A Revisit to Capital Control Policies When Bitcoin Is in Town

Yang(Gloria) Yu

⇤Jinyuan Zhang

†

ABSTRACT

This paper presents a novel notion of Law of One Price violation between Bitcoin (BTC) and Foreign

Exchange markets. We first document the magnitude and statistical properties of the trading gains of

triangle arbitrage which results from the price discrepancy among BTC, US dollars, and another fiat

currency. We further show that violations increase in the capital control intensity of the arbitrage-

linked countries. We argue that this triangular arbitrage opportunity, originating from the price

gap of the domestic currency between two markets, incorporates information about local investors’

demand for the domestic currency. When economic and political uncertainties surge in the home

country, the gap widens. We further test the predictability of our gap measure for foreign exchange

markets and find that the predictability peaks in three days and decays afterward. The pattern is

stronger in recent years. We provide suggestive evidence that this predictability reflects a real shift

in the demand for domestic currency without future reversals.

⇤Yang(Gloria) Yu is at INSEAD, Department of Finance, Fontainebleau, France (Email: gloria.yu@insead.edu)†Jinyuan Zhang is at INSEAD, Department of Finance, Fontainebleau, France (Email: jinyuan.zhang@insead.edu)

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1 Introduction

As of Oct 7th, 2017 the entire cryptocurrency market capitalization totals $148.6BN, with Bit-

coin(BTC) accounting for $72.7BN of it. The space represented by BTC receives mounting attention

from investors, blue-chip financial firms, as well as regulators. The Wall Street Journal reports that

Goldman Sachs Group Inc. is planning to dedicate a new trading operation to BTC and other cryp-

tocurrencies1. Christine Lagarde, Managing Director of the International Monetary Fund has spec-

ulated the triumph of BTC and other cryptocurrencies over central banks and conventional banking2.

Over the years, BTC has been evolving from a geeks’ game coin to a novel asset class. As its

user base, and performance arena shifts to the financial world, it is important to ask whether BTC

trades reflect information beyond the cryptocurrency space. What are the implications of cryptocur-

rency prices on other financial assets? Can BTC serve as a new mechanism to circumvent existing

financial regulations? Will macroeconomic policies such as capital controls become less e↵ective with

cryptocurrencies coming into play?

To shed some light on these questions, we investigate the interplay between the BTC market and

fiat currency markets, and the role of capital control policies and economic and political uncertainties

in the mechanism.

We choose BTC and the foreign exchange (forex) market for two reasons. First, the two are

fundamentally linked by an arbitrage parity as BTC is traded in 88 fiat currencies. In this sense,

BTC is a black market, mirroring the forex market. Second, the two markets are two extremes of

the spectrum regarding being subject to the scrutiny of government regulations. The forex market is

arguably the most monitored due to capital control and foreign exchange policies. Even in countries

with low capital controls, one would expect government interventions in the wake of uncertainties

threatening the home currency value. BTC, however, is still by large out of the regulator’s watch.

In a frictionless capital market, one would expect the absence of any triangular arbitrage oppor-

1https://www.wsj.com/articles/goldman-sachs-explores-a-new-world-trading-bitcoin-15069591282https://fee.org/articles/imf-head-predicts-the-end-of-banking-and-the-triumph-of-cryptocurrency/

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tunities resulting from the price discrepancy between these two markets. However, this is not the

case in reality. In this paper, we establish and document a novel notion of Law of One Price violation

(LOPV) between BTC and other fiat currencies. We document a non-trivial magnitude of triangle

arbitrage profitability between BTC, USD, and another Fiat currency. On average, the daily gross

triangular profitability is 5.76% over all the BTC-fiat trading pairs in our sample, meaning 5.76%

deviations from the arbitrage-free price. LOPV varies over time and cross countries, with a 9.44%

standard deviation.

LOPV and triangular arbitrage profitabilities exist because the two markets are not perfectly

integrated. The LOPV reflects the de jure control and the cost for investors to circumvent those

restrictions. Indeed, we find evidence that the magnitude of LOPV varies across countries due to the

di↵erential country-wise controls and frictions. Cross-country di↵erences in the frictions underneath

open positions of BTC-Fiat arbitrage come mostly from fiat currency market. Across all countries,

frictions in the cryptocurrency market di↵er primarily in the transaction fees charged by exchanges,

which do not count much in percentage terms relative to the total arbitrage return size (LOPV). How-

ever, cross-country di↵erences in the fiat market frictions are not trivial. Frictions in fiat currency

markets come from the institutional environment such as capital controls, forex controls, government

intervention, banking system quality and so on so forth. Indeed, one standard deviation increase

of the capital control intensity index as defined in Fernandez et al. (2016) is accompanied by 19%

higher LOP. We also find that higher trading volume reduces LOPV contemporaneously. The e↵ect

of capital controls on LOPV is more pronounced when trading volume is higher because high trad-

ing volume in low capital control countries is associated with even lower LOPV as more arbitrage

activities are involved.

Next, we argue that LOPV essentially originates from the wedge between forex rates and the BTC

implied forex rates. BTC implied forex rates means the ratio between BTC prices denoted in two

di↵erent fiat currencies. For simplicity, we keep USD as the base currency for forex rates and BTC

implied forex rates throughout the analysis. The signed wedge between the two rates reflects the

currency valuation di↵erence in BTC and forex markets. We take the signed wedge as our measure

for currency di↵erential valuation and call it GAP hereafter. We find that GAP varies both over time

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and across countries. Since GAP results from market frictions, we postulate that GAP changes as

the frictions are more or less binding as a result of market conditions. Specifically, we attribute the

variation in GAP to economic and political uncertainties proxied by the Economic Policy Uncertainty

(EPU) measure in Baker et al. (2016a).

When the EPU surges in the home country, one would expect the demand for the home country

currency shifts down and hence the home currency to depreciate. Similarly, a less favorable expecta-

tion about the US economy explains the appreciation of the domestic currency which takes USD as

base currency. In line with this argument, we find a negative relation between GAP and next month’

EPU in US and a positive relation between GAP and up-to-current EPU in domestic country. This

evidence suggests that investors anticipate future US economic policy uncertainties and adjust their

investment strategies ex-ante. This expectation can be interpreted as expectation for FOMC meet-

ings, presidential election etc.

A fundamental di↵erence between cryptocurrencies and traditional fiat currencies lies in the de-

centralized nature deriving from the blockchain technology, which eliminates intermediaries’ and

governments’ ability to regulate the network. Fiat currencies, on the contrary, are backed by the

credibility of the central government, and hence they are inevitably subject to regulation and gov-

ernment intervention. These cross-border transaction barriers make cryptocurrencies attractive as

an alternative tool to facilitate global transactions owing to the low cost and the fast transaction,

especially when investors have urgent needs to exchange currency or transfer their wealth o↵shore

and when they are restricted to do so via traditional banking. Since GAP incorporates information

on local demand shift for the domestic currency, we would expect GAP to hold predictability for

forex markets where information is not masked from forex. Hence our third prediction is that the

GAP has predictability for forex.

In line with our predictions, we find suggestive evidence of short-term predictability. We conduct

both Panel and Fama-Macbeth return predictablity regressions where the dependant variable is the

one day leading forex return, and the predictors are concurrent, one, two, three, and four day lagged

GAP changes. We find one-day lag and two-day lag of GAP changes as significant and positive

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predictors. We also find evidence showing that predictability decays after four days. The R square

for our prediction specification is 30 to 80 basis points, which is not trivial considering the di�culty

to predict the forex market which is the largest and most liquid asset class, see survey paper by Rossi

(2013). This predictability is more pronounced with higher magnitude in the more recent sample in

particular after 2015 when the BTC market sees the explosive increase in trading volume. This is

because the BTC market boom after 2015 contributes to the acceleration of information incorpora-

tion process and pricing e�ciency in the BTC market.

Given the predictability of our GAP measure, a natural question to ask is whether the change

of GAP reflects a real shift in the demand for local currencies. If the GAP change results from the

overshooting reactions in the BTC market, then we would observe the forex return to reverse shortly.

If the temporal GAP change is due to fundamental changes in domestic currencies, then we would

expect a stable decay of predictability instead of reversals. To distinguish the two possibilities, We

run both Panel and Fama Macbeth regressions between future cumulative forex returns and current

GAP change. We change the cumulative return window for the dependent variables. The sign and

magnitude of the current GAP change predicting future forex cumulative return keeps up four days

into the future but the statistical power disappears. We can observe that magnitude of predictability

coe�cients first picks up and persists to be positive without reversing to zero or negative. This

evidence supports the interpretation that GAP reflects the real demand shift without reversals.

Concerning contributions, we first note that this paper contributes to the understanding of mar-

ket frictions leading to the violation of LOP on which the modern finance builds, particular when

financial innovations lead to more choices for investment. We potentially open up entire series of

discussions on the arbitrage opportunities between traditional financial assets and innovative asset

classes that are put under spotlight. We empirically demonstrate institution qualities and capital

control policies are candidates as to limits to arbitrate in the cryptocurrency and fiat currency mar-

kets. These findings complement the existing literature which often cites behavioral and rational

demand shocks as sources for limits of arbitrage (see Lamont and Thaler 2003; Gromb and Vayanos

2010, among others). The closest paper to ours in this regard is Pasquariello (2017) which attributes

currency market fictions to government interventions.

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Second, by studying the time-series and cross-sectional variations of the LOPV and GAP mea-

sures, we tie the BTC market with macroeconomic regulations such as capital controls and market

conditions like political and economic uncertainties. We uncover the function of BTC as a mechanism

for local investors to move wealth cross-border when they are constrained to do so via existing avenues

provided by traditional financial assets. Capital flight can be accomplished through various channels,

such as cross-listed shares (e.g., Domowitz et al. 1998; Auguste et al. 2006; Edison and Warnock

2008) or cross-border mergers and acquisitions (e.g., Di Giovanni 2005) or FDI (e.g., Alfaro et al.

2008). Compared to cross-listed shares which are typically listed in no more than ten countries, BTC

and other major cryptocurrencies are traded in up to eighty major fiat currencies, a↵ording a larger

and more flexible choice set for capital flight. Moreover, we have access to high frequency quotes in

the BTC market. In future research, we would also leverage the higher frequency quotes of BTC and

forex, to further pin down the dynamics between the two markets and thus better understand the

LOPV process.

Hence, we suggest possible externalities of cryptocurrencies on macro policies e↵ectiveness. There

is not much research work taking this angle. A related work in a broader sense are Raskin and Yer-

mack (2016) and Yermack (2017) which qualitatively evaluate the possible changes to central banking

policies and corporate governance Blockchain technology could bring. Our empirical finding provides

new facts to consider when regulators are contemplating traditional policies in the current era. This

is closely related to rising discussions about BTC, e.g., Fantazzini et al. (2017); Glaser et al. (2014);

Bohme et al. (2015); Cong et al. (2017).

Third, we also empirically show the predictability of our GAP measure for forex price movements

in the following days. We find the predictability peaks around day three and decays gradually after-

ward in both economic and statistic sense. As summarized by Rossi (2013), plenty of research works

have attempted to forecast exchange rates using economic models and various econometric method-

ologies. Most of them investigate predictabilities at monthly, quarterly, and even yearly frequencies.

Papers, which focus on daily data, mainly study the impacts of macroeconomic news announcements

on exchange rates (see Faust et al. 2007; Fratzscher 2009 among others). One exception is Ferraro

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et al. (2015), which find that the lagged oil price shocks has short-term out-of-sample predictability

for exchange rates. For future steps, we will do out-of-sample analysis for our GAP measure as well

and run batteries of horse-race tests agaisnt measures proposed in Ferraro et al. (2015).

To our knowledge, our paper is the first to empirically evaluate and show the relative merits of

the BTC pricing process. We show that the implied price formation process of fiat currencies by

BTC incorporates the real demand shift for domestic currencies. Note that we are not arguing that

BTC price or return itself is explaining or predicting exchange rates of other fiat currencies and thus

qualify as a universal currency, as many research has discussed about (e.g., Yermack 2013).

A lot of debates in the media center on whether FinTech is a disruptive force to financial market

e�ciencies not, and some guru in the field of finance call for a full-fledged regulation framework

for FinTech. Our exercise provides empirical facts for regulators to evaluate when addressing these

debates.

The paper is organized as follows. In Section 2 we provide institutional details about BTC trad-

ing. In Section 3, we develop our hypothesis and provide empirical analysis accordingly. Section 4

concludes.

2 Introduction to Bitcoin Trading

BTC was the first decentralized cryptocurrency created in 2009 by a pseudonymous developer Satoshi

Nakamoto. It is open-sourced and features itself with the peer-to-peer network and proof-of-work

scheme. Discussions about BTC have centered around its potential as an alternative monetary sys-

tem, and a payment system to replace the existing commercial banking3

BTC started out as a digital cash and online payment system, with its first transaction taking

place in 2010 when two pizzas were procured with 10,000 BTC. In its infant days, its user base covers

mostly geeky programmers and sometimes criminals who use BTC to facilitate illegal transactions.

3Harvey (2014) and Harvey (2016) provide in-depth descriptions about the space.

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Transferring BTC between “wallets” usually takes up to one hour to find the block and confirm the

transaction. In 2011 Feb, BTC took parity with US dollar and received increasing attention from

traders and investors. As its price took o↵, the demand to trade BTC as an asset soared. We need

further investigation as to the sophistication of BTC traders. Investors can trade BTC: 1) at over-

the-counter (OTC) marketplace which o↵ers low-fee escrow service and a marketplace to exchange

currencies between PayPal and BTC4, 2) through P2P exchanges, 3) through centralized exchanges.

In this paper, we focus on the case of centralized exchanges. We choose 3) because centralized ex-

changes o↵er observable trading volumes and their data quality is higher.

As of 2017 May 31st, the units of BTC price include 88 fiat countries and 15 alternative cryp-

tocurrencies such as ETHEREUM and DASH. There are 59 exchanges across 25 countries o↵ering

trading platforms for BTC. Table 8 lists the top 20 fiat currencies with which BTC can be traded

and the respective markets and countries for each trading pair. BTC can be traded with USD across

23 exchanges in 12 countries and 6 countries provide trading platforms between CNY and BTC.

E↵ectively, each BTC-Fiat trading pair attracts both home and international investors.

Completing BTC-Fiat trades on a chosen exchange incurs several types of fees including exchange

fees, trade fees, and deposit/withdrawal fees. These transaction fees vary quite a lot across these

exchanges, depending on the liquidities, market size, and service qualities of the exchanges5.

Exchchange fees are the basic fees for operations. Trade fees include a maker fee which is the

cost to make an o↵er to sell currencies, and a taker fee which is the cost charged to take others’

o↵er. Depositing or withdrawing cryptocurrencies incurs no charges. Fees are charged when traders

deposit fiat currencies to the exchange account and withdraw fiat currencies from the exchange to

bank accounts. Some exchanges allow credit card transactions.

For example, Kraken, one of the biggest European BTC trading platforms, a↵ords BTC-USD,

BTC-EUR, BTC-CAD, BTC-JPY, and BTC-GBP trading pairs. Traders can deposit or withdraw

fiat currencies to their Kraken accounts with debit cards. In terms of Bank deposit and withdrawal

4See details at https://en.bitcoin.it/wiki/OTC Exchange5See details at http://crowdsourcingweek.com/blog/bitcoin-exchange-comparison/

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fees, international wire incurs 0%-0.19% or fixed commission depending on the deposit and with-

drawal currency. For trade fees, maker fee is 0% 0.36% depending on volume and currency pair,

while taker fee is 0.1% 0.36%. In terms of transaction time, depositing and withdrawal fiat currencies

takes 1 to 5 business days. Market or limit orders take seconds to go through, and transfer BTC in

and out of the Kraken account requires 10 minutes to 1 hour depending on the tra�cs.

3 Empirical Analysis

In this session, we introduce the law of one price (“LOP” for brevity) for BTC market spot foreign

exchange rate (“forex”) markets. We develop our hypothesis in Section 3.1, and describe the data in

Section 3.2. Section 3.3 to Section 3.6 contain the econometric analysis.

3.1 Hypotheses

As discussed in section 2, investors can easily convert one fiat to another through BTC with relatively

low transaction costs, instead of the traditional banking or forex channels. The interaction between

the two markets serves as an ideal laboratory to test the LOPV.

To complete the round-trip or triangular arbitrage between BTC and two other fiat currencies,

one needs to buy BTC with one fiat currencies and sell instantaneously to the other currency which

then gets exchanged back to the starting currency at the spot exchange rates between the two fiat

currencies. A classic notebook riskless arbitrage requires the simultaneous realization of all trades.In

real operations with delays, the key risk is the price movements of BTC given its high volatility.

Since BTC transactions and order executions are speedy, this risk is less of a concern. Regarding the

time delay due to the BTC-fiat transfer as introduced in section 2, we can be less worried because

the intra-day foreign exchange rate fluctuation is rather minimal on regular days. Note that we also

need to factor in the non-zero total transaction costs incurred throughout the round-trip arbitrage.

As mentioned in section 2, these costs vary quite a lot depending on the exchange platform, the

type of fiat currencies involved, the amount of transaction, and the payment method. We can only

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estimate the upper and lower bound for the transaction cost component. For the simplicity of our

analysis, we abstract from the transaction costs and time delays in completing the triangular arbi-

trage for now.

We use the following definitions:

• Eit = the spot exchange rate, currency i per USD

• --BUSDt = the BTC price in USD

• --Bit = the BTC price in currency i

The following relationship follows the LOP for BTC at any time t:

Eit =

--Bit

--BUSDt

:= --Eit, (1)

where --EA/Bt is the implied exchange rate, currency i per USD .

In the frictionless world, when LOP is violated, arbitrageurs can step in and profit from the

triangle arbitrage strategies. LOP will thus be restored. For example, at January 2, 2017, --BCNY =

7365.55, --BUSD = 1019.21, and ECNY = 6.86, that is, BTC in CNY was priced higher than in USD.

Arbitragers in the US can purchase 0.098 BTC using 100 USD, sell it to 722.67 CNY, and then

exchange back to get 105.45 USD, capturing 5.45% raw return. Similarly, arbitrageurs in China also

can earn the same return by exchanging CNY to USD, buy BTC in USD and sell it for CNY. In

doing so, they can push up --BUSD and push down --BCNY , eliminating potential arbitrage opportunities.

In reality, however, the Chinese government only allows each individual to exchange 50k USD

per year. This regulation prevents arbitrageurs in both markets from executing triangle arbitrage

strategies once their exchange quotas are reached, leading to a divergence between the spot exchange

rate Eit and the implied exchange rate --Ei

t. This phenomenon illustrates how violations of LOP could

be closely related to capital controls and foreign exchange controls. Similar to Pasquariello (2017)’s

argument, “government intervention results in greater LOPV in equilibrium”, we hypothesize that

H1: LOPV in equilibrium increases in the intensity of capital controls.

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We define the measurement of LOPV, namely “VLOP”, as:

LOPV it =

���--Eit

Eit

� 1��� ⇤ 100. (2)

LOPV is equivalent to the absolute percentage return from the triangle arbitrage strategy. This mea-

surement is not signed as arbitrageurs can either long Eit and short Ei

t or the way around. Moreover,

LOPV has the price-scale invariant property such that LOPV in di↵erent currencies are comparable6.

The violations of LOP also implies a segmentation of market making (see Pasquariello (2017)),

meaning that the diverging prices in two markets reflect di↵erent demands. Following the example

above, the BTC price is higher when denoted in CNY than in USD. Let us consider a case when

there is a sudden incentive to transfer assets overseas, potentially due to an increase in the economic

policy uncertainty in the domestic country. When EPU in the domestic country rises, investors lose

confidence in their local economy. So they are willing to transfer their local currency to other assets

such as BTC or USD, hedging against local political turbulence.

As Chinese investors do not have easy access to the US exceeding 50k per year through the bank-

ing system, they could choose convert CNY to USD through the BTC market. The high demand

for BTC pushes up --BCNY and the selling pressure in US BTC market pushes down --BUSD. The

USD-CNY foreign exchange market, however, far outsizes BTC market, so the spot FX rate will

not be a↵ected by the temporary CNY selling pressure from the BTC market. As a consequence,

the di↵erence between implied exchange rate and spot exchange rate is exacerbated, and CNY is

depreciated vis-a-vis USD in the BTC market relative to the forex market. The rise of price gap

reflects the premium Chinese investors would pay to evade the capital control. Let us recall that the

arbitrageurs could not jump in to reduce this gap as they are already constrained in the forex market.

The case is slight di↵erent for countries with less restricted foreign exchange controls. BTC is

potentially an alternative investment opportunity given its amazing performance. When domestic

economic policy uncertainty increase, investors would shift a proportion of wealth to BTC market.

6Another measurement of LOPV is via less-than-perfect price correlations among identical assets (e.g., Gromb andVayanos (2010); Pasquariello (2017). Since correlation is time invariant, we do not include it in our main analysis.

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Then, an increase in GAP also hints for an extra demand of BTC for domestic investors than US

investors.

Therefore, a sudden increase in price gap reflects the unexpected demand shift of domestic cur-

rency and the depreciation of domestic currency vis-a-vis USD in BTC market in comparison to the

forex market.

We define “GAP” as

GAP it =

⇣ --Eit

Eit

� 1⌘⇤ 100. (3)

When GAP increases, currency i is depreciated vis-a-vis USD in BTC market in comparison to the

forex market. Discussions above lead to our second hypothesis:

H2: GAP increases in EPU at the domestic country, conditional on invariant capital

control and foreign exchange policies.

We further argue that GAP could have a short-term predictability for foreign exchange rate due

to its informativeness about domestic investors’ demand shift for foreign currencies or assets. So our

third hypothesis is:

H3: Gap has a short term forecasting power for forex.

At last, if our GAP measure reflects right demand shift for domestic currencies, we should not

observe the predicted change in forex by GAP to reverse in the future. Hence we expect its pre-

dictability to first increase and peak, then decay gradually over time but not reverse in sign. So here

comes our last hypothesis:

H4: The predicted change in forex by GAP persists without future reversals.

We provide empirical analysis on our hypothesis testing in later subsections.

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3.2 Data

For cryptocurrency pricing and volume data, we access the aggregating website 7 with API. The

website aggregates open-high-low-close price and volume data from over 40 exchanges globally. We

have BTC pricing denoted in other cryptos and fiat currencies at each exchange in daily frequency.

Our volume data is denoted by the number of BTC traded per day (24 hours) at each exchange.

Price information is quoted at 00:00:00 Greenwich Mean Time (GMT) across all markets and coun-

tries. To capture the daily increase of bitcoin supply on the bitcoin network, we download the entire

blockchain data from an aggregating website8. For control variables, we cross-checked the validity

of the BTC supply increase data by adding up coins mined per day and transactions fees per day

converted to units of BTC, using other data sources.

Our sample covers the period from September 06, 2011 to May 31, 2017. At any day, the aggre-

gated price is the weighted average of prices on all exchanges. A weight belonging to an exchange

is the ratio of the 24 hour trading volume of a currency pair to the total volume of the pair on all

exchanges. In total, 100 out of 162 fiats have quotes for BTC. Some trading pairs have minimal

amount of volume. To rule out the concern of illiquidity, we only keep fiats with at least 20 units

of BTC traded per day. We are left with 22 trading fiats in our analysis sample: AUD (Australia),

BRL (Brazil), CAD (Canada), CNY (China), EUR (Euro Zone), GBP (United Kingdom), HKD

(Hong Kong), IDR (Indonesia), INR (India), JPY (Japan), KRW (Korea), MXN (Mexico), MYR

(Malaysia), NGN (Nigeria), PHP (Philippines), PLN (Poland), RUB (Russia), SEK (Sweden), SGD

(Singapore), THB (Thailand), USD, and ZAR (South Africa).

Another filtering we apply to data is to delete data points from February 7, 2014, to February

25, 2014. Mt. Gox, the then largest bitcoin exchange, halted all bitcoin withdrawals citing technical

issues in February of 2014, resulting in an abnormal trading pattern and extremely high GAP in that

month. After data cleaning, we have in total 24571 observations in our final sample.

The foreign exchange rate data is extracted from Bloomberg, where Eit are opening quotes at

7https://www.cryptocompare.com/

8https://blockchain.info

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00:00:00 (GMT). We adopt capital control index constructed by Fernandez et al. (2016). We use

Economic Policy Uncertainty Index9 constructed by Baker et al. (2016a) as a proxy for demand

shocks. Equity market index returns come from Datastream.

3.3 Summary statistics

Table 1 presents the summary statistics of daily Volume, daily LOPV and daily GAP, monthly eco-

nomic policy uncertainty index (EPU) and yearly capital control index (CC) in our sample. Firstly,

we notice that only CNY, EUR, GBP and JPY were traded with BTC before 2013, and these fiats

had the greater trading volume per day than other fiats. Secondly, there is significantly non-zero

LOPV across di↵erent fiats, indicating the violations of LOP. There is substantial cross-country het-

erogeneity for LOPV. LOPV are most prominent for CNY, BRL, ZAR and INR, and least for EUR,

PLN, and JPY. The link between LOPV and CC is not very clear from the Table. Thirdly, GAP is

significantly left skewed with most GAP being positive, indicating that BTC is usually priced higher

in countries compared to US. Notice that NGN is an outlier, whose LOPV and GAP are both five

times larger than those of other currencies on average. We hence exclude NGN from the following

analysis.

Even for currencies with fewer restrictions like EUR, there are significantly price gaps (triangle

arbitrage returns). For currencies with foreign exchange controls, such as CNY, THB, INR, the price

gaps are substantially higher. Since we can long or short price gaps, the returns of triangle arbitrage

trading strategy are the absolute of return, shown in the left panel of Table 1. On average, there is

7% return on the table, which is quite shocking.

Figure 1 shows the time series plot of daily equally weighted LOPV over all fiats. There exhibits

a structural change around March 2013, due to an upheaval in the number of currencies that trades

with BTC. Despite a declining trend in both the variance and the level of LOPV, its level remains

above 5%, indicating the persistent di�culty to implement triangle arbitrage strategy.

9Data website: http://www.policyuncertainty.com/

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Figure 1. Time series plot of daily average LOPV over CNY, JPY, EUR and GBP

3.4 Relationship between LOPV and Capital Controls

Following the discussions in Section 3.1, the LOPV in equilibrium are expected to be higher when

there are tighter regulations over capital inflows and outflows. To test this relationship, we employ

country-level capital control index (“CC”) constructed by Fernandez et al. (2016) to approximate

capital regulation restrictiveness10. Index CC ranges from 0 to 1 and increases in the intensity of

capital control policies for all asset categories. Even though CC is a yearly panel data, the time-series

variation is rather small as the capital control policies are usually stable over time. In this analysis,

we only focus on cross-sectional relationships.

We attempt to test this relationship using two regression approaches, the panel regression with

date fixed e↵ect and the Fama-Macbeth regression method:

log(LOPV im) ⇠ ↵m + �1CCi,

where log(LOPV im) is the logarithm of the monthly average of daily LOPV, LOPV t

i . Fama-Macbeth

regression includes two steps: 1) cross section regression log(LOPV im) ⇠ �m

1 CCi for each month, and

10As the capital control only has data until 2015, we fill in later years using the average of previous five years data.Note that we are interested in the cross-sectional di↵erence and the capital control does not change that much over theyears. So this procedure should not a↵ect our analysis that much.

15

2) take the time-series average of �m1 .

As shown in Column (1), (2) and (4) in Table 3, the coe�cient o CCi is significantly positive,

meaning that when CC index increases 0.1 (unit), wed expect the raw return from triangular arbi-

trage strategies increase by 5.89 percent. This evidence supports our hypothesis H1 that violations

of LOP is prominent in countries with restricted capital controls.

To further understand the mechanism, we employ the trading volume as a moderator of the

relationship. High trading volume reflects increased demand for BTC, when arbitrageurs require

more capital to trade such that capital control constraint is more likely to be binding. Turnover is

defined as the trading volume divided by the number of shares outstanding on a given day:

Turnovert = log⇣ VolumetTotal coint

⌘.

Column (3) and (5) show the results of the regression after including the moderator as an inter-

action term:

log(LOPV im) ⇠ ↵m + �1CCi + �2Turnover

im + �3CCi ⇤ Turnoverim.

The results show that the LOPV is higher in countries with more restricted capital controls. A

significantly positive coe�cient on the interaction term indicates that violations of LOP are more

prominent in high turnover months. Also, when the turnover is higher, the LOPV is lower, indicating

that investors are more engaged in arbitrage. Last, around 27% of the cross sectional variation in

monthly LOPV is explained by Turnoverim and CCi ⇤Turnoverim, hinting the crucial role of turnover

as a moderator to measure the bindingness of capital control constrains.

In appendix Table 11, we perform the same analysis at yearly frequency and the it yields similar

results. The economic and statistical power are similar. Furthermore, we provide scatter plots for

yearly average of log(LOPV ) and CC from 2014 to 2017 in Figure 2. The slope increases from -0.29

(insignificant) in 2014 to 1.0 (significant at 5%) in 2017. This provides further evidence that capital

control policy is positively associated with violations of LOP, especially after BTC trading becomes

popular.

16

3.5 Relationship between GAP and EPU

To test hypothesis H2, i.e., controlling for capital control and foreign exchange policies, the higher

the demand for USD among local investors, the larger the price gap, we apply the Economic Policy

Uncertainty Index (“EPU”) constructed by Baker et al. (2016a) as a demand shifter for domestic

currency. When EPU in the domestic country rises, investors lose confidence in their local economy.

So they would like to transfer their local currency to other assets such as BTC or USD, hedging

against local political turbulence at home. As regulations tighten in the forex market, investors

could turn to BTC market to convert domestic currencies to USD. As a result, domestic currency is

depreciated vis-a-vis USD in the BTC market relative to the forex market and price gap increases.

We only have monthly EPU measurements for thirteen countries in our sample: Australia, Brazil,

Canad, China, Euro Zone, India, Japan, Korea, Russia, Singapore, Sweden, United Kingdom and

the United States. This measurement mainly quantifies three components: newspaper coverage of

policy-related economic uncertainty, the number of federal tax code provisions set to expire and dis-

agreement among economic forecasters. These components are country-specific, so the relationship

between GAP and EPU across countries may not be comparable. Therefore, we focus on time series

analysis.

To match the frequency of EPU, we first aggregate GAP to monthly level by taking the average.

We then test above hypothesis with the following regression:

100 ⇤�GAPim ⇠ ↵i + ��1 ⇤�EPU i

m�1 + �0�EPU im + �1�EPU i

m+1 (4)

+ �US�1 ⇤�EPUUS

m�1 + �US0 �EPUUS

m + �US1 �EPUUS

m+1,

where the residuals are clustered at fiat and month levels. For robustness checking, we report results

from both the panel regression and the Fama-Macbeth regression11.

Both GAP and EPU have strong autocorrelation, hence we adopt the change in the measures to

mitigate the bias induced by potential non-stationarity (e.g., Hamilton 1994). The coe�cient ��1,

�0 and �1 in Equation 5 captures the past, contemporaneous, and future impacts of domestic eco-

11We first run time-series regression for each fiat, and then examine of the cross-sectional averages of coe�cients.

17

nomic policy uncertainties on the price gaps, respectively, while �US�1 , �

US0 and �US

1 corresponds to

the impacts of US economic policy uncertainty on the price gaps. The reason to include �EPUi,m+1

and �EPUUS,m+1 is to capture the potential anticipation of future economic policy uncertainties,

such as presidential election.

Table 2 presents the regression results, where GAP i,⇤m is standardized GAP i

m. �0 is positive

and statistically significant for domestic EPU change, but ��1 and �1 are insignificant. This result

means that the additional depression pressure of domestic currency vis-a-vis USD in the BTC market

compared to forex is only responding to the contemporaneous economic and political uncertainty

changes, not past information or expectation. On average, a one standard deviation increase of

monthly domestic EPU (around 58.43 unit see Table 2) is associated with 0.722 * 58.43 = 42.19bps

higher GAP this month.

The impact is much stronger when US EPU surges. Significantly negative �US�1 and �US

0 suggest

that investors respond to both past and contemporaneous economic policy uncertainties in US. Com-

bining both e↵ects together, a one standard deviation rise in US EPU corresponds to an increase in

GAP by (3.37 + 2.80) * 26.59 = 163.44bps or 25.1% of the sample standard deviation of �GAP im

– 650.44bps. Therefore, when US economy becomes turbulent, the demand for BTC surges in US ,

resulting in USD depreciated in the BTC market compared to forex.

3.6 Predictability of GAP

3.6.1 Predictability of GAP

In section 3.5, we establish a relation between GAP and EPU which can proxy for the shift in domestic

currency demand. In this session, we formally test hypothesis H3 and examine whether GAP has

predictive power for foreign exchange rates. If so, GAP does capture information about investors’

excessive demand shock for domestic currencies which are consistent with our results and intuitions

so far.

To empirically test hypothesis 3, we first define

RE,it,t+k = 100 ⇤

⇣log(Ei

t+k)� log(Eit)⌘

(5)

18

as the log return of spot exchange rate for the next k days. Essentially, we want to test weather past

daily change of GAP can predict the future forex return.

We first fix k at 1 and test the predictability of GAP for daily forex return next day. We specify

the following predictability regression:

100 ⇤RE,it,t+1 ⇠ ↵i + �0�GAP i

t + �1�GAP it�1 + ...+ �4�GAP i

t�4 + �RE,it�1,t. (6)

We only take the level change of GAP since GAP is already the return from triangle arbitrage strat-

egy and its percentage change could explode if GAP in the denominator is close to 0. To control the

possible daily autocorrelation in forex return time series, we include RE,it�1,t as a regressor as well.

This regression identifies the delay with which forex rates respond to GAP changes if forex re-

turns are relatively constant over the daily horizons. This regression can answer two key questions:

1) whether past daily change of GAP can predict future spot forex return, 2) how long does it take for

forex to fully absorb the information in GAP. If the current or past daily GAP change has predictive

power, then some �j will be significantly di↵erent from zero. If predictive power dies out after h day,

then �j becomes insignificant when j > h.

Table 4 reports the regression results. Consistent with our hypothesis, past three-day lagged

GAP changes predict forex return next day. When GAP increased from two- day before, indicating

an additional depreciation pressure of domestic currency in BTC market than forex market, one-day-

ahead forex return increase or in other words the domestic currency depreciates one day down the

road. An alternative interpretation of this result is that it takes (less than) three days for demand

shift information in GAP to be fully incorporated into the forex market.

Since BTC becomes widely known and its trading becomes more and more active over the years,

we expect the predictability patterns would vary over di↵erent sample periods. We split the data

into three sub-sample periods. The magnitudes of �1 increase from 14.353 to 43.959 in the recent

one year. This provides suggestive evidence that along with the development in BTC market, more

19

information about investors’ demand shift gets incorporated in the price. Tables included in the

later section will provide more supportive evidence for this argument. Coe�cients in column (4) lose

statistical significance possibly because the length of each fiat’s time series drops a lot.

For robustness checks, we also run Fama-Macbeth regressions for equation 612. Table 12 reassures

the three-day head predictability of GAP as the statistical significance remain similar as in the panel

regression. In the unreported results, we also repeated the panel regression after excluding one fiat

series from the sample at one time, and found the similar results as in Table 4. This step makes sure

that our results are not driven by one fiat.

3.6.2 GAP Predictability Decomposition

To further understand where predictability comes from, we decompose �GAP it into three parts. We

Start from the identical equations for BTC price quoted in fiat i and in USD:

1 = (Ret--B,it,t+1)�1 --B

it+1

--Bit

= (Ret--B,USt,t+1 )

�1 --BUSt+1

--BUSt

Ret--B,it,t+1

Ret--B,USt,t+1

=--Bit+1

--BUSt+1

⇣ --Bit

--BUSt

⌘�1=

--Eit+1

--Eit

.

DividingEi

t+1

Eit

on both sides of the equation yields:

Ret--B,it,t+1

Ret--B,USt,t+1

⇣Eit+1

Eit

⌘�1=

--Eit+1

Eit+1

⇣ --Eit

Eit

⌘�1=

GAPt+1/100 + 1

GAPt/100 + 1.

After taking log on both sides and expanding log(1 + x) around x = 0, we get:

�GAPt+1 ⇡ R--B,it,t+1 �R--B,US

t,t+1 �RE,it,t+1,

where R--B,it,t+1 (R--B,US

t,t+1 ) is log returns for BTC quoted in fiat i (USD).

Table 5 presents the FX predictability regression using the components of GAP it . We find that

the statistical significance of R--B,it�2,t�1 and R--B,i

t�3,t�2 survive, but not others. So BTC Return denoted

12We first run time-series regression for each fiat, and then examine of the cross-sectional averages of coe�cients.This correct for time-series autocorrelation in each fiat

20

by home currency drives the predictability of GAP previously found, rather than the BTC return

quoted in USD or foreign exchange rate of the home currency. We interpret this evidence as in

line with our conjectured mechanism that home country investors resort to BTC when expecting

depreciation of home currency and thus push up the BTC price quoted in the home currency.

3.6.3 GAP Predictability and Capital Controls Intensity

To investigate how GAP Predictability varies when the capital controls intensity is di↵erent, we split

the sample into countries with high (HCC) and low capital controls (LCC). As we can see from

Table 6, the magnitude of coe�cient for R--B,it�2,t�1 is larger in HCC, but R--B,i

t�2,t�1 is larger in LCC. It

is an interesting yet counterintuitive finding that FX return responds at a faster speed in HCC than

LCC. We are further looking into the explanations.

3.6.4 Predictability Decay of GAP

In the previous section, we find the forecasting ability of GAP for future forex returns in the short run.

However, it is still unclear whether this predictability comes from a real shift in demand for domestic

currency or a temporary shock possibly due to contagions across di↵erent markets. We hypothesize

that if the predictability corresponds to the permanent demand shift, then the predictability in forex

should at least persist a couple of days, until the arrival of the next demand shift. If systematically,

the predictability always gets back to zero beyond three days then it is probably the BTC market

that is over reacting before reversals.

To verify the conjecture above, we regress the future k-day cumulative forex return RE,it,t+k on

�GAP it :

100 ⇤RE,it+1,t+k ⇠ ↵i + �k�GAP i

t + �RE,it�1,t, k = 2, ..., 10 (7)

where residuals are clustered using Newey-West method with k�1 lags to overcome overlapping infor-

mation in RE,it+1,t+k+1. Again, we include RE,i

t�1,t to remove serial correlation forex return. Since there

seems a bit of issue with respect to the relation between one-day ahead forex return and GAP change

today as explained in Section 3.6.1, we use cumulative return RE,it+1,t+k after one day instead of RE,i

t,t+k.

21

Regression specification 7 helps us understand over many days into the future we can predict

forex returns using all the up-to-current information of GAP. And by checking the pattern of coe�-

cient estimates signs, magnitudes, and statistical power, we can draw more convincing conclusion as

to whether GAP contains real information about domestic currency demand shift.

Table 7 presents estimates �k and their standard error, along with R2 of for the regression. Con-

sistent with findings for hypothesis H3, we show that current �GAP it has forecasting power for future

cumulative forex return. The most important message from this table is that after the predictability

peaks, the sign of the coe�cient before GAP change remains positive and does not drop to zero or

reverse to be negative. The coe�cients lose significance because more noise and information gets

compounded into the forex as the return windows extend. This means future forex incorporates

information in GAP and the impact of GAP persists for more days without going back to zero.

The regression is in line with findings from 4. First, the predictability peaks around Day two

and three, as the �kcoe�cient magnitude and R2 for k = 2 are the highest across all rows for in the

same columns. Second, by comparing the coe�cient magnitudes from columns (1) to (4), we also

find that the predictability power is stronger for most recent days.

For robustness checks, we take the Fama-Macbeth approach to test 7. The economic and statis-

tical power of �k first picks up till Day 3 and decays gradually while remaining positive. Again, the

magnitude di↵erence between Panel and Fama-Macbeth can be attributed to the variation di↵erence

of GAP along time-series and cross-section dimensions. The take-way is the same. GAP does contain

information about domestic currency demand which does not get reversed afterward.

4 Conclusion

For concluding remarks, we propose a novel LOPV notion between BTC market and forex market

and empirically show its magnitude, statistically properties, distributions over time and across coun-

22

tries. We explain variations in LOPV concerning capital control policies and show that LOPV has

the more substantial magnitude in high capital countries. By documenting this new phenomenon

that emerges as FinTech boom unfolds, we can potentially open up entire series of discussions on the

arbitrage opportunities between traditional financial assets and innovative asset classes that are put

under spotlight.

This paper also suggests a mostly unstudied role of BTC as a cross-currency wealth transfer

device. We propose GAP, a BTC-price-based measure integrating information on home investors’

demand for domestic currencies to forecast Foreign Exchange rates in short run. To our knowledge,

our paper is the first to empirically evaluate and show the relative merits of the BTC pricing process.

We show that the implied price formation process of fiat currencies by BTC incorporates the real

demand shift for domestic currencies.

In contrast to Fintech’s exponential growth, governments around the world are still in the infant

stage of setting up regulations and rules for the area. Using BTC as an example, our paper speaks

to the broad debate as to whether FinTech can disrupt or improve the financial market e�ciency.

We are not advocating for a completely free and unregulated market for FinTech, but we do suggest

that policymakers and regulators should be aware that FinTech can enjoy the innate superior market

e�ciency and pricing formation process. Hence, monitoring of the FinTech sector can come at the

cost of its advantages in market e�ciency and price informativeness.

We hope our work can provoke more thoughts and empirical work on the interactions among

FinTech related new financial products, existing traditional financial assets, and macroeconomic

policies. Externalities posed by FinTech on the current financial system seem to be a fruitful and

promising research theme to follow in the future.

23

Table 1. Summary Statistics

This Table presents the summary statistics of key fiat currencies BTC is traded in. The sample period is from 2011 to 2017 and thefrequency of BTC related variables is daily. LOPV and GAP are defined as in 1 and 3. EPU is a measure of Economic Policy Uncertaintyconstructed in Baker et al. (2016b) at monthly level. CC is yearly capital controls intensity measured in Fernandez et al. (2016)

LOPV (daily) GAP (daily) EPU (monthly) CC (yearly)Fiat Start Date End Date Count Volume Mean SE SD q5. q95 Mean SE SD Mean MeanAUD 2013-03-11 2017-07-31 1130 380.22 5.18 0.18 5.94 0.33 12.30 3.37 0.21 7.13 100.73 0.16BRL 2013-03-18 2017-07-31 1085 143.92 7.61 0.21 6.86 1.53 14.61 5.49 0.26 8.65 124.77 0.64CAD 2013-03-12 2017-07-31 1129 175.09 6.36 0.51 17.19 0.26 14.80 3.78 0.53 17.94 121.42 0.05CNY 2011-09-02 2017-07-31 1483 348400.53 3.72 0.15 5.66 0.19 10.85 -0.91 0.17 6.72 131.68 0.90EUR 2011-08-29 2017-07-31 1530 6385.66 1.04 0.03 1.21 0.11 2.35 0.07 0.04 1.59 121.56 0.14GBP 2011-09-06 2017-07-31 1524 1109.16 2.04 0.09 3.34 0.21 4.40 1.17 0.10 3.73 121.56 0.03HKD 2013-04-03 2017-07-31 1113 399.21 5.17 0.21 6.92 0.25 11.76 0.80 0.26 8.60IDR 2013-05-14 2017-07-31 1067 937.55 9.61 0.42 13.84 0.35 28.90 4.26 0.50 16.30 0.65INR 2013-03-20 2017-07-31 1035 93.95 7.72 0.53 17.04 0.57 16.75 4.29 0.57 18.20 97.01 0.95JPY 2011-08-29 2017-07-31 1530 21087.81 5.26 0.34 13.31 0.27 9.85 3.59 0.35 13.85 101.62 0.00KRW 2013-08-08 2017-07-31 1023 2844.98 10.74 0.43 13.78 1.17 30.90 7.18 0.50 15.93 106.59 0.13MXN 2013-03-11 2017-07-31 1130 92.85 8.45 0.70 23.67 0.97 15.96 5.88 0.73 24.43 0.60MYR 2013-06-26 2017-07-31 1044 96.37 6.63 0.29 9.36 0.45 16.01 1.12 0.35 11.42 0.82NGN 2013-09-12 2017-07-31 992 43.02 26.17 0.70 21.98 2.82 58.82 21.00 0.86 26.97 0.17PHP 2013-04-01 2017-07-31 1111 73.21 6.69 0.38 12.60 0.43 12.64 1.35 0.43 14.21 0.88PLN 2013-06-24 2017-07-31 1056 707.79 2.88 0.15 5.00 0.26 6.41 0.16 0.18 5.77 0.62RUB 2013-03-28 2017-07-31 1116 505.62 10.98 0.90 30.13 0.57 14.96 5.81 0.94 31.54 123.56 0.41SEK 2013-03-12 2017-07-31 1129 40.43 8.28 0.58 19.53 1.35 13.02 6.21 0.60 20.28 105.26 0.02SGD 2013-03-25 2017-07-31 1120 353.25 6.54 0.31 10.37 0.22 16.67 3.67 0.35 11.70 117.33 0.12THB 2013-03-26 2017-07-31 1119 34.85 8.56 0.30 10.19 0.80 18.83 1.85 0.39 13.19 0.75ZAR 2013-04-15 2017-07-31 1105 185.34 7.62 0.16 5.39 1.98 13.59 6.33 0.21 6.86 0.68All 2011-08-29 2017-07-31 24571 23115.25 7.09 0.09 14.52 0.33 15.65 3.84 0.10 15.70

24

Table 2. Monthly relationship between GAP and EPU

This table tabulates the regression results from

100 ⇤�GAPim ⇠ ↵i + ��1 ⇤�EPUm�1 + �i

0�EPUm + �i1�EPU i

m+1

+ �US�1 ⇤�EPUUS

m�1 + �US0 �EPUUS

m + �US1 �EPUUS

m+1

The dependent variable in odd columns �GAPim is the monthly Change of GAP i

m, and �GAPi,⇤m in even

columns is the standardized change of GAPim by demean and dividing the standard deviation. �EPUm�1,

�EPUm and �EPUm+1 refer to the lagged, concurrent, and future monthly change of EPU in country i.�EPUUS

m�1, �EPUUSm and �EPUUS

m+1 refer to the lagged, concurrent, and future monthly change of EPU incountry US. Column (1) to (4) under Panel Regression report regression results from panel regression. Column(3) and (4) control for Month fixed e↵ects. The standard errors (in parentheses) are clustered at the Monthlevel. Column (5) and (6) under Fama-Macbeth report results using Fama-Macbeth methodology. The fullsample period is 2011-08-29 to 2017-07-31.

Panel Regression Fama-Macbeth

�GAPim �GAPi,⇤

m �GAPim �GAPi,⇤

m �GAPim �GAPi,⇤

m

(1) (2) (3) (4) (5) (6)

�EPU im�1 �0.157 �0.008 �0.177 �0.009 0.645 0.049

(0.372) (0.034) (0.385) (0.035) (0.406) (0.034)

�EPU im 0.722⇤⇤ 0.050⇤ 0.705⇤⇤ 0.049⇤ 1.398⇤⇤ 0.120⇤⇤⇤

(0.353) (0.026) (0.356) (0.026) (0.606) (0.041)

�EPU im+1 �0.396 �0.032 �0.410 �0.033 �0.501 �0.033

(0.371) (0.040) (0.372) (0.040) (0.511) (0.038)

�EPUUSm�1 �2.801⇤⇤ �0.150 �2.783⇤⇤ �0.150 �4.530⇤⇤⇤ �0.278⇤⇤

(1.306) (0.105) (1.305) (0.105) (1.449) (0.112)

�EPUUSm �3.374⇤⇤ �0.278⇤⇤ �3.354⇤⇤ �0.278⇤⇤ �4.965⇤⇤⇤ �0.383⇤⇤⇤

(1.488) (0.121) (1.500) (0.122) (1.204) (0.066)

�EPUUSm+1 �0.652 �0.079 �0.633 �0.078 �0.747 �0.080

(1.331) (0.130) (1.345) (0.130) (0.995) (0.104)

Constant �11.001 �0.222 �8.437 �0.008(33.474) (3.190) (11.994) (0.824)

Fiat FE N N Y Y

Observations 663 663 663 663 663 663Adjusted R2 0.013 0.010 �0.001 �0.005 �0.017 �0.019

Note:

⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

25

Table 3. Monthly cross-sectional regression of LOPV on capital controls

This table presents results from the following regression

log(LOPV im) ⇠ ↵m + �1CCi + �2Turnover

im + �3CCi ⇤ Turnoverim.

The dependent variable log(LOPV im) is the monthly discrepancy between shadow foreign exchange rate for

currency i and the actual spot rate i/USD, capturing the triangle arbitrage strategy. Turnover is average ofthe daily trading volume divided by the number of shares outstanding across one month. CC is the intensityof capital controls. Column (1) to (3) under Panel Regression report regression results from panel regressioncontrolling for Month fixed e↵ects. Column (4) and (5) under Fama-Macbeth report results using Fama-Macbeth methodology. The full sample period is 2011-08-29 to 2017-07-31.

Panel Regression Fama-Macbeth

log(LOPV im)

(1) (2) (3) (4) (5)

CCi 0.589⇤⇤⇤ 0.598⇤⇤⇤ 1.233⇤⇤⇤ 0.598⇤⇤⇤ 0.878⇤⇤⇤

(0.106) (0.093) (0.220) (0.099) (0.310)

Turnoverim �0.157⇤⇤⇤ �0.153⇤⇤⇤

(0.009) (0.013)

Turnoverim ⇤ CCi 0.063⇤⇤⇤ 0.037⇤

(0.017) (0.021)

Constant 1.035⇤⇤⇤ 1.042⇤⇤⇤ �0.772⇤⇤⇤

(0.059) (0.102) (0.192)

Month FE N Y Y NA NA

Observations 958 958 958 958 958Adjusted R2 0.030 0.255 0.522 0.256 0.523

Note:

⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

26

Table 4. Predictability Regression (panel regression)

This table presents results from prediction regression

100 ⇤RE,it,t+1 ⇠ ↵i + �0�GAP i

t + �1�GAP it�1 + ...+ �4�GAP i

t�4 + �RE,it�1,t.

The dependent variable RE,it,t+1 is one-day log return of spot exchange rate currency i/USD from t to t + 1.

�GAP it is the change in GAP between t � 1 and t for currency i/USD. Each row records the estimated

coe�cient, and the standard errors (in parentheses) are clustered at the date level. Fiat fixed e↵ect is includedin all regressions. The full sample period is 2011-08-29 to 2017-07-31. We also report the results for sub-sampleperiods: 2013-09-01 to 2017-07-31, 2015-01-01 to 2017-07-31 and 2016-06-01 to 2017-07-31.

100 ⇤REt,t+1

All Date >= 2013-09-01 Date >= 2015-01-01 Date >= 2016-06-01

(1) (2) (3) (4)

�GAP it 2.179 3.056 12.619 3.195

(6.446) (7.674) (10.888) (17.497)

�GAP it�1 14.353⇤⇤ 22.033⇤⇤ 37.478⇤⇤⇤ 43.959

(7.105) (8.744) (14.031) (28.537)

�GAP it�2 10.137 15.576⇤ 42.374⇤⇤⇤ 49.843⇤

(6.912) (8.508) (13.281) (26.777)

�GAP it�3 5.702 6.766 13.876 20.867

(6.428) (8.502) (14.042) (25.134)

�GAP it�4 �0.968 0.792 �5.335 0.167

(5.507) (6.547) (10.170) (18.369)

RE,it�1,t �248.848 �251.163 �126.597 �131.492

(230.478) (256.374) (252.167) (456.127)

Fiat FE Y Y Y Y

Observations 23,459 19,917 13,314 5,999R2 0.001 0.001 0.002 0.003

Note:

⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

27

Table 5. Decomposition of Predictability Regression (panel regression)

This table presents results from prediction regression

100 ⇤RE,it,t+1 ⇠ ↵i +R--B,i

t�1,t +R--B,USt�1,t +RE,i

t�1,t +R--B,it�2,t�1 +R--B,US

t�2,t�1 +RE,it�2,t�1 +R--B,i

t�3,t�2 +R--B,USt�3,t�2 +RE,i

t�3,t�2.

The dependent variable RE,it,t+1 is one-day log return of spot exchange rate currency i/USD from t to t + 1.

R--B,it�1,t is the log return of BTC quoted in fiat currency i from t � 1 to t. R--B,US

t�1,t is the log return of BTC

quoted in USD from t � 1 to t. RE,it,t+1 is one-day log return of spot exchange rate currency i/USD from t to

t+1. Each row records the estimated coe�cient, and the standard errors (in parentheses) are clustered at thedate level. Fiat fixed e↵ect is included in all regressions. The full sample period is 2011-08-29 to 2017-07-31.We also report the results for sub-sample periods: 2013-09-01 to 2017-07-31, 2015-01-01 to 2017-07-31 and2016-06-01 to 2017-07-31.

100 ⇤REt,t+1

All Date >= 2013-09-01 Date >= 2015-01-01 Date >= 2016-06-01

(1) (2) (3) (4)

R--B,it�1,t 2.936 4.811 15.339 10.344

(6.971) (8.288) (10.932) (17.174)

R--B,USt�1,t �4.432 �1.111 �1.244 37.125

(15.566) (19.199) (33.202) (47.819)

RE,it�1,t �251.305 �255.538 �145.619 �158.829

(231.399) (257.743) (250.403) (437.969)

R--B,it�2,t�1 15.298⇤⇤ 22.016⇤⇤⇤ 35.826⇤⇤⇤ 38.166⇤

(7.233) (8.280) (12.264) (20.285)

R--B,USt�2,t�1 �24.165 �31.029 �57.713 �86.509

(16.520) (20.622) (40.047) (72.379)

RE,it�2,t�1 �33.959 �21.420 �245.354 �239.381

(204.924) (226.656) (225.730) (352.178)

R--B,it�3,t�2 9.096 13.291⇤ 36.465⇤⇤⇤ 31.618⇤

(6.676) (7.156) (10.520) (16.931)

R--B,USt�3,t�2 �11.158 �12.253 �46.590 �105.211⇤

(15.461) (19.701) (35.558) (58.075)

RE,it�3,t�2 �43.603 �109.482 �212.077 �372.350

(195.325) (214.287) (225.832) (319.472)

Fiat FE Y Y Y Y

Observations 23,499 19,915 13,312 5,997R2 0.001 0.002 0.002 0.009Adjusted R2 0.0003 0.0002 0.0004 0.004

Note:

⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

28

Table 6. Predictability Regression by Capital Controls Intensity(panel regression)

This table presents results from prediction regression

100 ⇤RE,it,t+1 ⇠ ↵i +R--B,i

t�1,t +R--B,USt�1,t +RE,i

t�1,t +R--B,it�2,t�1 +R--B,US

t�2,t�1 +RE,it�2,t�1 +R--B,i

t�3,t�2 +R--B,USt�3,t�2 +RE,i

t�3,t�2.

The dependent variable RE,it,t+1 is one-day log return of spot exchange rate currency i/USD from t to t + 1.

R--B,it�1,t is the log return of BTC quoted in fiat currency i from t � 1 to t. R--B,US

t�1,t is the log return of BTC

quoted in USD from t � 1 to t. RE,it,t+1 is one-day log return of spot exchange rate currency i/USD from t to

t + 1. Each row records the estimated coe�cient, and the standard errors (in parentheses) are clustered atthe date level. Fiat fixed e↵ect is included in all regressions. We split our sample to HCC group – the fiatswith capital control index over 0.5, and LHH group – the fiats with capita control index less than 0.5. Wereport the results for sub-sample periods: 2013-09-01 to 2017-07-31, 2015-01-01 to 2017-07-31 and 2016-06-01to 2017-07-31.

100 ⇤REt,t+1

Date >= 2013-09-01 Date >= 2015-01-01 Date >= 2016-06-01

(HCC) (LCC) (HCC) (LCC) (HCC) (LCC)

(1) (2) (3) (4) (5) (6)

R--B,it�1,t 12.942⇤ �4.695 17.284⇤ 13.893 2.617 32.150

(7.729) (11.997) (10.036) (23.935) (16.772) (43.607)

R--B,USt�1,t �8.163 �5.323 �7.142 3.197 50.172 8.659

(19.088) (18.701) (34.645) (40.608) (46.648) (63.536)

RE,it�1,t �156.048 �324.129 65.375 �352.472 215.894 �580.838

(223.752) (359.909) (289.407) (298.984) (460.358) (556.500)

R--B,it�2,t�1 18.157⇤⇤ 15.161 37.260⇤⇤⇤ 34.230 23.939 76.436

(8.657) (11.001) (11.713) (25.028) (17.298) (53.502)

R--B,USt�2,t�1 �23.391 �27.401 �64.004 �51.892 �72.232 �125.170

(19.471) (20.253) (40.766) (47.008) (65.068) (102.838)

RE,it�2,t�1 �261.686 193.858 �302.386 �204.282 �508.564 32.691

(217.710) (303.616) (270.134) (254.233) (411.493) (365.841)

R--B,it�3,t�2 4.528 13.723 26.409⇤⇤ 57.856⇤⇤⇤ 6.523 91.959⇤⇤

(7.757) (10.218) (10.717) (20.434) (17.713) (35.709)

R--B,USt�3,t�2 �23.052 �7.446 �70.046⇤ �33.269 �100.618⇤ �144.124⇤

(18.886) (18.994) (36.342) (43.202) (54.277) (78.688)

RE,it�3,t�2 �49.989 2.332 �127.279 �297.558 �386.474 �338.900

(210.691) (289.426) (262.863) (271.312) (368.831) (326.867)

Fiat FE Y Y Y Y Y Y

Observations 10,768 11,037 6,593 6,717 2,967 3,028R2 0.002 0.003 0.003 0.004 0.013 0.011Adjusted R2 0.0001 0.001 0.0005 0.001 0.007 0.005

Note:

⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

29

Table 7. Predictability Decay of GAP

This table presents results from prediction regression

100 ⇤RE,it+1,t+1+k ⇠ ↵i + �k�GAP i

t + �RE,it�1,t.

The dependent variable RE,it+1,t+1+k is the future k-day cumulative log return of spot exchange rate currency

i/USD. �GAP it is the change in GAP between t�1 and t for currency i/USD. Each row records the estimated

coe�cient �k and R2 from OLS, and the standard errors (in parentheses) are adjusted by Newey-West methodwith k1 lags to overcome overlapping information in RE,i

t+1,t+1+k. Fiat fixed e↵ect is included in all regressions.The full sample period is 2011-08-29 to 2017-07-31. We also report the results for sub-sample periods: 2013-09-01 to 2017-07-31, 2015-01-01 to 2017-07-31 and 2016-06-01 to 2017-07-31.

k All Date >= 2013-09-01 Date >= 2015-01-01 Date >= 2016-06-01�k R2 (1e-4) �k R2(1e-4) �k R2 (1e-4) �k R2 (1e-4)

1 10.27⇤⇤ 1.58 14.05⇤⇤⇤ 2.58 12.75⇤ 1.41 19.27⇤ 2.49(4.46) (5.12) (7.64) (11.47)

2 12.79⇤⇤ 1.26 16.55⇤⇤⇤ 1.83 29.95⇤⇤⇤ 3.94 39.83⇤⇤⇤ 5.39(5.66) (6.10) (9.60) (15.31)

3 13.26⇤⇤ 0.91 14.33⇤⇤ 0.93 22.09⇤⇤ 1.46 34.69⇤ 2.81(6.47) (6.89) (11.30) (17.94)

4 8.56 0.29 11.20 0.43 6.31 0.09 20.94 0.80(6.83) (7.14) (10.65) (16.79)

5 9.00 0.26 6.55 0.12 9.24 0.16 34.02 1.74(6.91) (7.33) (10.69) (17.34)

Note:

⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

30

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A Appendix

A.1 Tables

32

Table 8. Summary Statistics for BTC exchanges for 22 Fiat Currencies in the Sample

Fiat NC NE Country MarketAUD 5 7 Australia, China, Finland, US, Japan BitSquare, BTCMarkets, LakeBTC, LocalBitcoins, MonetaGo, Quoine,

RemitanoBRL 1 3 Finland BitSquare, LocalBitcoins, MercadoBitcoinCAD 4 9 US, China, Finland, Canada BitSquare, Coinbase, Cryptsy, Kraken, LakeBTC, LocalBitcoins, Mon-

etaGo, QuadrigaCX, RemitanoCNY 7 16 China, British Virgin Islands, DK, Fin-

land, US, Japan, RussiaBitSquare, BTC38, BTCChina, BTER, CCEDK, CHBTC, Huobi, Jubi,LakeBTC, LocalBitcoins, MonetaGo, OKCoin, Quoine, Remitano, Vi-aBTC, Yunbi

EUR 11 26 Poland, UK, Russia, DK, US, ES, China,Livecoin, Finland, France, Japan

BitBay, BitMarket, BitSquare, Bitstamp, BTCE, CCEDK, Cexio, Coin-base, Coinfloor, Coinroom, Cryptsy, Exmo, Gatecoin, HitBTC, it-Bit, Kraken, LakeBTC, LiveCoin, LocalBitcoins, Lykke, MonetaGo,Paymium, Quoine, TheRockTrading, WavesDEX, Yacuna

GBP 6 13 Russia, DK, US, UK, China, Finland BitSquare, BTCE, CCEDK, Coinbase, Coinfloor, Coinroom, Kraken,LakeBTC, LocalBitcoins, Lykke, MonetaGo, Remitano, Yacuna

HKD 4 6 China, Finland, US, Japan BitSquare, Gatecoin, LakeBTC, LocalBitcoins, MonetaGo, QuoineIDR 3 3 Finland, United Kingdom, Japan LocalBitcoins, Luno, QuoineINR 4 5 Finland, US, Japan, India LocalBitcoins, MonetaGo, Quoine, Remitano, UnocoinJPY 4 11 Japan, US, China, Finland bitFlyer, bitFlyerFX, BitSquare, Coincheck, Kraken, LakeBTC, Local-

Bitcoins, Lykke, MonetaGo, Quoine, ZaifKRW 2 3 South Korea, Finland Bithumb, Korbit, LocalBitcoinsMXN 3 3 Mexico, Finland, US Bitso, LocalBitcoins, MonetaGoMYR 2 3 Finland, United Kingdom LocalBitcoins, Luno, RemitanoNGN 2 3 Finland, United Kingdom localbitcoins, lunoPHP 2 3 Finland, Japan LocalBitcoins, Quoine, RemitanoPLN 3 6 Poland, UK, Finland BitBay, BitMarket, BitSquare, Coinfloor, Coinroom, LocalBitcoinsRUB 5 5 Russia, DK, ES, Finland, US BTCE, CCEDK, Exmo, LocalBitcoins, MonetaGoSEK 2 4 Finland, US BitSquare, LocalBitcoins, MonetaGo, RemitanoSGD 5 7 US, China, Finland, United Kingdom,

JapanitBit, LakeBTC, LocalBitcoins, Luno, MonetaGo, Quoine, Remitano

THB 2 2 Finland, US LocalBitcoins, MonetaGoUSD 13 34 Poland, British Virgin Islands, UK, US,

Russia, DK, Germany, ES, China, Live-coin, Finland, Canada, Japan

BitBay, Bitfinex, BitSquare, Bitstamp, BitTrex, BTCE, CCEDK,CCEX, Cexio, Coinbase, Coinfloor, Coinroom, Coinsetter, Cryptsy,Exmo, Gatecoin, Gemini, HitBTC, Huobi, itBit, Kraken, LakeBTC,LiveCoin, LocalBitcoins, Lykke, MonetaGo, OKCoin, Poloniex, Quadri-gaCX, Quoine, Remitano, TheRockTrading, WavesDEX, Yobit

ZAR 3 5 Finland, United Kingdom, US BitSquare, LocalBitcoins, Luno, MonetaGo, Remitano

33

Table 9. Summary Statistics for EPU

This table presents the summary statistics for monthly EPU and its change over 2013 to 2017. We obtain

EPU data from Baker et al. (2016a).

EPUm �EPUm

Fiat N Mean SD Mean SD

AUD 55 103.12 48.32 -0.27 55.00

BRL 56 237.77 127.09 2.31 104.51

CAD 55 196.04 63.01 -0.16 65.37

CNY 55 224.59 155.40 -0.76 106.29

EUR 56 197.78 69.68 -2.28 61.47

GBP 56 197.78 69.68 -2.28 61.47

INR 55 90.47 37.24 -1.57 33.09

JPY 55 108.02 29.69 -0.39 23.70

KRW 53 143.61 79.96 0.83 59.66

RUB 55 199.89 81.41 -0.30 101.80

SEK 53 104.23 13.60 -0.21 17.56

SGD 55 144.54 61.75 -0.74 43.07

USD 55 108.96 23.39 -0.70 26.59

All 158.22 66.17 -0.50 58.43

34

Table 10. Autocorrelation for Key Variables from 2013 to 2017

This table presents the autocorrelation for daily log return of spot exchange rate fiat currency i/USD, Rit,t+1,

daily GAP, GAP it , change in daily GAP�GAP i

t , monthly EPU, EPU im and change in monthly EPU,�EPU i

m.

The sample period is 2013 to 2017.

Fiat Rit,t+1 GAP i

t �GAP it EPU i

m �EPU im

AUD -0.02 0.43 -0.33 0.36 -0.33

BRL -0.12 0.85 -0.18 0.67 -0.13

CAD -0.01 0.21 -0.13 0.47 -0.38

CNY 0.06 0.79 -0.18 0.77 -0.30

EUR -0.00 0.63 -0.28 0.61 -0.23

GBP -0.03 0.28 -0.58 0.61 -0.23

HKD -0.07 0.44 -0.49

IDR 0.02 0.91 -0.15

INR -0.04 0.41 -0.28 0.60 -0.28

JPY -0.02 0.72 -0.36 0.68 -0.00

KRW -0.07 0.90 -0.20 0.72 -0.16

MXN 0.02 0.39 -0.07

MYR 0.09 0.76 -0.46

PHP 0.03 0.53 -0.40

PLN -0.04 0.72 -0.36

RUB -0.07 0.64 -0.55 0.23 -0.38

SEK -0.05 0.22 -0.21 0.18 -0.37

SGD -0.01 0.71 -0.35 0.76 -0.18

THB 0.13 0.43 -0.47

ZAR 0.02 0.73 -0.34

35

Table 11. Yearly cross-sectional regression of violations of LOP (LOPV) on capitalcontrols (CC)

This table presents results from the following regression

log(LOPV iy ) ⇠ ↵y + �1CCi + �2Turnover

iy + �3CCi ⇤ Turnoveriy.

The dependent variable log(LOPV iy ) is the yearly discrepancy between shadow foreign exchange rate

for currency i and the actual spot rate i/USD, capturing the triangle arbitrage strategy. Turnover isaverage of the daily trading volume divided by the number of shares outstanding across one year. CCis the intensity of capital controls. Column (1) to (3) under Panel Regression report regression resultsfrom panel regression controlling for Month fixed e↵ects. Column (4) and (5) under Fama-Macbethreport results using Fama-Macbeth methodology. The full sample period is 2011-08-29 to 2017-07-31.

Panel Regression Fama-Macbeth

log(LOPV it )

(1) (2) (3) (4) (5)

CCi 0.693⇤⇤ 0.694⇤⇤⇤ 1.510⇤⇤⇤ 0.689⇤⇤ 1.346⇤⇤

(0.276) (0.240) (0.529) (0.280) (0.665)

Turnoverit �0.163⇤⇤⇤ �0.155⇤⇤⇤

(0.022) (0.040)

Turnoverit ⇤ CCi 0.079⇤⇤ 0.064⇤

(0.040) (0.038)

Constant 1.217⇤⇤⇤ 1.218⇤⇤⇤ �0.662(0.154) (0.304) (0.594)

Year FE N Y Y NA NA

Observations 95 95 95 95 95Adjusted R2 0.053 0.288 0.626 0.288 0.616

Note:

⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

36

Figure 2. Scatter plots of yearly average of log(LOPViy) and CCi from 2014 to 2017.

Each point represents one fiat.

37

Table 12. Predictability Regression (Fama-Macbeth regression)

This table presents results from prediction regression using Fama-Macbeth approach

100 ⇤RE,it,t+1 ⇠ ↵i + �0�GAP i

t + �1�GAP it�1 + ...+ �4�GAP i

t�4 + �RE,it�1,t.

The dependent variable RE,it,t+1 is one-day log return of spot exchange rate currency i/USD from t to t + 1.

�GAP it is the change in GAP between t � 1 and t for currency i/USD. Each row records the estimated

coe�cient and the standard errors (in parentheses). The full sample period is 2011-08-29 to 2017-07-31.We also report the results for sub-sample periods: 2013-09-01 to 2017-07-31, 2015-01-01 to 2017-07-31 and2016-06-01 to 2017-07-31.

REt,t+1

All Date >= 2013-09-01 Date >= 2015-01-01 Date >= 2016-06-01

(1) (2) (3) (4)

�GAP it 13.294 23.261 49.713⇤ 83.300

(10.951) (15.248) (27.187) (96.194)

�GAP it�1 20.818⇤⇤ 36.508⇤⇤⇤ 90.070⇤⇤⇤ 166.939

(9.353) (8.672) (29.697) (109.658)

�GAP it�2 11.883 23.695⇤⇤ 129.986⇤⇤⇤ 164.870⇤⇤⇤

(9.447) (10.625) (44.485) (50.923)

�GAP it�3 6.441 18.794⇤⇤ 43.053 24.108

(11.973) (9.414) (45.852) (29.989)

�GAP it�4 �5.756 7.597 �27.558 �96.539⇤⇤

(13.202) (8.641) (33.387) (38.380)

RE,it�1,t �52.076 �48.875 75.064 �175.700

(107.444) (119.093) (116.609) (161.975)

Constant 202.175⇤⇤⇤ 158.367⇤⇤⇤ 100.399⇤⇤⇤ �129.572⇤⇤

(32.621) (31.299) (23.721) (53.009)

Observations 23,459 19,917 13,314 5,999R2 0.002 �0.0002 0.005 0.004

Note:

⇤p<0.1; ⇤⇤p<0.05; ⇤⇤⇤p<0.01

38