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Why Do Banks Speculate and Hedge on Derivatives?

Kai Chen

Yong-Cheol Kim

Lubar School of Business, University of Wisconsin-Milwaukee

PO Box 742, Milwaukee, WI 53021

ykim@uwm.edu

Phone: 414-229-4997, or FAX: 414-229-5999

Abstract

We use notional amounts of derivative contracts to measure U.S. banks’ derivatives activities

and estimate the models which explain their derivatives behaviors. Banks engage in speculative

derivatives activities to make off-balance-sheet incomes to improve their earnings performance,

and they speculate out of risk-seeking impetus and take advantage of market volatility. Banks

hedge their risk exposures with derivatives to smooth their cash flows and liquidity, but their

derivative hedging does not balance the overall risks that banks are undertaking. Though there

are common features, banks’ speculative derivatives activities in FX markets seem to be more

aggressive than in other derivatives markets.

JEL Classification Number: G21, G3, D81

Keywords: Commercial Banks, Derivatives, Speculation, Hedge, Risk Management

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Why Do Banks Speculate and Hedge on Derivatives?

1. Introduction

Although academics generally conceive that derivatives are used as an instrument to

hedge risk or for speculative purposes, commercial banks in fact engage in three kinds of

derivatives activities: hedging, dealing, and speculating. When used for hedging, a derivative

position is employed to offset or reduce the risks associated with an existing balance sheet

position or a future planned transaction. Dealing in derivatives is an intermediary business in

which banks, as dealer, make contracts available for customers to earn fees. Derivatives positions

in dealing activities may offset each other. If not, dealers may enter into offsetting positions with

other customers or manage derivatives risks in other ways. As speculators, banks can enter

derivatives transactions in order to profit from expectations that are different from the market

expectations about how the prices of the underlying assets will move. Even though a dealing

transaction is different from a pure speculative transaction in terms of their purposes, both have

some characteristics in common. First, both transactions are based on the dealer/speculator’s

expectations about the direction of the markets. If the market expectation and the price of

contracts are favorable, the dealer/speculator would like to have more transactions. Second, the

risk that the expectation may fail exists in dealing activities as well as in speculative activities.

Once the derivatives positions are established, the dealer/speculator would, to varying degrees,

be exposed to the market risks. Thus, both pure speculative and dealing activities can be

considered the activities with speculative characteristics. In this sense, we can say that

commercial banks use derivative contracts for two purposes: to speculate on anticipated price

moves or to hedge some positions exposed to a variety of risks.

Derivatives have served an increasingly important role in the bank portfolio management.

Derivatives activities at U.S. bank holding companies, as measured by the total notional amount

of $$288.26 trillion as of December 31, 2013, have ever since been growing dramatically.

Meanwhile, more commercial banks, as many as 660 U.S. bank holding companies in the fourth

quarter of 2013 according to our study, have engaged in derivatives transactions. To our surprise,

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however, the financial research does not match the growth of bank activities in the derivatives

markets. Though there are a few studies investigating derivatives use by non-financial firms for

hedging (e.g., Nance et al., 1993; Mian, 1996; Geczy et al., 1997; Guay and Kothari, 2003), we

find only a couple of papers studying the commercial banks’ hedging activities using derivative

contracts. Among them, Purnanandam (2007) compares the effects of bank characteristics and

macroeconomic shocks on interest rate risk management behavior between derivatives user

banks and derivatives non-user banks. Minton, Stulz, and Williamson (2009) investigate how

much banks use credit derivatives to hedge loans.1 Furthermore, researches on derivatives

speculation are few and far between. The latest study that concerns bank derivatives activities is

Ellul and Yerramilli (2013), which examines the strength and independence of bank risk

management system using notional amounts of derivatives contracts as a control variable.

Obviously, there are many open questions concerning the bank engagements in derivatives,

especially under the circumstances that banks are playing a key role in financial derivatives

markets.

A fundamental question is concerned with the incentives for banks to use derivatives.

Why do banks participate in financial derivatives markets? The answers are neither complete nor

clear. Until now, there is no empirical research about what factors specifically motivate banks to

speculate in financial derivatives markets. Even though the hedge theory points out that firms

would hold derivative contracts to hedge risks inherent in their positions and, in turn, to smooth

their cash flows and profits, the empirical evidence for commercial banks is rare.

1 On the financial markets, credit derivatives and derivative contracts are two different derivatives instruments in

terms of the nature of the risk that they transfer. Credit derivatives are bilateral financial contracts with payoffs

linked to a credit related event such as non-payment of interest, a credit downgrade, or a bankruptcy filings. Either

party involved in a credit derivative contract can use it to transfer some or all of the credit risk of a debt to the other

party or to take on the risk transferred by the contract. Derivatives contracts are contracts with values linked to the

underlying assets, such as stocks, bonds, commodities, currencies, interest rates, and market indexes. The risk of

fluctuation in price of these underlying assets is transferred between the parties of the contracts. Credit derivatives

usually account for 5-6% of the total derivatives amounts, according to OCC’s quarterly reports. In general, the term

derivatives refers to the derivative contracts. This paper uses the term in this sense.

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By examining the notional amounts of the derivative contracts reported by U.S. bank

holding companies in their quarterly FR Y-9C filings with the Federal Reserve, this paper tries to

answer this question. First, our analyses show that the notional amounts for trading-purposes and

for non-trading purposes are, respectively, appropriate proxies for banks’ speculative and

hedging derivatives activities. Then we find the evidence that the banks speculate on derivatives

under the pressure to improve their earnings performance and out of impetus to take risk.

Specifically, the banks’ speculative derivatives activities, though not in foreign exchange

markets, are negatively associated with their past cash flows as well as net incomes, indicating

that one motivation for banks to speculate is to make off-balance-sheet incomes to improve their

profitability. The banks with higher historical risk-seeking characteristics tend to engage in more

speculative derivatives activities, and the banks speculate more when the underlying assets

markets are volatile. Also, we get the evidence that the banks hedge risk exposures of their

balance-sheet positions to smooth their cash flows and liquidity. The past fluctuations of the

banks’ cash flow and liquidity are positively associated with their non-trading notional amounts

of interest rate derivatives, and the past fluctuations of the banks’ foreign interest incomes and

expenses, used to proxy for their cash flows related to foreign business, are positively associated

with their non-trading notional amounts of foreign exchange derivatives. However, the banks’

hedging derivatives activities are quite operational; they do not balance the overall risks that the

banks are undertaking. The additional evidence shows that the bank derivatives behaviors differ

somewhat across the markets of four categories of derivatives. For example, the banks speculate

in foreign exchange derivatives markets just spurred by their risk-seeking incentives which tend

to be sensitive to volatility of foreign exchange markets. These differences implicate that banks

might be more aggressive in foreign exchange derivatives markets than in other derivatives

markets.

Studying why banks use derivatives provides us with a new perspective to look at bank

behaviors of hedging and taking risks. Traditionally, banks are thought to be able to control the

risks they are experiencing, for instance, through the tools of risk managements such as Value at

Risk (VaR). However, an important cause of the financial crisis of 2007-2008 is the overdue

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risk-taking that was driven by the greediness of some financial managers in the course of the

financial innovations. A further understanding of bank behaviors dealing with risks will be

helpful to improve the risk managements of financial institutions as well as the banking

regulations.

The paper proceeds as follows. Section 2 develops hypotheses about the motivations of

commercial banks to speculate and hedge in derivatives markets. Section 3 describes our data

and documents the derivatives activities at U.S. bank holding companies from 1995 to 2013.

Section 4 introduces the variables that we use in the analyses and summarizes their descriptive

statistics. Section 5 presents our empirical results. We conclude the paper in Section 6.

2. Literature Summery and Hypothesis Development

Derivatives are generally used as an instrument to hedge risks, and can also be used for

speculative purposes. Academics have shown inertest on the incentives for firms to hedge and

speculate as more financial information about the use of derivatives becomes available. In this

section, after briefly reviewing the relevant studies, we develop the hypotheses about why banks

hedge and speculate on derivatives.

2.1. Incentives to hedge using derivatives

Risk management theory has examined the value that a firm can obtain from hedging

activities. In general, stockholders benefit when the reduction of risks from hedging lowers the

risk-adjusted discount rates, increasing the present value of firms. Guary and Kothari (2003)

summarize four aspects through which hedging might improve a firm’s value by suppressing the

costly volatility.2 Froot, Scharfstein, and Stein (1993) argue that hedging can be a value-

increasing activity if it more closely matches fund inflows with outflows, thereby lowering the

probability that a firm needs to turn to the more expensive external financing. Smith and Stulz

2 These four aspects are (a) external financing (Froot, Scharfstein, and Stein, 1993); (b) financial distress costs

(Myers, 1977; Smith and Stulz, 1985); (c) taxes (Smith and Stulz, 1985; Stulz, 1996; Leland, 1998); (d) cost of

managerial risk aversion (Stulz, 1984; Smith and Stulz, 1985).

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(1985) maintain that hedging narrows the distribution of firm-value outcomes and, in turn,

reduces the expected costs of financial distress, therefore increasing the value of a levered firm.

Froot, Scharfstein, and Stein (1993) consider bankruptcy costs and predict that the reduction of

variability reduces sub-optimal investments. On the other hand, because financial distress might

make equity-holders to decline positive net present value projects if the gains accrue primarily to

fixed claimholders (Myers, 1977), hedging the firm value reduces the probability of distress and

the likelihood that equity-holders would find it beneficial to pass up valuable projects. There also

exist tax incentives to hedge the volatility of cash flows and income. One such benefit arises

from the concavity of corporate taxes in a firm’s expected profits (Smith and Stulz, 1985).

Further, Stulz (1996) and Leland (1998) argue that a reduction in cash flow volatility through

hedging can increase debt capacity and generate greater tax benefits.

Smith and Stulz (1985) analyze managerial motivation to hedge in terms of managerial

wealth maximization. The reduction of the variability of expected cash flows have different

impact on managers with different interests in their firms. A risk-averse manager with a large

share of a firm’s equity has an incentive to reduce the variability of the stock price as the

managerial wealth is tied with the firm value. On the other hand, managers whose compensation

is partly based on the unexercised options have an incentive to increase the variability of their

firm values. The net incentive effect for managers is likely to depend on the relative managerial

wealth of stock ownership and option value. Besides the ownership incentives, managers are also

motivated to smooth the earnings performance by hedging on derivatives due to the accounting-

based compensations (Guary and Kothari, 2003).

Given that the incentives of managers and shareholders are aligned with each other,

banks, fundamentally, would like to stabilize their cash flows to reduce the cost of volatility on

the firm value. Straightforwardly, we come up with the first hypothesis about the bank hedging

motivations—commercial banks use derivatives contracts as hedging instruments in order to

smooth their cash flows—the cash flow stabilization hypothesis.

Liquidity, affecting both safety and profitability of a firm, is a more important

characteristic for banking institutions than for industrial firms. A lower short-term liquidity is

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thought to increase bankruptcy costs (Nance, Smith, and Smithson, 1993), while a higher

liquidity would be kept at the cost of profitability. Considering the importance of liquidity for

banking institutions, we put forward the liquidity stabilization hypothesis that banks use

derivatives to hedge so as to smooth their liquidity.

The hedging theories (Smith and Stulz, 1985; Froot, Scharfstein, and Stein, 1993) predict

that banks may have a risk self-control function—optimally balancing the risks in different areas

and matching risk-taking and risk control—and Ellul and Yerramilli (2013) refer to this

mechanism as “hedging channel”. Brewer, Jackson, and Moser (2001) find that banks using

derivatives to manage interest rate risk hold lower levels of capital, maintaining that derivatives

usage allows banks to substitute risk management for capital, which they claim is expensive

compared with risk management. If bank managers are feeling that their bank is experiencing

higher risks in other areas, say, a lower equity and/or more risky assets, they might use the

derivatives hedging, if the cost of hedging is acceptable, to mitigate the risks that the bank is

undertaking. This hypothesis that banks use derivatives to balance the overall risks is called the

risk balancing hypothesis.

2.2. Incentives to speculate using derivatives

Although speculative use of derivatives can change bank risks, prior banking literature

generally discusses the bank risk taking in conventional businesses, for example, the mismatch

of assets and liabilities. Boyd and Nocolo (2005), in their revisit of the theory of bank risk-taking

and competitions, suggest considering the loan side of the balance sheet together with deposit

taking in determining the overall risk-taking behavior, a perspective expanding the prior

literature that focuses on the deposit taking and the deposit insurance. Gatev et al. (2007)

examine liquidity risk in banks and conclude that the deposit-lending synergy mitigates liquidity

risk. Despite lots of studies on bank risk taking and risk management, the case of bank

derivatives speculating is, till now, an incipient research area as the relevant data are lacking

even after 1995 when the Federal Reserve mandates the reporting of derivatives activities.

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In their trailblazing work, Geczy, Minton and Schrand (2007) suggest in a survey study

that firms view speculation as a profitable activity, not merely a risk-seeking activity because

they have information and cost advantages. Inspired by this idea, we hypothesize that earnings

performance pressure might be a factor that motivates commercial banks to speculate on

derivatives. If banks are experiencing a lower level of cash flow and/or net income than expected,

they would more aggressively engage in off-balance-sheet activities to seek out extra incomes.

One source of these “easy” moneys is the derivatives markets. Thus, we propose the earnings-

improving hypothesis: banks use derivatives as a speculative instrument to improve their

profitability. If a bank is under the pressure of earnings performance, we expect it to speculate

more in derivatives markets, ceteris paribus.

A bank may have evolved its own business culture over time, which, featuring being

aggressive or conservative, determines the choice of risk and the strength of risk management.

Ellul and Yerramilli (2013) refer to this endogenously-built business culture as “business model

channel” vis-à-vis “hedging channel” in bank risk control mechanism. Fahelnbrach, Prilmeier,

and Stulz (2012) argue that this business model is persistent because they find that U.S. banks’

performance in 1998 crisis forecasts their performance during the financial crisis of 2007-2008.

A bank with a risk-seeking culture may prefer to hire aggressive managers and/or to insert risk-

taking incentives into executive compensation contracts. Meanwhile, a manager may be

inculcated by this risk-favoring culture or stimulated by the risk-taking terms in his contract to be

more aggressive in business. (DeYoung, Peng, and Yan (2013) find a strong link between the

risk-taking incentives in the CEO compensation contracts and the financially risky business

policies of the U.S. commercial banks). On the other hand, bank managers, including board

directors, personally have varying risk preferences. While they are running business, their

psychological personalities would affect the bank risk-taking policies, contributing to the bank

risk-taking culture in long run. As a result, the business model or risk culture might be incarnated

in a bank’s past risk-taking behaviors. A bank with a historical risk-seeking propensity might

own more risky assets but fewer secured assets, finance its assets with fewer deposits but more

market borrowings, and, not surprisingly, engage in more speculative derivatives activities. Here

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comes the risk-favoring hypothesis: banks speculate on derivatives because in nature they are

risk-seeker. The more inclined to seek risk a bank is, the more speculative derivatives the bank

would use. In this paper, we test these hypotheses to explain why banks hedge and speculate on

derivatives.

3. Sample Selection and Overall Description of U.S. BHCs’ Derivatives Activities

Bank holding companies (BHC, thereafter) file their consolidated financial statements to the

Federal Reserve in the form of FR Y-9C each quarter.3 After reporting the use of credit

derivatives in Schedule HC-L, BHCs report their holdings of derivative contracts in four

categories: interest rate contracts (IR derivatives, thereafter), foreign exchange contracts (FX

derivatives, thereafter), equity derivative contracts (EQ derivatives, thereafter), and commodity

and other contracts (CM derivatives, thereafter). For each category, BHCs report the gross

amounts (notional amounts) classified by the types of contracts—futures contracts, forward

contracts, option contracts, and swaps—as well as the total gross amount of derivative contracts

separated as for trading purposes and for purposes other than trading. The Federal Reserve

System defines the derivative trading and non-trading activities as follows. Derivative trading

activities include (a) regularly dealing in interest rate contracts, foreign exchange contracts,

equity derivative contracts, and other off-balance-sheet commodity contracts, (b) acquiring or

taking positions in such items principally for the purpose of selling in the near term or otherwise

with the intent to resell (or repurchase) in order to profit from short-term price movements, or (c)

acquiring or taking positions in such items as an accommodation to customers. Derivative

instruments used to hedge trading activities are also included as trading activities. The

derivatives activities for non-trading purposes include (a) off-balance-sheet contracts used to

hedge debt and equity securities classified as available-for-sale, (b) foreign exchange contracts

3 By the Bank Holding Company Act of 1956, a bank holding company is broadly defined as "any company which

has control over any bank”. All bank holding companies in the United States are required to register with the Board

of Governors of the Federal Reserve System.

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that are designated as, and are effective as, economic hedges of a net investment in a foreign

office, (c) intercompany foreign exchange contracts of a long-term investment nature when the

parties to the contract are consolidated, combined or accounted for by the equity method, and (d)

off-balance-sheet contracts used to hedge other assets or liabilities not held for trading purposes

that are accounted for at market value.4 According to the definitions above, the notional amount

of derivative contracts for non-trading purposes can be thought of as representing the bank

hedging activities, while the notional amount of derivative contracts for trading purposes can be

regarded as a measure of, or at least a proxy for the bank speculative activities. Our tests prove

that these two kinds of notional amounts of derivative contracts are appropriate proxies for bank

speculative and hedging derivatives activities.

We use the FR Y-9C dataset to test our hypotheses. The bank-quarter observations are

screened from the dataset based on the following criteria: (a) U.S. bank holding companies; (b)

BHCs with a non-missing value of total assets (BHCK2170); (c) BHCs with total loans

(BHCK2122) greater than zero (we exclude the non-commercial banks such as investment

banks); (d) BHCs with non-zero and non-missing data of the notional amounts of derivative

contracts in a quarter as well as the same BHCs in the neighboring quarters of the same calendar

year, even if the derivatives positions are reported as zero or missing in the neighboring quarters.

If one bank-quarter observation is selected for reported derivatives positions, then the four

quarters from the same calendar year, if available, are included, with the missing value of

notional amounts, if any, converted into zero.5 The study period begins from Quart 1, 1995, the

first time BHCs reported their holdings of derivative contracts, and ends on Quarter 4, 2013, the

last quarter the latest FR Y-9C data was available when this study was updated. These criteria

create the main sample (including all four categories of derivative contracts) that we use to

document the U.S banks’ overall derivatives activities over time and to report the summary

4 See the website of the Federal Reserve Board: http://www.federalreserve.gov/apps/mdrm/data-dictionary

5 Some BHCs discontinued their derivative activities occasionally for a while during their entire derivatives activity

career. We include these pause quarters in our samples because these observations provide us with information to

explain why these banks discontinued and then resume their derivative activities. If the pause period is beyond a

calendar year, this period is not included in the samples.

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statistics. The main sample comprises 31,849 bank-quarter observations involving 1,519 unique

commercial banks. Applying the same screening criteria to four categories of derivative contracts

individually, we build IR, FX, EQ, and CM derivatives samples for regression analysis.

To have a bird’s eye view of the derivatives activities by U.S. banks, we report,

respectively in Panel A, B, C, and D of Table 1, an overall description of the IR, FX, EQ, and

CM derivatives activities at the year-ends of the sample period. In each panel, we first report the

number of BHCs that use the derivative contracts for trading or non-trading purposes. Then, we

show, separately for trading purposes and for non-trading purposes, the number of users, the

notional amounts of the derivative contracts in total, and the aggregate notional amounts over the

aggregate assets across all BHCs involved (Times of Assets). Here, we summarize only the main

interesting features.

First of all, the IR derivative contracts dominate the derivatives activities at BHCs in a

cross-sectional view. The numbers of the BHCs using IR derivatives are greater than the

numbers of the BHCs using FX derivatives, and much greater than the numbers of the BHCs

using EQ and CM derivatives. So are the total notional amounts of IR derivatives, especially the

total amounts for trading purposes, which is up to $240.99 trillion in 2013. The extents to which

the BHCs use the derivatives compared to their assets, measured by the aggregate notional

amounts scaled by the aggregate bank book assets across all BHCs involved at the year-ends, are

also much greater for IR contracts than for other categories of derivatives. At the end of 2010,

U.S. BHCs hold as high as almost 18 times as many notional amounts of IR derivative contracts

for trading purposes as their total assets. FX contracts play a considerable part in the BHCs’

derivatives activities. But the roles of EQ and CM contracts are limited in general. This evidence

is consistent with the fact reported by OCC that derivative contracts remain concentrated in IR

products, which usually comprise 80 of total derivative notional amounts.

Secondly, in a time-series view, the IR derivatives activities behave differently from any

of its counterparts. Since the year of 2000, the number of the BHCs using IR contracts increases

sharply, especially for the contracts for non-trading purposes. So do the notional amounts of IR

contracts, especially for the contracts for trading purposes. Even though the number of BHCs

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using FX products does not change much, the notional amounts of FX derivatives for both

trading and non-trading purposes grow gradually. This time-series pattern also applies to the EQ

and CM categories, yet with their notional amounts at a lower order.

Finally, in a view of comparison, the derivatives activities for trading purposes dominate

those for non-trading purposes in terms of both the level of notional amounts and the times as

many the notional amounts as the assets in all four categories, though at different orders across

the categories. However, in terms of the number of the BHCs, the most remarkable impression is

that the BHCs using IR derivatives for non-trading purposes outnumber those using IR

derivatives for trading purposes, obviously since 2001.

4. Variables and their Summary Statistics

For narrative convenience, we define the key variables in issue before moving on. In our

empirical analysis, we may use the variants of some variables. The complete variables and their

detailed definitions are given in the Appendix: Variable Definitions.

We use the gross amount (notional amount) of derivative contracts scaled by the bank

assets—Notional—to proxy for a bank’s derivatives activities. When combined with Spec. or

Hedg., it refers to the scaled notional amount of derivative contracts for trading or non-trading

purposes. For instance, IR Spec.Notional is used to refer to a bank’s total notional amount of

interest rate products for trading purposes scaled by the total assets. For the reasoning that we

have discussed, the total notional amount for trading purposes is used to proxy for the

speculative derivatives activities and the total amount for non-trading purposes to proxy for

hedging activities.

A series of variables is introduced to describe the bank earnings performance. The two

key variables that we use in our analysis are CF, measuring the cash flow (the sum of total

interest income, total noninterest income, and realized gains (losses) on held-to-maturity

securities and available-for-sale securities), and ROA, measuring the net income. All these

variables are on the basis of scaling on the total assets.

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Inspired by Guay and Kothari (2003), we develop two variables to capture the

fluctuations in cash flow and liquidity. waveCF, measuring the oscillation of a bank’s cash flow,

is defined as the absolute changes of quarterly CF averaged over a period of previous four

quarters. waveLiq, measuring the variation of a bank’s liquidity, is calculated in the same way

except for replacing the cash flow with the liquid assets which consist of cash and short term

securities as traditionally defined.

We develop an index to describe the condition of a bank’s overall risk taking. A bank

confronts risks coming from several respects of banking management. Traditionally, the ratio of

total equity to total assets, Cap.ratio, is used to measure a bank’s risk taking in capital

management. Commercial and industrial loans are usually thought to be the most risky loans

since these loans are exposed to market fluctuations. We use commercial and industrial loans

over total loans, C&I.ratio, to measure the risk taking in loan portfolio management. As banks

diversify their fund sources to the overnight loan markets, such as the federal funds market, and

the new financial instruments, such as negotiable CDs, on the financial markets, the deposits

have become the relatively cheap and stable financing of the bank assets. Among the total

deposits, the core deposits, calculated as total deposits minus total time deposits of over

$100,000 and total brokered retail deposits, are the most stable source of funds for lending

because they are less vulnerable than other fund sources to changes in short-term interest rates.

We use core deposits over total assets, Depo.ratio, to describe a bank’s risk taking in fund

raising management. Banks have to keep a reasonable level of liquidity assets in case of

unexpected deposit withdrawals and expenditures, and therefore, the ratio of liquidity assets to

total assets, Liq.ratio, reflects a bank’s risk taking in liquidity management. All these measures

are designed in the way, say, one minus total equities over total assets for Cap.ratio, so that the

greater value of a variable indicates the higher risk taking in the respective area. Then we

construct a Risk Condition Index (RCI) by taking the first principal component of the above four

risk taking variables to measure the overall risk condition in each bank-quarter.6 By construction,

6 We do not include the risk taking in the off-balance-sheet activities, which Ellul and Yerramilli (2013) measure by

a ratio of non-interest income to the sum of interest and non-interest incomes, because derivatives trading is a part of

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the greater the value of the index, the higher level of overall risk a bank is taking on. The four

risk-taking variables may not necessarily correlate with each other in the same direction—they

are actually not slightly in our main sample—because these variables also reflect the bank

policies beyond risk-taking, such as leverage choice and clientele preference, and/or because

banks balance their risks in different areas. The main advantage of this approach is that we can

measure the across-the-board risk profile of a bank with a single, variance-maximized variable

by reducing the dimensionality of the dataset. Meanwhile, the extent to which these variables are

connected to “non-risk-taking” components are minimized by putting them together into an

index based on the Principal Components Analysis (Bharath, Pasquariello, and Wu, 2009). RCI

is computed using the main sample including four quarters prior to the quarter when a BHC

began reporting its derivatives positions so that we save more information.7 After getting RCI,

we compute the average of RCI over a period of previous four quarters, avg.RCI, to proxy for a

bank’s risk-taking propensity.

Four other bank characteristics, Size, Growth, L2A, and Lig, are also used in our analysis.

Size is the logarithm of bank total assets (in millions) adjusted by annual CPI of 2013. Larger

banks are more likely to get involved in the derivatives activities possibly because of the

economy of scale (Nance, Smith, and Smithson, 1993; Brewer, Jackson, and Moser, 2001;

Graham and Rogers, 2002). The growth rate of the net income over the previous four quarters,

denoted Growth, controls for the bank growing potential. A commercial bank with more loans

may have fewer speculative activities but more hedging activities (Brewer, Jackson, and Moser,

2001) so we include the ratio of total loans to total assets, denoted L2A, in the regressions as a

control variable. Liquidity, a fundamental characteristic concerning bank safety and profitability,

is calculated as total liquid assets divided by total assets and labeled as Liq. As a matter of fact,

Liq.ratio =1– Liq, by definition.

off-balance-sheet activities and the inclusion of the risk taking in off-balance-activities in a risk conditions index

may lead to endogeneity bias in our regressions. 7 This expanded sample is winsorized at 0.5

th and 99.5

th percentiles.

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The other variable used in our analysis is the standard deviation of the financial indexes

which proxy for the volatility of the markets of the underlying assets of the derivatives.

Corresponding to our quarterly database, this variable, labeled as STD, is computed on quarterly

basis after the indexes are converted to daily yields (percent changes).

Before starting regression analysis, we present, in Table 2, the summary statistics of the

key variables in the main sample. Though we will carry out our regressions on the samples of the

four categories of derivatives contracts, these summary statistics provide us with a general

picture about the distributions of the key variables in the regression samples.

Panel A shows that the variables describing BHCs’ derivatives activities are highly

censored, with a very large proportion of the observations having a value of zero. The structure

of our samples—the main sample and the samples used in regressions—is very complicated. As

a matter of fact, there are four types of observations in a sample: (a) the observations without

derivatives positions (we keep this type of observations because one or more than one derivatives

positions appear(s) in neighboring quarters of the same calendar year); (b) the observations with

only hedging positions; (c) the observations with only speculative positions; (d) the observations

with both. We include all these observations to avoid possible sample selection bias. The issue of

censored variable is the most serious in the sample of IR derivative contracts, with

approximately three fourths of the observations having zero value for Spec. Notional. To address

the censored data, we apply tobit model to our regression analysis.

Panel B shows that the size of BHCs, in terms of the CPI-adjusted book value of total

assets, has a highly skewed distribution—more than three quarters of the entire sample have a

size below the sample mean, while the distribution of Size, the logarithm of the book value of

assets, is much less skewed, with its mean approximately equal to its median. RCI is normalized

within a range from zero to one. The bank characteristic variables, including RCI, are not highly

skewed. However, some variables, such as Growth and Cap.ratio, have obvious outliers. The

samples used in the regressions will be winsorized at the 0.5th

and 99.5th

percentiles in case the

outliers affect our results.

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Before we end this section, we have a brief look at the correlations between the variables

in the regression analysis. L2A and Liq have a correlation coefficient as high as -88.68%, so we

do not include Liq in the regressions to avoid collinearity. The second highest correlation is

between L2A and RCI, -57.57%. When we remove L2A or replace it with Liq, which has a

correlation coefficient of 46.06% with RCI, in the models, our results (not reported) remain

qualitatively similar. The other pairs of variables that might lead to collinearity are CF and ROA,

33.47%, waveCF and waveLiq, 22.72%. We will address them in the regressions.

5. Results

In this section, we test the hypotheses proposed in Section 2 using the pooled cross-sectional

time series samples selected from the FR Y-9C dataset. We begin with the speculation

hypotheses and then examine the hedging hypotheses. After we get the basic results, we check

for robustness with Heckman test and then have a closer look at some results. Next, we do a

couple of interesting tests derived from the speculation hypotheses. Finally, we check the effects

of the top banks which dominate the derivatives markets on our results.

5.1. Results on speculation activities

The earnings-improving hypothesis states that banks use derivatives as a speculative instrument

to improve their earnings performance, which is measured by a series of variables such as cash

flow and net income. Specifically, if these earnings variables do not perform satisfactorily for a

while, banks would be driven to speculate more on derivatives in an attempt to make extra

money. So, we associate the averages of earnings variables over previous four quarters—avg.CF,

and avg.ROA—with the speculative activities—Spec.Notional. We expect a negative relationship

if the hypothesis holds. In this section, we focus on the effect of avg.CF and avg.ROA on

Spec.Notional. Later on, we will have a closer look at which part of the cash flow drives the

results.

The risk-favoring hypothesis asserts that the bank speculative derivatives activities can be

attributed to the bank culture and/or bankers’ adventurous personality of seeking risk. This

16

hypothesis can be tested in two perspectives. Principally, we examine the effects of the variables

that proxy for the bank propensity to seek risk. The variable, RCI, which we have developed to

describe the bank overall risk condition, is improper to proxy for the bank risk preference

because it describes the current risk profile, which is determined as a result of the interaction of

many bank characteristics, including, not limited to the bank risk propensity. For example,

Brewer, Minton, and Moser (2000) suggest that derivatives usage complements business lending

and Brewer, Jackson, and Moser (2001) show that the previous equity ratio is positively related

to the growth of business lending. Instead, we use the average of RCI over previous four quarters

to capture the effect of the bank propensity for risk-seeking because we believe that the culture

and leadership of an organization can be accumulated and manifested somewhat in the

organization’s historical behaviors. Thus, we expect that a bank with a higher avg.RCI would

exhibit more speculative activities on the derivatives markets in current quarters, if the risk-

favoring hypothesis is true.

Additionally, we examine the risk-favoring hypothesis by looking at how banks respond

to changes in the underlying-assets markets. Risk-seekers love and take advantage of the market

volatility, which is measured by the standard deviation of the indexes that represent prices of

underlying assets. If banks are risk-seekers, we expect that Spec.Notional of a certain category of

derivatives would be positively related to STD of the corresponding index. The additional benefit

of this examination is that we can verify whether our use of banks’ notional derivative contracts

for trading purposes as a proxy for their speculative activities is appropriate. If bank notional

derivative contracts for trading purposes are positively associated with the market volatility, it

strongly indicates that our proxy captures the characteristics of speculative behaviors.

Using the pooled cross-sectional time series samples selected from FR-Y9C database, we

test the speculation hypotheses in the following regression specifications.

Spec.Notional = Size + Size2 + Growth + L2A + avg.CF + avg.RCI

+ STD (Quarter Dummies) (1)

Spec.Notional = Size + Size2 + Growth + L2A + avg.ROA + avg.RCI

+ STD (Quarter Dummies) (2)

17

Spec.Notional = Size + Size2 + Growth + L2A + avg.CF+ avg.ROA

+ avg.RCI + STD (Quarter Dummies) (3)

Since avg.CF and avg.ROA have a high correlation between them, we check their effects

separately in the specifications (1) and (2), and then put them together in the specification (3) to

see whether their correlation affects the overall results. Because STD is a time variant variable

that might correlate with some unobservable time trend, we also run regressions that replace STD

with quarter dummies to make sure that our main results would not be affected by the possible

bias resulting from endogeneity. In each specification, we control Size, Growth, and L2A. As the

size distribution of BHCs is highly skewed, we also include Size2 to control for the possible non-

linear relationship between BHCs’ size and their derivatives activities. Following Ellul and

Yerramilli (2013), we orthogonalize Size and Size

2 before including them in the regressions.

It is very cautious for the academics to use notional amounts of derivative contracts for

trading (non-trading purposes) to proxy for bank speculative (hedging) derivatives activities.

Except for Ellul and Yerramilli (2013) that uses them as control variables, we have not seen any

studies doing so. To prove that our proxies are proper, we run regressions with the exactly same

specifications but with the dependent variable replaced by Hedg.Notional. By comparison, we

can see how differently the two dependent variables respond to the bank characteristics as well

as the market signals, and this comparison may provide evidence that our proxies are reasonable.

Considering that each category of IR, FX, EQ, and CM derivatives contracts has its own

characteristics related to the markets of its underlying assets, though they have something in

common as derivatives, we apply our analysis to each category of contracts. Applying the same

screening rules to four categories of derivative contracts, we construct four regression samples

and then estimate (after winsorizing at the 0.5th

and 99.5th

percentiles) the tobit regressions on

each sample separately. The standard deviations of four underlying-assets market indexes, AAA-

STD for AAA bonds yields, Partner-STD for OITP Index, S&P-STD for S&P 500 Index, and

18

Gold-STD for S&P gold price index, are used respectively in each sample to capture the effects

of the market volatility on IR, FX, EQ, and CM derivatives activities.8

We present the results in Table 3 in which Panel A, B, C, and D are for four categories of

derivatives, respectively. In each panel, Part I shows the main results of the models with

Spec.Notional as dependent variables, and Part II displays the results of the compared models

with Hedg.Notional as dependent variables.

Overall, the results in Part I of each panel provide the evidence supporting the

speculation hypotheses at varying degrees. First of all, in the models (1), (2), (3), and (4) for IR,

EQ, and CM samples, avg.CF and avg.ROA are, both or individually, negatively associated with

Spec.Notional at different levels of significance. When we put avg.CF and avg.ROA together in

the models (5) and (6), it is understandable that avg.ROA alone in Panel A and D, or both avg.CF

and avg.ROA in Panel C become insignificant because avg.CF and avg.ROA are relatively highly

correlated. These results support the earnings-improving hypothesis. However, even though the

coefficients of avg.CF and avg.ROA in FX sample are negative in most cases, they are not

significant.

Secondly, the coefficients of avg.RCI in all the cases in Panel A, B, and C have the

positive signs at different levels of significance as predicted by the risk-favoring hypothesis. In

Panel D, even though the coefficients are not significant in the models of (1) and (5), their p-

values are close to 10%. These results have proved that the quantity of banks’ speculative

derivatives activities is also determined by the measure of their risk-seeking dispositions, which

have been embodied in their historical risk-taking behaviors.

Thirdly, in the IR and FX derivatives samples, all models with STD embedded produce

positive coefficients of STD at conventional significance levels, most of which are above 5%.

These results indicate that banks take advantage of the market volatility when they are

speculating on derivatives and therefore support the risk-favoring hypothesis. However, this

8AAA bonds yields come from the Federal Reserve’s website: http://www.federalreserve.gov/releases/h15/update/.

OITP Index is the Federal Reserve’s Nominal Other Important Trading Partners Dollar Index from the Federal

Reserve’s website: http://www.federalreserve.gov/releases/h10/summary/.

19

conclusion does not apply to the bank EQ and CM derivatives activities because the coefficients

of STD are not significant in the models in Panel C and D.

As far as the control variables are concerned, the coefficients of Size in all models are

positively significant, indicating that larger banks would get involved more in the derivatives

activities to take advantage of the economy of scale. And the coefficients of L2A in all models

are negative but not necessarily significant. This result is consistent with a reasonable conjecture

that a bank with more loan business would get involved less in the derivatives markets.

Finally, in the compared models shown in Part II of each panel, we find that

Hedg.Notional respond in different ways to some explanatory variables. Here we point out some

important differences but do not explain in detail for the sake of space: (a) almost all coefficients

of avg.CF are significantly positive or insignificant; (b) all coefficients of avg.ROA are

insignificant, except the models (11) and (12) in Panel A, in which the coefficients become

negatively significant because of the collinearity between avg.ROA and avg.CF; (c) all

coefficients of avg.RCI are insignificant; (d) the coefficients of STD are insignificant in Panel A

and B, but positively significant in Panel D; (e) the coefficients of Size are significantly negative

in Panel C and D; (f) In IR sample, the coefficients of L2A are significantly positive. Combined

with other minor differences that we do not mention, all these results show that the notional

amount of derivative contracts for trading purposes is different from that for non-trading

purposes in nature.

5.2. Results on hedging activities

The cash flow stabilization hypothesis maintains that banks use derivatives to smooth their cash

flows. Thus, a bank having experienced higher fluctuation in cash flow would use more

derivatives for hedging. The liquidity stabilization hypothesis asserts that banks use derivatives

to smooth their liquidity. Thus, a bank with less stable liquidity would perform more hedging

derivatives activities. If these hypotheses are true, we expect that Hedg.Notional be positively

associated with waveCF and/or waveLiq. The risk balancing hypothesis points out that when

bank managers sense that they are currently undertaking a serious overall risk exposure, they

20

would use more hedging derivatives to mitigate it. If this hypothesis holds, RCI, which is used to

proxy for the bank current risk profile, is expected to explain Hedg.Notional significantly in the

regressions, and the predicted sign of the coefficients should be positive. In addition, there are

two alternative predictions about the response of Hedg.Notional to the market signals. Because a

bank’s hedging contracts are scheduled based on its operational needs or on the exposures of the

planned positions in its balance sheet to the future uncertainties, hedging derivatives activities

have nothing to do with market volatility. This reasoning expects that the coefficients of STD

would not be statistically different from zero. The competing prediction is that a bank would

hedge more using derivatives instruments when the markets are more volatile, so the coefficients

of STD are expected to be positive.

To test these hypotheses, we use the specifications designed as follows.

Hedg.Notional = Size + Size2 + Growth + L2A + waveCF + RCI

+ STD (Quarter Dummies) (4)

Hedg.Notional = Size + Size2 + Growth + L2A + waveLiq + RCI

+ STD (Quarter Dummies) (5)

Hedg.Notional = Size + Size2 + Growth + L2A + waveCF + waveLiq

+ RCI + STD (Quarter Dummies) (6)

We apply the models to IR, FX, EQ, and CM samples, with the different STDs selected for the

corresponding sample. In addition, as we have already known from Part II in Panel C and D of

Table 2 that EQ and CM Hedg.Notionals are negatively associated with Size, we remove Size2

when running the regressions on EQ and CM samples. We present the results in Table 4 which

has four panels showing the results for four categories of derivatives respectively. In each panel,

Part I shows the main results of the models with Hedg.Notional as dependent variables, and Part

II displays the results of the compared models with Spec.Notional as dependent variables and

with all independent variables exactly the same.

We begin our discussion with the main results in Part I of each panel. First of all, in IR

sample, all coefficients of both waveCF and waveLiq are positive at 1% level of significance.

21

This evidence strongly supports the cash flow and liquidity stabilization hypotheses. But these

associations do not appear in the samples of FX, EQ, and CM derivatives.

Secondly, in all panels, RCI does not have significant effects on Hedg.Notional. In light

of these results, we cannot conclude that the risk balancing hypothesis holds. Looking forwards

at the results from the compared models with Spec.Notional as dependent variable, shown in Part

II on the right of this table, we find a positive association between RCI and Spec.Notional.

Looking back at the results from both main models and compared models in Table 3, we recall

that avg.RCI is positively associated with Spec.Notional, but not with Hedg.Notional. Putting all

these results together, we now have a whole picture about the relationships of Spec.Notional and

Hedg.Notional with avg.RCI and RCI. Because of the high correlation between RCI and avg.RCI,

both of them are positively associated with Spec.Notional but not associated with Hedg.Notional.

The reasonable explanation of this picture is that a bank’s risk-seeking propensity, represented

by its avg.RCI, leads to the bank’s current risk condition as well as its speculative derivatives

activities, while its hedging derivatives activities are unrelated to its current as well as historical

overall risk condition.

Thirdly, the results concerning the market signals vary across the samples. In Panel A, B,

and C, all coefficients of STD are insignificant; only in the model (2) in Panel B is it negatively

significant with a p-value on the border of 10%. As there is no reasonable explanation for a

negative effect of STD, these results indicate that the bank hedging activities using IR, FX, and

EQ derivatives do not respond to the market volatility. In Panel D, Gold-STD has positive

coefficients that are significant at 10% levels in the models (2), (4), and (6). One conjecture

about this result is that banks might hedge more on gold when the gold market is more volatile to

protect the value of their gold reserves.

For the control variables, the coefficients of Size in all models in Panel A and B are

significantly positive, consistent with the theory of the economy of scale. However, it is

surprising that smaller banks get involved more in hedging activities in EQ and CM derivatives

markets since the coefficients of Size in Panel C and D are negative significantly. In addition, it

22

is reasonably expected that L2A is significantly positively associated with IR Hedg.Notional in

Panel A since IR derivative contracts are usually used to hedge interest rate risk inherent in loans.

At last, we point out some differences shown in the compared models in Part II which we

have not discussed yet. An impressive result is that in IR derivatives sample, the coefficients of

waveCF and waveLiq are negative, completely opposite to their counterparts in Part I.

Before ending our across-the-board analysis of BHCs’ speculative and hedging

derivatives activities, we summarize the key differences between them which reveal more

intriguing implications. Although the speculative and hedging behaviors have something in

common—larger banks engage more in both speculative and hedging activities on IR and FX

derivatives markets, they differ in many respects. The speculative derivatives activities are

usually associated with lower cash flows and net incomes, but the hedging derivatives activities

are not. The speculative derivatives activities are usually associated with higher historical as well

as current risk-taking in comprehensive measure, but the hedging derivatives activities are not.

The speculative IR and FX derivatives activities positively respond to the market volatility, but

the hedging IR and FX derivatives activities do not. The hedging IR derivatives activities are

positively associated with the past fluctuations of the bank cash flow and liquidity, but the

speculative IR derivatives activities are not. The hedging IR derivatives activities are positively

correlated with the loans in total assets, but the speculative IR derivatives activities are not. Our

analyses have captured these differences as well as commons, indicating that our methodology is

reflective and suitable.

5.3. Robust test for the speculation hypotheses: Heckman selection model

Our test of the speculation hypotheses is carried out on the samples in which a bank-quarter is

included if the BHC reports positions of derivatives contracts—for trading or for non-trading

purposes—at the end of the quarter or the neighboring quarter(s) in a calendar year during our

study period. Careful inspection reveals that a lot of observations in the samples have only

derivatives positions for non-trading purposes but no positions for trading purposes. For example,

in the IR derivatives sample, 21,317 out of 27,970 observations have the value of zero for IR

23

Spec.Notional. To avoid the selection bias, we include these observations in our analysis of the

speculation hypotheses on the ground that the variable of derivatives positions for trading

purposes has value of zero, rather than unobservable, in these observations and that the tobit

model is effective in handling the issue of the censored dataset. Nevertheless, we are concerned

that our results about the speculative activities might be biased by this extremely skewness of the

dependent variable. To substantiate our results, we apply the Heckman (1979) selection model to

the IR and FX samples. From now on, we focus on the IR and FX derivatives activities, the two

main derivatives activities at BHCs.

Following the Heckman maximum likelihood estimation procedure, we first use a probit

model to estimate the probability for a bank to be derivatives speculator or not—a BHC is

defined as a speculator in a quarter if it announces a non-zero and non-missing notional amount

of the derivatives contracts for trading purposes in any quarters of the same calendar year.

Though the factors affecting a bank’s decision to be a speculator are theoretically the same as the

factors influencing its level of speculative activities, we make some changes in the specification

of the selection equations to meet the requirement of exclusion restriction (Leung and Yu, 1996;

Puhani, 2000) and to reach the convergence of maximum likelihood estimation: (1) we include a

new variable, avg.D2L (total deposits over total loans averaged across previous four quarters) as

an instrument to replace L2A; (2) we decompose avg.RCI into its components, including in the

selection equation avg.Cap.ratio, avg.C&I.ratio, and avg.Depo.ratio—avg.Liq.ratio is not

included because of its high correlation with avg.D2L; (3) we use avg.Size, as well as the

resultant avg.Size2, instead of the current Size. For simplicity, Growth is excluded from the

selection equation as it is insignificant if it were in. In addition, the quarter dummies are not

included in both selection and outcome equations as they interrupt with the convergence of

maximum likelihood estimation. Specifically, the selection equations corresponding to the six

outcome equations in the second stage are as follows:

Select = avg.Size + avg.Size2 + avg.D2L + avg.CF + avg.Cap.ratio

+ avg.C&I.ratio + avg.Depo.ratio (+ STD) (7)

Select = avg.Size + avg.Size2 + avg.D2L + avg.ROA + avg.Cap.ratio

24

+ avg.C&I.ratio + avg.Depo.ratio (+ STD) (8)

Select = avg.Size + avg.Size2 + avg.D2L + avg.CF + avg.ROA + avg.Cap.ratio

+ avg.C&I.ratio + avg.Depo.ratio (+ STD) (9)

in which, Select is a binomial variable with the value of one if a bank is identified as a speculator

in a quarter, or zero otherwise. In the second stage, the outcome equations follow exactly the

specifications of the main models in Table 3, with the Inverse Mills Ratio included. The results

of the second stage of Heckman estimation on IR and FX samples are presented in Panel A and

B, Table 5, respectively.

The results from Heckman test are consistent with the results from the tobit analysis

above: avg.CF and avg.ROA have significantly negative coefficients in IR sample but not in FX

sample; avg.RCI has positive effects on speculative activities of both IR and FX derivatives;

BHCs’ speculative activities are sensitive to the market volatility, signaled by AAA-STD for IR

derivatives and by Partner-STD for FX derivatives. These results indicate that our main results

are robust and that the tobit model is effective in handling the issue of high degree of censoring.

5.4. The earnings-improving hypothesis revisited: which part of cash flow matters?

We have strong evidence to show that BHCs speculate on IR derivatives in order to improve

their cash flows. Furthermore, we want to find out which part of cash flow drives their

speculative activities. We classify the bank cash flow into three parts: interest incomes generated

by loan assets, interest incomes generated by non-loan assets, and non-interest incomes, and then

compute three variables, avg.II-loan, avg.II-nonloan, and avg.non-II, following the way of

calculating avg.CF. We substitute these three variables, respectively, for avg.CF in the

specifications to check which part has the effects on the banks’ speculative IR derivatives

activities. The results are presented in Table 6. To our surprise, it is the interest incomes from

non-loan assets and non-interest incomes that drive the BHCs to speculate in IR derivatives

markets.9 The coefficients of avg.II-nonloan, and avg.non-II in the models (3) - (6) are all

9 In our previous version, we hypothesize that banks speculate to make extra money to supplement the incomes from

their main business, traditionally, the loan business. We use a variable, the interest incomes from both loan and non-

25

strongly significantly negative, while the coefficients of avg.II-loan are insignificant in the

model (1) and positively significant in the model (2). This finding implicates that non-loan

business has played such an important role in bank earnings performance that a lower level of

income from these non-traditional areas would push them to engage in risky business for extra

money. Furthermore, as non-interest incomes are mostly coming from risky, non-traditional

businesses, this finding implicates that a lower income from risky business may lead to more

activities in risky business.

5.5. Hedging FX derivatives activities revisited: stabilization of foreign cash flows

Though the results about the hedging FX derivatives activities (Panel B, Table 4) do not support

the cash flow stabilization hypothesis, banks may hedge on FX derivatives to smooth a specific

category of cash flows related to their foreign business. Now we examine this alternative

hypothesis. Using foreign interest incomes and foreign interest expenses respectively in place of

total cash flows in the calculation of waveCF, we compute two variables, waveXCF-in and

waveXCF-out, to proxy for the fluctuations of bank cash-in-flows from, and cash-out-flows to

foreign countries. These two variables are then put, separately, in the regressions to examine

whether banks’ hedging FX derivatives activities are associated with the fluctuations of their

cash flows in or out of the home country. In Table 7 that reports the results, we find that all

coefficients of waveXCF-in and waveXCF-out are positive at levels of 10% or above in the

models (1) –(4). When we put both variables in a specification, shown in the models (5) and (6),

the significance of waveXCF-out disappears while waveXCF-in remains significantly positive

because of the correlation between them. This evidence indicates that banks would hedge in FX

derivatives markets to stabilize their cross-border cash flows.

5.6. Do banks that are more risk-seeking speculate more when markets are volatile?

loan assets scaled by total assets to test this hypothesis. A further classification of interest incomes reveals that this

hypothesis is not true.

26

In this section, we examine the additional hypothesis related to the risk-favoring hypothesis.

Naturally, we expect that banks that are more inclined to take risk would engage in more

speculative derivatives activities when the financial markets are volatile—they would like to take

more advantage of the market volatility. To test this hypothesis, we add two kinds of interactions,

separately, into the tobit models. One interaction is simply the product of avg.RCI and STD. The

other is STD multiplied by a dummy, up-avg.RCI, which equals one when avg.RCI is above a

cut-off, or zero otherwise. Because avg.RCI has a significantly positive coefficient in the

regressions, we are sure that we can find a cut-off of avg.RCI so that up-avg.RCI surely has a

significant positive coefficient if it replaces avg.RCI in the regressions. After error-and-trials, we

obtain this cut-off of 70 (55) percentiles in IR (FX) sample. If this additional hypothesis holds,

we expect a positive sign for the coefficients of the interactions.

We perform our analysis on the IR and FX derivatives samples and show the results

respectively in Panel A and B, Table 8. In both panels, the models (1), (2), and (3) include

avg.RCI*STD, and the models (4), (5), and (6) substitute up-avg.RCI and up-avg.RCI*STD for

avg.RCI. In (1), (2), (3) of Panel A for IR sample, the coefficients of avg.RCI are significantly

positive as we expect, but the coefficients of avg.RCI*STD are significantly negative, while the

coefficients of STD are still significantly positive. Obviously, the unreasonable signs of the

coefficients of avg.RCI*STD result from the highly correlation between avg.RCI*STD and

avg.RCI. The results from the models (4), (5), and (6) are very similar; the coefficients of up-

avg.RCI*STD are significantly negative. These results from IR sample do not support our

hypothesis.

Results in Panel B for FX sample are different; both interactions, avg.RCI*STD and up-

avg.RCI*STD have significantly positive coefficients, the coefficients of avg.RCI and up-

avg.RCI are significantly positive, but the significances of STD disappear. These results indicate

that on FX derivatives markets, banks that are more risk-seeking would like to take more

advantage of the market volatility than would banks that are less risk-seeking. Comparing with

the results from IR sample, we may conclude that bank speculative behaviors are more

aggressive in FX derivatives markets than in IR derivatives markets.

27

5.7. Which banks take advantage of market volatility?

Our study has shown that large banks engage in more speculative derivatives activities than

small banks do. However, it does not necessarily mean that large banks would like to take more

risks. The large banks may do so possibly because of the economy of scale and their advantages

in information collection associated with large scales. On the other hand, large banks may

undertake more risks because they are “too big to fail” (Stern and Feldman, 2004; Ennis and

Malek, 2005; Marques, Correa, and Sapriza, 2013; Afonso, Santos, and Traina, 2014). It is

difficult to differentiate these two kinds of motivations in large banks’ risk-taking behaviors. By

investigating which banks, large or small, would take advantage of the market volatility, our

study can help to answer this question in a view of speculative use of derivatives. If large banks

engage in more speculative activities than small banks do when markets are volatile, after

controlling the effects of size which proxies for advantages in scale and information, we might

conclude that large banks take more risks because they are “too big to fail”. We create three size

portfolios of small banks, middle banks, and large banks, with the total assets less than $10

billion, between $10 and $50 billion, and more than $50 billion, respectively, and then include in

the tobit model the newly-created size dummies and their interactions with STD, replacing Size,

Size2, and STD. The coefficients of these three interactions capture the extents to which the

banks in different portfolios take advantage of market volatility. The results are presented in

Table 9.

We are not surprised that the coefficients of L-Banks and M-Banks are significantly

positive, which explains that large and middle banks engage in more speculative activities

because of their advantages over small banks in terms of scale and information. And yet, we are

surprised at the comparison between the coefficients of the three interactions in the IR sample

and their counterparts in the FX sample. The coefficients of the large bank interactions are

significantly (at 5% level) greater than zero in the FX derivatives sample, but not in the IR

derivatives sample, while the coefficients of the small bank interactions manifest vice versa,

though their effects in IR sample are not so strong. The results implicates that in the FX

28

derivatives markets, large bank speculators respond actively to market volatility but middle and

small bank speculators do not, whereas in the IR derivatives markets, small bank speculators

respond actively to market volatility but middle and large bank speculators do not. Banks behave

in different ways in different derivatives markets. Even though the results are mixed for the “too

big to fail” issue, they are useful for banking regulations.

5.8. Robust test to control the effects of top banks

OCC quarterly reports point out that usually the top four banks with the most derivatives

activities hold above 90% of all derivative contracts. We are concerned that the activities of these

top banks would drive the results of our analysis. To make sure that our results are universal,

rather than the consequences of the behaviors of the top banks, we identify the top four banks in

terms of the sum of four categories of derivative contracts in each quarter in the main sample,

remove the top four banks from the regression samples, and then repeat our analysis. We find

that the removal of the top four banks does not change our conclusions at large. We report part of

our repetition—the analysis of speculative IR and FX derivatives activities—in Table 10.

The results in Table 10 are basically similar as the results in Table 3, though the effects of

AAA-STD in Panel A for IR sample become weak. Thus, we conclude that the dominance of

large banks on derivatives markets does not bias our main results. The characteristics of the bank

derivatives behaviors revealed in our study are universal.

6. Conclusion

Using the total notional amounts of derivative contracts for trading and non-trading

purposes, reported in the Federal Reserve Y-9C filings, to proxy for the bank speculative and

hedging derivatives activities, this paper analyzes commercial banks’ motivations to speculate

and hedge in derivatives markets. We hypothesize that banks speculate on derivatives under the

pressure to improve their earnings performance and out of impetus to take risk and that banks use

derivatives to hedge their risk exposure in order to smooth their cash flow as well as liquidity.

The evidence from our analysis strongly supports these hypotheses. The bank speculative

29

activities, especially on the IR derivatives markets, though not on FX derivatives markets, are

negatively associated with their previous cash flows and/or net incomes, indicating that banks

speculate to make off-balance-sheet incomes to improve their profitability. In particular, we

show that speculative IR derivatives activities are driven by the poor performance of non-

traditional businesses. We construct the index (RCI) that integrates a series of bank

characteristics—risk taking in equity, loans, fund raising, and liquidity—to proxy for banks’

overall risk condition, and find that the higher historical RCIs, representing banks’ higher risk

preference, generally lead to more speculative activities in all four categories of derivatives.

More interestingly, the results from IR and FX samples show that the bank speculative

derivatives activities are positively associated with the volatility of the underlying-assets markets,

adding evidence to our hypothesis that risk-seeking is a significant factor in banks’ culture and/or

bankers’ personalities that would affect their decision concerning speculative activities.

Furthermore, we find that the bank hedging derivatives activities are positively related to

the previous fluctuations in cash flows and liquidity in IR sample, and to the previous

fluctuations in cash flows associated with foreign business in FX sample. These results indicate

that the motivation for banks to hedge is to smooth their cash flow and liquidity and, in turn, to

enhance the bank value. However, we do not have evidence that banks’ current risk profile,

measured by RCI, affects their hedging derivatives activities; the coefficients of RCI are always

insignificant in our regressions. Possibly, it implicates that the bank hedging derivatives

activities are simply operational, associated only with specific transactions.

Though there are common features, there are sort of differences in the bank activities on

different derivatives markets. We find, among others, that banks seem to be more aggressive in

FX derivatives markets because their speculation on FX derivatives is merely driven by their

risk-seeking incentives—their past earning performance does not have significant effects on FX

derivatives activities, and their risk-seeking incentives are sensitive to the volatility of foreign

exchange markets.

Current literature does not have many studies on commercial banks’ speculative

behaviors, probably because banks usually put their dealing activities and speculative activities

30

together and their pure speculative activities are unobservable. Using notional amounts of

derivative contracts for trading and non-trading purposes, our models capture the fundamental

characteristics that distinguish the speculative behavior from the hedging behavior, which are

especially pertinent to IR and FX derivatives, the two main derivatives instruments on the

markets. The trading notional amounts are usually negatively associated with previous cash

flows and/or net incomes, but the non-trading notional amounts are not. The higher trading

notional amounts usually accompany higher overall risk condition indexes, historical as well as

current, but the non-trading notional amounts do not. The IR and FX trading notional amounts

respond to the market volatility, but their non-trading counterparts do not. The IR non-trading

notional amounts are positively associated with the fluctuations of the bank cash flow and

liquidity and with the account of loans in the assets, but their trading counterparts are not. These

results not only support our speculation and hedge hypotheses, meanwhile, also reveal that the

notional amounts of derivative contracts for trading and non-trading purposes are appropriate

proxies for the bank speculative and hedging activities.

Still, there are puzzles in the bank behaviors in derivatives markets. For instance, why do

large and small banks speculate on IR and FX derivatives differently when the markets are

volatile? Why do small banks hedge more in EQ and CM derivatives markets? Why do banks

hedge more actively when the gold market is more volatile? Furthermore, we are wondering how

the speculative derivatives activities affect banks’ main business, performance, and risks. We

expect more studies in this area so that we can better understand the bank behaviors and improve

the risk management and the banking regulations.

References

Afonso, Gara, João Santos, and James Traina, 2014, Do “too-big-to-fail” banks take on more

risk?, Economic Policy Review 20, 1-18.

Bharath, Sreedhar T., Paolo Pasquariello, and Guojun Wu, 2009, Does Asymmetric information

drive capital structure Decisions?, Review of Financial Studies 22, 3211-3043.

31

Boyd, John and Gianni Nicolo, 2005, The theory of bank risk taking and competition revisited,

Journal of Finance 60, 1329-1343.

Brewer, Elijah III, Bernadette A. Minton, and James T. Moser, 2000, Interest rate derivatives and

bank lending, Journal of Banking and Finance 24, 353-379.

Brewer, Elijah III, William Jackson III, and James Moser, 2001, The value of using interest rate

derivatives to manage risk at U.S. banking organizations, Economic Perspectives 25, 49-66

DeYoung, Robert, Emma Y. Peng, Meng Yan, 2013, Executive compensation and policy choices

at U.S. commercial banks, Journal of Financial and Quantitative Analysis 48, 165-196.

Ellul, Andrew, and Vijay Yerramilli, 2013, Stronger risk controls, lower risk: evidence from U.S. bank

holding companies, Journal of Finance 68, 1757–1803.

Fahlenbrach, Rudiger, Robert Prilmeier, and Rene M. Stulz, 2012, This time is the same: using

bank performance in 1998 to explain bank performance during the recent financial crisis,

Journal of Finance 67, 2139-2186.

Froot, Kenneth, David Scharfstein, and Jeremy Stein, 1993, Risk management: coordinating

corporate investment and financing policies, Journal of Finance 48, 1629-1658.

Ennis, Huberto M., and H.S. Malek, 2005, Bank risk of failure and the too-big-to-fail policy,

Economic Quarterly 91/2, 21-44.

Gatev, E., Til Schuermann, and Philip E. Strahan, 2007, How Do Banks Manage Liquidity Risks?

Evidence from the Equity and Deposit Markets in the Fall of 1998, In M. Carey, & R. M.

Stulz, The Risks of Financial Institutions, 105-132, University of Chicago Press.

Geczy, Chris, Bernadette Minton, and Catherine Schrand, 1997, Why firms use currency

derivatives, Journal of Finance 52, 1323-1354.

Geczy, Chris, Bernadette Minton, and Catherine Schrand, 2007, Taking a view: corporate

speculation, governance and compensation, Journal of Finance 62, 2405-2443.

Graham, John R., and Daniel A. Rogers, 2002, Do firms hedge in response to tax incentives?

Journal of Finance 57, 815-839.

Guay, Wayne, and S.P. Kothari, 2003, How much do firms hedge with derivatives? Journal of

Financial Economics 70, 423-461.

32

Heckman, James J., 1979, Sample selection bias as a specification error, Econometrica 47, 153-

161.

Hentschel, Ludger, and S.P. Kothari, 2001, Are corporations reducing or taking risks with

derivatives? Journal of Financial and Quantitative Analysis 36, 93-118.

Ivashina, Victoria, and David Scharfstein, 2010, Bank lending during the financial crisis of 2008,

Journal of Financial Economics 97, 319-338.

Leland, Hayne E., 1998, Agency costs, risk management, and capital structure, Journal of

Finance 53, 1213-1243.

Leung, Siu Fai, and Shihti Yu, 1996, On the choice between sample selection and two-part

models, Journal of Econometrics 72, 197-229.

Marques, Luis B., Ricardo Correa, and Horacio Sapriza, 2013, International evidence on

government support and risk taking in the banking sector, Working Paper No. 13/94, IMF.

Mian, Shehzad L., 1996, Evidence on corporate hedging policy, Journal of Financial and

Quantitative Analysis 31, 419-439.

Minton, Bernadette A., Rene Stulz, and Rohan Willianmson, 2009, How march do banks use

credit derivatives to hedge loans?, Journal of Financial Services Research 35, 1-31.

Myers, Stewart C., 1977, Determinants of corporate borrowing, Journal of Financial Economics

5, 147-175.

Nance, Deanne, Clifford Smith, and Charles Smithson, 1993, On the determinants of corporate

hedging, Journal of Finance 48, 267-284.

Office of the Comptroller of the Currency, 2013, OCC's Quarterly Report on Bank Trading and

Derivatives Activities First Quarter 2013, Washington, D.C. (http://www.occ.gov).

Purnanandam, Amiyatosh, 2007, Interest rate derivatives at commercial banks: An empirical

investigation, Journal of Monetary Economics 54, 1769-1808.

Puhani, Patrick A., The Heckman correction for sample selection and its critique, Journal of

Economic Surveys 14, 53-68.

Smith, Clifford W., and Rene Stulz, 1985, The determinants of firms’ hedging policies, Journal

of Financial and Quantitative Analysis 20, 391-405.

33

Stern, Gary H., and Ron J. Feldman, 2004, Too big to fail: the hazards of bank bailouts,

Brookings Institution Press, Washington, D.C.

Stulz, Rene, 1984, Optimal hedging policies, Journal of Financial and Quantitative Analysis 19,

127-140.

Stulz, Rene, 1996, Rethinking risk management, Journal of Financial and Quantitative Analysis

9, 8-24.

34

Appendix: Variable Definitions

Variables Definition

Panel A: Variables of Derivatives Activities(1)

Spec.Notional notional amounts of derivative contracts for trading purposes / total assets (BHCK2170)

Hedg.Notional notional amounts of derivative contracts for non-trading purposes / total assets

Panel B: Variables of Earnings Performance (2)

II-loan interest incomes generated by loan assets (BHCK4435+BHCK4436+BHCKF821+BHCK4059

+BHCK4065) / the total assets

II-nonloan interest incomes generated by non-loan assets (BHCK4107– BHCK4435 – BHCK4436 –

BHCKF821 – BHCK4059 –BHCK4065) / the total assets

non-II non-interest incomes (BHCK4079+BHCK3521+BHCK3196) / the total assets

CF cash flow (BHCK4107+BHCK4079+BHCK3521+BHCK3196) / total assets

ROA net income (BHCK4340) / total assets

Panel B: Variables Describing the Fluctuations in Cash Flow and Liquidity

waveCF average of the absolute changes in CF across previous four quarters

waveLiq average of the absolute changes in Liq across previous four quarters

waveXCF-in foreign interest incomes (BHCK4059) / the total assets

waveXCF-out foreign interest expense (BHCK4172) / the total assets

Panel D: Variables Describing Risk Conditions

Cap.ratio 1 – (total equity / total assets) = 1 – (BHCK3210 / BHCK2170)

C&I.ratio commercial and industrial loans (BHCK1763 + BHCK1764) / total loans (BHCK2122)

Depo.ratio 1 – (core deposits / total assets); core deposits = (total deposits – total time deposits of

over $100,000 – total brokered retail deposits) = (BHCB2210 + BHCB3187 + BHCB2389 +

BHCB6648 + BHOD3189 + BHOD3187 + BHOD2389 + BHOD6648 – BHDMA243 – BHDMA164)

Liq.ratio 1 – Liq

RCI the first principal component of the correlation matrix of Cap.ratio, C&I.ratio,

Depo.ratio, and Liq.ratio

avg.RCI the average of RCI across previous four quarters

up-avg.RCI a dummy equal to one when avg.RCI is above the 70 (55) percentiles in IR (FX)

sample, or zero otherwise

Panel E: Other Variables

Assets book value of total assets (BHCK2170) adjusted by annual CPI of 2013

Size logarithm of total assets (BHCK2170) adjusted by annual CPI of 2013

35

Growth growth rate of net income across previous four quarters

L2A total loans / total assets

D2L total deposits / total loans

Liq total liquid assets / the total assets; total liquid assets = (BHCK0081 + BHCK0395 +

BHCK0397 + BHCK1754 + BHCK1773 + BHDMB987 + BHCKB989 + BHCK1350)

Panel F: Variables of Financial Markets

STD standard deviation of the financial indexes representing prices of the underlying assets

on the quarterly basis after the indexes are converted to daily yields (percent changes)

AAA-STD STD for the interest rate of AAA bonds

Partner-STD STD for the Nominal Other Important Trading Partners Dollar Index

S&P-STD STD for S&P 500 index

Gold-STD STD for S&P gold price index

(1) When combined with IR, FX, EQ, or CM, it refers to the notional amounts of the specific derivative

contracts for trading or non-trading purposes. For instance, IR Spec.Notional is the notional amounts of

the interest rate derivative contracts for trading purposes divided by the total assets, used to proxy for the

speculative interest rate derivatives activities.

(2) We add a prefix of avg. to a bank characteristic variable to refer to the average of the variable across

previous four quarters. For example, in the analysis of the speculation hypotheses, we use avg.RCI to

proxy for the bank risk-seeking propensity.

36

Table 1

Overall Description of U.S. BHCs’ Derivatives Activities from 1995 to 2013 This table describes the overall U.S. BHCs’ derivatives activities represented by the notional amounts of derivative contracts at the year-ends

from 1995 to 2013. The year-end samples include the BHCs that, with non-missing total assets and total loans greater than zero, report

derivatives positions in their FR Y-9C forms of fourth quarter. Panel A, B, C, and D are for IR, FX, EQ, CM derivative contracts,

respectively. In each panel, we first report the number of BHCs using the derivative contracts, regardless of their purposes. Then, we describe

their derivatives activities for trading purposes and for non-trading purposes respectively; we show the number of users, the aggregate

notional amounts of the derivative contracts, and the aggregate notional amounts over the aggregate assets across all BHCs involved (Times

of Assets).

Year

Panel A: IR Derivative Contracts Panel B: FX Derivative Contracts

#

of BHCs

For Trading Purposes For Non-Trading Purposes #

of BHCs

For Trading Purposes For Non-Trading Purposes

#

of

BHCs

Notional

Amounts

(trillion)

Times

of

Assets

#

of

BHCs

Notional

Amounts

(trillion)

Times

of

Assets

#

of

BHCs

Notional

Amounts

(trillion)

Times

of

Assets

#

of

BHCs

Notional

Amounts

(trillion)

Times

of

Assets

1995 147 93 $10.520 3.033 87 $0.109 0.047 103 87 $5.469 1.585 31 $0.079 0.050

1996 125 76 $12.853 3.518 82 $0.116 0.043 93 75 $6.278 1.705 31 $0.106 0.063

1997 124 74 $17.362 4.077 88 $0.177 0.059 78 68 $7.392 1.779 26 $0.144 0.062

1998 109 72 $31.317 5.745 74 $0.312 0.088 68 60 $9.287 1.740 26 $0.178 0.049

1999 113 66 $37.682 5.728 82 $0.375 0.080 66 52 $9.712 1.553 32 $0.179 0.042

2000 122 63 $47.470 6.487 93 $0.380 0.070 71 55 $10.470 1.480 34 $0.197 0.044

2001 255 67 $56.346 7.122 244 $3.101 0.333 87 51 $10.241 1.349 59 $0.114 0.017

2002 315 77 $72.135 8.105 298 $3.476 0.348 91 55 $11.046 1.337 57 $0.158 0.023

2003 403 84 $93.873 9.316 387 $3.819 0.333 95 62 $12.888 1.367 56 $0.136 0.016

2004 462 92 $123.382 9.932 442 $4.579 0.328 97 68 $15.973 1.337 56 $0.340 0.031

2005 560 104 $129.871 9.889 533 $4.691 0.318 92 61 $14.737 1.182 56 $0.289 0.027

2006 417 80 $108.934 10.658 402 $3.447 0.304 71 48 $11.751 1.240 41 $0.234 0.030

2007 440 93 $129.750 11.422 417 $2.631 0.206 65 45 $16.583 1.559 38 $0.289 0.031

2008 471 105 $137.153 11.740 436 $2.356 0.179 68 42 $15.058 1.357 45 $0.228 0.022

2009 527 115 $237.083 17.700 486 $2.850 0.188 69 47 $20.973 1.620 44 $0.431 0.035

2010 533 121 $239.212 17.834 492 $2.530 0.164 78 55 $24.673 1.875 49 $0.357 0.027

2011 533 117 $238.576 17.321 498 $4.971 0.314 72 51 $28.765 2.168 47 $0.710 0.052

2012 617 137 $223.447 15.282 580 $4.106 0.246 79 53 $30.397 2.180 50 $0.861 0.062

2013 650 143 $240.993 15.878 611 $4.149 0.241 79 56 $32.097 2.149 49 $0.794 0.056

37

Year

Panel C: EQ Derivative Contracts Panel D: CM Derivative Contracts

#

of BHCs

For Trading Purposes For Non-Trading Purposes #

of BHCs

For Trading Purposes For Non-Trading Purposes

# of

BHCs

Notional Amounts

(trillion)

Times of

Assets

# of

BHCs

Notional Amounts

(trillion)

Times of

Assets

# of

BHCs

Notional Amounts

(trillion)

Times of

Assets

# of

BHCs

Notional Amounts

(trillion)

Times of

Assets

1995 15 14 $0.250 0.150 2 <$0.001 0.001 21 17 $0.143 0.079 5 <$0.001 0.001

1996 14 14 $0.206 0.113 3 <$0.001 0.002 21 16 $0.180 0.086 7 <$0.001 <0.001

1997 18 16 $0.340 0.143 7 $0.006 0.014 22 19 $0.172 0.063 4 <$0.001 0.001

1998 28 22 $0.698 0.180 8 $0.002 0.002 17 15 $0.211 0.057 3 <$0.001 <0.001

1999 29 26 $1.037 0.200 6 $0.002 0.002 18 17 $0.295 0.062 1 <$0.001 <0.001

2000 33 25 $1.382 0.238 11 $0.002 0.002 20 18 $0.339 0.060 2 <$0.001 <0.001

2001 49 27 $1.286 0.197 27 $0.006 0.001 22 18 $0.285 0.047 5 <$0.001 0.001

2002 64 30 $1.191 0.164 41 $0.002 <0.001 23 22 $0.374 0.057 2 <$0.001 0.001

2003 83 31 $1.310 0.159 56 $0.009 0.002 20 19 $0.386 0.055 2 $0.001 0.002

2004 95 29 $1.887 0.183 74 $0.004 0.001 27 25 $0.575 0.059 2 $0.006 0.017

2005 104 30 $2.405 0.226 79 $0.006 0.002 37 32 $0.897 0.085 5 $0.006 0.011

2006 61 20 $3.210 0.403 43 $0.007 0.004 25 21 $0.932 0.116 6 $0.005 0.004

2007 62 21 $3.899 0.417 46 $0.010 0.002 24 20 $1.182 0.131 8 $0.005 0.003

2008 54 19 $3.451 0.359 43 $0.032 0.006 27 21 $1.113 0.119 10 $0.006 0.003

2009 59 24 $5.315 0.462 43 $0.057 0.008 28 23 $3.251 0.291 8 $0.006 0.002

2010 63 26 $5.239 0.451 47 $0.072 0.017 30 25 $3.699 0.332 9 $0.014 0.003

2011 56 22 $5.937 0.513 41 $0.054 0.016 28 21 $4.115 0.372 9 $0.031 0.007

2012 62 22 $5.486 0.466 48 $0.026 0.005 34 21 $3.918 0.351 16 $0.018 0.003

2013 58 24 $6.704 0.534 43 $0.018 0.003 35 22 $3.493 0.297 16 $0.012 0.002

38

Table 2

Summary Statistics of The Main Sample (Entire Panel) The pooled cross-sectional time series sample has bank-quarter observations of U.S. BHCs which, with non-

missing total assets and total loans greater than zero, report notional amounts of any categories of derivative

contracts in any quarter of a calendar year during the period from 1995 to 20013. The sample comprises 31,849

bank-quarter observations involving 1,519 unique commercial banks. Panel A presents the descriptive statistics

of BHCs’ derivatives activities, and Panel B presents the descriptive statistics of the key BHCs’ characteristics.

The detailed definitions of the variables are provided in the Appendix.

Variable Mean Std. Dev. Min P25 Median P75 Max Obs.

Panel A: Variables of Derivatives Activities

IR Spec.Notional 0.310 2.579 0 0 0 0 56.173 31709

FX Spec.Notional 0.070 0.457 0 0 0 0 7.179 31696

EQ Spec.Notional 0.008 0.079 0 0 0 0 2.529 31679

CM Spec.Notional 0.005 0.049 0 0 0 0 2.444 31680

IR Hedg.Notional 0.066 0.311 0 0.001 0.013 0.050 13.894 31768

FX Hedg.Notional 0.003 0.021 0 0 0 0 1.053 31708

EQ Hedg.Notional 0.001 0.007 0 0 0 0 0.509 31696

CM Hedg.Notional 0.000 0.002 0 0 0 0 0.142 31690

Panel B: Variables of Bank Characteristics

Assets (billion) 31.078 156.508 0.083 0.747 1.623 7.200 2649.610 31849

Size 7.928 1.788 4.419 6.616 7.392 8.882 14.790 31849

Growth -0.173 25.766 -1468.300 -0.030 0.038 0.135 1591.070 29774

L2A 0.655 0.140 0.002 0.593 0.676 0.745 0.979 31849

Liq 0.272 0.123 0,004 0.189 0.256 0.335 0.969 31849

CF 0.018 0.010 -0.234 0.014 0.016 0.020 0.277 31849

ROA 0.002 0.005 -0.316 0.001 0.002 0.003 0.115 31842

waveCF 0.002 0.003 0.000 0.001 0.001 0.001 0.192 29775

waveLiq 0.019 0.014 0.001 0.010 0.015 0.023 0.242 29775

Cap.ratio 0.909 0.043 0.182 0.897 0.914 0.928 1.678 31849

C&I.ratio 0.176 0.120 0.000 0.096 0.154 0.225 1.000 31849

Depo.ratio 0.405 0.155 0.043 0.304 0.377 0.465 1.000 31849

Liq.ratio 0.728 0.123 0.040 0.665 0.746 0.811 0.996 31849

RCI 0.275 0.093 0 0.215 0.259 0.313 1 31849

39

Table 3

Analysis of Speculative Derivatives Activities This table shows the results of the regression analysis to examine the factors that motivate the bank speculative derivatives activities. We estimate tobit model

on the pooled cross-sectional time series samples that have bank-quarter observations and span from 1995 to 20013. Panel A, B, C, and D are for IR, FX, EQ,

and CM derivatives samples, respectively. In each panel, Part I presents the results of the main models using Spec.Notional as dependant variable and Part II

shows the results of the compared models using Hedg.Notional as dependant variable, both dependent variables having a left-censored limit of zero. In the

compared models, we use exactly the same explanatory variables as those in the main models so that we can show how speculative and hedging activities are

different from each other. The independent variables relevant to our speculation hypotheses are avg.CF, avg.ROA, avg.RCI, and STDs. The intercept is included

in each specification but not reported. The detailed definitions of the variables are provided in the Appendix. Robust standard errors are clustered at the BHC

level and t-statistics based on the robust standard errors are reported in parentheses underneath the coefficients. The symbols ***, **, and * denote statistical

significance at the 1%, 5%, and 10% levels, respectively.

40

Panel A: IR Derivative Contracts

Part I: Main Models Dependent Variable: Spec.Notional

Part II: Compared Models Dependent Variable: Hedg.Notional

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Size 0.937*** 0.983*** 0.931*** 0.959*** 0.937*** 0.984*** 0.028*** 0.022*** 0.028*** 0.023*** 0.028*** 0.022***

(7.99) (7.85) (7.96) (7.87) (8.01) (7.86) (6.76) (5.87) (6.74) (6.12) (6.79) (5.91)

Size2 0.289*** 0.270*** 0.284*** 0.270*** 0.289*** 0.270*** 0.006*** 0.007*** 0.006*** 0.007*** 0.006*** 0.007***

(5.32) (5.11) (5.16) (5.04) (5.32) (5.11) (4.28) (4.70) (4.16) (4.59) (4.20) (4.67)

Growth -0.013 -0.014 -0.012 -0.010 -0.012 -0.011 -0.000 -0.000 0.000 0.000 0.000 0.000

(-0.84) (-0.87) (-0.80) (-0.67) (-0.83) (-0.73) (-0.32) (-0.24) (0.42) (0.37) (0.29) (0.32)

L2A -0.211 -0.321 -0.456 -0.622 -0.210 -0.314 0.134*** 0.111*** 0.136*** 0.122*** 0.135*** 0.112***

(-0.18) (-0.27) (-0.39) (-0.53) (-0.17) (-0.26) (3.02) (2.64) (3.12) (2.84) (3.05) (2.66)

avg.CF -88.974*** -65.746***

-88.678*** -62.757*** 5.106*** 2.843***

5.538*** 3.213***

(-3.33) (-3.72)

(-3.23) (-3.42) (4.34) (3.52)

(4.59) (3.76)

avg.ROA

-53.241** -67.253*** -1.973 -18.057

0.213 -0.134 -3.378*** -2.570***

(-2.11) (-3.09) (-0.08) (-0.82)

(0.24) (-0.16) (-3.43) (-2.87)

avg.RCI 3.541*** 3.479*** 3.358*** 3.106** 3.542*** 3.496*** 0.022 0.016 0.056 0.049 0.027 0.022

(2.93) (2.81) (2.68) (2.49) (2.93) (2.83) (0.37) (0.29) (0.97) (0.87) (0.47) (0.38)

AAA-STD

0.352**

0.269*

0.324**

0.005

0.004

0.001

(2.20)

(1.73)

(2.07)

(0.77)

(0.62)

(0.10)

Quarter FEs yes no yes no yes no yes no yes no yes no

# of BHCs 1349 1349 1349 1349 1349 1349 1349 1349 1349 1349 1349 1349

Obs. 27970 27970 27970 27970 27970 27970 27970 27970 27970 27970 27970 27970

Left Bound Obs. 21317 21317 21317 21317 21317 21317 4597 4597 4597 4597 4597 4597

Uncensored Obs. 6653 6653 6653 6653 6653 6653 23373 23373 23373 23373 23373 23373

41

Panel B: FX Derivative Contracts

Part I: Main Models Dependent Variable: Spec.Notional

Part II: Compared Models Dependent Variable: Hedg.Notional

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Size 0.387*** 0.364*** 0.384*** 0.363*** 0.388*** 0.363*** 0.003 0.004** 0.004* 0.004** 0.003 0.004**

(7.63) (7.25) (7.53) (7.19) (7.61) (7.23) (1.57) (2.11) (1.76) (2.16) (1.63) (2.17)

Size2 0.036* 0.032 0.035* 0.032 0.036* 0.032 0.002*** 0.002*** 0.002*** 0.002*** 0.002*** 0.002***

(1.75) (1.56) (1.73) (1.58) (1.75) (1.57) (2.90) (3.11) (2.90) (3.10) (2.89) (3.10)

Growth 0.004 0.006 0.007 0.005 0.004 0.004 0.000 0.000 0.000 0.000 0.001 0.000

(0.48) (0.75) (0.91) (0.62) (0.57) (0.56) (0.79) (0.50) (0.51) (0.56) (1.29) (1.05)

L2A -1.614* -1.599* -1.618** -1.609* -1.612* -1.603* -0.032 -0.033 -0.031 -0.030 -0.031 -0.032

(-1.95) (-1.92) (-1.96) (-1.94) (-1.95) (-1.93) (-0.77) (-0.80) (-0.73) (-0.73) (-0.76) (-0.78)

avg.CF -16.125 -1.421

-15.584 -2.861 1.497*** 0.787

1.691*** 0.981*

(-1.55) (-0.16)

(-1.39) (-0.29) (2.72) (1.58)

(2.96) (1.91)

avg.ROA

-25.958 4.127 -3.691 8.851

1.419 0.479 -1.306 -1.181

(-1.15) (0.22) (-0.17) (0.48)

(0.96) (0.40) (-1.31) (-1.49)

avg.RCI 1.747*** 1.597*** 1.767*** 1.595*** 1.747*** 1.598*** 0.039 0.039 0.037 0.040 0.039 0.039

(3.08) (2.84) (3.13) (2.85) (3.07) (2.84) (1.04) (1.07) (0.96) (1.08) (1.06) (1.09)

Partner-STD

0.258***

0.268***

0.269***

-0.009

-0.010

-0.011

(2.84)

(2.94)

(2.97)

(-1.30)

(-1.48)

(-1.55)

Quarter FEs yes no yes no yes no yes no yes no yes no

# of BHCs 272 272 272 272 272 272 272 272 272 272 272 272

Obs. 6457 6457 6457 6457 6457 6457 6457 6457 6457 6457 6457 6457

Left Bound Obs. 2202 2202 2202 2202 2202 2202 3322 3322 3322 3322 3322 3322

Uncensored Obs. 4255 4255 4255 4255 4255 4255 3135 3135 3135 3135 3135 3135

42

Panel C: EQ Derivative Contracts

Part I: Main Models Dependent Variable: Spec.Notional

Part II: Compared Models Dependent Variable: Hedg.Notional

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Size 0.080*** 0.083*** 0.078*** 0.082*** 0.080*** 0.083*** -0.002*** -0.002*** -0.002*** -0.002*** -0.002*** -0.002***

(5.80) (5.78) (5.73) (5.77) (5.76) (5.69) (-4.39) (-4.33) (-4.39) (-4.31) (-4.45) (-4.40)

Size2 0.009*** 0.008*** 0.010*** 0.009*** 0.009*** 0.008*** 0.000 0.000 0.000 0.000 0.000 0.000

(2.98) (2.56) (3.14) (2.73) (3.03) (2.60) (1.06) (1.17) (1.10) (1.27) (1.05) (1.12)

Growth 0.004 0.004 0.004* 0.004* 0.004 0.004 -0.000 -0.000* -0.000 -0.000* -0.000 -0.000**

(1.50) (1.53) (1.76) (1.73) (1.58) (1.61) (-1.29) (-1.84) (-1.22) (-1.69) (-1.40) (-1.99)

L2A -0.348** -0.317* -0.348** -0.320* -0.346** -0.315* 0.001 -0.002 0.001 -0.001 0.001 -0.002

(-2.08) (-1.84) (-2.08) (-1.84) (-2.06) (-1.83) (0.08) (-0.18) (0.10) (-0.14) (0.08) (-0.18)

avg.CF -4.517 -3.076*

-4.258 -2.673 -0.030 -0.095*

-0.079 -0.165*

(-1.64) (-1.73)

(-1.27) (-1.01) (-0.48) (-1.90)

(-0.76) (-1.69)

avg.ROA

-9.771** -7.888** -1.429 -1.867

-0.004 -0.120 0.195 0.279

(-2.30) (-2.03) (-0.21) (-0.30)

(-0.02) (-0.84) (0.70) (0.99)

avg.RCI 0.369** 0.427** 0.367* 0.417** 0.369** 0.428** 0.021 0.018 0.021 0.017 0.021 0.018

(2.03) (2.24) (1.92) (2.16) (2.03) (2.24) (1.13) (0.95) (1.13) (0.93) (1.13) (0.95)

S&P-STD

-0.006

-0.012

-0.007

-0.000

-0.000

0.000

(-0.85)

(-1.44)

(-0.95)

(-0.29)

(-0.53)

(0.04)

Quarter FEs yes no yes no yes no yes no yes no yes no

# of BHCs 202 202 202 202 202 202 202 202 202 202 202 202

Obs. 4111 4111 4111 4111 4111 4111 4111 4111 4111 4111 4111 4111

Left Bound Obs. 2471 2471 2471 2471 2471 2471 1656 1656 1656 1656 1656 1656

Uncensored Obs. 1640 1640 1640 1640 1640 1640 2455 2455 2455 2455 2455 2455

43

Panel D: CM Derivative Contracts

Part I: Main Models Dependent Variable: Spec.Notional

Part II: Compared Models Dependent Variable: Hedg.Notional

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Size 0.043*** 0.043*** 0.042*** 0.040*** 0.044*** 0.043*** -0.002*** -0.002*** -0.002*** -0.002*** -0.002*** -0.002***

(4.98) (4.89) (4.86) (4.75) (4.96) (4.88) (-3.39) (-3.10) (-3.28) (-3.13) (-3.40) (-3.13)

Size2 0.002 0.002 0.001 0.001 0.002 0.001 0.000 0.000 0.000 0.000 0.000 0.000

(0.68) (0.52) (0.34) (0.47) (0.53) (0.43) (0.92) (1.07) (1.26) (1.16) (0.92) (1.07)

Growth 0.001 0.001 0.000 0.001 -0.000 -0.000 0.000* 0.000* 0.000 0.000 0.000 0.000

(0.83) (0.96) (0.38) (0.50) (-0.31) (-0.15) (1.66) (1.71) (1.36) (1.45) (1.60) (1.54)

L2A -0.176 -0.176 -0.183 -0.203 -0.192 -0.186 -0.007 -0.011 -0.010 -0.011 -0.007 -0.011

(-1.37) (-1.30) (-1.32) (-1.37) (-1.52) (-1.42) (-0.93) (-1.11) (-0.99) (-1.09) (-0.94) (-1.17)

avg.CF -8.786** -7.568***

-10.197** -8.553*** 0.757** 0.265

0.754** 0.238

(-2.16) (-3.03)

(-2.44) (-3.02) (2.18) (1.16)

(2.18) (1.02)

avg.ROA

0.611 -3.531 10.083 7.325

0.784 0.547 0.024 0.248

(0.11) (-0.76) (1.60) (1.31)

(1.09) (1.00) (0.04) (0.50)

avg.RCI 0.243 0.247* 0.306* 0.260* 0.247 0.251* 0.001 -0.009 -0.006 -0.009 0.001 -0.009

(1.61) (1.66) (1.91) (1.71) (1.61) (1.67) (0.05) (-0.49) (-0.34) (-0.53) (0.05) (-0.48)

Gold-STD

-0.017

0.008

-0.014

0.003**

0.003**

0.004**

(-1.00)

(0.52)

(-0.86)

(2.28)

(1.99)

(2.28)

Quarter FEs yes no yes no yes no yes no yes no yes no

# of BHCs 113 113 113 113 113 113 113 113 113 113 113 113

Obs. 2038 2038 2038 2038 2038 2038 2038 2038 2038 2038 2038 2038

Left Bound Obs. 553 553 553 553 553 553 1595 1595 1595 1595 1595 1595

Uncensored Obs. 1485 1485 1485 1485 1485 1485 443 443 443 443 443 443

44

Table 4

Analysis of Hedging Derivatives Activities This table shows the results of the regression analysis to examine the factors that motivate the bank hedging derivatives activities. We estimate tobit model on

the pooled cross-sectional time series samples that have bank-quarter observations and span from 1995 to 20013. Panel A, B, C, and D are for IR, FX, EQ,

and CM derivatives samples, respectively. In each panel, Part I presents the results of the main models using Hedg.Notional as dependant variable and Part II

presents the results of the compared models using Spec.Notional as dependant variable, both dependent variables having a left-censored limit of zero. In the

compared models, we use exactly the same explanatory variables as those in the main models so that we can show how hedging and speculative activities are

different from each other. The independent variables relevant to our hedging hypotheses are waveCF, waveLiq, RCI, and STDs. The intercept is included in

each specification but not reported. The detailed definitions of the variables are provided in the Appendix. Robust standard errors are clustered at the BHC

level and t-statistics based on the robust standard errors are reported in parentheses underneath the coefficients. The symbols ***, **, and * denote statistical

significance at the 1%, 5%, and 10% levels, respectively.

45

Panel A: IR Derivative Contracts

Part I: Main Models Dependent Variable: Hedg.Notional

Part II: Compared Models Dependent Variable: Spec.Notional

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Size 0.028*** 0.023*** 0.030*** 0.025*** 0.029*** 0.024*** 0.925*** 0.948*** 0.920*** 0.946*** 0.922*** 0.949***

(6.79) (6.13) (7.09) (6.38) (7.06) (6.32) (7.96) (7.85) (7.98) (7.87) (7.99) (7.88)

Size2 0.006*** 0.007*** 0.006*** 0.007*** 0.006*** 0.007*** 0.287*** 0.272*** 0.286*** 0.271*** 0.287*** 0.272***

(4.13) (4.59) (4.14) (4.61) (4.11) (4.59) (5.19) (5.06) (5.17) (5.05) (5.19) (5.07)

Growth 0.000 0.000 0.000 0.000 0.000 0.000 -0.019 -0.022 -0.018 -0.021 -0.018 -0.022

(0.23) (0.22) (0.10) (0.09) (0.03) (0.10) (-1.23) (-1.33) (-1.19) (-1.32) (-1.21) (-1.34)

L2A 0.145*** 0.127*** 0.149*** 0.130*** 0.155*** 0.136*** -0.515 -0.690 -0.515 -0.651 -0.548 -0.669

(3.32) (2.97) (3.43) (3.05) (3.59) (3.19) (-0.43) (-0.58) (-0.42) (-0.53) (-0.44) (-0.55)

waveCF 9.558*** 8.735***

8.388*** 7.923*** -64.447** -61.471**

-62.158** -63.001**

(4.91) (4.62)

(4.26) (4.13) (-2.32) (-2.26)

(-2.30) (-2.37)

waveLiq

1.212*** 0.895*** 0.828*** 0.556**

-4.385 -1.152 -1.890 1.185

(4.90) (3.95) (3.44) (2.50)

(-0.80) (-0.24) (-0.35) (0.26)

RCI 0.030 0.016 0.037 0.026 0.025 0.013 3.541*** 3.278*** 3.472*** 3.160** 3.553*** 3.271***

(0.52) (0.28) (0.63) (0.46) (0.43) (0.23) (2.79) (2.58) (2.72) (2.49) (2.80) (2.59)

AAA-STD

-0.001

0.004

-0.001

0.389**

0.357**

0.389**

(-0.19)

(0.57)

(-0.15)

(2.40)

(2.23)

(2.40)

Quarter FEs yes no yes no yes no yes no yes no yes no

# of BHCs 1349 1349 1349 1349 1349 1349 1349 1349 1349 1349 1349 1349

Obs. 27973 27973 27973 27973 27973 27973 27973 27973 27973 27973 27973 27973

Left Bound Obs. 4596 4596 4596 4596 4596 4596 21317 21317 21317 21317 21317 21317

Uncensored Obs. 23377 23377 23377 23377 23377 23377 6656 6656 6656 6656 6656 6656

46

Panel B: FX Derivative Contracts

Part I: Main Models Dependent Variable: Hedg.Notional

Part II: Compared Models Dependent Variable: Spec.Notional

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Size 0.004* 0.004** 0.004* 0.004* 0.004* 0.004* 0.383*** 0.366*** 0.373*** 0.360*** 0.375*** 0.363***

(1.87) (2.18) (1.75) (1.93) (1.69) (1.88) (7.52) (7.20) (7.60) (7.24) (7.58) (7.22)

Size2 0.002*** 0.002*** 0.002*** 0.002*** 0.002*** 0.002*** 0.036* 0.032 0.037* 0.032 0.037* 0.032

(2.78) (3.00) (2.83) (3.07) (2.82) (3.06) (1.78) (1.59) (1.80) (1.59) (1.82) (1.60)

Growth 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.005 0.002 0.005 0.002 0.005

(1.21) (0.79) (1.16) (0.77) (1.24) (0.83) (0.19) (0.59) (0.32) (0.66) (0.28) (0.61)

L2A -0.030 -0.031 -0.031 -0.033 -0.031 -0.033 -1.642** -1.583* -1.712** -1.614* -1.713** -1.608*

(-0.70) (-0.74) (-0.71) (-0.77) (-0.71) (-0.77) (-1.96) (-1.88) (-1.99) (-1.87) (-1.99) (-1.87)

waveCF 0.621 0.363

0.720 0.543 -18.124 -17.907

-13.783 -16.271

(0.99) (0.62)

(1.12) (0.90) (-1.42) (-1.39)

(-1.09) (-1.26)

waveLiq

-0.061 -0.136 -0.090 -0.155

-5.036* -2.096 -4.556 -1.560

(-0.33) (-0.82) (-0.48) (-0.93)

(-1.78) (-0.92) (-1.61) (-0.68)

RCI 0.035 0.036 0.037 0.038 0.036 0.037 1.810*** 1.711*** 1.835*** 1.688*** 1.852*** 1.720***

(0.88) (0.94) (0.95) (1.03) (0.93) (1.00) (3.08) (2.87) (3.15) (2.87) (3.16) (2.90)

Partner-STD

-0.012*

-0.008

-0.008

0.246***

0.307***

0.292***

(-1.72)

(-1.35)

(-1.31)

(2.78)

(2.70)

(2.62)

Quarter FEs yes no yes no yes no yes no yes no yes no

# of BHCs 272 272 272 272 272 272 272 272 272 272 272 272

Obs. 6457 6457 6457 6457 6457 6457 6457 6457 6457 6457 6457 6457

Left Bound Obs. 3322 3322 3322 3322 3322 3322 2202 2202 2202 2202 2202 2202

Uncensored Obs. 3135 3135 3135 3135 3135 3135 4255 4255 4255 4255 4255 4255

47

Panel C: EQ Derivative Contracts

Part I: Main Models Dependent Variable: Hedg.Notional

Part II: Compared Models Dependent Variable: Spec.Notional

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Size -0.002*** -0.002*** -0.002*** -0.002*** -0.002*** -0.002*** 0.100*** 0.101*** 0.098*** 0.099*** 0.098*** 0.099***

(-4.30) (-4.30) (-4.36) (-4.32) (-4.40) (-4.37) (5.64) (5.76) (5.72) (5.86) (5.72) (5.85)

Growth -0.000 -0.000* -0.000 -0.000* -0.000 -0.000* 0.003 0.003 0.003 0.003 0.003 0.003

(-1.24) (-1.95) (-1.25) (-1.90) (-1.25) (-1.94) (1.23) (1.22) (1.39) (1.34) (1.38) (1.34)

L2A -0.000 -0.003 0.000 -0.003 0.000 -0.003 -0.423** -0.393** -0.452** -0.416** -0.452** -0.416**

(-0.04) (-0.31) (0.03) (-0.25) (0.02) (-0.26) (-2.36) (-2.14) (-2.30) (-2.09) (-2.30) (-2.09)

waveCF -0.042 -0.197

-0.081 -0.219 -2.588 -1.662

-1.026 -0.119

(-0.22) (-1.06)

(-0.39) (-1.08) (-0.75) (-0.51)

(-0.29) (-0.04)

waveLiq

0.036 0.010 0.039 0.020

-1.688 -1.471 -1.640 -1.465

(0.51) (0.14) (0.54) (0.29)

(-1.33) (-1.18) (-1.27) (-1.15)

RCI 0.019 0.014 0.019 0.014 0.019 0.014 0.252 0.301 0.247 0.304 0.251 0.305

(0.95) (0.72) (0.95) (0.70) (0.95) (0.71) (1.07) (1.32) (1.07) (1.37) (1.08) (1.35)

S&P-STD

-0.000

-0.000

-0.000

-0.008

-0.007

-0.007

(-0.39)

(-0.54)

(-0.39)

(-0.92)

(-0.95)

(-0.91)

Quarter FEs yes no yes no yes no yes no yes no yes no

# of BHCs 202 202 202 202 202 202 202 202 202 202 202 202

Obs. 4110 4110 4110 4110 4110 4110 4110 4110 4110 4110 4110 4110

Left Bound Obs. 1656 1656 1656 1656 1656 1656 2470 2470 2470 2470 2470 2470

Uncensored Obs. 2454 2454 2454 2454 2454 2454 1640 1640 1640 1640 1640 1640

48

Panel D: CM Derivative Contracts

Part I: Main Models Dependent Variable: Hedg.Notional

Part II: Compared Models Dependent Variable: Spec.Notional

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Size -0.002*** -0.002*** -0.002*** -0.002*** -0.002*** -0.002*** 0.044*** 0.042*** 0.042*** 0.039*** 0.042*** 0.040***

(-3.29) (-3.20) (-3.40) (-3.30) (-3.38) (-3.27) (4.44) (4.37) (4.29) (4.18) (4.36) (4.29)

Growth 0.000* 0.000* 0.000** 0.000* 0.000** 0.000* 0.001 -0.000 0.001 0.000 0.001 0.000

(1.89) (1.88) (2.04) (1.96) (2.06) (1.95) (0.65) (-0.03) (0.78) (0.09) (0.82) (0.09)

L2A -0.012 -0.013 -0.013 -0.013 -0.013 -0.013 -0.181 -0.209 -0.193 -0.224 -0.194 -0.221

(-1.12) (-1.23) (-1.14) (-1.23) (-1.13) (-1.23) (-1.32) (-1.42) (-1.32) (-1.44) (-1.33) (-1.43)

waveCF 0.036 -0.031

0.091 0.012 -3.532 -5.071**

-2.717 -4.137**

(0.15) (-0.13)

(0.40) (0.05) (-1.63) (-2.33)

(-1.26) (-2.12)

waveLiq

-0.039 -0.029 -0.043 -0.029

-0.883 -0.983 -0.762 -0.787

(-0.49) (-0.44) (-0.53) (-0.43)

(-1.01) (-1.21) (-0.86) (-0.98)

RCI -0.013 -0.016 -0.013 -0.016 -0.013 -0.016 0.328** 0.291* 0.327* 0.284* 0.328* 0.293*

(-0.73) (-0.87) (-0.73) (-0.87) (-0.73) (-0.87) (1.96) (1.82) (1.94) (1.78) (1.94) (1.81)

Gold-STD

0.003*

0.003*

0.003*

0.013

0.009

0.010

(1.91)

(1.79)

(1.77)

(0.88)

(0.66)

(0.74)

Quarter FEs yes no yes no yes no yes no yes no yes no

# of BHCs 113 113 113 113 113 113 113 113 113 113 113 113

Obs. 2038 2038 2038 2038 2038 2038 2038 2038 2038 2038 2038 2038

Left Bound Obs. 1595 1595 1595 1595 1595 1595 553 553 553 553 553 553

Uncensored Obs. 443 443 443 443 443 443 1485 1485 1485 1485 1485 1485

49

Table 5

Analysis of Speculative Derivatives Activities with Heckman Selection Model This table shows the results from the second stage in the Heckman test to examine whether the heavy censored data affect our main results. We estimate

Heckman maximum likelihood model on the pooled cross-sectional time series samples that have bank-quarter observations and span the time period

from 1995 to 20013. In the first stage, the probability for a bank to be derivatives speculator or not in a quarter is estimated using the equations (7), (8),

and (9) in the text. In the second stage, the outcome equations follow exactly the specifications of the main models in Table 3, with the Inverse Mills

Ratio included but not reported. Panel A and B are for IR and FX derivatives samples, respectively. The intercepts are included in both selection and

outcome equations but not reported. The detailed definitions of the variables are provided in the Appendix. STDs are AAA-STD and Partner-STD for IR

and FX samples, respectively. Robust standard errors are clustered at the BHC level and z-statistics based on the robust standard errors are reported in

parentheses underneath the coefficients. The symbols ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

50

Panel A: IR Derivatives Contracts Panel B: FX Derivative Contracts

(1) (2) (3) (4) (5) (6) (1) (2) (3) (4) (5) (6)

Outcome Equation Outcome Equation

Size 0.884*** 0.884*** 0.867*** 0.867*** 0.885*** 0.884*** 0.339*** 0.339*** 0.338*** 0.338*** 0.338*** 0.336***

(7.03) (7.03) (6.99) (6.99) (7.03) (7.03) (5.96) (5.96) (6.01) (6.01) (5.91) (5.92)

Size2 0.252*** 0.252*** 0.251*** 0.251*** 0.251*** 0.252*** 0.044** 0.045** 0.045*** 0.045*** 0.044*** 0.045***

(4.66) (4.65) (4.58) (4.58) (4.64) (4.64) (2.54) (2.55) (2.56) (2.57) (2.57) (2.58)

Growth 0.002 0.002 0.003 0.003 0.003 0.003 0.001 0.001 0.001 0.001 0.002 0.001

(0.74) (0.80) (1.08) (1.11) (1.44) (1.47) (0.45) (0.44) (0.60) (0.57) (0.85) (0.81)

L2A 0.347 0.346 0.220 0.220 0.359 0.359 -0.082 -0.085 -0.108 -0.111 -0.090 -0.093

(1.03) (1.03) (0.66) (0.66) (1.08) (1.08) (-0.31) (-0.31) (-0.38) (-0.39) (-0.34) (-0.35)

avg.CF -54.257*** -54.215***

-53.215*** -52.274*** -5.586 -5.301

-7.507 -7.544

(-3.43) (-3.43)

(-3.27) (-3.27) (-0.66) (-0.63)

(-0.82) (-0.83)

avg.ROA

-51.168** -50.709** -8.591 -7.975

-0.963 1.003 10.448 12.511

(-2.41) (-2.40) (-0.41) (-0.38)

(-0.05) (0.06) (0.63) (0.75)

avg.RCI 3.780*** 3.783*** 3.538*** 3.541*** 3.850*** 3.853*** 2.076*** 2.075*** 2.035*** 2.036*** 2.127*** 2.128***

(4.07) (4.08) (3.79) (3.80) (4.10) (4.10) (4.02) (4.03) (4.11) (4.11) (4.09) (4.09)

STD

0.326**

0.270**

0.316**

0.244***

0.262***

0.267***

(2.33)

(2.01)

(2.31)

(2.73)

(2.98)

(3.03)

# of BHCs 1370 1370 1369 1369 1369 1369 274 274 274 274 274 274

Obs. 28276 28276 28273 28273 28273 28273 6478 6478 6478 6478 6478 6478

Censored Obs. 20765 20765 20762 20762 20762 20762 1885 1885 1885 1885 1885 1885

Uncensored Obs. 7511 7511 7511 7511 7511 7511 4593 4593 4593 4593 4593 4593

51

Table 6

Which part of cash flow matters? This table shows the results of the regression analysis to examine which part of cash flow motivates the bank

speculative activities on IR derivatives markets. We estimate tobit model on the pooled cross-sectional time

series sample that have bank-quarter observations and span from 1995 to 20013. The dependent variable is IR

Spec.Notional, with a left-censored limit of zero. The independent variables of interest are avg.II-loan, avg.II-

nonloan, and avg.non-II, representing three components of bank cash flows. The intercepts are included but not

reported. The detailed definitions of the variables are provided in the Appendix. Robust standard errors are

clustered at the BHC level and t-statistics based on the robust standard errors are reported in parentheses

underneath the coefficients. The symbols ***, **, and * denote statistical significance at the 1%, 5%, and 10%

levels, respectively.

(1) (2) (3) (4) (5) (6)

Size 0.929*** 0.967*** 0.900*** 0.968*** 0.952*** 0.980***

(7.98) (7.90) (7.92) (7.87) (7.95) (7.84)

Size2 0.283*** 0.269*** 0.287*** 0.269*** 0.285*** 0.271***

(5.05) (5.02) (5.24) (5.03) (5.25) (5.13)

Growth -0.018 -0.015 -0.016 -0.015 -0.015 -0.016

(-1.20) (-1.00) (-1.07) (-1.01) (-0.97) (-0.99)

L2A -0.807 -0.882 -0.207 -0.419 -0.890 -1.102

(-0.66) (-0.75) (-0.18) (-0.36) (-0.75) (-0.93)

avg.II-loan 45.991 53.750***

(0.68) (3.37)

avg.II-nonloan

-165.535*** -36.064***

(-3.03) (-3.24)

avg.non-II

-74.821*** -82.336***

(-2.73) (-2.91)

avg.RCI 3.156** 3.053** 2.651** 3.110** 3.544*** 3.297***

(2.53) (2.42) (2.19) (2.47) (2.88) (2.67)

AAA-STD

-0.005

0.127

0.333**

(-0.03)

(0.84)

(2.10)

Qtr Fixed Effects yes no yes no yes no

# of BHCs 1349 1349 1349 1349 1349 1349

Obs. 27970 27970 27970 27970 27970 27970

Left Bound Obs. 21317 21317 21317 21317 21317 21317

Uncensored Obs. 6653 6653 6653 6653 6653 6653

52

Table 7

Hedging FX Derivatives Activities Revisited This table shows the results of the regression analysis to examine whether banks hedge on FX derivatives to

stabilize their foreign cash-in-flows and cash-out-flows. We estimate tobit model on the pooled cross-sectional

time series sample that have bank-quarter observations and span from 1995 to 20013. The dependent variable

is FX Hedg.Notional, with a left-censored limit of zero. The independent variables of interest are waveXCF-in,

and waveXCF-out, representing the fluctuations of BHCs’ two-way cash flows. The intercepts are included but

not reported. The detailed definitions of the variables are provided in the Appendix. Robust standard errors are

clustered at the BHC level and t-statistics based on the robust standard errors are reported in parentheses

underneath the coefficients. The symbols ***, **, and * denote statistical significance at the 1%, 5%, and 10%

levels, respectively.

(1) (2) (3) (4) (5) (6)

Size 0.003 0.003* 0.003 0.004* 0.002 0.003*

(1.33) (1.73) (1.40) (1.92) (1.20) (1.70)

Size2 0.002** 0.002*** 0.002*** 0.002*** 0.002** 0.002***

(2.45) (2.71) (2.70) (2.97) (2.45) (2.71)

Growth 0.000 0.000 0.000 0.000 0.000 0.000

(0.92) (0.59) (0.92) (0.59) (0.86) (0.57)

L2A -0.039 -0.039 -0.034 -0.035 -0.039 -0.039

(-0.93) (-0.94) (-0.80) (-0.82) (-0.93) (-0.94)

waveXCF-in 46.808*** 41.378***

38.512*** 40.364***

(3.35) (3.09)

(3.01) (3.15)

waveXCF-out

51.445** 32.040* 21.862 2.482

(2.54) (1.77) (1.21) (0.15)

RCI 0.012 0.015 0.005 0.016 0.003 0.013

(0.31) (0.38) (0.12) (0.40) (0.08) (0.35)

Partner-STD

-0.012*

-0.011

-0.012*

(-1.78)

(-1.60)

(-1.77)

Qtr Fixed Effects yes no yes no yes no

# of BHCs 272 272 272 272 272 272

Obs. 6457 6457 6457 6457 6457 6457

Left Bound Obs. 3322 3322 3322 3322 3322 3322

Uncensored Obs. 3135 3135 3135 3135 3135 3135

53

Table 8

Do banks that are more risk-seeking speculate more when markets are volatile? This table shows the results of the regression analysis to examine whether risk-seeking banks speculate more than risk-averse banks when markets are

volatile. We estimate tobit model on the pooled cross-sectional time series samples that have bank-quarter observations and span from 1995 to 20013. Panel

A and B are for IR and FX derivatives samples, respectively. The dependent variable is Spec.Notional, with a left-censored limit of zero. The independent

variables of interest are two interactions, avg.RCI*STD and up-avg.RCI*STD. up-avg.RCI is a dummy that equals one when avg.RCI is above the 70 (55)

percentile in IR (FX) sample, or zero otherwise. STDs are AAA-STD and Partner-STD for IR and FX samples, respectively. The intercepts are included but

not reported. The detailed definitions of the variables are provided in the Appendix. Robust standard errors are clustered at the BHC level and t-statistics

based on the robust standard errors are reported in parentheses underneath the coefficients. The symbols ***, **, and * denote statistical significance at the

1%, 5%, and 10% levels, respectively.

54

Panel A: IR Derivatives Contracts Panel B: FX Derivatives Contracts

(1) (2) (3) (4) (5) (6) (1) (2) (3) (4) (5) (6)

Size 0.983*** 0.959*** 0.984*** 1.011*** 0.986*** 1.012*** 0.364*** 0.363*** 0.363*** 0.354*** 0.353*** 0.354***

(7.85) (7.87) (7.86) (8.02) (8.07) (8.03) (7.25) (7.19) (7.23) (7.25) (7.18) (7.22)

Size2 0.270*** 0.270*** 0.270*** 0.264*** 0.264*** 0.264*** 0.032 0.032 0.032 0.029 0.029 0.029

(5.11) (5.04) (5.11) (5.00) (4.93) (5.00) (1.55) (1.57) (1.56) (1.45) (1.48) (1.47)

Growth -0.014 -0.010 -0.011 -0.016 -0.013 -0.014 0.006 0.005 0.004 0.006 0.005 0.005

(-0.87) (-0.67) (-0.72) (-1.04) (-0.86) (-0.91) (0.74) (0.63) (0.57) (0.82) (0.65) (0.57)

L2A -0.323 -0.625 -0.316 -1.176 -1.419 -1.171 -1.603* -1.612* -1.606* -1.788** -1.800** -1.790**

(-0.27) (-0.53) (-0.26) (-1.00) (-1.23) (-0.99) (-1.92) (-1.95) (-1.93) (-2.22) (-2.26) (-2.23)

avg.CF -65.768***

-62.806*** -63.001***

-60.321*** -1.363

-2.690 -1.865

-3.621

(-3.72)

(-3.42) (-3.57)

(-3.28) (-0.15)

(-0.28) (-0.21)

(-0.38)

avg.ROA

-67.115*** -17.887

-62.811*** -16.229

3.709 8.158

4.758 10.757

(-3.09) (-0.81)

(-2.93) (-0.74)

(0.20) (0.44)

(0.25) (0.57)

avg.RCI 4.053*** 3.633*** 4.059***

1.183** 1.183** 1.192**

(3.05) (2.74) (3.06)

(2.00) (1.99) (1.99)

avg.RCI*STD -3.889** -3.567** -3.823**

1.811** 1.804** 1.774**

(-2.22) (-2.13) (-2.18)

(2.08) (2.05) (2.01)

up-avg.RCI

0.482** 0.415* 0.484**

0.225** 0.224** 0.229**

(2.18) (1.88) (2.19)

(2.11) (2.06) (2.11)

up-avg.RCI*STD

-1.163*** -1.045*** -1.154***

0.355** 0.355** 0.344**

(-3.24) (-2.99) (-3.21)

(2.09) (2.06) (2.00)

STD 1.461*** 1.289** 1.415*** 0.748*** 0.634*** 0.720*** -0.366 -0.355 -0.343 0.093 0.105 0.111

(2.67) (2.47) (2.59) (3.56) (3.10) (3.44) (-1.36) (-1.27) (-1.23) (1.23) (1.28) (1.39)

# of BHCs 1349 1349 1349 1349 1349 1349 272 272 272 272 272 272

Obs. 27970 27970 27970 27970 27970 27970 6457 6457 6457 6457 6457 6457

Left Bound Obs. 21317 21317 21317 21317 21317 21317 2202 2202 2202 2202 2202 2202

Uncensored Obs. 6653 6653 6653 6653 6653 6653 4255 4255 4255 4255 4255 4255

55

Table 9

Which banks take advantage of market volatility?

This table shows the results of the regression analysis to examine which banks, in terms of their size, take

advantage of market volatility. We estimate tobit model on the pooled cross-sectional time series samples that have

bank-quarter observations and span from 1995 to 20013. Panel A and B are for IR and FX derivatives samples,

respectively. The dependent variable is Spec.Notional, with a left-censored limit of zero. The independent variables

of interest are S-Banks*STD, M-Banks*STD, and L-Banks*STD, representing the interactions of the dummies of

small, middle, and large bank portfolios, respectively, with STDs. STDs are AAA-STD and Partner-STD for IR and

FX samples, respectively. The intercepts are included but not reported. The detailed definitions of the variables are

provided in the Appendix. Robust standard errors are clustered at the BHC level and t-statistics based on the robust

standard errors are reported in parentheses underneath the coefficients. The symbols ***, **, and * denote

statistical significance at the 1%, 5%, and 10% levels, respectively.

Panel A: IR Derivatives Contracts Panel B: FX Derivatives Contracts

(1) (2) (3) (1) (2) (3)

L-Banks 6.259*** 6.127*** 6.263*** 1.203*** 1.196*** 1.199***

(5.18) (5.18) (5.19) (5.63) (5.62) (5.62)

M-Banks 2.728*** 2.589*** 2.731*** 0.515*** 0.510*** 0.514***

(5.53) (5.56) (5.54) (3.71) (3.74) (3.71)

Growth -0.018 -0.013 -0.015 0.003 0.002 0.001

(-0.83) (-0.64) (-0.70) (0.34) (0.19) (0.13)

L2A -2.381 -2.564* -2.371 -2.249*** -2.261*** -2.254***

(-1.60) (-1.68) (-1.59) (-2.91) (-2.95) (-2.94)

avg.CF -63.246***

-59.428*** -1.803

-3.724

(-3.00)

(-2.80) (-0.18)

(-0.35)

avg.ROA

-73.984** -23.828

5.540 11.739

(-2.53) (-0.93)

(0.26) (0.63)

avg.RCI 5.313*** 4.892*** 5.336*** 1.500** 1.497** 1.501**

(3.58) (3.36) (3.59) (2.38) (2.38) (2.38)

S-Banks*STD 0.490* 0.324 0.451* -0.008 -0.001 0.002

(1.91) (1.31) (1.78) (-0.03) (-0.00) (0.01)

M-Banks*STD -0.454 -0.266 -0.437 0.117 0.127 0.127

(-0.98) (-0.56) (-0.94) (1.09) (1.20) (1.20)

L-Banks*STD -0.198 -0.138 -0.235 0.464** 0.483** 0.485**

(-0.45) (-0.31) (-0.53) (2.34) (2.44) (2.46)

# of BHCs 1349 1349 1349 272 272 272

Obs. 27970 27970 27970 6457 6457 6457

Left Bound Obs. 21317 21317 21317 2202 2202 2202

Uncensored Obs. 6653 6653 6653 4255 4255 4255

56

Table 10

Robust Test of Speculation Hypotheses to Control the Effects of Top Banks This table shows the results of the robust test of the speculation hypotheses to control the effects of top banks. From the pooled cross-sectional time series

samples that have bank-quarter observations and span the time period from 1995 to 20013, we remove the top four banks in terms of the total notional amount

of derivative contracts of all four categories in each quarter. Then we repeat our main regression analysis on the reduced samples. Panel A and B display the

results of IR and FX derivatives samples, respectively. The dependent variable is Spec.Notional, with a left-censored limit of zero. The independent variables

relevant to our speculation hypotheses are avg.CF, avg.ROA, avg.RCI, and STDs. STDs are AAA-STD and Partner-STD for IR and FX samples, respectively.

The intercepts are included but not reported. The detailed definitions of the variables are provided in the Appendix. Robust standard errors are clustered at the

BHC level and t-statistics based on the robust standard errors are reported in parentheses underneath the coefficients. The symbols ***, **, and * denote

statistical significance at the 1%, 5%, and 10% levels, respectively.

57

Panel A: IR Derivatives Contracts Panel B: FX Derivatives Contracts

(1) (2) (3) (4) (5) (6) (1) (2) (3) (4) (5) (6)

Size 0.566*** 0.586*** 0.562*** 0.573*** 0.567*** 0.587*** 0.297*** 0.276*** 0.293*** 0.275*** 0.297*** 0.276***

(7.53) (7.66) (7.53) (7.77) (7.54) (7.68) (5.52) (5.25) (5.45) (5.23) (5.51) (5.25)

Size2 0.136*** 0.126*** 0.132*** 0.125*** 0.135*** 0.126*** 0.009 0.005 0.008 0.006 0.010 0.006

(4.61) (4.37) (4.47) (4.34) (4.60) (4.36) (0.38) (0.21) (0.33) (0.23) (0.38) (0.24)

Growth 0.001 0.001 0.002 0.003 0.002 0.002 0.005 0.007 0.007 0.005 0.005 0.005

(0.10) (0.09) (0.25) (0.36) (0.19) (0.28) (0.77) (1.01) (1.03) (0.81) (0.74) (0.75)

L2A 0.475 0.427 0.387 0.294 0.479 0.432 -1.273 -1.238 -1.274 -1.246 -1.274 -1.241

(0.73) (0.66) (0.60) (0.46) (0.73) (0.67) (-1.49) (-1.46) (-1.50) (-1.48) (-1.49) (-1.47)

avg.CF -41.657*** -32.924***

-40.585*** -30.923*** -10.468 -1.174

-10.753 -2.869

(-3.13) (-3.14)

(-2.98) (-2.84) (-1.19) (-0.16)

(-1.17) (-0.36)

avg.ROA

-31.599** -36.513*** -7.183 -12.108

-13.659 5.579 1.930 10.302

(-2.12) (-2.80) (-0.47) (-0.91)

(-0.69) (0.33) (0.11) (0.69)

avg.RCI 2.598*** 2.539*** 2.500*** 2.361*** 2.603*** 2.550*** 1.525*** 1.397*** 1.542*** 1.396*** 1.525*** 1.396***

(3.68) (3.59) (3.52) (3.38) (3.70) (3.62) (3.23) (2.97) (3.27) (2.99) (3.23) (2.98)

STD

0.192*

0.143

0.173*

0.149**

0.160**

0.162**

(1.95)

(1.49)

(1.79)

(1.97)

(2.11)

(2.13)

Qtr Fixed Effects yes no yes yes no yes yes no yes yes no yes

# of BHCs 1347 1347 1347 1347 1347 1347 270 270 270 270 270 270

Obs. 27680 27680 27680 27680 27680 27680 6167 6167 6167 6167 6167 6167

Left Bound Obs. 21317 21317 21317 21317 21317 21317 2202 2202 2202 2202 2202 2202

Uncensored Obs. 6363 6363 6363 6363 6363 6363 3965 3965 3965 3965 3965 3965