assessing and combining financial conditions indexes · assessing and combining financial...

Assessing and Combining Financial ConditionsIndexes∗

Sirio Aramonte,a Samuel Rosen,b and John W. Schindlera

aFederal Reserve BoardbUniversity of North Carolina at Chapel Hill, Kenan-Flagler

Business School

We evaluate the short-horizon predictive ability of finan-cial conditions indexes for stock returns and macroeconomicvariables. We find reliable predictability only when the sampleincludes the 2008 financial crisis, and we argue that this resultis driven by tailoring the indexes to the crisis and by non-synchronous trading. In addition, we suggest a simple proce-dure for aggregating the various indexes into a single proxy forfinancial conditions, which can help to reduce the uncertaintyfaced by policymakers when monitoring financial conditions.

JEL Codes: E32, G01, G17.

1. Introduction

The severity and the economic impact of the 2008 financial crisishave led to a proliferation of indexes that proxy for financial con-ditions or financial stress, which we collectively refer to as finan-cial conditions indexes (FCIs). An open question in the literature is

∗We would like to thank Kathryn Smith for research assistance, and partici-pants of several Federal Reserve Quantitative Surveillance Group meetings. Wewould also like to thank Malcolm Spittler and Troy Matheson for providing dataand guidance for, respectively, the Citi Financial Conditions Index and the Inter-national Monetary Fund U.S. Financial Conditions Index, and Mark Carlson,Kurt Lewis, and William Nelson, and Roberto Cardarelli, Selim Elekdag, andSubir Lall for providing data and guidance for, respectively, their Financial Mar-ket Stress Index and the International Monetary Fund U.S Financial Stress Index.This article represents the views of the authors and should not be interpreted asreflecting the views of the Board of Governors of the Federal Reserve System orother members of its staff. Corresponding author: [email protected]; Tel.:+1-202-912-4301.

1

2 International Journal of Central Banking February 2017

whether these indexes should be used as indicators of current finan-cial conditions, or whether they also have predictive power for futurefinancial and economic activity and could be used as early-warningindicators. In this paper we evaluate whether FCIs can predict stockreturns or innovations to macroeconomic variables over a one-monthor a one-quarter horizon. In addition, we study the Granger causalityrelation between the FCIs and several measures of credit availabilitythat we use as proxies for the “financial cycle” (Borio 2014). Finally,we suggest a simple procedure for aggregating the various FCIs intoa single proxy for financial conditions.

We find that most FCIs can predict monthly and quarterlyreturns on the S&P 500 and on a portfolio of financial companies andalso innovations to a number of macroeconomic variables. However,this predictability is not robust to excluding the period around the2008 financial crisis (2007–12). We posit three possible explanationsfor this pattern. The first is threshold effects, in the sense that finan-cial conditions may matter only after deteriorating beyond a certainthreshold. The second is data mining, because most of the FCIs wereintroduced after the financial crisis, and they are built using variablesthat displayed significant movements during the crisis. And the thirdis non-synchronous data, given that most FCIs include one variablederived from the prices of options that trade after equity marketsclose, hence FCIs include, by construction, information that willbe incorporated in stock prices the next day. We provide evidencethat all three effects are present, but we argue that predictability ismainly driven by data mining and by non-synchronous trading.

The FCIs we study measure financial conditions in one of twoways. The first approach is to evaluate whether broad financial con-ditions are loose or tight by historical standards (e.g., the Bloombergand Chicago Fed indexes). Alternatively, other indexes purport tomeasure whether the financial system is experiencing historicallyunusual stress (e.g., the Carlson, Lewis, and Nelson 2014 index).Despite these and other methodological differences that we detail insection 2, the FCIs exhibit a large amount of common variation, ascan be seen in the top graph in figure 1. Such a result is expectedbecause, while the concept of “financial conditions” is often onlyloosely defined by the articles that build the various FCIs, changesin the state of the financial system directly affect many of the vari-ables used to build the FCIs. However, the top graph in figure 1

Vol. 13 No. 1 Assessing and Combining Financial Conditions 3

Figure 1. Time-Series Plots of the Twelve FinancialConditions Indexes

Notes: The graphs show the time series of the twelve FCIs we study. For scalereasons, the International Monetary Fund (IMF) U.S. Financial Stress Index andthe Carlson, Lewis, and Nelson (2014) Financial Stress Index are not shown. Thebottom graph focuses on the years surrounding the 2008 financial crisis, and itshows the averages of standardized versions of (i) Citi FCI and IMF FCI, (ii)MS FCI and ANFCI, and (iii) the remaining FCIs. See table 1 for a list of FCIacronyms.


shows that, at certain points in time, different indexes provide aconflicting reading of financial conditions. This was the case, forinstance, in 1997–98 for the Citi FCI and 2004–05 for one of theBloomberg FCIs. In section 3, we propose a method to extract asingle and more precise measure of financial conditions from the setof indexes we consider.

Having a reliable measure of financial conditions is importantfor policymakers because, as the literature has shown, financialconditions matter in a variety of ways. The importance of a well-functioning financial system to the broad economy is highlightedby the results in Bernanke and Blinder (1992), Kashyap, Stein,and Wilcox (1993), Kashyap, Lamont, and Stein (1994), Peek andRosengren (1997), and Paravisini (2008), who show that tight mon-etary policy, binding capital ratios, and bank financing constraintscan reduce the supply of credit, especially in the case of small bankswith less liquid assets (Kashyap and Stein 2000). A reduced creditsupply has particularly negative effects for small firms (Gertler andGilchrist 1994, Khwaja and Mian 2008) and bank-dependent bor-rowers (Chava and Purnanandam 2011). A tight credit supply ulti-mately affects investment (Campello, Graham, and Harvey 2010),inventories (Kashyap, Lamont, and Stein 1994), and the broadereconomy (Bernanke 1983; Peek and Rosengren 1997, 2000; Calomirisand Mason 2003).

We assess predictability using simple predictive regressions of aset of stock returns or innovations to macroeconomic variables onlagged FCI values. Our analysis differs from Hatzius et al. (2010),who also evaluate the predictive power of several FCIs, in a fewimportant ways. First, we only consider FCIs that are updated atleast monthly (we should emphasize that, as shown in figure 1, theFCIs still have meaningful monthly variation), and we study theirpredictive power for stock returns and innovations to macroeconomicvariables one month and one quarter ahead. By using such horizons,our predictability results are unlikely to reflect business-cycle effects,especially in the case of one-month-ahead regressions. Second, westudy a larger number of FCIs and a larger number of financial andmacroeconomic variables. Third, we explicitly discuss whether thepredictability that arises in coincidence with the 2008 financial crisisis hard-wired in the FCIs, in the sense that the variables includedin the FCIs may have been chosen on the basis of whether they


experienced large fluctuations in 2007 and 2008.1 Finally, asdescribed in more detail in section 2, we assess the statistical signif-icance of predictability with a methodology that is robust to biasesgenerated by the high persistence that typically characterizes FCIs,which we found had noticeable effects on the results discussed insection 2.

Our conclusions differ from those of English, Tsatsaronis, andZoli (2005), who focus on four- and eight-quarter horizons, and con-sider a longer sample than we do. They aggregate financial variableswith principal component analysis and find that the resulting proxiesfor financial conditions have some predictive power for macroeco-nomic variables. Our conclusions, however, are more in line withthose of Hatzius et al. (2010), because we find limited value in usingFCIs as reliable early-warning indicators, especially when (i) exclud-ing the period surrounding the 2008 financial crisis from the sample,and (ii) acknowledging that the choice of the variables that entermany FCIs may have been influenced by knowing, in hindsight, whatprecipitated the financial crisis.

In the second part of our paper, we discuss a simple two-stepmethodology for combining the various FCIs into a single proxy forfinancial conditions. The large number of FCIs is itself indicative ofthe uncertainty that surrounds the measurement of financial con-ditions. In the top graph in figure 1, one can see that the FCIsgenerally move together in the long run, but they can provide con-flicting assessments at a given point in time—even shortly beforea major financial crisis. For instance, the bottom graph in figure1 shows the averages of three sets of (standardized) FCIs between2006 and 2009. The shaded area highlights the period between Marchand August 2008, which corresponds to the months between the pur-chase of The Bear Stearns Companies, Inc. by JPMorgan Chase &Co. (March 16, 2008) and the bankruptcy filing of Lehman BrothersHoldings Inc. on September 15, 2008. The chart clearly shows that

1For example, Hatzius et al. (2010) develop their own FCI in addition to eval-uating others. They note on page 21 that “the better performance during themost recent five years . . . may reflect selection bias in our choice of variables toinclude in the index: naturally, our selection was governed in part by an under-standing of the types of financial variables that were used for monitoring andmeasuring the recent financial crisis. In this sense, we did not seek to mitigateobserver bias.”


different FCIs give conflicting readings of the state of the financialsystem in the second and third quarters of 2008, ranging from anoticeable deterioration to a large improvement.

Aggregating the individual indexes can help average out modeluncertainty and provide policymakers with a single variable thatreflects the information available in all of the FCIs. We first identifythe indexes that best summarize the information provided by theremaining FCIs. In the second step we form all combinations of theidentified indexes and select the “best” combination on the basis ofhow well it summarizes the information in the remaining FCIs. Thisprocedure is discussed in detail in section 3.

2. Assessing the Predictive Ability of FCIs

We consider twelve FCIs that (i) focus on the United States, (ii)are updated at least every month, and (iii) have a sufficiently longhistory.2 Table 1 contains a detailed list of data sources and includesthe abbreviated FCI names we use in the paper.

The construction of the FCIs varies considerably, although allof them are largely based on financial market variables, includingimplied volatilities, Treasury yields, yield spreads, commercial paperrates, stock returns, and exchange rates (see Kliesen, Owyang, andVermann 2012 for a detailed list of variables that underlie a range ofthe FCIs we study here). Some FCIs only include a small set of vari-ables, as in the case of the Bloomberg Financial Conditions Index,which is based on ten underlying variables. One index, the ChicagoFed Adjusted National Financial Conditions Index, has more than100 underlying variables.

In general, the constituent variables are aggregated using prin-cipal component analysis or simple weighted sums. Principal com-ponent analysis is used extensively in the literature to extract infor-mation from a large set of macroeconomic or financial variables forforecasting purposes. For example, Ludvigson and Ng (2007) rely

2In order to increase the power of our test, we require that the FCIs coverthe stressful episodes that characterized the late 1990s (the Long-Term Capi-tal Management collapse, and the Asian and Russian financial crises). For thisreason, we do not include the HSBC Financial Clog Index (Bloomberg tickerHSCLOG Index) or the Westpac U.S. Financial Stress Index (Bloomberg tickerWRAISTRS Index), whose series start in 2007 and 1998, respectively.


Tab

le1.

Sum

mar

yD

escr

iption

sof

the

Tw

elve

FC

Is

Ava

ilab

ility

Shor

tan

dSta

rtY

ear

Full

Nam

eN

ame

Agg

rega

tion

Yea

rEst

.R

efer

ence

Dat

aSou

rce

Blo

ombe

rgU

.S.Fin

anci

alC

ondi

tion

sIn

dex

BFC

ID

-WA

1994

2009

Ros

enbe

rg(2

009)

Blo

ombe

rg

Blo

ombe

rgU

.S.Fin

anci

alC

ondi

tion

sIn

dex

Plu

sB

FC

I+D

-WA

1994

2009

Ros

enbe

rg(2

009)

Blo

ombe

rg

Cle

vela

ndFin

anci

alSt

ress

Inde

xC

FSI

D-W

A19

9120

09O

etet

al.(2

011)

Cle

vela

ndFe

d

Mor

gan

Stan

ley

Fin

anci

alC

ondi

tion

sIn

dex

U.S

.M

SFC

ID

-WA

1995

N/A

Blo

ombe

rg

Fin

anci

alM

arke

tSt

ress

Inde

xC

LN

FSI

W-F

PC

1994

2003

Car

lson

,Lew

is,an

dN

elso

n(2

014)

Aut

hors

Nat

iona

lFin

anci

alC

ondi

tion

sIn

dex

NFC

IW

-FP

C19

7320

06B

rave

and

But

ters

(201

0)C

hica

goFe

d

Adj

uste

dN

atio

nalFin

anci

alC

ondi

tion

sIn

dex

AN

FC

IW

-FP

C19

7320

06B

rave

and

But

ters

(201

0)C

hica

goFe

d

St.Lou

isFe

dFin

anci

alSt

ress

Inde

xST

LFSI

W-F

PC

1993

2009

Klie

sen

and

Smit

h(2

010)

St.Lou

isFe

d

Kan

sas

City

Fin

anci

alSt

ress

Inde

xK

CFSI

M-F

PC

1990

2009

Hak

kio

and

Kee

ton

(200

9)K

ansa

sC

ity

Fed

Cit

iFin

anci

alC

ondi

tion

sIn

dex

Cit

iFC

IM

-WA

1983

2000

D’A

nton

io(2

008)

Cit

iR

esea

rch

IMF

U.S

.Fin

anci

alC

ondi

tion

sIn

dex

IMF

FC

IM

-DFM

1994

2009

Mat

heso

n(2

012)

Aut

hor

IMF

U.S

.Fin

anci

alSt

ress

Inde

xIM

FFSI

M-W

A19

8020

11C

arda

relli

,E

lekd

ag,

and

Lal

l(2

011)

Aut

hors

Note

s:T

heta

ble

prov

ides

sum

mar

yin

form

atio

non

the

twel

veFC

ISw

est

udy.

The

abbr

evia

tion

sin

the

“Ava

ilabi

lity

and

Agg

rega

-ti

on”

colu

mn

indi

cate

whe

ther

the

inde

xis

upda

ted

daily

(D),

wee

kly

(W),

orm

onth

ly(M

),an

dw

heth

erth

eva

riab

les

unde

rlyi

ngth

ein

dex

are

aggr

egat

edw

ith

aw

eigh

ted

aver

age

(WA

),fir

stpr

inci

palco

mpon

ent

(FP

C),

ordy

nam

icfa

ctor

mod

el(D

FM

).T

heFC

Iin

Car

lson

,Lew

is,an

dN

elso

n(2

014)

isba

sed

onan

inde

xde

velo

ped

in20

03.A

llin

dexe

sar

eex

pres

sed

asz-

scor

es,w

ith

the

exce

ptio

nof

the

CLN

FSI

,w

hich

isa

prob

abili

ty.In

the

empi

rica

lan

alys

is,w

ere

plac

eth

eC

LN

FSI

wit

hit

sna

tura

llo

gari

thm

(the

inde

xne

ver

isex

actl

yze

roin

our

sam

ple)

.


on dynamic factor analysis to summarize a broad cross-section ofvariables, and find that several of the resulting factors can predictone-quarter-ahead excess stock returns. Stock and Watson (2002)use principal component analysis to build factors used to predictseveral macroeconomic variables in the medium run (six, twelve,and twenty-four months ahead).

For indexes constructed as weighted sums, weights are typicallyassigned subjectively by the authors, although a few of the indexesuse more sophisticated methods. The Cleveland Financial StressIndex (CFSI), for instance, calculates weights dynamically basedon the relative dollar flow observed in the Federal Reserve Board’sFlow of Funds statistical release (Z.1) (Oet et al. 2011). All theindexes are expressed in terms of z-scores, with the exception of theFinancial Stress Index of Carlson, Lewis, and Nelson (2014), whichis expressed in terms of probabilities.

2.1 Predictive Regressions

We evaluate the predictive power of the twelve FCIs with a series ofmonthly and quarterly predictive regressions of the form

yt = α + β × FCIt−1 + εt.

The FCIs enter the regression in levels, rather than changes,because the developers of most of the FCIs emphasize that theirindexes are ordinal measures of financial conditions/stress.

We focus on one-month-ahead and one-quarter-ahead regressionsbecause our predictability results might in part reflect business-cycleeffects if the dependent variable were measured over longer hori-zons. In particular, many FCIs include the implied volatility indexVIX as a constituent variable. Bollerslev, Tauchen, and Zhou (2009)find that the variance risk premium, which is the difference betweenthe squared value of VIX and a measure of realized variance, canpredict stock returns about three to six months out, with the asso-ciated adjusted R2 peaking at a five-month horizon and declininggradually at longer horizons. The variance risk premium is drivenby the dynamics of both volatility risk and risk aversion; the latteris embedded in VIX, and its evolution over time has a business-cyclecomponent. Note that we do not control for the predictive power ofother variables, like the variance risk premium, because the results


from regressions in which FCIs are the only predictor already suggesta lack of reliable predictive power at the horizons we focus on.

The FCI coefficient is estimated with OLS, and we assess itsstatistical significance with either heteroskedasticity-consistent stan-dard errors or the local-to-unity asymptotics procedure of Campbelland Yogo (2006). Local-to-unity asymptotics are useful in evaluatingthe statistical significance of persistent predictors, because, in suchcases, the standard t-test can give a rejection rate that is inconsistentwith its nominal size.

As is clear from the time-series plots in figure 1, FCIs tend tobe quite persistent. The high autocorrelation that characterizes theFCIs is also evident in the confidence intervals for the autoregressiveroots shown in table 2.3 We report confidence intervals, rather thanpoint estimates, to highlight that, after accounting for statisticaluncertainty, the FCIs plausibly have autoregressive roots very closeto one. In five cases, we are actually unable to reject the hypothesisthat the FCIs have a unit root. Given the nature of the problem westudy, asymptotic standard errors would normally be biased againstfinding predictability, and the local-to-unit asymptotics procedure ofCampbell and Yogo (2006) corrects for this bias. Our results showthat the Campbell and Yogo (2006) procedure finds predictability25 percent more often than OLS for innovations to macroeconomicvariables, and six times as often for stock returns.4

3The procedure for calculating the confidence intervals in table 2 is describedin the online appendix to Campbell and Yogo (2006).

4The direction and severity of the bias depend on the persistence of the pre-dictor, on the correlation between the innovations to the predictor and to thepredicted variable (δ), and on the sign of the predictive relation (see the discus-sion on the skewness of the t-statistic distribution in section 3.2 of Campbell andYogo 2006). Given the persistence of the FCIs, and assuming that poor finan-cial conditions lead to negative returns/innovations to macroeconomic variables,OLS should find predictability less often than the Campbell and Yogo (2006)procedure for FCIs that increase in value when financial conditions deteriorate.In order to run the Campbell and Yogo (2006) procedure, we need to make surethat the correlation δ is negative, and we do so by multiplying an FCI by –1 ifneeded. The figures mentioned in the text refer to predictive regressions basedon the full sample and where the FCIs, after being multiplied by –1 if needed,increase in value when financial conditions deteriorate. This is the case for all thefull-sample regressions that predict returns, and for 78 percent of the full-sampleregressions that predict innovations to macroeconomic variables.


Tab

le2.

Sum

mar

ySta

tist

ics

ofth

eT

wel

veFC

Is

Val

ue

on90

%C

Ifo

rA

RM

ean

Med

ian

10th

90th

Sep

t.20

08#

Obs.

Root

Dai

ly

BFC

I0.

426

0.06

0−

0.88

71.

910

7.65

144

350.

922

0.99

6B

FC

I+0.

264

−0.

057

−1.

237

1.78

25.

980

4435

0.90

10.

986

CFSI

0.13

80.

024

−1.

065

1.61

32.

592

4435

0.93

91.

004

MS

FC

I0.

075

−0.

139

−1.

144

1.71

80.

764

4429

0.83

40.

947

Wee

kly

CLN

FSI

−4.

311

−4.

625

−7.

264

−0.

660

−0.

001

887

0.92

30.

997

NFC

I−

0.36

1−

0.53

4−

0.78

30.

229

1.84

788

70.

936

1.00

2A

NFC

I−

0.03

0−

0.22

4−

0.81

50.

923

3.31

088

70.

757

0.89

8ST

LFSI

0.05

0−

0.12

7−

0.90

50.

909

2.90

488

70.

936

1.00

2

Mon

thly

KC

FSI

0.13

2−

0.11

5−

0.80

01.

010

2.73

020

40.

945

1.00

6C

itiFC

I0.

137

0.01

3−

1.17

21.

694

3.22

420

40.

916

0.99

3IM

FFC

I0.

078

−0.

189

−0.

781

1.10

02.

930

204

0.97

41.

015

IMF

FSI

−0.

216

−1.

065

−3.

425

3.39

38.

930

204

0.80

70.

930

Note

s:T

heta

ble

show

sth

em

ean,

med

ian,

10th

and

90th

per

cent

iles,

pea

kcr

isis

valu

e(S

epte

mber

2008

),to

talob

serv

atio

ns,an

d90

per

cent

confi

denc

ein

terv

alfo

rth

eA

R(1

)au

tore

gres

sive

root

(see

Cam

pbel

lan

dY

ogo

2006

).T

ime

per

iod:

July

1995

toJu

ne20

12.


In practice, we assume that each of the FCIs follows an AR(1)process, and use local-to-unity asymptotics unless the autoregressiveroot of the FCI is sufficiently distant from one, in a sense definedin footnote 5, or unless there is no correlation between the innova-tions to the FCI’s autoregressive process and the innovations in aregression of the predicted variable on the FCI.5 Note that both apersistent predictor and a non-zero correlation are necessary for thestandard OLS asymptotics to be inappropriate.

The dependent variables in our predictive regressions are (i)returns on a broad market index and on various industry portfo-lios, and (ii) autoregressive residuals of log-changes in several eco-nomic variables; we study residuals because, unlike returns, changesin macroeconomic variables can be autocorrelated. We considermonthly and quarterly returns on the S&P 500 and on sevenequally weighted industry portfolios: finance, construction, manu-facturing, transportation, wholesale trade, retail trade, and services.The macroeconomic variables we consider measure the availability ofcredit (total consumer credit, and commercial and industrial loans),the state of the housing market (housing starts), and manufactur-ing activity (durable goods orders, industrial production, and totalmanufacturing inventory).6

We present the results for the predictability of stock returns intables 4 through 7. For each portfolio/FCI combination we report thecoefficient on the FCI (βFCI) and the regression root mean squarederror (RMSE). We show an asterisk next to a coefficient when thecoefficient is statistically significant at the 90 percent confidencelevel.

5Using the notation in Campbell and Yogo (2006), we rely onheteroskedasticity-consistent standard errors if the DF-GLS statistic is less than–5 (a more negative DF-GLS statistic shifts the confidence interval for the autore-gressive root of the predictor away from one), or if the parameter δ̂ (which meas-ures the correlation between the innovations) is equal to zero. When needed,we linearly interpolate the values obtained from the lookup tables in the onlineappendix to Campbell and Yogo (2006), which only provide a discrete set ofvalues for δ̂ and of the DF-GLS statistic.

6Stock returns are from the Center for Research in Security Prices (CRSP)through Wharton Research Data Services. The macroeconomic data are from theFederal Reserve Economic Data (FRED) database of the Federal Reserve Bankof St. Louis. See table 3 for summary statistics and additional details on theconstruction of the industry portfolios.

12 International Journal of Central Banking February 2017Tab

le3.

Sum

mar

ySta

tist

ics

ofIn

dust

rySto

ckR

eturn

san

dLog

-Chan

ges

ofM

acro

Var

iable

s

Mea

nStd

.D

ev.

Med

ian

10th

90th

#O

bs.

Stoc

kRet

urns

S&P

500

0.00

60.

046

0.01

0−

0.06

00.

059

204

Fin

ance

0.00

90.

046

0.01

2−

0.03

30.

059

204

Con

stru

ctio

n0.

012

0.08

00.

016

−0.

085

0.10

020

4M

anuf

actu

ring

0.01

20.

073

0.01

5−

0.08

10.

099

204

Tra

nspo

rtat

ion

0.00

90.

061

0.01

7−

0.07

10.

072

204

Who

lesa

leTra

de0.

012

0.06

70.

020

−0.

070

0.08

520

4R

etai

lTra

de0.

011

0.07

20.

009

−0.

066

0.07

820

4Se

rvic

es0.

012

0.08

40.

020

−0.

086

0.10

120

4

Mac

roVar

iabl

es

C&

I0.

004

0.01

00.

005

−0.

011

0.01

520

4C

onsu

mer

Cre

dit

0.00

50.

006

0.00

4−

0.00

30.

009

204

Dur

able

Goo

ds0.

002

0.04

20.

003

−0.

047

0.04

920

4H

ousi

ngSt

arts

−0.

003

0.06

9−

0.00

7−

0.08

90.

080

204

Ind.

Pro

duct

ion

0.00

20.

007

0.00

2−

0.00

60.

009

204

Tot

alIn

vent

ory

0.00

20.

007

0.00

2−

0.00

80.

010

204

Note

s:T

heta

ble

repor

tssu

mm

ary

stat

isti

csfo

rth

ere

turn

son

the

S&P

500

inde

xan

don

indu

stry

por

tfol

ios,

and

for

log-

chan

ges

inse

lect

edm

acro

econ

omic

vair

able

s.T

hein

dust

rypor

tfol

ios

are

equa

llyw

eigh

ted

and

are

built

usin

gC

RSP

data

onth

eba

sis

ofSt

anda

rdIn

dust

rial

Cla

ssifi

cati

on(S

IC)

code

s(fi

nanc

e:60

00to

6231

and

6712

to67

26;co

nstr

ucti

on:15

21to

1799

;m

anuf

actu

ring

:20

11to

3999

;tr

ansp

orta

tion

:40

11to

4971

;w

hole

sale

trad

e:50

12to

5199

;re

tail

trad

e:52

11to

5999

;se

rvic

es:

7011

to89

99).

The

mac

roec

onom

icva

riab

les

are

(wit

hth

eFe

dera

lR

eser

veB

ank

ofSt

.Lou

is’s

FR

ED

mne

mon

icin

pare

nthe

ses)

:co

mm

erci

alan

din

dus-

tria

llo

ans

atal

lco

mm

erci

alba

nks

(BU

SLO

AN

S),

tota

lco

nsum

ercr

edit

owne

dan

dse

curi

tize

d(T

OTA

LSL

),m

anuf

actu

rers

’ne

wor

ders

for

dura

ble

good

s(D

GO

RD

ER

),ho

usin

gst

arts

(HO

UST

),in

dust

rial

prod

ucti

on(I

ND

PR

O),

and

valu

eof

man

ufac

ture

rs’t

otal

inve

ntor

ies

(AM

TM

TI)

.T

ime

per

iod:

July

1995

toJu

ne20

12.


Table 4 shows results for the 1995–2012 sample, and it indicatesthat eleven of the twelve FCIs can predict returns on the financeindustry portfolio, that four can predict the S&P 500, and that theMorgan Stanley index can predict returns on the finance and threeadditional industry portfolios. There is noticeable dispersion in theRMSEs across industry portfolios for a given FCI, with the construc-tion and services portfolios generally showing the largest RMSE andthe finance portfolios the lowest, but the RMSEs are remarkablysimilar across FCIs for a given portfolio. The statistically significantcoefficients have the expected negative sign, indicating that higherlevels of financial stress (or tighter financial conditions) tend to befollowed by lower returns.

The severity of the 2008 financial crisis naturally raises the ques-tion of whether the predictive power of the FCIs mainly arises fromthe events that started in early 2007, or whether it is also present inthe broader sample. In table 5 we report the coefficients and RMSEsestimated over the 1995–2006 sample, and the results highlight that,essentially, there is no meaningful predictability left. Later in thissection we discuss whether the lack of predictive power outside ofthe 2008 crisis is due to predictability only being there during peri-ods of financial stress, or whether it is the result of the FCIs beingtailored to the recent financial crisis.

The conclusions that we can draw from monthly returns alsocarry over to quarterly returns (tables 6 and 7). Five FCIs have pre-dictive power for returns on the S&P 500 or on the financial industryportfolio in the 1995–2012 sample. Similar to the monthly analysis,returns on the remaining industry portfolios are not predictable.

Restricting the sample to the pre-crisis period (1995–2006, table7) all but eliminates the predictability, with the exception of theMorgan Stanley index, which can now predict returns on five port-folios. Note that the coefficients for the Morgan Stanley index arepositive, while they were negative at the monthly horizon in thefull sample (table 4). This difference is consistent with the possibil-ity that measuring returns over longer horizons captures the earlysigns of a turning business cycle. The Morgan Stanley index maysignal poor financial conditions when the business cycle is close toits trough, and, while one-month-ahead returns may still be negativeas the economy bottoms out, one-quarter-ahead returns may alreadycapture the initial pickup in economic activity.


Tab

le4.

Pre

dic

tive

Reg

ress

ions:

Mon

thly

Sto

ckR

eturn

s,19

95–2

012

BFC

IB

FC

I+C

FSI

MS

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−0.

432∗

4.60

4−

0.28

84.

629

−0.

180

4.64

90.

022

4.65

2Fin

ance

−0.

624∗

4.51

7−

0.42

0∗4.

569

−0.

778∗

4.55

0−

0.24

1∗4.

611

Con

stru

ctio

n−

0.16

48.

079

−0.

054

8.08

20.

291

8.07

7−

0.22

78.

079

Man

ufac

turi

ng−

0.26

37.

265

−0.

055

7.27

60.

186

7.27

4−

0.41

5∗7.

262

Tra

nspo

rtat

ion

−0.

315

6.12

2−

0.11

96.

138

0.00

56.

141

−0.

369∗

6.12

7W

hole

sale

Tra

de−

0.30

36.

676

−0.

100

6.69

00.

115

6.69

1−

0.30

1∗6.

684

Ret

ailTra

de−

0.19

47.

227

0.02

97.

233

0.11

47.

232

0.01

67.

233

Serv

ices

−0.

108

8.39

30.

111

8.39

30.

481

8.38

0−

0.18

28.

392

AN

FC

IN

FC

IC

LN

FSI

ST

LFSI

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−0.

069

4.65

2−

1.23

9∗4.

596

−0.

117

4.64

4−

0.51

14.

622

Fin

ance

−1.

068∗

4.54

5−

1.84

3∗4.

491

−0.

271∗

4.57

3−

0.66

0∗4.

567

Con

stru

ctio

n−

0.20

58.

081

−0.

500

8.07

7−

0.02

78.

082

0.23

08.

079

Man

ufac

turi

ng0.

020

7.27

6−

0.57

57.

269

0.03

57.

276

0.07

67.

276

Tra

nspo

rtat

ion

0.04

26.

141

−0.

840

6.12

1−

0.05

76.

139

−0.

116

6.13

9W

hole

sale

Tra

de−

0.36

86.

686

−0.

697

6.68

00.

074

6.69

00.

080

6.69

2R

etai

lTra

de−

0.41

77.

226

−0.

223

7.23

20.

100

7.22

90.

444

7.21

8Se

rvic

es0.

279

8.39

2−

0.39

38.

391

0.17

98.

384

0.27

58.

390

(con

tinu

ed)


Tab

le4.

(Con

tinued

)

Cit

iFC

IIM

FFSI

KC

FSI

IMF

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−0.

566

4.60

8−

0.04

14.

650

−0.

583∗

4.60

8−

0.92

0∗4.

565

Fin

ance

−0.

406

4.59

5−

0.20

0∗4.

558

−0.

766∗

4.54

2−

0.77

0∗4.

557

Con

stru

ctio

n0.

030

8.08

30.

078

8.07

70.

063

8.08

2−

0.17

08.

081

Man

ufac

turi

ng−

0.00

77.

276

0.03

97.

275

−0.

013

7.27

6−

0.32

87.

269

Tra

nspo

rtat

ion

−0.

281

6.13

20.

000

6.14

1−

0.20

16.

137

−0.

587

6.11

4W

hole

sale

Tra

de−

0.06

06.

692

−0.

000

6.69

2−

0.04

86.

692

−0.

186

6.69

0R

etai

lTra

de−

0.07

17.

232

0.10

77.

222

0.28

27.

226

0.16

77.

231

Serv

ices

−0.

122

8.39

30.

078

8.38

90.

130

8.39

3−

0.20

98.

392

Note

s:T

heta

ble

repor

tsth

eco

effici

ents

and

root

mea

nsq

uare

der

rors

(bot

hin

%)

ofpr

edic

tive

regr

essi

ons

ofon

e-m

onth

-ahe

adst

ock

retu

rns

onFC

Ile

vels

.Se

eta

bles

1an

d3

for

mor

ede

tails

onth

eFC

Isan

dfo

rth

ede

finit

ion

ofin

dust

rypor

tfol

ios.

Ast

eris

ksin

dica

tesi

gnifi

canc

eat

the

90per

cent

leve

l.St

atis

tica

lsi

gnifi

canc

eis

eval

uate

don

the

basi

sof

hete

rosk

edas

tici

ty-c

onsi

sten

tst

anda

rder

rors

or,w

here

appr

opri

ate,

wit

hth

elo

cal-to

-uni

tyas

ympt

otic

sof

Cam

pbel

lan

dY

ogo

(200

6)(s

eese

ctio

n2

for

deta

ils).

Tim

eper

iod:

July

1995

toJu

ne20

12.


Tab

le5.

Pre

dic

tive

Reg

ress

ions:

Mon

thly

Sto

ckR

eturn

s,19

95–2

006

BFC

IB

FC

I+C

FSI

MS

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

0.14

74.

277

0.03

84.

279

0.71

84.

245

0.43

64.

253

Fin

ance

0.18

03.

392

0.27

93.

384

−0.

040

3.39

50.

512

3.35

0C

onst

ruct

ion

0.52

46.

737

0.05

76.

751

1.08

06.

703

−0.

027

6.75

1M

anuf

actu

ring

0.97

96.

980

0.35

17.

019

1.73

96.

907

0.04

57.

028

Tra

nspo

rtat

ion

0.62

55.

983

0.20

96.

002

1.29

15.

928

0.11

96.

004

Who

lesa

leTra

de0.

715

6.17

00.

210

6.19

61.

192

6.13

50.

310

6.19

0R

etai

lTra

de0.

444

6.14

00.

154

6.14

90.

911

6.11

30.

707

6.10

4Se

rvic

es1.

474

8.85

30.

861

8.89

52.

400

8.75

70.

327

8.93

2

AN

FC

IN

FC

IC

LN

FSI

ST

LFSI

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

1.11

7∗4.

228

−0.

070

4.27

90.

045

4.27

8−

0.33

14.

275

Fin

ance

−0.

547

3.38

00.

214

3.39

50.

156

3.38

20.

889

3.36

6C

onst

ruct

ion

0.09

66.

751

1.12

06.

746

0.24

26.

735

1.14

96.

726

Man

ufac

turi

ng0.

856

7.01

03.

335

6.98

10.

434

6.97

81.

724

6.97

4Tra

nspo

rtat

ion

1.02

75.

975

1.16

95.

999

0.24

25.

988

0.77

35.

993

Who

lesa

leTra

de0.

053

6.19

91.

695

6.18

50.

428

6.14

41.

529

6.15

1R

etai

lTra

de−

0.10

76.

151

1.01

56.

146

0.38

96.

105

1.34

76.

113

Serv

ices

1.54

28.

892

3.74

88.

891

0.73

28.

825

2.44

08.

853

(con

tinu

ed)


Tab

le5.

(Con

tinued

)

Cit

iFC

IIM

FFSI

KC

FSI

IMF

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−0.

451

4.25

30.

177

4.26

0−

0.49

34.

268

−0.

929

4.23

9Fin

ance

0.21

83.

388

−0.

069

3.39

2−

0.02

93.

395

0.59

03.

375

Con

stru

ctio

n0.

089

6.75

10.

029

6.75

10.

421

6.74

60.

265

6.74

9M

anuf

actu

ring

0.40

17.

016

0.26

77.

003

1.00

57.

001

0.39

67.

024

Tra

nspo

rtat

ion

−0.

063

6.00

50.

248

5.98

00.

373

6.00

1−

0.37

16.

001

Who

lesa

leTra

de0.

222

6.19

50.

084

6.19

60.

575

6.18

90.

606

6.18

8R

etai

lTra

de−

0.02

06.

151

0.02

96.

151

0.47

96.

144

0.83

86.

129

Serv

ices

0.14

58.

937

0.36

98.

900

1.25

28.

905

0.56

08.

932

Note

s:T

heta

ble

repor

tsth

eco

effici

ents

and

root

mea

nsq

uare

der

rors

(bot

hin

%)

ofpr

edic

tive

regr

essi

ons

ofon

e-m

onth

-ahe

adst

ock

retu

rns

onFC

Ile

vels

.Se

eta

bles

1an

d3

for

mor

ede

tails

onth

eFC

Isan

dfo

rth

ede

finit

ion

ofin

dust

rypor

tfol

ios.

Ast

eris

ksin

dica

tesi

gnifi

canc

eat

the

90per

cent

leve

l.St

atis

tica

lsi

gnifi

canc

eis

eval

uate

don

the

basi

sof

hete

rosk

edas

tici

ty-c

onsi

sten

tst

anda

rder

rors

,or

,w

here

appr

opri

ate,

wit

hth

elo

cal-to

-uni

tyas

ympt

otic

sof

Cam

pbel

lan

dY

ogo

(200

6)(s

eese

ctio

n2

for

deta

ils).

Tim

eper

iod:

July

1995

toD

ecem

ber

2006

.


Tab

le6.

Pre

dic

tive

Reg

ress

ions:

Quar

terl

ySto

ckR

eturn

s,19

95–2

012

BFC

IB

FC

I+C

FSI

MS

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−1.

099∗

8.88

2−

0.66

88.

989

−0.

650∗

9.02

70.

800

9.00

5Fin

ance

−1.

401∗

9.04

6−

0.96

09.

188

−2.

191∗

9.04

30.

041

9.31

4C

onst

ruct

ion

−0.

006

15.6

160.

203

15.6

121.

144

15.5

720.

481

15.6

06M

anuf

actu

ring

−0.

025

15.0

420.

565

15.0

161.

181

14.9

940.

436

15.0

34Tra

nspo

rtat

ion

−0.

498

12.5

290.

165

12.5

510.

141

12.5

530.

196

12.5

52W

hole

sale

Tra

de0.

179

14.1

990.

714

14.1

560.

919

14.1

711.

210

14.1

35R

etai

lTra

de0.

620

14.8

851.

278

14.7

781.

101

14.8

751.

897

14.7

60Se

rvic

es0.

462

17.0

741.

090

17.0

021.

927

16.9

772.

035

16.9

32

AN

FC

IN

FC

IC

LN

FSI

ST

LFSI

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−0.

946

9.01

9−

3.64

7∗8.

787

−0.

175

9.04

2−

1.17

18.

972

Fin

ance

−3.

325∗

8.91

7−

4.61

5∗8.

900

−0.

611

9.19

7−

1.38

79.

206

Con

stru

ctio

n−

1.65

715

.558

−1.

267

15.5

970.

432

15.5

811.

217

15.5

66M

anuf

actu

ring

−1.

195

15.0

11−

0.72

615

.036

0.56

714

.980

1.47

914

.966

Tra

nspo

rtat

ion

−1.

208

12.5

15−

2.06

612

.493

0.18

712

.545

0.55

912

.540

Who

lesa

leTra

de−

1.62

614

.140

−0.

311

14.2

000.

713

14.0

971.

882

14.0

71R

etai

lTra

de−

1.26

514

.882

1.25

614

.898

0.87

114

.769

3.08

514

.580

Serv

ices

−1.

424

17.0

51−

0.70

117

.085

0.98

116

.926

1.94

716

.974

(con

tinu

ed)


Tab

le6.

(Con

tinued

)

Cit

iFC

IIM

FFSI

KC

FSI

IMF

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−1.

408

8.90

3−

0.07

89.

047

−1.

467∗

8.91

8−

2.32

4∗8.

756

Fin

ance

−0.

630

9.28

6−

0.49

49.

148

−1.

665∗

9.14

7−

0.74

39.

154

Con

stru

ctio

n1.

202

15.5

530.

232

15.5

940.

602

15.6

03−

0.17

315

.615

Man

ufac

turi

ng1.

201

14.9

780.

384

14.9

811.

092

14.9

980.

034

15.0

42Tra

nspo

rtat

ion

0.05

012

.553

0.20

612

.532

0.05

412

.553

−0.

921

12.5

21W

hole

sale

Tra

de1.

122

14.1

420.

448

14.1

131.

456

14.1

180.

653

14.1

87R

etai

lTra

de1.

309

14.8

390.

650

14.7

392.

559

14.6

711.

777

14.8

13Se

rvic

es0.

861

17.0

610.

440

17.0

191.

339

17.0

320.

235

17.0

88

Note

s:T

heta

ble

repor

tsth

eco

effici

ents

and

root

mea

nsq

uare

der

rors

(bot

hin

%)

ofpr

edic

tive

regr

essi

ons

ofon

e-qu

arte

r-ah

ead

stoc

kre

turn

son

FC

Ile

vels

.Se

eta

bles

1an

d3

for

mor

ede

tails

onth

eFC

Isan

dfo

rth

ede

finit

ion

ofin

dust

rypor

tfol

ios.

Ast

eris

ksin

dica

tesi

gnifi

canc

eat

the

90per

cent

leve

l.St

atis

tica

lsi

gnifi

canc

eis

eval

uate

don

the

basi

sof

hete

rosk

edas

tici

ty-c

onsi

sten

tst

anda

rder

rors

or,w

here

appr

opri

ate,

wit

hth

elo

cal-to

-uni

tyas

ympt

otic

sof

Cam

pbel

land

Yog

o(2

006)

(see

sect

ion

2fo

rde

tails

).T

ime

per

iod:

July

1995

toJu

ne20

12.


Tab

le7.

Pre

dic

tive

Reg

ress

ions:

Quar

terl

ySto

ckR

eturn

s,19

95–2

006

BFC

IB

FC

I+C

FSI

MS

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

0.37

58.

351

0.16

88.

355

1.12

28.

316

2.05

8∗7.

984

Fin

ance

1.33

86.

788

1.17

36.

776

−0.

921∗

6.85

31.

698∗

6.57

9C

onst

ruct

ion

3.09

413

.512

1.31

413

.707

2.63

913

.638

1.06

213

.717

Man

ufac

turi

ng4.

004

13.9

131.

732

14.2

255.

299

13.7

991.

906

14.1

57Tra

nspo

rtat

ion

2.07

711

.938

1.02

012

.025

2.76

911

.899

1.73

411

.893

Who

lesa

leTra

de3.

342

12.1

321.

416

12.3

852.

961

12.2

812.

681∗

12.0

52R

etai

lTra

de3.

199

12.5

071.

841

12.6

652.

178

12.7

103.

560∗

12.0

80Se

rvic

es5.

677

17.2

993.

557

17.5

957.

327

17.1

573.

757∗

17.4

12

AN

FC

IN

FC

IC

LN

FSI

ST

LFSI

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−1.

628

8.30

1−

3.30

38.

308

0.25

78.

341

−0.

956

8.34

2Fin

ance

−2.

247

6.75

61.

664

6.87

10.

398

6.84

13.

035

6.70

5C

onst

ruct

ion

−2.

492

13.6

962.

746

13.7

550.

958

13.6

444.

777

13.5

52M

anuf

actu

ring

−1.

420

14.3

155.

456

14.2

621.

710

13.9

336.

194

13.9

77Tra

nspo

rtat

ion

−0.

722

12.0

65−

1.53

212

.065

0.99

011

.912

2.81

311

.985

Who

lesa

leTra

de−

2.84

612

.358

2.63

312

.452

1.50

912

.109

5.26

312

.172

Ret

ailTra

de−

2.43

112

.730

2.91

912

.786

1.54

012

.442

4.96

412

.551

Serv

ices

−1.

607

17.9

604.

453

17.9

442.

613∗

17.2

228.

253

17.4

71

(con

tinu

ed)


Tab

le7.

(Con

tinued

)

Cit

iFC

IIM

FFSI

KC

FSI

IMF

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−1.

548

8.18

60.

073

8.35

5−

1.73

08.

284

−2.

812

8.16

1Fin

ance

0.93

96.

810

−0.

399

6.81

40.

438

6.88

12.

134

6.75

0C

onst

ruct

ion

1.38

813

.693

−0.

299

13.7

551.

901

13.7

231.

514

13.7

42M

anuf

actu

ring

1.87

714

.195

0.19

714

.331

2.81

314

.229

1.48

514

.309

Tra

nspo

rtat

ion

0.07

612

.073

0.09

512

.070

0.50

012

.069

−0.

890

12.0

59W

hole

sale

Tra

de1.

257

12.3

98−

0.12

512

.469

1.67

312

.428

1.71

012

.425

Ret

ailTra

de0.

851

12.7

78−

0.18

512

.803

1.74

512

.763

2.55

312

.707

Serv

ices

1.38

517

.922

0.42

817

.953

3.29

117

.864

1.84

717

.947

Note

s:T

heta

ble

repor

tsth

eco

effici

ents

and

root

mea

nsq

uare

der

rors

(bot

hin

%)

ofpr

edic

tive

regr

essi

ons

ofon

e-qu

arte

r-ah

ead

stoc

kre

turn

son

FC

Ile

vels

.Se

eta

bles

1an

d3

for

mor

ede

tails

onth

eFC

Isan

dfo

rth

ede

finit

ion

ofin

dust

rypor

tfol

ios.

Ast

eris

ksin

dica

tesi

gnifi

canc

eat

the

90per

cent

leve

l.St

atis

tica

lsi

gnifi

canc

eis

eval

uate

don

the

basi

sof

hete

rosk

edas

tici

ty-c

onsi

sten

tst

anda

rder

rors

or,w

here

appr

opri

ate,

wit

hth

elo

cal-to

-uni

tyas

ympt

otic

sof

Cam

pbel

land

Yog

o(2

006)

(see

sect

ion

2fo

rde

tails

).T

ime

per

iod:

July

1995

toD

ecem

ber

2006

.


We now discuss whether the FCIs are informative about futureinnovations to the macroeconomic variables we mentioned earlier inthis section. In order to implement the Campbell and Yogo (2006)procedure, the only covariate we include in the predictive regressionsis one of the FCIs, and we account for autocorrelation in macro-economic variable changes by using the residuals from log-changeautoregressions as the dependent variable, where the number of lagsis chosen on the basis of the Schwarz Bayesian information criterion.7

The first set of results (table 8) focuses on one-month-aheadpredictability, with the sample running from 1995 to 2012.8 TheChicago Fed, St. Louis Fed, Kansas City Fed, and InternationalMonetary Fund (IMF) FCI indexes predict all the variables; mostother indexes predict at least three of the six variables. When thesample excludes the 2008 financial crisis (table 9), however, the evi-dence in favor of predictability is weak, with most indexes beingable to predict only one macroeconomic variable, typically indus-trial production. Similar conclusions can be drawn when focusing onquarterly horizons, as shown in tables 10 and 11 (only the MorganStanley index can predict more variables in the short sample thanin the full sample).9

7For the monthly series, we use two lags for total consumer credit, commercialand industrial loans, industrial production, and total manufacturing inventory,and one lag for durable goods orders and housing starts. For the quarterly series,we use two lags for total consumer credit and total manufacturing inventory, onelag for commercial and industrial loans and industrial production, and zero lagsfor durable goods orders and housing starts.

8While we expect a negative relation between a worsening of current financialconditions and future economic activity, some coefficients in tables 8 and 9 arepositive. The reason is that the Campbell and Yogo (2006) procedure requires anegative correlation between current innovations to the dependent variable andthe predictor, hence we sometimes need to multiply the FCI by –1. We system-atically check that the sign of the estimated coefficient is as expected: positiveif the FCI (multiplied by –1 as applicable) signals stress when low, and negativeotherwise.

9In an unreported analysis, we obtain similar results when, instead of exclud-ing the period around the financial crisis, we orthogonalize the innovations rela-tive to the appropriately lagged Chicago Fed National Activity Index, which isa proxy for the current state of the economy. The similarity of the results when(i) excluding the crisis and (ii) controlling for the state of the economy is notsurprising, especially in a relatively short sample like ours, because the financialcrisis also coincided with a severe recession, which exerts a strong leverage effect.


Tab

le8.

Pre

dic

tive

Reg

ress

ions:

Innov

atio

ns

toM

onth

lyLog

-Chan

ges

inM

acro

econ

omic

Var

iable

s,19

95–2

012

BFC

IB

FC

I+C

FSI

MS

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I0.

078∗

0.66

20.

105∗

0.65

20.

072

0.66

9−

0.05

10.

671

Con

sum

erC

redi

t−

0.03

40.

519

−0.

032

0.51

9−

0.04

70.

519

0.01

80.

521

Dur

able

Goo

ds−

0.88

3∗3.

733

−0.

765∗

3.78

1−

0.92

0∗3.

860

−0.

667∗

3.90

5H

ousi

ngSt

arts

−1.

184∗

6.24

1−

0.89

5∗6.

344

−0.

759∗

6.45

6−

0.76

4∗6.

448

Ind.

Pro

duct

ion

−0.

120∗

0.64

5−

0.09

4∗0.

654

−0.

167∗

0.64

9−

0.12

6∗0.

657

Tot

alIn

vent

ory

−0.

059∗

0.43

7−

0.05

4∗0.

438

−0.

043

0.44

4−

0.07

1∗0.

439

AN

FC

IN

FC

IC

LN

FSI

ST

LFSI

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I0.

059

0.67

20.

240∗

0.65

8−

0.03

80.

667

0.18

0∗0.

647

Con

sum

erC

redi

t−

0.02

50.

521

−0.

129∗

0.51

6−

0.01

70.

520

−0.

073∗

0.51

6D

urab

leG

oods

−1.

147∗

3.87

5−

2.36

6∗3.

725

−0.

317∗

3.90

0−

1.15

3∗3.

786

Hou

sing

Star

ts−

1.87

5∗6.

342

−2.

984∗

6.26

5−

0.32

1∗6.

457

−1.

220∗

6.37

6In

d.P

rodu

ctio

n−

0.05

10.

670

−0.

321∗

0.64

50.

072∗

0.64

9−

0.16

2∗0.

650

Tot

alIn

vent

ory

−0.

050

0.44

4−

0.14

3∗0.

438

−0.

021

0.44

3−

0.07

3∗0.

439

(con

tinu

ed)


Tab

le8.

(Con

tinued

)

Cit

iFC

IIM

FFSI

KC

FSI

IMF

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I0.

119∗

0.66

00.

034∗

0.66

10.

168∗

0.64

70.

183∗

0.64

9C

onsu

mer

Cre

dit

−0.

034

0.52

0−

0.01

30.

519

−0.

061∗

0.51

7−

0.06

8∗0.

517

Dur

able

Goo

ds−

0.83

4∗3.

860

−0.

335∗

3.77

2−

1.24

7∗3.

731

−1.

356∗

3.74

6H

ousi

ngSt

arts

1.16

2∗6.

369

−0.

414∗

6.31

7−

1.28

2∗6.

349

−1.

390∗

6.35

9In

d.P

rodu

ctio

n0.

180∗

0.64

0−

0.04

4∗0.

651

0.17

7∗0.

643

−0.

213∗

0.63

8Tot

alIn

vent

ory

−0.

075∗

0.43

8−

0.01

7∗0.

442

−0.

071∗

0.43

9−

0.10

6∗0.

434

Note

s:T

heta

ble

repor

tsth

eco

effici

ents

and

root

mea

nsq

uare

der

rors

(bot

hin

%)

ofpr

edic

tive

regr

essi

ons

ofon

e-m

onth

-ahe

adin

nova

tion

sto

log-

chan

ges

inm

acro

econ

omic

vari

able

son

FC

Ile

vels

.Se

eta

bles

1an

d3

for

mor

ede

tails

onth

eFC

Isan

don

the

mac

roec

onom

icva

riab

les.

Ast

eris

ksin

dica

tesi

gnifi

canc

eat

the

90per

cent

leve

l.St

atis

tica

lsi

gnifi

canc

eis

eval

uate

don

the

basi

sof

hete

rosk

edas

tici

ty-c

onsi

sten

tst

anda

rder

rors

or,w

here

appr

opri

ate,

wit

hth

elo

cal-to

-uni

tyas

ympt

otic

sof

Cam

pbel

lan

dY

ogo

(200

6)(s

eese

ctio

n2

for

deta

ils).

Tim

eper

iod:

July

1995

toJu

ne20

12.


Tab

le9.

Pre

dic

tive

Reg

ress

ions:

Innov

atio

ns

toM

onth

lyLog

-Chan

ges

inM

acro

econ

omic

Var

iable

s,19

95–2

006

BFC

IB

FC

I+C

FSI

MS

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I−

0.08

50.

583

−0.

087∗

0.58

00.

042

0.58

6−

0.04

10.

586

Con

sum

erC

redi

t0.

030

0.33

20.

040

0.33

10.

005

0.33

3−

0.02

90.

332

Dur

able

Goo

ds0.

339

3.68

30.

309

3.68

10.

420

3.68

1−

0.17

03.

690

Hou

sing

Star

ts0.

225

5.24

90.

382

5.23

80.

414

5.24

40.

455

5.23

0In

d.P

rodu

ctio

n0.

138∗

0.54

70.

059

0.55

6−

0.09

20.

555

−0.

082∗

0.55

2Tot

alIn

vent

ory

−0.

040

0.38

3−

0.05

20.

380

−0.

022

0.38

4−

0.07

5∗0.

375

AN

FC

IN

FC

IC

LN

FSI

ST

LFSI

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I−

0.05

80.

586

0.21

70.

585

−0.

057∗

0.57

7−

0.22

1∗0.

576

Con

sum

erC

redi

t−

0.05

80.

332

−0.

118

0.33

20.

015

0.33

2−

0.01

60.

333

Dur

able

Goo

ds0.

179

3.69

32.

128

3.65

7−

0.18

53.

677

0.66

73.

679

Hou

sing

Star

ts0.

120

5.25

20.

465

5.25

10.

145

5.24

5−

0.82

75.

236

Ind.

Pro

duct

ion

−0.

054

0.55

90.

363∗

0.55

20.

058∗

0.54

80.

153

0.55

4Tot

alIn

vent

ory

0.04

80.

383

−0.

179

0.38

2−

0.03

3∗0.

379

−0.

098

0.38

1

(con

tinu

ed)


Tab

le9.

(Con

tinued

)

Cit

iFC

IIM

FFSI

KC

FSI

IMF

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I0.

076

0.58

20.

009

0.58

70.

128

0.58

20.

227∗

0.57

0C

onsu

mer

Cre

dit

0.02

10.

333

0.02

0∗0.

330

0.05

80.

332

0.05

50.

332

Dur

able

Goo

ds−

0.27

43.

683

0.18

73.

670

0.87

63.

655

−0.

807

3.65

9H

ous.

Star

ts−

0.25

65.

246

−0.

091

5.24

90.

144

5.25

20.

490

5.24

4In

d.P

rodu

ctio

n0.

132∗

0.54

20.

041∗

0.55

20.

169∗

0.55

0−

0.14

7∗0.

552

Tot

alIn

vent

ory

0.05

20.

380

−0.

016

0.38

3−

0.07

90.

381

−0.

149∗

0.37

3

Note

s:T

heta

ble

repor

tsth

eco

effici

ents

and

root

mea

nsq

uare

der

rors

(bot

hin

%)

ofpr

edic

tive

regr

essi

ons

ofon

e-m

onth

-ahe

adin

nova

tion

sto

log-

chan

ges

inm

acro

econ

omic

vari

able

son

FC

Ile

vels

.Se

eta

bles

1an

d3

for

mor

ede

tails

onth

eFC

Isan

don

the

mac

roec

onom

icva

riab

les.

Ast

eris

ksin

dica

tesi

gnifi

canc

eat

the

90per

cent

leve

l.St

atis

tica

lsi

gnifi

canc

eis

eval

uate

don

the

basi

sof

hete

rosk

edas

tici

ty-c

onsi

sten

tst

anda

rder

rors

or,w

here

appr

opri

ate,

wit

hth

elo

cal-to

-uni

tyas

ympt

otic

sof

Cam

pbel

lan

dY

ogo

(200

6)(s

eese

ctio

n2

for

deta

ils).

Tim

eper

iod:

July

1995

toD

ecem

ber

2006

.


Tab

le10

.P

redic

tive

Reg

ress

ions:

Innov

atio

ns

toQ

uar

terl

yLog

-Chan

ges

inM

acro

econ

omic

Var

iable

s,19

95–2

012

BFC

IB

FC

I+C

FSI

MS

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I0.

406∗

1.22

90.

477∗

1.16

10.

571∗

1.26

0−

0.41

4∗1.

305

Con

sum

erC

redi

t−

0.09

91.

003

−0.

122

0.99

6−

0.12

11.

007

−0.

029

1.01

4D

urab

leG

oods

−1.

858∗

5.30

8−

1.63

7∗5.

483

−1.

413∗

5.89

6−

1.45

0∗5.

840

Hou

sing

Star

ts−

1.37

3∗8.

645

−0.

803

8.82

2−

1.07

7∗8.

846

0.78

18.

870

Ind.

Pro

duct

ion

−0.

248∗

1.29

1−

0.16

71.

324

−0.

371∗

1.29

6−

0.34

8∗1.

290

Tot

alIn

vent

ory

−0.

285∗

1.11

2−

0.25

7∗1.

128

−0.

238∗

1.17

5−

0.19

71.

179

AN

FC

IN

FC

IC

LN

FSI

ST

LFSI

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I0.

548∗

1.31

41.

238∗

1.17

60.

204∗

1.29

80.

752∗

1.15

5C

onsu

mer

Cre

dit

−0.

064

1.01

4−

0.32

90.

996

−0.

014

1.01

4−

0.17

40.

999

Dur

able

Goo

ds−

2.64

3∗5.

679

−4.

935∗

5.31

1−

0.70

8∗5.

824

−2.

278∗

5.60

4H

ousi

ngSt

arts

−2.

657

8.65

1−

4.17

1∗8.

562

−0.

386

8.86

5−

0.78

48.

878

Ind.

Pro

duct

ion

−0.

061

1.34

9−

0.65

7∗1.

292

−0.

163∗

1.29

1−

0.28

9∗1.

317

Tot

alIn

vent

ory

0.42

0∗1.

151

−0.

578∗

1.15

0−

0.10

1∗1.

175

−0.

258∗

1.17

1

(con

tinu

ed)


Tab

le10

.(C

ontinued

)

Cit

iFC

IIM

FFSI

KC

FSI

IMF

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I0.

526∗

1.24

60.

201∗

1.19

10.

780∗

1.11

70.

812∗

1.13

2C

onsu

mer

Cre

dit

−0.

089

1.01

0−

0.03

81.

006

−0.

145

1.00

3−

0.20

8∗0.

994

Dur

able

Goo

ds−

1.63

5∗5.

765

−0.

635∗

5.63

7−

2.41

5∗5.

508

−2.

866∗

5.36

9H

ousi

ngSt

arts

−1.

025

8.83

4−

0.37

58.

815

−1.

163

8.82

9−

2.01

1∗8.

690

Ind.

Pro

duct

ion

−0.

384∗

1.27

4−

0.11

7∗1.

285

−0.

393∗

1.28

5−

0.43

0∗1.

281

Tot

alIn

vent

ory

0.33

4∗1.

136

−0.

094∗

1.15

3−

0.28

2∗1.

163

−0.

375∗

1.14

1

Note

s:T

heta

ble

repor

tsth

eco

effici

ents

and

root

mea

nsq

uare

der

rors

(bot

hin

%)

ofpr

edic

tive

regr

essi

ons

ofon

e-qu

arte

r-ah

ead

inno

vati

ons

tolo

g-ch

ange

sin

mac

roec

onom

icva

riab

les

onFC

Ile

vels

.Se

eta

bles

1an

d3

for

mor

ede

tails

onth

eFC

Isan

don

the

mac

roec

onom

icva

riab

les.

Ast

eris

ksin

dica

tesi

gnifi

canc

eat

the

90per

cent

leve

l.St

atis

tica

lsi

gnifi

canc

eis

eval

uate

don

the

basi

sof

hete

rosk

edas

tici

ty-c

onsi

sten

tst

anda

rder

rors

or,w

here

appr

opri

ate,

wit

hth

elo

cal-to

-uni

tyas

ympt

otic

sof

Cam

pbel

lan

dY

ogo

(200

6)(s

eese

ctio

n2

for

deta

ils).

Tim

eper

iod:

July

1995

toJu

ne20

12.


Tab

le11

.P

redic

tive

Reg

ress

ions:

Innov

atio

ns

toQ

uar

terl

yLog

-Chan

ges

inM

acro

econ

omic

Var

iable

s,19

95–2

006

BFC

IB

FC

I+C

FSI

MS

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I0.

431∗

1.11

10.

399∗

1.09

50.

436

1.12

7−

0.29

21.

118

Con

sum

erC

redi

t0.

058

0.60

70.

028

0.60

90.

094

0.60

6−

0.06

50.

605

Dur

able

Goo

ds0.

956

4.42

6−

0.96

04.

389

0.74

84.

469

−0.

768∗

4.40

8H

ousi

ngSt

arts

1.51

35.

349

1.75

8∗5.

190

1.21

05.

434

1.50

6∗5.

203

Ind.

Pro

duct

ion

−0.

261

1.09

7−

0.09

61.

115

−0.

087

1.11

8−

0.21

6∗1.

089

Tot

alIn

vent

ory

−0.

364∗

0.87

6−

0.37

4∗0.

845

−0.

232

0.91

5−

0.31

6∗0.

851

AN

FC

IN

FC

IC

LN

FSI

ST

LFSI

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I0.

061

1.17

21.

375∗

1.11

0−

0.19

1∗1.

109

0.76

1∗1.

104

Con

sum

erC

redi

t−

0.17

60.

601

−0.

352

0.60

20.

030

0.60

70.

027

0.60

9D

urab

leG

oods

0.48

04.

494

3.93

04.

372

−0.

492∗

4.39

6−

1.33

74.

450

Hou

sing

Star

ts0.

501

5.49

92.

044

5.47

8−

0.33

65.

467

2.54

65.

347

Ind.

Pro

duct

ion

−0.

160

1.11

6−

0.69

31.

103

−0.

118

1.09

5−

0.31

01.

108

Tot

alIn

vent

ory

0.00

90.

931

−1.

189∗

0.87

2−

0.16

5∗0.

872

−0.

625∗

0.87

3

(con

tinu

ed)


Tab

le11

.(C

ontinued

)

Cit

iFC

IIM

FFSI

KC

FSI

IMF

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

C&

I0.

344∗

1.11

10.

137∗

1.12

10.

699∗

1.08

50.

810∗

1.05

2C

onsu

mer

Cre

dit

0.05

00.

607

−0.

047

0.59

80.

191

0.59

7−

0.06

60.

608

Dur

able

Goo

ds−

0.41

34.

481

−0.

320

4.43

21.

607

4.38

6−

2.05

4∗4.

308

Hou

sing

Star

ts0.

255

5.50

0−

0.08

55.

503

0.51

55.

497

1.04

35.

467

Ind.

Pro

duct

ion

0.36

2∗1.

049

−0.

142∗

1.06

2−

0.42

6∗1.

087

−0.

303

1.10

3Tot

alIn

vent

ory

0.31

7∗0.

865

0.14

2∗0.

861

0.52

3∗0.

870

−0.

605∗

0.84

7

Note

s:T

heta

ble

repor

tsth

eco

effici

ents

and

root

mea

nsq

uare

der

rors

(bot

hin

%)

ofpr

edic

tive

regr

essi

ons

ofon

e-qu

arte

r-ah

ead

inno

vati

ons

tolo

g-ch

ange

sin

mac

roec

onom

icva

riab

les

onFC

Ile

vels

.Se

eta

bles

1an

d3

for

mor

ede

tails

onth

eFC

Isan

don

the

mac

roec

onom

icva

riab

les.

Ast

eris

ksin

dica

tesi

gnifi

canc

eat

the

90per

cent

leve

l.St

atis

tica

lsi

gnifi

canc

eis

eval

uate

don

the

basi

sof

hete

rosk

edas

tici

ty-c

onsi

sten

tst

anda

rder

rors

or,w

here

appr

opri

ate,

wit

hth

elo

cal-to

-uni

tyas

ympt

otic

sof

Cam

pbel

lan

dY

ogo

(200

6)(s

eese

ctio

n2

for

deta

ils).

Tim

eper

iod:

July

1995

toD

ecem

ber

2006

.


2.2 Granger Causality Tests

In the final part of our predictability analysis, we study the relationbetween each of the FCIs and selected measures of credit availability.Borio (2014) highlights that the financial cycle can amplify macro-economic fluctuations and contribute to significant economic dislo-cations, and that one key component of the financial cycle is creditavailability. Borio (2014) also suggests that peaks in the financialcycle are closely associated with financial crises, which implies thatthe results of our tests in this section can provide additional evidenceon whether FCIs are a useful signal for an upcoming financial crisis.The measures of credit availability that we consider are consumercredit, mortgage credit, and the issuance of commercial mortgage-backed securities (CMBS), all expressed as a fraction of nominalgross domestic product. The last two variables are directly relatedto the real estate boom that set the stage for the 2008 financial crisis,with one of them also measuring the importance of credit provisionthrough the securitization channel.10

We depart from the methodology of Campbell and Yogo (2006)when studying the relation between the FCIs and the measures ofcredit availability, and we conduct a series of Granger causality testsinstead. The reason is that it is the overall level of credit availabil-ity, rather than its changes or innovations, that contributes to thebuildup of financial vulnerabilities during financial booms and thatsets the stage for future financial crises (see the discussion in Borio2014). By using Granger causality tests based on quarterly vectorautoregressions with an appropriate number of lags, we can focuson levels and still account for the persistence of both the FCIs andthe credit availability variables. We use the procedure described inToda and Yamamoto (1995) to account for the fact that the FCIsand the credit availability variables may not only be persistent butalso potentially integrated in small samples.

10The consumer credit and mortgage credit data is from the Federal Reserve’sZ.1 release. Consumer credit is series LA153166000.Q (household and non-profitorganizations; consumer credit), while mortgage credit is series LA153165105.Q(household and non-profit organizations; home mortgages). CMBS issuance istotal issuance less Fannie Mae/Freddie Mac, resecuritized, and foreign propertyissuance, and it is provided by Commercial Mortgage Alert. Nominal gross domes-tic product is the series GDP from the FRED database of the Federal ReserveBank of St. Louis.


For each FCI/credit variable pair, we select the optimal num-ber of vector autoregression lags (m) with the Schwarz Bayesianinformation criterion. We then use augmented Dickey-Fuller teststo determine each series’ order of integration, and define n as themaximum order of integration for the two series. Next, we estimatea vector autoregression with m + n lags. Using heteroskedasticity-consistent standard errors, we construct Wald test statistics to deter-mine the joint statistical significance of the coefficients on the firstm lags of the FCI in the equation for the credit availability variable,and the joint statistical significance of the coefficients on the first mlags of the credit availability variable in the equation for the FCI.These are the Granger causality tests, and the null hypothesis isthat the coefficients are jointly zero. Table 12 reports the resultingp-values.

The top panel of table 12 shows the p-values of tests of whetherthe FCIs Granger-cause the indicated credit availability measure,and the bottom panel shows the results of tests that the credit avail-ability measures Granger-cause the FCIs. In the left panels we usethe full sample, which runs from 1995 to 2012. In the right panelswe evaluate to what extent the results are affected by observationscorresponding to the financial crisis by setting the 2008 observa-tions of the dependent variables to missing.11 We assess the robust-ness of the results to the financial crisis by excluding only one year(2008), because the credit availability variables have a strong trendbefore the crisis and only after 2007 do they exhibit significant vari-ation (Borio 2014 highlights that the financial cycle evolves moreslowly than the economic cycle). Truncating the sample after 2007would have eliminated the variation that is likely most importantfor identifying the effect we are interested in.

In the full sample, consumer credit, mortgage credit, and CMBSissuance are Granger-caused by five, four, and three FCIs, respec-tively. After setting the 2008 observations of the credit availabilitymeasures to missing, no FCIs Granger-cause CMBS issuance, andthree out of twelve still Granger-cause consumer credit and mort-gage credit. Focusing on whether the FCIs are Granger-caused bycredit availability, only six tests of the seventy-two that we report

11We set the observations to missing rather than dropping them to avoid using2007:Q4 as the first lag for 2009:Q1.


Tab

le12

.FC

Isan

dM

easu

res

ofC

redit

Ava

ilab

ility:G

range

rC

ausa

lity

Tes

ts

Full

Sam

ple

Excl

udin

g20

08

CC

R/

MT

G/

CM

BS/

CC

R/

MT

G/

CM

BS/

GD

PG

DP

GD

PG

DP

GD

PG

DP

FC

IsG

rang

er-C

ausi

ngC

redi

tA

vaila

bilit

y

BFC

I0.

120.

170.

340.

360.

200.

66B

FC

I+0.

200.

160.

690.

720.

620.

75C

FSI

0.62

0.51

0.06

∗0.

680.

570.

16M

SFC

I0.

650.

740.

980.

870.

800.

59C

LN

FSI

0.21

0.90

0.56

0.21

0.93

0.71

NFC

I0.

03∗∗

0.06

∗0.

100.

07∗

0.20

0.23

AN

FC

I0.

04∗∗

0.40

0.07

∗0.

270.

06∗

0.30

STLFSI

0.13

0.02

∗∗0.

210.

200.

160.

16K

CFSI

0.07

∗0.

06∗

0.16

0.13

0.15

0.33

Cit

iFC

I0.

01∗∗

∗0.

430.

230.

02∗∗

0.26

0.73

IMF

FC

I0.

01∗∗

∗0.

140.

09∗

0.01

∗∗∗

0.08

∗0.

11IM

FFSI

0.34

0.06

∗0.

510.

460.

05∗∗

0.61

(con

tinu

ed)


le12

.(C

ontinued

)

Full

Sam

ple

Excl

udin

g20

08

CC

R/

MT

G/

CM

BS/

CC

R/

MT

G/

CM

BS/

GD

PG

DP

GD

PG

DP

GD

PG

DP

Cre

ditA

vaila

bilit

yG

rang

er-C

ausi

ngFC

Is

BFC

I0.

420.

190.

930.

170.

270.

63B

FC

I+0.

230.

09∗

0.66

0.07

∗0.

320.

45C

FSI

0.27

0.82

0.33

0.39

0.73

0.56

MS

FC

I0.

110.

800.

780.

07∗

0.56

0.34

CLN

FSI

0.17

0.72

0.25

0.19

0.54

0.12

NFC

I0.

610.

300.

810.

410.

780.

84A

NFC

I0.

950.

440.

180.

770.

920.

50ST

LFSI

0.29

0.14

0.61

0.15

0.36

0.52

KC

FSI

0.98

0.06

∗0.

500.

270.

710.

70C

itiFC

I0.

100.

720.

340.

10∗

0.94

0.65

IMF

FC

I0.

560.

05∗∗

0.33

0.41

0.16

0.26

IMF

FSI

0.75

0.40

0.73

0.80

0.53

0.89

Note

s:T

heta

ble

show

sp-

valu

esfr

omte

sts

that

the

give

nFC

IG

rang

er-c

ause

sth

ein

dica

ted

mea

sure

ofcr

edit

avai

labi

lity

(top

pane

l)an

dth

atth

egi

ven

cred

itav

aila

bilit

ym

easu

reG

rang

er-c

ause

sth

ein

dica

ted

FC

I(b

otto

mpa

nel)

.T

hem

easu

res

are

cons

umer

cred

itov

erno

min

alG

DP

(CC

R/G

DP

),m

ortg

age

cred

itov

erno

min

alG

DP

(MT

G/G

DP

),an

dis

suan

ceof

com

mer

cial

mor

tgag

e-ba

cked

secu

riti

esov

erno

min

alG

DP

(CM

BS/

GD

P).

We

choo

seth

enu

mber

ofla

gsfo

rth

eve

ctor

auto

regr

essi

ons

unde

rlyi

ngth

ete

sts

bysu

mm

ing

the

opti

mal

lag

chos

enw

ith

the

Schw

arz

Bay

esia

nin

form

atio

ncr

iter

ion

and

the

max

imum

orde

rof

inte

grat

ion

ofth

etw

ose

ries

inth

eve

ctor

auto

regr

essi

onch

osen

wit

hau

gmen

ted

Dic

key-

Fulle

rte

sts

(Tod

aan

dY

amam

oto

1995

).W

eco

nclu

deth

atth

ere

isG

rang

erca

usal

ity

ifa

Wal

dte

stre

ject

sth

enu

llhy

pot

hesi

sth

atth

ela

gged

valu

esof

the

inde

pen

dent

vari

able

are

equa

lto

zero

.N

ote

that

,fo

llow

ing

Tod

aan

dY

amam

oto

(199

5),

we

only

test

that

the

first

mla

gsar

eeq

ual

toze

ro,

whe

rem

isth

enu

mber

ofla

gsid

enti

fied

asop

tim

alw

ith

the

Schw

arz

crit

erio

n.T

hesy

mbol

s**

*,**

,an

d*

indi

cate

stat

isti

calsi

gnifi

canc

eat

the

1per

cent

,5

per

cent

,an

d10

per

cent

leve

ls,re

spec

tive

ly.


in the bottom panel reject the null of no Granger causality. Theseresults suggest that, if we exclude 2008, neither the FCIs nor thecredit availability measures Granger-cause each other. The reasonis that only six tests out of thirty-six (16.7 percent) reject the nullthat the FCIs do not Granger-cause the credit variables, and thenumber drops to three out of thirty-six (8.3 percent) when we testwhether the credit variables Granger cause the FCIs. In both cases,the incidence of rejections is roughly in line with the expected num-ber of false positives for tests that have a significance level of 10percent. In the full sample, the evidence is less clear on whether theFCIs Granger-cause the credit variables, because twelve tests out ofthirty-six (33.3 percent) reject the null of no Granger causality. Thenumber of tests that reject the null that the credit variables do notGranger-cause the FCIs remains three out of thirty-six.

2.3 Interpreting Our Results

Overall, our opinion is that the empirical evidence in favor of theFCIs having reliable predictive power is weak. Given that the FCIsare built by combining public data for typically highly liquid finan-cial instruments, they can hardly be characterized as containingprivileged information. We discuss three possible explanations forthe predictability we find in some of our results: threshold effects,data mining, and non-synchronous trading. We ultimately concludethat those results are mainly driven by data mining and by non-synchronous trading.

The predictability could be the result of threshold effects, in thatfinancial conditions matter only after they deteriorate sufficiently.12

In figure 2 we show a scatter plot of innovations to log-changesof total inventories against the St. Louis FCI, where observationsfor which the index is above its 80th percentile are highlighted bydifferent shapes. Triangles indicate an observation from between

12In Kashyap, Lamont, and Stein (1994), for instance, financing constraints atthe firm level only become binding in tight-money environments. Hubrich andTetlow (2012) find that macroeconomic dynamics crucially depend on financialstress, with stress being “of negligible importance in ‘normal’ times, but of criticalimportance when the economy is in a high-stress . . . state” (page 30).


Figure 2. Threshold Effects

Notes: The plot shows a scatter of innovations to log-changes in total invento-ries against the Federal Reserve Bank of St. Louis’s Financial Stress Index. Thesquares and triangles correspond to observations for which the index is above its80th percentile, with the triangles indicating an observation from between 2007and 2012 and the squares indicating an observation from outside that range. Timeperiod: July 1995 to June 2012.

2007 and 2012, and squares indicate an observation from outsidethat range.13

The evidence in figure 2 does not clearly point to a thresh-old effect. There is indeed a negative relationship between the FCIand log-changes in inventories; however, this negative relationship isstrong only in the observations from the recent crisis. One possibleexplanation is that the variables underlying the many FCIs con-structed after the 2008 crisis were chosen based on their movementsduring the crisis. For instance, the FCIs typically include variableslike the spread between three-month LIBOR and the three-monthTreasury-bill rate (the TED spread), which had unusually largemovements during the 2008 crisis. Figure 3 shows the time series of

13Figure 2 is representative of other index/variable combinations. The unre-ported figures are available upon request from the authors.


Figure 3. Time Series of the TED Spread

Notes: The graph shows the time series of the spread, in basis points, betweenthe three-month USD LIBOR and the three-month U.S. Treasury yield, which iscommonly referred to as the “TED spread.” Data are from the British Bankers’Association and the Federal Reserve H.15 statistical release. The vertical lineshighlight five events that range from the Asian crisis in 1997 to the Europeandebt crisis in 2010.

the TED spread from 1986 onwards, and it highlights the unusuallyhigh level of the TED spread that was a feature of the 2008 finan-cial crisis. Unfortunately, in practice it is difficult to distinguish athreshold effect from possible data mining, not least because onlyone major financial crisis is included in the sample.

Another potential source of predictability is non-synchronoustrading across markets. Many FCIs, for instance, include the impliedvolatility index VIX, which is derived from the prices of S&P 500index options. These options trade for fifteen minutes after tradingin the underlying index ends (4:15 p.m. versus 4:00 p.m. easternstandard time)14 with the consequence that VIX on day t containsinformation that will be reflected in stock prices only on day t + 1—afact that can generate spurious predictability (see Atchison, Butler,and Simonds 1987 for a discussion of the effects of non-synchronous

14Details on the trading hours of S&P 500 index options can be found athttp://www.cboe.com/products/indexopts/spx spec.aspx


trading on the autocorrelation of equity index returns). We explorethis possibility by running predictive regressions on returns thatexclude the first day of each holding period.

Table 13 shows that now only five of the twelve FCIs have sta-tistically significant predictive power for the finance portfolio, downfrom eleven in table 4, suggesting that non-synchronous trading doesplay a role but is not the sole driver of predictability. In unreportedresults, we find that excluding the first day of each quarter does notchange the significance patterns of tables 6 and 7; most likely, theeffect of non-synchronous trading is diluted by the larger variationof returns measured over longer horizons.

The notion that FCIs only have weak predictive power is alsosupported by a series of Granger causality tests. These tests focuson the dynamic relation between the FCIs and a set of measuresfor the availability of consumer credit, mortgage credit, and CMBSissuance. Two of these variables are closely related to the housingboom that characterized the 2000s, and CMBS issuance also meas-ures the importance of the securitization channel for the provisionof commercial real estate credit. Even though both the variables ofinterest and the statistical approach are different than in the predic-tive regression analysis, this additional set of results supports ourmain conclusion that the FCIs do not have predictive power if oneexcludes the 2008 financial crisis from the sample.

Finally, we should emphasize that we do not conduct an out-of-sample analysis, which would be a more stringent test of pre-dictability, because our in-sample analysis already finds a lack ofreliable predictive power.

3. Combining FCIs

We now turn to the question of how to consolidate the increasinglylarge number of indexes into a single proxy for financial conditions.The FCIs themselves are already an aggregation of underlying vari-ables, and the procedure we describe below can be seen as a higher-level consolidation that aggregates across different variable sets andmethodologies, with the objective of reducing model uncertainty forpolicymakers. As discussed in the introduction, the various FCIsfollow similar long-run trends, but they can give significantly differ-ent readings on financial conditions at a given point in time (e.g.,

Vol. 13 No. 1 Assessing and Combining Financial Conditions 39Tab

le13

.P

redic

tive

Reg

ress

ions:

Mon

thly

Sto

ckR

eturn

sExcl

udin

gth

eFir

stD

ayof

the

Mon

th,19

95–2

012

BFC

IB

FC

I+C

FSI

MS

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−0.

230

4.62

0−

0.11

04.

630

−0.

027

4.63

30.

308

4.62

1Fin

ance

−0.

453∗

4.58

1−

0.26

34.

615

−0.

634∗

4.58

90.

007

4.63

4C

onst

ruct

ion

0.07

18.

295

0.19

88.

289

0.43

88.

283

0.19

28.

293

Man

ufac

turi

ng−

0.06

07.

146

0.12

47.

144

0.31

47.

139

−0.

039

7.14

6Tra

nspo

rtat

ion

−0.

156

6.06

70.

013

6.07

20.

117

6.07

0−

0.05

76.

071

Who

lesa

leTra

de−

0.09

16.

529

0.09

16.

529

0.23

86.

526

0.08

66.

530

Ret

ailTra

de0.

062

7.19

00.

247

7.18

00.

278

7.18

50.

448

7.17

4Se

rvic

es0.

075

8.14

90.

273

8.13

90.

579

8.12

90.

216

8.14

7

AN

FC

IN

FC

IC

LN

FSI

ST

LFSI

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

0.11

34.

633

−0.

670

4.61

7−

0.04

14.

632

−0.

193

4.62

9Fin

ance

−0.

922∗

4.58

0−

1.34

9∗4.

567

−0.

197

4.61

0−

0.38

64.

616

Con

stru

ctio

n0.

039

8.29

50.

188

8.29

50.

078

8.29

30.

602

8.27

1M

anuf

actu

ring

0.13

17.

146

−0.

081

7.14

60.

121

7.14

00.

355

7.13

7Tra

nspo

rtat

ion

0.12

96.

071

−0.

422

6.06

70.

008

6.07

20.

103

6.07

1W

hole

sale

Tra

de−

0.19

96.

529

−0.

186

6.53

00.

147

6.52

10.

361

6.52

0R

etai

lTra

de−

0.17

57.

189

0.36

47.

187

0.18

77.

177

0.76

27.

147

Serv

ices

0.36

98.

145

0.04

28.

150

0.25

88.

127

0.52

18.

132

(con

tinu

ed)


Tab

le13

.(C

ontinued

)

Cit

iFC

IIM

FFSI

KC

FSI

IMF

FC

I

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

βFCI

RM

SE

S&P

500

−0.

318

4.61

90.

050

4.63

0−

0.32

94.

619

−0.

633∗

4.59

2Fin

ance

−0.

260

4.62

5−

0.12

14.

612

−0.

540∗

4.59

6−

0.54

74.

603

Con

stru

ctio

n0.

189

8.29

30.

183

8.26

70.

360

8.28

60.

160

8.29

4M

anuf

actu

ring

0.17

37.

144

0.12

57.

131

0.19

27.

143

−0.

085

7.14

6Tra

nspo

rtat

ion

−0.

139

6.07

00.

067

6.06

6−

0.04

16.

071

−0.

399

6.05

9W

hole

sale

Tra

de0.

089

6.53

00.

078

6.52

40.

159

6.52

80.

054

6.53

1R

etai

lTra

de0.

155

7.18

80.

204

7.15

10.

531

7.16

70.

424

7.17

9Se

rvic

es0.

048

8.15

00.

147

8.13

20.

296

8.14

40.

003

8.15

0

Note

s:T

heta

ble

repor

tsth

eco

effici

ents

and

root

mea

nsq

uare

der

rors

(bot

hin

%)

ofpr

edic

tive

regr

essi

ons

ofon

e-m

onth

-ahe

adst

ock

retu

rns

onFC

Ile

vels

.R

etur

nsar

eca

lcul

ated

afte

rex

clud

ing

the

first

day

ofea

chm

onth

.Se

eta

bles

1an

d3

for

mor

ede

tails

onth

eFC

Isan

dfo

rth

ede

finit

ion

ofin

dust

rypor

tfol

ios.

Ast

eris

ksin

dica

tesi

gnifi

canc

eat

the

90per

cent

leve

l.St

atis

tica

lsi

gnifi

canc

eis

eval

uate

don

the

basi

sof

hete

rosk

edas

tici

ty-c

onsi

sten

tst

anda

rder

rors

or,w

here

appr

opri

ate,

wit

hth

elo

cal-to

-uni

tyas

ympt

otic

sof

Cam

pbel

lan

dY

ogo

(200

6)(s

eese

ctio

n2

for

deta

ils).

Tim

eper

iod:

July

1995

toJu

ne20

12.


see figure 1). When aggregating the FCIs, we first select a smallersubsample of indexes, and then search for the “best” combination.

In the first step, we sort the individual indexes on the basis of howwell they capture the information contained in the remaining FCIs.We measure this ability to capture information using the adjustedR2 from regressions15 of changes in the first principal component ofall indexes except for index i on changes in index i. Letting i denotethe FCI of interest, with i = 1, . . . , 12 and “fpc” the first principalcomponent,

PC−i = fpc({FCIj}j �=i)

ΔPC−i = γ + δ × ΔFCIi + εt.

Table 14 reports the adjusted R2s of the regressions described above,which we run on two different samples, 1995–2006 and 1995–2012.The rankings in the two samples are quite similar, with the St. LouisFCI, in particular, having a noticeable margin over the other indexes.We use the ranking to select the five FCIs with the highest adjustedR2 for further aggregation. The two Bloomberg FCIs are rankedamong the top five when the sample includes the period surround-ing the 2008 financial crisis; however, given that the two indexes arebuilt in a similar way, and that BFCI+ ranks noticeably worse thanBFCI in the shorter sample, we exclude BFCI+ from the set of best-performing indexes. We replace BFCI+ with the Kansas City index,which ranks sixth in the 1995–2012 sample and fifth when excludingthe years around the 2008 financial crisis.

Three aspects of our setup warrant further discussion. First, theranking criterion does not compare the FCIs with a benchmark,because financial conditions are an unobservable factor. We also donot use forecasting errors to select or optimally combine the variouspredictors (e.g., Timmermann 2006), because in the first part of thepaper we find that FCIs are not particularly reliable at forecastingeither returns or macroeconomic variables. Second, it is in princi-ple possible that a regression yields a low R2 because index i is aradically better proxy for financial conditions and does not span theremaining FCIs. However, the overlap and the encompassing nature

15We use robust regressions (Hamilton 1991), which reduce the influence ofoutliers.


Tab

le14

.Expla

nat

ory

Pow

erof

FC

Is

July

1995

–Dec

.20

06Ju

ly19

95–J

une

2012

ΔP

CR

es.Δ

Ave

rage

Ran

kΔ

PC

Res

.ΔA

vera

geR

ank

BFC

I48

.10

44.3

346

.22

256

.99

52.9

854

.98

4B

FC

I+34

.99

30.7

632

.88

658

.35

54.2

056

.27

3C

itiFC

I34

.43

37.1

835

.81

444

.62

45.3

945

.00

5C

FSI

32.4

929

.44

30.9

724

.70

21.8

923

.30

NFC

I44

.36

41.3

442

.85

365

.07

60.7

962

.93

2A

NFC

I15

.85

11.6

213

.74

35.9

229

.30

32.6

1IM

FFSI

22.3

327

.99

25.1

633

.35

35.6

034

.48

KC

FSI

35.4

431

.61

33.5

25

42.2

336

.41

39.3

26

IMF

FC

I14

.04

19.1

816

.61

29.3

638

.77

34.0

6M

SFC

I17

.79

17.7

817

.79

21.3

424

.40

22.8

7C

LN

FSI

29.9

627

.70

28.8

333

.61

30.9

632

.28

STLFSI

63.7

260

.01

61.8

61

75.6

373

.82

74.7

21

Note

s:T

heta

ble

repor

tsth

ead

just

edR

2s

(in

%)of

robu

stre

gres

sion

s(H

amilt

on19

91)of

(i)ch

ange

sin

the

first

prin

cipa

lcom

pon

ent

ofal

lth

eFC

Is(a

side

from

the

one

indi

cate

din

each

row

)on

chan

ges

inth

ein

dica

ted

FC

I(c

olum

nsla

bel

ed“Δ

PC

”),

and

of(i

i)on

e-la

gau

tore

gres

sive

resi

dual

sof

chan

ges

inth

efir

stpr

inci

palco

mpon

ent

ofal

lth

eFC

Is(a

side

from

the

one

indi

cate

din

each

row

)on

one-

lag

auto

regr

essi

vere

sidu

als

ofth

ech

ange

sin

the

indi

cate

dFC

I(c

olum

nsla

bel

ed“R

es.Δ

”).


of the variables that underlie the different indexes make such a pos-sibility unlikely. Third, we choose to select five FCIs, and the choiceof this number is admittedly arbitrary, but the key point is that weare able to reduce the number of combinations we consider, thuslowering the likelihood that our results are driven by data mining.

In the second step we form all combinations of the five indexesselected above,16 calculate each combination’s first principal compo-nent, and regress17 changes in the first principal component of theFCIs that are not in the combination under consideration (out ofthe twelve we study) on changes in the first principal component ofthe combination. Letting C denote the combination of interest,

PC/∈C = fpc({FCIj}j /∈C)

PC∈C = fpc({FCIj}j∈C)

ΔPC/∈C = γ + δ × ΔPC∈C + εt.

In order to minimize the risk of overfitting, the regressions are runon several subsamples, and we use the resulting set of adjusted R2 toselect the “best” combination of FCIs. Specifically, we calculate, foreach combination and in each subsample, the squared deviation ofthe combination’s adjusted R2 relative to the highest adjusted R2 ineach subsample. We then average, for each combination, the squareddeviations across time periods, and use the averages to identify the“best” composite FCI. Table 15 reports five averages: the first (col-umn A) shows arithmetic averages; in the second (B) the averageis weighted by the ratio of daily S&P 500 return volatility in eachsubsample over the volatility in the full sample; in the third (C) itis weighted by the ratio of the average VIX level in each subsam-ple over the average VIX level in the full sample; in the fourth (D)weights are based on the volatility of daily VIX changes; in the fifth(E) the arithmetic average is calculated on the four non-overlappingsamples (7/95–12/98 through 1/06–6/12).

The criterion we use to rank the FCIs is, of course, one of poten-tially many. For example, we could have selected the FCIs with the

16We form thirty-one different combinations: five individual indexes, ten setsof two indexes, ten sets of three indexes, five sets of four indexes, and one set offive indexes.

17We again use robust regressions (see Hamilton 1991).


le15

.Expla

nat

ory

Pow

erof

the

Fir

stP

rinci

pal

Com

pon

ent

ofFC

IC

ombin

atio

ns

7/95

–7/

95–

7/95

–1/

99–

1/02

–1/

06–

Ave

rage

Squar

edD

evia

tion

Com

bin

atio

n6/

1212

/06

12/9

812

/05

12/0

56/

12A

BC

DE

STLFSI

–B

FC

I70

.36

58.6

856

.74

63.4

449

.81

76.6

91.

481.

411.

461.

431.

80ST

LFSI

–N

FC

I76

.12

62.1

760

.36

63.2

453

.72

81.6

80.

840.

800.

840.

791.

14ST

LFSI

–K

CFSI

70.2

757

.41

51.5

552

.77

63.8

071

.02

2.41

2.41

2.49

2.39

3.11

STLFSI

–C

itiFC

I73

.31

54.2

363

.40

53.7

750

.30

83.1

61.

721.

691.

771.

571.

97B

FC

I–

NFC

I62

.43

53.9

548

.65

58.8

945

.55

67.2

03.

453.

393.

433.

463.

99B

FC

I–

KC

FSI

66.1

061

.43

59.8

164

.21

56.6

571

.91

1.30

1.35

1.33

1.36

1.44

BFC

I–

Cit

iFC

I70

.96

57.9

365

.49

51.2

551

.55

77.3

41.

801.

851.

911.

692.

26N

FC

I–

KC

FSI

61.0

348

.32

48.9

148

.74

47.1

463

.46

4.76

4.80

4.85

4.77

5.34

NFC

I–

Cit

iFC

I64

.41

48.9

752

.43

42.6

440

.08

77.5

84.

644.

534.

724.

315.

48C

itiFC

I–

KC

FSI

58.7

845

.10

40.3

439

.41

42.3

665

.57

7.11

7.00

7.22

6.90

8.29

STLFSI

–B

FC

I-

NFC

I71

.32

61.9

660

.88

70.8

350

.99

76.0

40.

910.

880.

880.

901.

12ST

LFSI

–B

FC

I–

KC

FSI

73.7

366

.84

68.0

071

.86

61.5

173

.02

0.41

0.48

0.43

0.49

0.56

STLFSI

–B

FC

I–

Cit

iFC

I77

.09

64.2

970

.55

62.9

455

.97

81.6

60.

480.

490.

500.

440.

65ST

LFSI

–N

FC

I–

KC

FSI

69.6

659

.32

54.6

656

.15

63.3

372

.18

1.85

1.86

1.91

1.85

2.35

STLFSI

–N

FC

I–

Cit

iFC

I78

.31

62.2

272

.37

57.6

255

.16

86.0

70.

770.

790.

830.

691.

04ST

LFSI

–C

itiF

CI

–K

CFSI

71.3

658

.55

52.9

052

.85

54.6

078

.65

2.09

2.03

2.15

1.98

2.74

BFC

I–

NFC

I–

KC

FSI

67.9

664

.93

63.8

570

.14

56.9

768

.97

0.98

1.08

1.01

1.11

1.16

BFC

I–

NFC

I–

Cit

iFC

I71

.05

62.8

667

.08

60.4

455

.34

75.3

20.

940.

990.

990.

931.

18B

FC

I–

Cit

iFC

I–

KC

FSI

74.8

963

.92

72.1

658

.57

57.0

479

.65

0.72

0.76

0.78

0.68

0.99

NFC

I–

Cit

iFC

I–

KC

FSI

69.5

254

.51

55.6

848

.79

47.9

575

.87

2.83

2.80

2.92

2.67

3.53

(con

tinu

ed)


Tab

le15

.(C

ontinued

)

7/95

–7/

95–

7/95

–1/

99–

1/02

–1/

06–

Ave

rage

Squar

edD

evia

tion

Com

bin

atio

n6/

1212

/06

12/9

812

/05

12/0

56/

12A

BC

DE

STLFSI

–B

FC

I–

NFC

I–

KC

FSI

72.8

068

.44

69.1

176

.10

59.1

871

.79

0.46

0.53

0.48

0.55

0.61

STLFSI

–B

FC

I–

NFC

I–

Cit

iFC

I75

.70

67.1

171

.43

68.0

357

.75

81.4

30.

230.

240.

240.

220.

32

STLFSI

–B

FC

I–

Cit

iFC

I–

KC

FSI

74.7

567

.53

73.3

463

.24

60.3

179

.45

0.39

0.43

0.43

0.39

0.55

STLFSI

–N

FC

I–

Cit

iFC

I–

KC

FSI

74.2

862

.26

61.2

256

.73

57.1

780

.84

1.09

1.09

1.15

1.02

1.48

BFC

I–

NFC

I–

Cit

iFC

I–

KC

FSI

76.2

667

.30

73.2

862

.83

57.9

879

.01

0.44

0.48

0.48

0.43

0.65

STLFSI

–B

FC

I–

NFC

I–

Cit

iFC

I-

KC

FSI

75.4

568

.96

72.4

568

.46

60.1

277

.55

0.26

0.29

0.28

0.28

0.36

Note

s:T

hefir

stsi

xco

lum

nsre

por

tth

ead

just

edR

2s

(in

%)

ofro

bust

regr

essi

ons

(Ham

ilton

1991

)of

(i)

chan

ges

inth

efir

stpr

inci

pal

com

pon

ent

ofal

lth

eFC

Is(a

side

from

thos

ein

dica

ted

inea

chro

w)

onch

ange

son

the

first

prin

cipa

lco

mpon

ent

ofth

ein

dica

ted

FC

Ico

mbi

nati

on(t

here

sult

sfo

rin

divi

dual

FC

Isar

eun

tabu

late

d).

The

last

five

colu

mns

show

the

aver

ages

,ac

ross

sub-

per

iods

,of

the

squa

red

devi

atio

nsof

each

com

bina

tion

’sad

just

edR

2fr

omea

chti

me

per

iod’

sla

rges

tad

just

edR

2.C

olum

nA

show

ssi

mpl

eav

erag

esac

ross

the

six

sub-

per

iods

.C

olum

nsB

–Dre

por

tw

eigh

ted

aver

ages

,w

here

the

wei

ghts

assi

gned

toea

chsu

b-per

iod

are

base

don

the

vola

tilit

yof

daily

S&P

500

retu

rns

(B),

onth

eav

erag

eim

plie

dvo

lati

lity

inde

xV

IX(C

),an

don

the

vola

tilit

yof

daily

chan

ges

inV

IX(D

).T

hew

eigh

tsar

eca

lcul

ated

byno

rmal

izin

gth

evo

lati

litie

s/av

erag

esin

each

sub-

per

iod

wit

hth

evo

lati

lity/

aver

age

over

the

full

sam

ple.

Col

umn

Esh

ows

sim

ple

aver

ages

acro

ssth

efo

urno

n-ov

erla

ppin

gsu

b-per

iods

(7/9

5–12

/98

thro

ugh

1/06

–6/1

2).


lowest volatility. Such choice would have implied an assumption onthe way financial conditions change over time, namely that theyevolve smoothly. Choosing the FCIs with the highest volatility wouldhave implied that we assume financial conditions can change rapidly,and that we are looking for a more reactive proxy. Precisely to avoidimposing strong assumptions on the nature of the process for finan-cial conditions, we have adopted a criterion that only assumes that(i) all the indexes we study provide some information about financialconditions, and (ii) none of the indexes is likely to contain uniquelyaccurate information about financial conditions.

The results in table 15 show (in bold) that the first principalcomponent of the St. Louis Fed, Bloomberg, Chicago Fed, and Citiindexes has the lowest average squared deviations in all columns(A)–(E): hence we consider such combination our composite FCI(CFCI). Figure 4 highlights that the CFCI follows the generalpattern of the individual FCIs, but its volatility exhibits differentregimes depending on whether financial conditions are loose or tight.The three graphs in figure 4 show the CFCI against three alter-native proxies for financial conditions: STLFSI, which is the best-performing individual FCI; the first principal component of STLFSIand of the index with the lowest correlation with STLFSI (MS FCI);and the implied volatility index VIX, which is one of the variablesunderlying many FCIs. The CFCI tracks STLFSI closely, althoughthe latter is less volatile in the years following the bull market of thelate 1990s. The first principal component of STLFSI and of MS FCIis more volatile than the CFCI, especially in the earlier part of thesample, and it points to much more improved conditions than theCFCI in early 2008, just before the crisis gained full traction.

A comparison of VIX and the CFCI shows that the two trackeach other quite well, with the exception of the period between mid-2007 and late 2008, when VIX remains stable, and the CFCI showsa largely steady deterioration in financial conditions. In addition,the CFCI points to loose financial conditions in the second half ofthe 1990s until late 1998, while, over the same period, VIX pointsto slowly deteriorating conditions starting in 1995. In figure 5, weplot the twenty-four-month exponentially weighted rolling correla-tion between VIX changes and changes in the CFCI, where obser-vations are weighted so that the weight decays by 50 percent everytwelve months (see figure 5 for details). The correlation is initially


Figure 4. The Composite FCI and Other Proxies ofFinancial Conditions

Notes: Each of the three graphs shows the composite FCI (CFCI, solid line)against one of three alternative proxies for financial conditions: the STLFSI (topgraph), which is the individual FCI that best summarizes the information in theremaining FCIs (see table 14); the first principal component of the STLFSI and ofthe index that is least correlated with the STLFSI (the MS FCI—middle graph);and the implied volatility index VIX (bottom graph). VIX data are from theChicago Board Options Exchange.


Figure 5. Exponentially Weighted Correlation betweenVIX Changes and Changes in the Composite FCI

Notes: The graph shows exponentially weighted correlation (dotted line) betweenmonthly changes in the volatility index VIX and monthly changes in the com-posite FCI, together with the VIX index, which is standardized for scale reasons(dashed line), and the composite FCI (CFCI, solid line). The correlation is cal-culated on the basis of a twenty-four-month rolling window, where the weightsdecay by 50 percent every twelve months. Specifically, the weights assigned toobservations {t − i}23

i=0 are given by 10.75 · α · (1 − α)i, where α = 1 − e

−ln(4)24 .

low, but it jumps to about 80 percent with the Russian default inAugust 1998. With the exception of two relatively short periods inlate 2000 and 2005/06, it stays mostly above 50 percent, and it hasbeen around 80–90 percent since the events of the late summer of2008.

4. Conclusion

We provide an assessment of the one-month-ahead and one-quarter-ahead predictive power that a selection of financial conditionsindexes (FCIs) have for returns on a broad equity index and a setof equity industry portfolios and for innovations to log-changes inmacroeconomic variables. Our analysis is based on local-to-unityasymptotics, which allows for accurate statistical inference in the


presence of persistent predictors. We find that the evidence for pre-dictive power at the horizons we consider is generally weak, unlessthe financial crisis is included. Further, part of the predictive poweris driven by the effects of non-synchronous trading and, potentially,data mining. We also study the relation between the FCIs andconsumer credit, mortgage credit, and the issuance of commercialmortgage-backed securities, and find only limited evidence that theFCIs Granger-cause these variables. Based on these results, we con-clude that FCIs are better used as aggregate indicators of currentfinancial conditions.

We also observe that, even in the months leading to the 2008financial crisis, different FCIs can provide conflicting assessments offinancial conditions. We suggest a procedure for combining the vari-ous FCIs into a single proxy, which can help reduce the uncertaintyfacing policymakers when monitoring financial conditions. Our pro-cedure builds on the modest assumptions that all the FCIs we studyprovide some information about financial conditions, yet no indexcontains uniquely accurate information.

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