FINANCE DISCIPLINE GROUP

UTS BUSINESS SCHOOL

WORKING PAPER NO.179 January 2014

The Stock Market, the Real Economy and Contagion Dirk G Baur Isaac Miyakawa

ISSN: 1837-1221 http://www.business.uts.edu.au/finance/

The Stock Market, the Real Economy and

Contagion

Dirk G. Baur∗

&

Isaac Miyakawa

UTS Business School, University of Technology, Sydney

First version: August 2013, This version: January 2014

Abstract

In this paper we analyze the link between stock market performance and macroe-

conomic performance for a large number of countries. We study the short-run and

long-run relationships and find that stock market returns do not coherently predict

future macroeconomic changes for the majority of countries, i.e. the estimates vary

considerably both across prediction horizons and across countries. Moreover, we test

whether the financial and real economy dynamic linkages increased in the financial

crisis in 2008 implying “macro-financial” contagion. The crisis-specific analysis of

macro-financial linkages broadens the perspective of existing studies of financial con-

tagion. Our findings indicate that the stock market does not merely reflect future

economic conditions but also influences them justifying policy responses as witnessed

during the 2008 financial and economic crisis.

JEL classification: C22; C32; E44; G01; G14; G15; G18

Keywords: global stock markets; real economic activity; predictive regressions; con-

tagion; financial crises; co-integration

∗corresponding author, address: PO Box 123, Broadway, Sydney, NSW 2007, Australia Email:dirk.baur@uts.edu.au

1

“Money values do not simply mirror the state of affairs in the real world;

valuation is a positive act that makes an impact on the course of events. Mon-

etary and real phenomena are connected in a reflexive fashion; that is, they

influence each other mutually.”

George Soros

The above quote states that the stock market is not a “sideshow” that merely reflects the

state of the real economy but instead that it affects the real economy through valuation.

The valuation provided by the stock market feeds back into the real economy and creates

a reflexive relationship.

This relationship also highlights the importance of the efficiency of the stock market. If

the stock market is not efficient and if prices regularly deviate from their true values

including bubble episodes, the signals do not enhance the allocation of resources and a

feedback effect can distort the real economy.

Theoretically, equity prices should be related to future economic activity because a firm’s

projected earnings growth depends on the future state of the economy (e.g. see Fama,

1990, Hamilton and Lin, 1996 and Schwert, 1990 and for more recent studies MSCI Barra,

2010 and Cornell, 2010). In other words, a firm’s stock price is based on the discounted

sum of all future cash flows which depend on the state of the real economy. If there is

empirical evidence that equity valuations are related to future economic activity, the stock

market (if efficient) can be used as a signal and predictor of future economic conditions.

The relationship between economic growth and stock market growth is particularly vital

for countries in which future pensions are tied to the performance of the stock market.

Australia and its pension scheme “superannuation” is one example of such a country. The

strength of the relationship thus determines to which degree households are exposed to

both stock market changes and economic performance.1

1There are many factors that can weaken the relationship between equity returns and the state of theeconomy and thus the quality of the signal. For example, the share of the company profits in the totaleconomy may change, investors’ valuation ratios may change, a country’s stock market (index) includesnon-domestic and possibly multinational companies etc.

2

If the stock market indeed signals future economic activity it will also influence it. For

example, if policy makers react to expected economic changes, e.g. a downturn, with

measures that counter the prediction the stock market does not only signal and predict

future economic performance but does also indirectly influence it. In this case, policy

makers influence the predicted value. Similarly, if households use the stock market as a

predictor of their future labor income, they might increase consumption levels in times of

rising stock prices thereby affecting the real economy and via a feedback effect the stock

market.2 Finally, if firms use high stock price valuations to issue more stock and use the

proceeds for investment the stock market will influence the real economy and not merely

reflect future economic activity.3

Hence, if the stock market does not only provide a signal and predict future economic

activity but if the signal also influences this future economic activity through the behavior

of firms, investors and consumers then the signal can enhance a boom phase and similarly

exacerbate a recession leading to greater fluctuations of the real economy performance

compared to a situation in which no signal is available. The cause is a positive feedback

from the stock market to the real economy. In contrast, policy makers can counteract

exacerbated fluctuations by raising interest rates or taxes in expansion periods and by

lowering interest rates or taxes in recession periods. They can thus use the signals from the

stock market to reduce the fluctuations of the real economy creating a negative feedback

effect.4

2The signal quality of the stock market may appear to be more accurate than it actually is due to theinfluence of the prediction on the predicted variables.

3Morck, Shleifer and Vishny (1990) identify four theories and hypotheses that explain the correlationbetween stock markets and subsequent investments. The hypotheses are “The Passive Information Hy-pothesis” which defines the stock market as a “sideshow” with no impact on the real economic activity,“The Active Information Hypothesis” which gives the stock market an active role in providing signals forthe real economy, “The Financing Hypothesis” which assumes the stock market provides equity capital tofirms and “The Stock Market Pressure Hypothesis” which assumes that managers (are forced to) react tochanges of the stock market value of their firms.

4Predictions generally influence the behaviour of human beings. For example, if the weather forecast forthe next day predicts warm weather and sunny conditions, people will be influenced by this prediction andmake plans aligned to the weather conditions. Whilst the weather forecast influences human behaviour,human behaviour does not immediately influence the weather conditions. This is a fundamental differenceto the stock market and real economy relationship. The weather is exogenous to the forecast while the realeconomy is not exogenous to the stock market forecast (e.g. see Giroud et al. (2012) for an empirical studyof ski resorts using snow as an exogenous weather instrument). If the stock market predicts positive (macro-)economic conditions in the future, people will not only be influenced by this prediction and potentially

3

The feedback effects are well captured by “reflexivity” as described by Soros (1988). To

identify feedback effects we focus on a period of financial turmoil and crisis and test

whether the relationship between the stock market and the real economy has intensified

during that period. This additional analysis can provide a justification for monetary and

fiscal policy changes as a response to stock market crashes or financial crises in general.

For example, many governments introduced special fiscal stimulus packages as a response

to the financial crisis in 2008 and major central banks significantly reduced the policy

interest rates to counteract the effects of the crisis. This response of policy makers to

significant changes in the stock markets is only justified if the stock market reflects real

economic activity both in normal times and in crisis times.

We aim to contribute to the literature in two major respects. First, we analyze the short-

run and long-run dynamic (lead-lag) relationships between real economy performance and

stock market performance for a large sample of countries and secondly, we study “macro-

financial” contagion, i.e. the increasing correlation between the stock market (“financial”)

and the macroeconomic variables representing economic performance (“macro”) in a crisis

period relative to a tranquil pre-crisis period. The crisis-specific analysis extends exist-

ing studies on contagion in equity markets to a study of contagion with a broader, i.e.

macroeconomic focus.5 We believe, to the best of our knowledge, that we are the first to

introduce this type of contagion.6

We find that stock market changes reflect and predict future economic conditions measured

by Industrial Production (IP) and GDP. However, the prediction is incoherent, i.e. the

quality of the signal varies both across prediction horizons and countries. A co-integration

buy or invest more, they will also influence the predicted value.5The contagion literature is often based on an analysis of changes in the co-movement of stock market

indices in a crisis period relative to a “tranquil” pre-crisis period (e.g. see Baig and Goldfajn, 1999 andForbes and Rigobon, 2002). If the co-movement increases in the crisis period there is evidence of contagion.Hence, in the context of this paper, if the co-movement between the stock market and the real economyincreases in a crisis triggered by the financial sector there is evidence for contagion of the real economywell extending the usual and narrower analysis of stock or bond market contagion.

6In a series of articles, Mian and Sufi (2009, 2010 and 2011) analyze macro-finance linkages with a focuson the 2007-2008 US mortgage default crisis. In contrast to this study, they focus on the US and do notanalyze changes in macroeconomic aggregates and the equity market within a contagion framework.

4

analysis based on levels further reveals that there is no compelling evidence for a long-run

(equilibrium) relationship between the stock market and Industrial Production or GDP

for most countries. One major contribution to this finding is the excess volatility of stock

valuations compared to IP and GDP levels. Finally, we focus on the financial crisis in

2008 and demonstrate that the correlation between the stock market and real economic

activity measured with IP or GDP increased consistent “macro-financial” contagion.

The remainder of this paper is structured as follows: Section I describes the econometric

framework to analyze the lead-lag, short-run and long-run relationships between the stock

market and the real economy. Section II contains the empirical analysis with a description

of the data and the presentation and discussion of the estimation results. Finally, section

III summarizes the main results and provides concluding remarks.

I. Econometric Framework

This section first describes the theoretical basis for the econometric analysis and then

outlines the econometric tests.

The value V of a firm is the sum of all expected future cash flows CF discounted with

the rate i:

Vt =

K∑

k=1

E(CFt+k)

(1 + i)k(1)

On the aggregate level using the assumption that future cash flows CF are related to the

future state of the economy X, the stock market value of all firms S is given by

St =

K∑

k=1

βkXt+k (2)

The above model could be estimated within a regression framework but would not provide

the predictive power of the stock market in forecasting future states of the real economy.

5

In contrast, the following regression model assesses the predictive power of S for different

k with k > 0:

Xt+k = βkSt (3)

Note that Equations 1 and 2 imply a contemporaneous correlation equal to zero (β0 = 0).

Only if there is a contemporaneous spillover from either the stock market to the real

economy or a feedback effect from the real economy to the stock market, a non-zero

contemporaneous correlation can originate. We will use this implication of the model to

test for feedback and crisis-specific contagion in a macro-financial context.7

Since both X and S are usually trending, non-stationary time-series the econometric

framework must account for this characteristic. Hence, section A focusses on the short-run

dynamics based on changes of the variables and Section B models the long-run dynamics

based on the levels. Finally, Section C presents a framework to analyze changes in the

short-run and long-run relationship implying macro-financial contagion.

A. Short-run Relationship

We analyze the relationship implied by Equation 3 with the following regression model

∆Xt+k = αk + βk∆St + et (4)

for k = 0, 1, 2, ...K where ∆St denotes relative changes in the stock market at t and ∆X

denotes relative changes of IP or GDP.

As an alternative to the parsimonious specification described above we also consider a

vector autoregression (VAR):

7The feedback effect can originate as follows: if St does not only predict but also influence Xt+1, Xt+1

will affect St+1 generating a feedback from the real economy to the stock market. The positive feedbackonto St+1 may then influence the prediction of Xt+2.

6

∆St = a1 +

K∑

k=1

b1,k∆St−k +

K∑

k=1

c1,k∆Xt−k + et (5)

∆Xt = a2 +

K∑

k=1

b2,k∆St−k +

K∑

k=1

c2,k∆Xt−k + et (6)

The model is estimated to analyze to what extent the inclusion of all lags of either S or X

predict future changes of X or S, respectively. In addition, we can analyze the existence

of any feedback from X to S not implied by Equations 1-3.

The theory in Equation 1 and the derived Equations 2 and 3 predict that the coefficient

estimates b2,k are statistically significant and positive. Moreover, if there is lagged feedback

from X to S then we expect at least one of the coefficient estimates c1,k to be statistically

significant and positive. For example, if St−2 predicts and influences Xt−1, this can result

in a lagged feedback affecting ∆St.8

B. Long-run Relationship

It can be assumed that stock market indices, IP and GDP levels are trending, non-

stationary and integrated time-series.9 Hence, instead of transforming the data to obtain

stationary series we can explicitly model the long-run relationship via co-integration. We

follow the approach of Engle and Granger (1987), i.e. establish that the time-series are

integrated of the same order and estimate the model124(4), pp. 1449-96.

St = βXt + et (7)

where St and Xt denote the level of the stock market index and IP or GDP of a specific

country at time t, respectively. If St and Xt are integrated of the same order and the error

term et (et = St − βXt) is stationary, S and X are co-integrated with a co-integrating

8If St predicts and influences Xt+1 it can create a contemporaneous feedback effect to St+1 (from Xt+1).If the feedback effect is not contemporaneous but lagged, then the feedback effect affects St+2 (from Xt+1),i.e. Xt+1 to St+2 or Xt to St+1 or Xt−1 to St.

9This assumption is tested in the empirical section.

7

vector [1,−β].

One of the advantages of this methodology is that we can explicitly distinguish between

a long-run relationship between S and X and short-run dynamics, that is, deviations of

S and X from their long-run relationship. This advantage is particularly useful for the

crisis-specific analysis since statistically significant deviations over T periods (e.g. months)

can be associated with specific periods of financial turmoil or crisis.

We also estimate the vector error correction model (VECM) applying the Johansen

methodology (Johansen, 1995). The VECM avoids misspecification of the data generating

process if there are any lead-lag short-run dynamics such as those suggested by Equation

1. For those countries for which a cointegrating relationship is established through the

trace and maximum eigenvalue test statistics, the following VECM is estimated:

[∆St,∆Xt]′ = a+Π(St−1 − βXt−1) +

K∑

i=1

bi∆St−i +

K∑

i=1

ci∆Xt−i + et (8)

where Π is the cointegrating matrix and a, bi and ci are vectors of coefficients. The lag

length K is determined by the Bayes Information Criterion.

Our attention will focus on Π and whether deviations from the equilibrium have both an

impact on ∆St and ∆Xt or just on one of the variables. More specifically, if equilibrium

errors (St−1−βXt−1) display a significant relationship with ∆Xt there is a feedback effect

rejecting the sideshow hypothesis of the stock market. Similarly, if the stock market

accurately predicts the future state of the real economy but also influences it due to the

reactions of households and firms, a feedback effect from the real economy to the stock

market is generated implying a positive error correction coefficient on ∆St. Note that

this test does not provide a way to determine whether the stock market causes changes

to future output. However, statistically significant error correction coefficient estimates

in both equations of the VECM support the feedback hypothesis and reflexivity.

8

C. Crisis-specific Relationships - Contagion

There are many studies that analyze crisis-specific changes in linkages (correlations) among

stock markets, bond markets or between stocks and bonds (e.g. Baig and Goldfajn, 1999,

Bekaert, Harvey and Ng, 2005, Boyer, Kumagai and Yuan, 2006, Dungey and Martin, 2007

and Forbes and Rigobon, 2002). However, to the best of our knowledge there is no study

on crisis-specific changes of the link between stock markets and macroeconomic variables

representing real economic activity. The justification for such an analysis is similar to

the more established and narrower definitions which focus on the financial sector. If the

contemporaneous link between the stock market and the real economy becomes stronger

at times when there is a financial or economic crisis, investors who participate in the real

economy and the stock market are more severely affected than in a situation in which the

stock market and the real economy do not co-move contemporaneously. If the stock market

predicts future economic conditions but does not affect current economic conditions, a

stock market crisis or crash can be compensated with the income or profits generated

in the real economy. However, a positive contemporaneous correlation may result in

severe losses in the stock market and in lower profits or income (even unemployment) for

investors.

Similar to existing studies on financial market contagion, we define macro-financial conta-

gion as the increase of the contemporaneous correlation between real economy performance

(“macro”) and stock returns (“financial”) during a financial crisis. Since the contempo-

raneous correlation should be zero in normal, non-crisis, periods10, a positive change of

the correlation in a crisis period indicates contagion. In other words, a positive change of

the contemporaneous correlation in a crisis period is evidence for a crisis-specific spillover

between the stock market to the real sector consistent with contagion. 11

10The contemporaneous correlation is zero if the stock market provides signals based on future expecta-tions and not signals based on contemporaneous effects (see equations (1) and (2)).

11The concept of macro-financial contagion relates closely to the extant literature that finds a strongrelationship between stock market volatility and economic recessions. Schwert (1989) finds that stockmarket volatility was two to three times higher during the Great Depression compared to its long-runmean. A similar finding is arrived at by Hamilton and Lin (1996), who show that monthly stock marketvolatility is positively related to recessions.

9

Crisis-specific correlation changes can be analyzed within the (i) short-run regression

framework and (ii) within the co-integration framework. The regression model based on

relative changes of the variables is given by

∆Xt = a+ b∆St + c∆StDt + et (9)

where Dt is a dummy variable that is equal to one if t lies within the crisis and zero

otherwise. A positive parameter c indicates that the link is stronger in the crisis period

than in “normal” periods.

We also estimate a VAR model that nests the parsimonious specification given by Equation

9.

∆Xt = a2 +

K∑

k=1

b2,k∆St−k +

K∑

k=1

c2,k∆Xt−k +

4∑

k=0

dk∆ lnSt−kDt−k + et (10)

There are two ways to analyze contagion within the co-integration framework (if the

involved series are indeed co-integrated). The first one is based on the model

St = βXt + γXtDt + et (11)

There is evidence for a stronger temporary linkage between S and X if γ is positive and

the residuals are stationary.

An alternative to this approach is a two-stage regression to analyze whether any deviation

from the long-run relationship (if such a relationship exists) coincides with a specific crisis

period. The residuals of a first-stage regression are tested for stationarity. If the null

hypothesis of non-stationarity is rejected, a second stage model is estimated as follows

et = γ1 + γ2Dt + et (12)

If there is a significant deviation from the equilibrium relationship indicated by the coef-

10

ficient γ2 it would justify policy action to bring variables back to equilibrium. However,

this approach does not indicate a strengthening of a pre-existing relationship consistent

with contagion. Instead, the two-stage approach is better suited to analyze crisis-specific

deviations from a long-run relationship.

II. Empirical Analysis

A. Descriptive Statistics

This section presents the descriptive statistics of the stock market, GDP and Industrial

Production time-series for a sample of 32 industrial and emerging, large and small coun-

tries. Table I presents the descriptive statistics for monthly stock returns (first panel),

quarterly stock returns (second panel), monthly IP log differences (third panel) and quar-

terly GDP log differences (fourth panel). Monthly and quarterly stock returns are used

to align them with Industrial Production which is available at a monthly frequency and

GDP which is only available at a quarterly frequency. The sample period spans a 33-year

period from the first quarter in 1980 until the first quarter in 2013. Both the Industrial

Production and GDP indices are seasonally adjusted. All prices are real prices, i.e. the

nominal prices have been corrected for price level changes to remove a common factor in

the data that may introduce a spurious relationship between the series. The industrial

production series is a volume index and the stock index and GDP index are deflated with

each countries’ CPI figures.

< Insert Table I about here >

Stock index data are the Datastream Total Market share indices and CPI series are from

the IMF International Financial Statistics database. The dataset of Industrial Production

and GDP indices are collated from multiple databases accessed through Datastream. For

each country, the database is chosen that provides the time series with the most complete

history.

11

A comparison of the average monthly stock returns with monthly Industrial Production

levels and quarterly stock returns with quarterly GDP levels reveals that monthly average

stock returns are larger and more volatile than IP and GDP figures. Figure 1 presents

examples and illustrates this finding for the stock market - GDP relationship of the US,

Brazil and Turkey.

< Insert Figure 1 about here >

The Figure demonstrates that stock price growth and GDP growth co-move to some extent

but that stock prices are more volatile and at times significantly deviate from the GDP

growth path.

An analysis of the cross-country correlations supports this conclusion. International stock

market returns are highly correlated with averages around 0.5 whilst the correlations

of GDP changes across countries are significantly lower on average, around 0.25. The

correlations of IP changes are similar to the monthly GDP correlations on average but

deviate significantly for some country pairs. Since stock market returns exhibit a greater

co-movement than GDP and IP changes across countries, the stock market - real economy

linkages will be lower on average.

Tables II and III present the Phillips-Perron (PP) test statistics for each country. The

test statistics illustrate that all series based on log differences are stationary. Changes of

stock market levels, GDP and IP are stationary without exception.

< Insert Table II about here >

< Insert Table III about here >

The descriptive statistics indicate that financial series are more volatile than macroeco-

nomic series and thus do not perfectly co-move. The following sections provide more

details about the static and dynamic relationships of the series.

12

B. Short-run Relationship

This section reports the results of the contemporaneous and predictive correlation coef-

ficient estimates of the stock market (St) and the real economy (Xt+k) proxied by GDP

and IP.

Table IV illustrates that the stock market is positively correlated with contemporaneous

and future GDP for the majority of countries. However, the correlations vary significantly

across countries and across time, i.e. across future periods k. The cross-sectional averages

of the contemporaneous and the predictive correlations are 0.114 and 0.208, 0.209, 0.133,

1.04 for k = 0, 1, 2, 3, 4, respectively. Consistent with the magnitudes of the average

coefficients, the number of statistically significant correlations is largest for k = 1 and

k = 2 with 21 significant estimates each. More specifically, the US exhibits positive

correlations for all k and highly significant and positive correlations for k = 1, 2, 3 whilst

Turkey exhibits negative correlations for k = 0, 2, 4 with only one significantly positive

correlation estimate for k = 3.

< Insert Table IV about here >

Table V presents the results for industrial production (IP), i.e. the contemporaneous and

predictive correlation estimates between the stock market and IP for 12 months. The

table shows that there are clearly less statistically significant coefficient estimates for IP

than for GDP with most statistically significant estimates clustered for k’s between k = 1

and k = 6.

< Insert Table V about here >

Two contrasting examples are the US and Norway. Whilst the US correlation estimates are

positive and statistically significant for almost all months k (except 10 and 11), Norway

does not exhibit any statistically significant correlation estimate. Given Norway’s oil-

centered structure of the economy this result is perhaps not too surprising.

We can summarize the results by stating that significant predictive correlations with

GDP are more frequent across countries than predictive correlations with IP but that

13

the magnitude and the sign of the predictive correlations vary considerably both across

prediction horizons and countries. The results illustrate that the stock market signals

future changes of GDP and of IP in around two thirds of the countries. However, given

the heterogeneity of the estimates both across countries and across prediction horizons

we can conclude that the stock market fails to provide strong and unambiguous signals

for future changes in economic activity in at least half of the countries analyzed. Finally,

the significant contemporaneous correlations for some countries are not implied by the

theoretical relationship given by the discounted cash flow model described by Equation

1 and indicate that the stock market does not merely reflect and predict future economy

activity but also influences it.

As an alternative to the predictive correlation analysis we also estimate the vector au-

toregression (VAR) as specified in Equations 5 and 6 to examine the lead-lag reflexive

relationship between stock market returns and GDP or IP changes.

Tables VI and VII present the estimation results for the stock market - GDP relationship

and Tables VIII and IX display the estimation result for the stock market - IP relationship.

< Insert Table VI about here >

< Insert Table VII about here >

The coefficient estimates for lagged changes of GDP on stock market changes reveal a

declining average coefficient for increasing lags of GDP changes. The first lag generally

exhibits a positive coefficient while the higher lags display negative coefficients in the

majority of cases.

The coefficient estimates representing the influence of (lagged) stock market changes on

changes of GDP are generally positive and significant with coefficients decreasing for

increasing lag lengths. The stronger results for GDP being the dependent variable are

consistent with the predictive role of the stock market. Similarly, the weaker results for

the stock market being the dependent variable are aligned with the underlying theory.

14

The estimation results for IP present a similar picture. The average coefficient estimates

are generally positive for lagged stock market returns influencing IP changes. In contrast,

lagged IP changes only exhibit a positive influence on stock market returns for lags up to

three months.

< Insert Table VIII about here >

< Insert Table IX about here >

The finding that stock returns can predict future changes in macroeconomic variables is

not surprising in the context of existing empirical literature that establishes the relevance

of various macroeconomic variables for future stock returns. For example, Cooper and

Priestley (2012) find that the world’s capital-output ratio is a strong predictor of future

stock market returns in six major stock markets. Rangvid (2006) arrives at a similar

conclusion across the US and G-7 countries yet uses the ratio of share prices to GDP.

Finally, we also estimated the model based on Equation 2. The structure of the equation

with the stock market returns on the left hand side of the equation perhaps better repre-

sents a role of the stock market that reflects future changes of GDP or IP rather than a

role that influences future changes. The coefficient estimates are fully consistent with the

theory. The further GDP or IP changes lie in the future the smaller (yet positive) is the

coefficient estimate.12

The next section analyzes the long-run relationship between stock markets and economic

activity based on the levels of the data in contrast to the changes analyzed in this section.

C. Long-run Relationship

This section presents the test results of a long-run and co-integrating relationship of

the stock market with GDP and IP. Table X presents the estimation results for GDP and

demonstrates that most countries do not display a long-run relationship between the stock

market and GDP. The table reports the Augmented Dickey Fuller (ADF) and the Phillips-

Perron (PP) test statistics. A rejection of the null hypothesis implies a stationary residual

12Results are not reported due to space considerations.

15

series based on a regression of the logarithmic stock market index level on the logarithmic

GDP level. The null hypothesis is only rejected for Canada, Czech Republic, Denmark

and Turkey. Therefore, the stock market and GDP exhibit a long-run relationship in only

four countries.

< Insert Table X about here >

The results for IP presented in Table XI illustrate that there are more long-run relation-

ships involving IP compared to GDP. Countries for which either the ADF test or the

PP test indicates rejection of the null hypothesis that there is no long-run relationship

between the stock market and IP are Canada, the US, Brazil, Denmark, France, Greece,

Italy, Norway, Portugal, Spain, Sweden and Turkey.

< Insert Table XI about here >

As an alternative to the Engle-Granger (1987) approach we estimate the co-integration

relationship using the Johansen (1995) technique. Consistent with the literature, this

approach yields more co-integration relationships as shown in Table XII.

< Insert Table XII about here >

We also estimate a vector error correction model (VECM) for the countries and variables

for which we find a co-integration relationship. The coefficient estimates measuring the

feedback effect, i.e. the error correction coefficients, are displayed in Table XIII and show

that there is a positive feedback effect both from the stock market to the macro-economy

and from the macro-economy to the stock market.

The VECM provides a direct test for the sideshow hypothesis of the stock market. If

the influence of the error correction term in the output equation and in the stock market

equation is not statistically different from zero, the sideshow hypothesis cannot be re-

jected. The estimates presented in the table show that the coefficients are all statistically

significant clearly rejecting the sideshow hypothesis of the stock market and confirming

the feedback effects.

16

< Insert Table XIII about here >

A comparison of the “short-run” and “long-run” results indicates that a short-run rela-

tionship does not imply a long-run relationship. The finding that there is no long-run

relationship between the stock market and GDP or IP for most countries is surprising

and partly due to the greater volatility of the stock market compared to GDP or IP. The

results reported in Shiller (1981) support this conclusion.

Given the rather weak evidence for long-run relationships, the crisis-specific analysis of

macro-financial contagion focusses on models that are not based on a long-run equilibrium

or co-integration assumption.13

D. Macro-financial Contagion

This section presents the estimation results of crisis-specific changes of the stock market -

real economy relationships. If the contemporaneous correlation between the stock market

and real economic activity (GDP or IP) increases during a financial crisis, there is evidence

for a crisis-specific feedback between the stock market and the real sector consistent with

contagion.14

Tables XIV and XV present the estimation results for a test of macro-financial contagion.

The crisis period is defined from July 2008 to March 2009 following the timeline provided

by the Bank for International Settlements (BIS, 2009). Eleven out of 32 countries exhibit

positive and statistically significant coefficients representing contagion from the stock mar-

ket to the real economy. The results indicate that the relationship has strengthened in the

crisis period or originated if there was no relationship before the crisis. The cross-sectional

average of all coefficients indicating crisis-specific changes of the contemporaneous corre-

lation of the stock market with GDP (c) is presented in the last row of the tables and

estimated at 0.069. This cross-sectional estimate indicates that contagion is not only a

13The finding that most countries do not exhibit an equilibrium relationship between financial and realeconomy variables is consistent with the hypothesis that markets tend towards disequilibrium (Soros, 1988).

14Recall that changes of stock market valuations should not contemporaneously affect GDP or IP levelsin normal periods and are inconsistent with the role of the stock market as a provider of signals to enhancethe future allocation of resources in an economy.

17

country-specific but also a systemic phenomenon. The results confirm that stock market

changes contemporaneously affect GDP changes during the crisis period consistent with

a feedback effect. Moreover, the results for k > 0 show that the predictive quality of the

stock market both increased for k = 1 and for k = 2 during the crisis.

< Insert Table XIV about here >

Table XV presents the estimation results for IP. Ten out of 32 countries exhibit positive

and statistically significant coefficient estimates consistent with a feedback effect and

contagion. The cross-sectional average of the coefficients is positive (0.083) and indicates

systemic contagion. Furthermore, there is also evidence for crisis-specific changes of the

predictive correlations which are stronger than the contemporaneous estimates. The cross-

sectional average is 0.178 and 0.210 for k = 1 and k = 2, respectively. Around 20 countries

exhibit significant predictive correlation estimates.

Note that the quality of a signal evidenced by the correlation should be relatively constant

to be reliable. If the quality of the signal increases in a crisis it can lead to an under- or

overreaction to the signal.15

< Insert Table XV about here >

We have defined contagion primarily as a contemporaneous spillover and correlation

change and thereby closely followed the definitions for financial contagion. However, from

a policymaker’s and household’s perspective, non-contemporaneous lead-lag spillovers can

be similarly contagious. That is, if a negative shock propagates to next quarter’s GDP,

this is just as important as if it propagates to contemporaneous GDP. Hence, extending

our (contemporaneous) contagion definition and including positive predictive correlation

changes as reported above further strengthens the evidence for contagion.16

15The weather forecast analogy used earlier may further clarify this point. If weather forecasts arecorrect in and for normal weather conditions and assumed to be similarly correct and of the same qualityin extreme conditions, people may equally react to a forecast predicting extreme weather conditions butpotentially overreact or underreact if the quality of the signal is different in such extreme conditions.

16Contagion could also be analyzed within the co-integrating framework and then reveal whether thelong-run relationship (if it existed) was disturbed during the crisis. This could be interpreted as a crisis-

18

It is well established in the contagion literature that the evidence for contagion depends

on the definition of the crisis period (e.g. see Dungey and Martin, 2007). As a robustness

check we estimated the contagion model for a longer crisis period starting in July 2007

(Q3 2007) and ending in March 2009 (Q1 2009). Surprisingly perhaps, the results are

remarkably similar compared with the shorter crisis period. For example, the cross-

sectional average of the contemporaneous correlation is 0.068 for GDP and 0.055 for IP

for the longer crisis period compared to 0.069 and 0.083 for the shorter crisis period,

respectively. The number of statistically significant estimates indicating a crisis-specific

change is larger for the longer crisis period involving GDP but similar for IP. The complete

results are presented in Tables XVI and XVII.

< Insert Tables XVI - XVII about here >

Finally, Tables XVIII-XXI report the coefficient estimates of the augmented VAR model

which fully support the main estimation results obtained with the more parsimonious

model.

< Insert Table XVIII - XXI about here >

Our findings have important implications for policy makers. If policy makers react strongly

to a financial crisis we would expect to find a weaker relationship between St and Xt (or

Xt+k) during the crisis. Yet we observe the opposite: a strengthening of the contempora-

neous and the predictive relationships and thus macro-financial contagion, going against

what we expect from the policy maker response. This implies an increased influence of

the stock market to the macro-economy driven by households and firms.17

In summary, almost all countries exhibit an increased correlation of the stock market

with either GDP or IP in the crisis period. Large industrial countries like the United

specific short-run deviation from the long-run relationship. However, this is not the usual interpretation ofcontagion and merely indicates a deviation rather than a strengthening of a pre-existing relationship. Sincemost countries do not display a long-run co-integration relationship between stock prices and macroeco-nomic variables, a test of deviations from such a relationship is not performed.

17Regarding the predictive relationships, it is also possible that investors are able to forecast cash flowsduring crisis conditions more accurately than during normal conditions. However, given the increased levelof uncertainty during financial crisis or turmoil conditions we deem this scenario as highly unlikely.

19

States, Japan, the UK and Germany all display increased contemporaneous correlations

consistent with contagion from the stock market to the real economy.

III. Summary and Concluding Remarks

This paper analyzed the short-run and long-run dynamic linkages between the stock mar-

ket and the real economy for a sample of 32 small and large, industrial and emerging coun-

tries. We find that the stock market reflects and signals future changes in real economic

activity for the majority of countries in the sample. However, the estimates vary signif-

icantly across countries and prediction horizons. Hence, the stock market provides only

incoherent signals across countries. We also find strong evidence for significant changes in

the contemporaneous and predictive correlations during the financial crisis in 2008 with

a similar heterogeneity across countries and prediction horizons. We argue that an in-

creased positive contemporaneous correlation is evidence for “macro-financial contagion”,

a form of contagion that is broader than more standard financial definitions of contagion.

Macro-financial contagion implies that the stock market does not only signal future eco-

nomic performance but does also contemporaneously affect the real economy. Despite its

importance regarding diversification and policy responses, this form of contagion has not

been analyzed in the literature before. The existence of macro-financial contagion is bad

news for investors, firms and households with exposure to both the stock market and the

real economy. If share prices and real economic activity decline in times of a financial

crisis the combined effects are more severe compared to a situation in which no contagion

existed. On the other hand, the stronger contemporaneous signal may help policy makers

to timely react to shocks affecting the real economy. The policy responses during the 2008

financial and economic crisis provide an example. To summarize, our findings indicate

that the stock market is not a sideshow but instead affects the real economy. Future

research could investigate the question whether the precision or mere existence of a stock

market signal amplifies or reduces economic cycles.

20

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22

Table I

Descriptive Statistics of Log-Returns Data

Descriptive statistics of log-returns data.

Mean S.D. Min. Max. Skew. Kurtosis

Monthly Stock Index (Log Returns)

CANADA 0.00 0.05 −0.26 0.13 −1.16 4.68MEXICO 0.01 0.07 −0.29 0.26 −0.41 1.75UNITED.STATES 0.00 0.05 −0.24 0.12 −0.85 2.60BRAZIL 0.00 0.07 −0.36 0.19 −0.86 3.05AUSTRIA 0.00 0.06 −0.32 0.32 −0.17 5.96BELGIUM 0.00 0.05 −0.29 0.17 −1.14 5.29BULGARIA 0.01 0.10 −0.36 0.32 −0.11 1.68CZECH.REPUBLIC 0.00 0.08 −0.25 0.51 0.76 8.36DENMARK 0.01 0.05 −0.21 0.17 −0.52 1.24FINLAND 0.00 0.08 −0.33 0.25 −0.20 1.26FRANCE 0.00 0.06 −0.24 0.19 −0.59 1.23GERMANY 0.00 0.05 −0.24 0.16 −0.83 2.12GREECE −0.00 0.10 −0.31 0.45 0.64 3.20HUNGARY −0.00 0.09 −0.43 0.45 −0.03 5.10IRELAND −0.00 0.06 −0.24 0.19 −0.81 1.34ITALY 0.00 0.07 −0.24 0.26 0.23 1.22LUXEMBOURG 0.00 0.05 −0.32 0.17 −1.09 5.68NETHERLANDS 0.00 0.05 −0.27 0.13 −1.29 4.24NORWAY 0.00 0.07 −0.32 0.20 −0.99 2.59PORTUGAL −0.00 0.05 −0.23 0.15 −0.43 1.55ROMANIA −0.00 0.13 −0.52 0.55 −0.15 3.08SLOVENIA −0.00 0.05 −0.18 0.13 −0.36 0.97SPAIN 0.00 0.06 −0.35 0.15 −0.98 3.50SWEDEN 0.01 0.07 −0.26 0.26 −0.51 1.60UNITED.KINGDOM 0.00 0.04 −0.15 0.13 −0.51 0.76INDIA 0.01 0.10 −0.39 0.54 0.09 3.88JAPAN 0.00 0.05 −0.24 0.17 −0.42 1.42KOREA 0.00 0.09 −0.33 0.40 0.31 1.98TURKEY 0.00 0.14 −0.54 0.52 0.23 1.98ISRAEL 0.00 0.06 −0.21 0.16 −0.55 0.73RUSSIAN.FEDERATION 0.01 0.12 −0.44 0.44 −0.37 2.60Quarterly Stock Index (Log Returns)

CANADA 0.01 0.08 −0.25 0.20 −0.79 1.23MEXICO 0.03 0.13 −0.31 0.42 0.13 0.18UNITED.STATES 0.01 0.08 −0.27 0.20 −0.73 0.70BRAZIL 0.01 0.14 −0.36 0.41 −0.27 0.44AUSTRIA 0.01 0.13 −0.44 0.63 0.39 4.77BELGIUM 0.01 0.10 −0.39 0.23 −1.10 2.59BULGARIA 0.02 0.22 −0.42 0.59 0.14 0.08CZECH.REPUBLIC 0.00 0.13 −0.39 0.29 −0.46 0.45DENMARK 0.02 0.10 −0.35 0.26 −0.70 0.64FINLAND 0.01 0.17 −0.47 0.64 0.19 1.01FRANCE 0.01 0.11 −0.38 0.25 −0.92 1.76GERMANY 0.01 0.11 −0.38 0.25 −0.92 1.37GREECE −0.00 0.19 −0.45 0.83 0.89 3.13HUNGARY −0.00 0.16 −0.45 0.34 −0.29 0.16IRELAND 0.01 0.13 −0.51 0.28 −0.99 2.14ITALY 0.01 0.13 −0.31 0.43 0.25 0.97LUXEMBOURG 0.01 0.11 −0.41 0.25 −0.88 2.39NETHERLANDS 0.01 0.10 −0.36 0.21 −1.34 2.63NORWAY 0.01 0.13 −0.55 0.28 −1.11 2.06

(continued)

23

(continued)

Mean S.D. Min. Max. Skew. Kurtosis

PORTUGAL −0.00 0.11 −0.29 0.34 −0.20 0.19ROMANIA −0.01 0.23 −0.83 0.59 −0.62 1.70SLOVENIA −0.01 0.12 −0.45 0.24 −0.92 1.87SPAIN 0.00 0.12 −0.34 0.33 −0.28 0.22SWEDEN 0.02 0.14 −0.40 0.36 −0.72 0.82UNITED.KINGDOM 0.01 0.08 −0.23 0.19 −0.54 0.35INDIA 0.01 0.20 −0.50 1.02 1.20 5.94JAPAN 0.01 0.11 −0.40 0.24 −0.56 0.74KOREA 0.01 0.17 −0.41 0.63 0.44 1.44TURKEY 0.01 0.23 −0.68 0.67 −0.04 0.50AUSTRALIA 0.01 0.09 −0.49 0.24 −1.41 5.56ISRAEL 0.00 0.12 −0.39 0.26 −0.64 0.24RUSSIAN.FEDERATION 0.01 0.24 −0.66 0.61 −0.49 0.89Monthly Industrial Production Log Returns)

CANADA 0.00 0.01 −0.04 0.03 −0.19 0.63MEXICO 0.00 0.01 −0.04 0.04 −0.01 1.29UNITED.STATES 0.00 0.01 −0.04 0.02 −1.15 5.17BRAZIL 0.00 0.03 −0.27 0.22 −1.38 23.71AUSTRIA 0.00 0.02 −0.06 0.07 0.12 0.76BELGIUM 0.00 0.03 −0.25 0.22 −0.47 14.45BULGARIA 0.00 0.02 −0.12 0.11 −0.22 6.09CZECH.REPUBLIC 0.00 0.04 −0.26 0.21 −0.26 12.79DENMARK 0.00 0.03 −0.17 0.14 −0.04 3.87FINLAND 0.00 0.02 −0.14 0.15 −0.22 7.82FRANCE 0.00 0.01 −0.05 0.04 −0.23 1.17GERMANY 0.00 0.02 −0.10 0.12 −0.13 8.74GREECE −0.00 0.03 −0.14 0.19 0.49 6.47HUNGARY 0.00 0.03 −0.14 0.10 −0.53 2.30IRELAND 0.01 0.05 −0.22 0.14 −0.56 2.64ITALY 0.00 0.02 −0.06 0.06 −0.03 1.34LUXEMBOURG 0.00 0.04 −0.19 0.12 −0.24 1.72NETHERLANDS 0.00 0.03 −0.11 0.14 0.07 3.30NORWAY 0.00 0.04 −0.39 0.34 −0.74 26.74PORTUGAL 0.00 0.03 −0.13 0.11 0.12 1.38ROMANIA 0.00 0.02 −0.09 0.08 −0.92 6.34SLOVENIA 0.00 0.02 −0.14 0.06 −1.92 9.56SPAIN 0.00 0.02 −0.07 0.09 0.21 2.51SWEDEN 0.00 0.03 −0.26 0.25 −0.34 47.08UNITED.KINGDOM 0.00 0.01 −0.05 0.03 −0.49 1.62INDIA 0.01 0.02 −0.05 0.08 0.46 2.52JAPAN 0.00 0.02 −0.17 0.06 −2.82 22.45KOREA 0.01 0.02 −0.12 0.14 −0.66 7.04TURKEY 0.00 0.06 −0.26 0.18 −0.52 2.51ISRAEL 0.00 0.02 −0.08 0.06 −0.47 1.17RUSSIAN.FEDERATION 0.00 0.02 −0.10 0.15 0.37 9.06Quarterly Gross Domestic Product (Log Returns)

CANADA 0.01 0.01 −0.03 0.02 −1.12 1.89MEXICO 0.01 0.03 −0.16 0.04 −2.53 9.72UNITED.STATES 0.01 0.01 −0.03 0.02 −1.05 2.77BRAZIL 0.01 0.02 −0.04 0.05 −0.25 0.04AUSTRIA 0.00 0.01 −0.02 0.03 0.08 0.35BELGIUM 0.00 0.01 −0.02 0.02 −0.11 0.10BULGARIA −0.00 0.09 −0.40 0.27 −1.41 6.31CZECH.REPUBLIC 0.01 0.03 −0.04 0.14 2.68 11.51DENMARK 0.00 0.01 −0.04 0.03 −0.49 0.45FINLAND 0.01 0.01 −0.04 0.05 −0.39 0.94

(continued)

24

(continued)

Mean S.D. Min. Max. Skew. Kurtosis

FRANCE 0.00 0.01 −0.02 0.02 −0.30 0.28GERMANY 0.00 0.01 −0.04 0.02 −1.35 3.33GREECE 0.00 0.03 −0.09 0.08 −0.14 −0.10HUNGARY 0.00 0.03 −0.11 0.05 −1.51 4.26IRELAND 0.01 0.02 −0.05 0.08 0.34 1.62ITALY 0.00 0.01 −0.03 0.04 0.15 2.11LUXEMBOURG 0.01 0.02 −0.04 0.10 0.54 3.15NETHERLANDS 0.00 0.01 −0.03 0.03 −0.99 2.04NORWAY 0.01 0.02 −0.08 0.06 −0.46 2.10PORTUGAL 0.01 0.01 −0.03 0.05 −0.15 0.18ROMANIA 0.01 0.13 −0.43 0.47 −0.32 3.69SLOVENIA 0.01 0.01 −0.03 0.04 −0.39 0.29SPAIN 0.01 0.01 −0.03 0.04 −0.04 0.19SWEDEN 0.01 0.01 −0.03 0.03 −0.99 1.46UNITED.KINGDOM 0.01 0.01 −0.03 0.03 −0.80 0.42INDIA 0.01 0.02 −0.04 0.05 −0.06 −0.13JAPAN 0.00 0.01 −0.03 0.04 −0.07 0.38KOREA 0.02 0.02 −0.08 0.05 −1.15 4.27TURKEY −0.00 0.08 −0.18 0.53 2.40 14.31AUSTRALIA 0.01 0.01 −0.03 0.05 −0.02 0.94ISRAEL 0.01 0.01 −0.02 0.03 −0.23 −0.55RUSSIAN.FEDERATION 0.02 0.17 −0.65 0.71 0.20 5.16

25

Table II

Phillips Perron: Stationarity Testing

Phillips Perron testing on quarterly stock index and GDP. The test statistic is presented with the numberof optimal lags in parentheses. The alternative hypothesis is that the data is stationary.

Countries Log Stock Index Stock Index Returns Log GDP GDP Growth

CANADA −2.928(4) −9.528(4)∗∗∗ −2.548(4) −6.066(4)∗∗∗

MEXICO −2.353(4) −8.714(3)∗∗∗ −2.177(4) −9.593(4)∗∗∗

UNITED.STATES −1.831(4) −10.433(4)∗∗∗ −1.189(4) −9.390(4)∗∗∗

BRAZIL −3.111(3) −7.186(3)∗∗∗ −2.838(3) −7.442(3)∗∗∗

AUSTRIA −2.078(4) −8.795(4)∗∗∗ −1.299(4) −11.844(4)∗∗∗

BELGIUM −2.068(4) −10.270(4)∗∗∗ −1.391(4) −7.194(4)∗∗∗

BULGARIA −0.930(3) −4.429(3)∗∗∗ −3.465(3)∗∗ −11.699(3)∗∗∗

CZECH.REPUBLIC −2.149(3) −8.991(3)∗∗∗ −2.488(3) −10.453(3)∗∗∗

DENMARK −3.487(4)∗∗ −8.403(4)∗∗∗ −1.287(4) −11.333(4)∗∗∗

FINLAND −1.548(4) −8.935(3)∗∗∗ −1.685(4) −8.630(4)∗∗∗

FRANCE −1.947(4) −10.659(4)∗∗∗ −0.918(4) −8.007(4)∗∗∗

GERMANY −2.152(3) −8.956(3)∗∗∗ −3.337(3)∗ −9.540(3)∗∗∗

GREECE −1.419(4) −9.252(3)∗∗∗ 0.094(4) −14.048(4)∗∗∗

HUNGARY −2.262(3) −8.882(3)∗∗∗ −0.497(3) −11.619(3)∗∗∗

IRELAND −1.571(4) −9.865(4)∗∗∗ −0.099(4) −13.197(4)∗∗∗

ITALY −2.506(4) −10.091(4)∗∗∗ 1.381(4) −11.876(4)∗∗∗

LUXEMBOURG −2.322(3) −8.220(3)∗∗∗ −2.232(4) −11.956(4)∗∗∗

NETHERLANDS −1.599(4) −9.851(4)∗∗∗ −1.074(4) −9.662(4)∗∗∗

NORWAY −3.120(4) −10.927(4)∗∗∗ −2.119(4) −9.448(4)∗∗∗

PORTUGAL −1.900(3) −8.058(3)∗∗∗ 0.760(4) −8.761(4)∗∗∗

ROMANIA −1.956(3) −6.641(3)∗∗∗ −3.324(3)∗ −14.220(3)∗∗∗

SLOVENIA −0.966(3) −4.705(3)∗∗∗ 0.656(3) −7.328(3)∗∗∗

SPAIN −1.650(4) −10.724(4)∗∗∗ 0.311(4) −12.553(4)∗∗∗

SWEDEN −2.962(4) −10.027(4)∗∗∗ −2.187(4) −9.406(4)∗∗∗

UNITED.KINGDOM −2.119(4) −9.729(3)∗∗∗ −0.015(4) −9.569(3)∗∗∗

INDIA −2.984(3) −9.438(3)∗∗∗ −2.033(3) −7.919(3)∗∗∗

JAPAN −2.367(4) −10.333(4)∗∗∗ −1.052(4) −12.256(4)∗∗∗

KOREA −2.822(4) −9.944(4)∗∗∗ 0.038(4) −9.415(4)∗∗∗

TURKEY −3.652(4)∗∗ −9.597(3)∗∗∗ −5.703(3)∗∗∗ −11.904(3)∗∗∗

AUSTRALIA −2.990(4) −11.070(4)∗∗∗ −2.193(4) −8.653(4)∗∗∗

ISRAEL −2.648(3) −8.712(3)∗∗∗ −2.395(3) −7.908(3)∗∗∗

RUSSIAN.FEDERATION −1.906(3) −7.634(3)∗∗∗ −3.744(3)∗∗ −13.733(3)∗∗∗

26

Table III

Phillips Perron: Stationarity Testing

Phillips Perron testing on monthly stock index and Industrial Production. The test statistic is presentedwith the number of optimal lags in parentheses. The alternative hypothesis is that the data is stationary.

Countries Log Stock Index Stock Index Returns Log IP IP Growth

CANADA −3.058(5) −18.021(5)∗∗∗ −1.255(5) −20.443(5)∗∗∗

MEXICO −2.494(5) −17.358(5)∗∗∗ −2.294(5) −21.614(5)∗∗∗

UNITED.STATES −1.661(5) −18.275(5)∗∗∗ −1.333(5) −15.796(5)∗∗∗

BRAZIL −2.596(4) −13.902(4)∗∗∗ −4.010(5)∗∗∗ −23.580(5)∗∗∗

AUSTRIA −1.845(5) −14.823(5)∗∗∗ −3.074(5) −30.582(5)∗∗∗

BELGIUM −1.811(5) −15.775(5)∗∗∗ −4.320(5)∗∗∗ −36.068(5)∗∗∗

BULGARIA −0.774(4) −10.497(4)∗∗∗ −0.906(4) −15.847(4)∗∗∗

CZECH.REPUBLIC −1.905(4) −12.791(4)∗∗∗ −3.089(5) −24.530(5)∗∗∗

DENMARK −3.071(5) −17.930(5)∗∗∗ −2.231(5) −26.029(5)∗∗∗

FINLAND −1.233(5) −13.417(5)∗∗∗ −1.510(5) −27.855(5)∗∗∗

FRANCE −1.720(5) −17.515(5)∗∗∗ −0.964(5) −26.234(5)∗∗∗

GERMANY −2.002(5) −15.024(5)∗∗∗ −3.527(5)∗∗ −25.389(5)∗∗∗

GREECE −1.241(5) −14.888(5)∗∗∗ −1.191(5) −40.825(5)∗∗∗

HUNGARY −2.059(5) −15.035(5)∗∗∗ −1.670(5) −26.132(5)∗∗∗

IRELAND −2.295(4) −10.841(4)∗∗∗ −0.975(5) −39.582(5)∗∗∗

ITALY −2.192(5) −18.211(5)∗∗∗ −0.574(5) −25.301(5)∗∗∗

LUXEMBOURG −2.150(5) −12.957(5)∗∗∗ −2.689(5) −32.352(5)∗∗∗

NETHERLANDS −1.353(5) −17.153(5)∗∗∗ −9.098(5)∗∗∗ −38.028(5)∗∗∗

NORWAY −3.262(5)∗ −17.037(5)∗∗∗ −0.950(5) −42.251(5)∗∗∗

PORTUGAL −1.678(5) −13.309(5)∗∗∗ −1.155(5) −41.206(5)∗∗∗

ROMANIA −1.867(4) −13.073(4)∗∗∗ −2.259(4) −15.930(4)∗∗∗

SLOVENIA −0.627(4) −9.459(4)∗∗∗ −1.776(4) −15.809(4)∗∗∗

SPAIN −1.435(5) −15.229(5)∗∗∗ −0.161(5) −31.362(5)∗∗∗

SWEDEN −2.561(5) −16.473(5)∗∗∗ −2.201(5) −29.449(5)∗∗∗

UNITED.KINGDOM −1.944(5) −16.041(5)∗∗∗ −0.200(5) −24.685(5)∗∗∗

INDIA −2.837(5) −14.515(5)∗∗∗ −2.258(4) −22.543(4)∗∗∗

JAPAN −2.185(5) −17.666(5)∗∗∗ −2.128(5) −19.449(5)∗∗∗

KOREA −2.636(5) −15.620(5)∗∗∗ −1.871(5) −22.085(5)∗∗∗

TURKEY −3.664(5)∗∗ −17.614(5)∗∗∗ −8.617(5)∗∗∗ −41.791(5)∗∗∗

ISRAEL −2.526(4) −13.887(4)∗∗∗ −2.837(5) −23.212(5)∗∗∗

RUSSIAN.FEDERATION −1.920(4) −11.150(4)∗∗∗ −3.201(4)∗ −16.732(4)∗∗∗

27

Table IV

ρ(∆ lnSt,∆ lnGDPt+k)

ρ(∆ lnSt,∆ lnGDPt+k) for k ∈ {0, 1, 2, 3, 4}. Quarter to quarter log-changes are used.

k

Countries 0 1 2 3 4

CANADA 0.269∗∗∗ 0.390∗∗∗ 0.388∗∗∗ 0.293∗∗∗ 0.097

MEXICO 0.183∗ 0.330∗∗∗ 0.229∗∗ −0.041 0.141

UNITED.STATES 0.119 0.223∗∗ 0.268∗∗∗ 0.250∗∗∗ 0.098BRAZIL 0.209∗ 0.342∗∗∗ 0.186 0.108 0.028

AUSTRIA 0.150∗ 0.270∗∗∗ 0.122 0.069 0.120

BELGIUM 0.247∗∗∗ 0.300∗∗∗ 0.178∗∗ 0.095 0.123BULGARIA 0.158 0.188 0.169 0.071 0.184

CZECH.REPUBLIC 0.055 −0.022 0.036 −0.190 0.204∗

DENMARK 0.016 0.255∗∗∗ 0.234∗∗∗ 0.070 0.195∗∗

FINLAND 0.215∗∗ 0.314∗∗∗ 0.329∗∗∗ 0.419∗∗∗ 0.312∗∗∗

FRANCE 0.107 0.259∗∗∗ 0.201∗∗ 0.148∗ 0.095

GERMANY 0.166 0.076 0.278∗∗∗ 0.081 0.074

GREECE 0.028 −0.104 0.373∗∗∗ −0.023 −0.117

HUNGARY −0.122 0.071 0.199∗ 0.190∗ 0.003

IRELAND 0.119 0.167∗ 0.158∗ 0.152∗ 0.124

ITALY −0.003 0.123 0.145 0.330∗∗∗ 0.048

LUXEMBOURG 0.201∗ 0.278∗∗ −0.070 0.020 0.091

NETHERLANDS 0.164∗ 0.228∗∗∗ 0.240∗∗∗ 0.222∗∗ 0.166∗

NORWAY 0.125 0.315∗∗∗ 0.199∗∗ 0.046 0.146

PORTUGAL 0.222∗∗ 0.243∗∗ 0.243∗∗ 0.211∗∗ 0.157

ROMANIA −0.196 0.212∗ 0.364∗∗∗ −0.245∗ −0.140

SLOVENIA 0.305∗∗ 0.467∗∗∗ 0.427∗∗∗ 0.348∗∗∗ 0.297∗∗

SPAIN 0.064 0.120 0.248∗∗ 0.082 0.184∗

SWEDEN 0.171∗ 0.215∗∗ 0.416∗∗∗ 0.224∗∗ 0.059

UNITED.KINGDOM 0.176∗ 0.180∗ 0.096 0.218∗∗ 0.054

INDIA 0.405∗∗∗ 0.109 0.184 0.134 0.134

JAPAN 0.130 0.067 0.206∗∗ 0.140 0.249∗∗∗

KOREA 0.124 0.337∗∗∗ 0.069 0.050 0.053

TURKEY −0.065 0.116 −0.090 0.198∗ −0.018AUSTRALIA −0.057 0.125 0.325∗∗∗ 0.147∗ 0.316∗∗∗

ISRAEL 0.087 0.207∗ −0.012 0.325∗∗∗ 0.049

RUSSIAN.FEDERATION −0.138 0.264∗∗ 0.345∗∗∗ 0.113 −0.213Average 0.114 0.208 0.209 0.133 0.104

28

Table V

ρ(∆ lnSt,∆ ln IPt+k)

ρ(∆ lnSt,∆ ln IPt+k) for k ∈ {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}. Month to Month log-changes are used.

k

Countries 0 1 2 3 4 5 6 7 8 9 10 11 12

CANADA 0.000 0.134∗∗∗ 0.139∗∗∗ 0.185∗∗∗ 0.089∗ 0.145∗∗∗ 0.148∗∗∗ 0.013 0.182∗∗∗ 0.080 0.126∗∗ 0.049 −0.004

MEXICO 0.070 0.119∗∗ 0.052 0.198∗∗∗ 0.137∗∗ 0.038 0.036 0.014 0.002 0.025 −0.053 0.043 0.051UNITED.STATES −0.067 0.101∗∗ 0.172∗∗∗ 0.286∗∗∗ 0.189∗∗∗ 0.120∗∗ 0.157∗∗∗ 0.125∗∗ 0.111∗∗ 0.061 0.073 0.123∗∗ 0.104

BRAZIL 0.083 0.106 0.270∗∗∗ 0.072 0.196∗∗∗ 0.044 0.122∗ −0.033 −0.060 −0.098 0.071 0.038 0.000

AUSTRIA 0.010 0.063 0.087∗ 0.045 0.135∗∗∗ 0.035 0.058 0.012 −0.037 0.057 0.067 −0.014 0.066

BELGIUM 0.062 −0.039 0.086∗ 0.032 0.076 0.001 0.026 −0.006 0.007 −0.008 −0.002 0.038 −0.019

BULGARIA 0.163∗∗ 0.146∗ 0.060 0.226∗∗∗ 0.208∗∗ 0.097 0.024 0.199∗∗ 0.057 0.111 0.020 0.079 0.184

CZECH.REPUBLIC −0.036 −0.039 0.135∗∗ −0.012 0.148∗∗ −0.000 0.005 0.055 −0.082 −0.066 0.014 0.169∗∗ 0.075

DENMARK 0.047 0.027 −0.026 0.079 0.033 0.069 0.110∗∗ 0.007 −0.011 −0.000 0.066 0.001 0.082

FINLAND 0.009 0.066 0.079 0.143∗∗ 0.050 0.111∗ 0.066 0.026 0.090 0.074 0.039 0.126∗∗ 0.061

FRANCE 0.025 0.065 0.102∗∗ 0.041 0.085∗ 0.051 0.041 0.009 0.077 0.044 0.088∗ 0.043 −0.002

GERMANY 0.199∗∗∗ 0.119∗ 0.124∗∗ 0.139∗∗ 0.173∗∗∗ 0.106∗ 0.073 0.021 0.102 0.011 0.072 0.013 0.097GREECE −0.020 0.039 −0.048 −0.028 0.050 −0.031 0.154∗∗∗ −0.028 0.062 0.004 0.031 0.003 −0.106

HUNGARY 0.092 0.044 0.094 0.076 0.091 0.002 0.071 0.069 0.060 0.017 0.180∗∗∗ −0.053 −0.010

IRELAND 0.018 0.018 −0.009 0.083 −0.035 0.012 0.126∗ −0.048 0.009 0.031 0.119 0.017 0.025

ITALY 0.015 0.087∗ 0.123∗∗ 0.019 0.129∗∗ 0.087∗ 0.042 0.024 0.092∗ 0.045 −0.036 0.058 0.065

LUXEMBOURG 0.050 −0.002 0.048 0.015 −0.005 0.139∗∗ −0.066 −0.094 0.098 0.019 0.065 0.016 −0.058

NETHERLANDS 0.044 0.039 0.075 0.023 0.080 0.106∗∗ −0.005 −0.048 −0.015 0.040 −0.024 −0.063 0.098NORWAY 0.049 −0.054 0.038 0.056 0.010 −0.047 −0.005 0.054 0.050 −0.029 0.001 −0.059 −0.002PORTUGAL 0.002 0.012 0.052 0.013 0.080 −0.004 0.035 0.002 0.070 −0.003 −0.029 0.008 0.090

ROMANIA 0.016 −0.009 0.277∗∗∗ 0.012 −0.076 0.048 −0.028 −0.031 0.048 −0.055 0.051 −0.027 0.067SLOVENIA 0.154∗∗ 0.193∗∗ 0.239∗∗∗ 0.031 0.153∗∗ 0.157∗∗ 0.125 −0.018 0.131∗ 0.093 0.092 0.032 −0.004

SPAIN 0.014 0.050 0.143∗∗ 0.041 0.092 0.015 0.101∗ 0.060 0.080 0.060 0.011 0.103∗ 0.078

SWEDEN 0.001 0.016 0.137∗∗∗ 0.105∗∗ 0.119∗∗ 0.041 0.036 0.048 0.120∗∗ 0.059 0.054 0.012 0.049

UNITED.KINGDOM 0.104∗ 0.056 0.020 0.082 0.143∗∗ 0.064 0.029 0.062 0.054 0.040 0.104∗ 0.029 0.026

INDIA 0.031 0.160∗∗ 0.005 0.083 0.127∗ −0.050 0.011 0.007 0.129∗ 0.010 −0.029 0.080 0.048

JAPAN 0.113∗∗ 0.075 0.116∗∗ 0.081 0.125∗∗ 0.116∗∗ 0.055 0.015 0.000 0.075 0.049 0.086∗ 0.001

KOREA 0.075 0.146∗∗ 0.136∗∗ 0.033 0.072 0.049 −0.005 0.062 0.016 0.045 0.034 0.014 −0.076

(continue

29

(continued)

k

Countries 0 1 2 3 4 5 6 7 8 9 10 11 12

TURKEY 0.057 0.041 0.105∗ 0.076 −0.005 −0.001 −0.123∗∗ 0.054 0.005 0.068 0.043 −0.103∗ 0.016

ISRAEL 0.004 0.029 0.042 0.065 0.202∗∗∗ 0.014 0.040 0.021 0.050 −0.039 0.049 −0.042 0.002

RUSSIAN.FEDERATION 0.284∗∗∗ 0.223∗∗∗ 0.040 −0.030 0.132∗ −0.053 0.046 −0.193∗∗ 0.079 0.169∗∗ 0.106 −0.128∗ −0.103Average 0.054 0.066 0.094 0.073 0.097 0.048 0.049 0.015 0.051 0.030 0.047 0.022 0.029

30

Table VI

VAR model: Stock Market and GDP

Quarterly changes. ∆ lnSt = a+4∑

k=1

bk∆ lnGDPt−k +4∑

k=1

ck∆ lnSt−k + et.

Countries b1 b2 b3 b4

CANADA 0.903 −1.615 −1.046 1.095MEXICO 0.112 0.726 −0.771 0.076

UNITED.STATES 1.939∗ −0.466 0.164 0.085BRAZIL −0.724 −1.442 1.131 −2.229∗∗

AUSTRIA 2.424∗ −1.587 −2.610∗ −1.425BELGIUM 1.485 −1.806 0.934 −2.138BULGARIA 1.350 1.647 −0.075 1.957CZECH.REPUBLIC −0.002 1.106 0.742 −0.374DENMARK 0.115 −0.154 −0.308 −0.411FINLAND 3.061 −2.426 −0.995 −0.668

FRANCE 3.492∗ −1.589 0.510 0.814GERMANY 1.729 −1.926 −1.534 0.353

GREECE 0.934 1.352∗ −0.314 0.312

HUNGARY 1.530∗ −0.792 −0.204 −0.908IRELAND 0.821 0.425 0.674 0.098

ITALY 2.965∗∗ −0.957 −1.852 −0.266LUXEMBOURG 0.488 0.027 −0.068 0.016NETHERLANDS −0.317 −1.798∗ −1.129 0.895NORWAY −0.108 −0.354 −0.091 −0.272PORTUGAL 0.166 0.941 −0.830 −0.923

ROMANIA −0.474 0.897∗ −0.339 0.130SLOVENIA 0.456 1.507 0.149 0.988SPAIN 0.857 1.100 −0.785 −0.487

SWEDEN 2.060 1.073 −1.444 −2.714∗∗

UNITED.KINGDOM 0.537 −0.798 0.314 −0.159INDIA −0.612 −1.730 0.079 −1.289

JAPAN 0.627 1.828∗∗ −1.179 −1.127

KOREA 2.956∗∗∗ −3.934∗∗∗ −1.306 −1.494TURKEY −0.135 −0.296 −0.187 −0.010

AUSTRALIA −0.532 −1.347∗ −1.232 0.931ISRAEL 1.257 −0.692 0.126 −0.267RUSSIAN.FEDERATION −0.517 −0.305 −0.390 −0.670∗

Average 0.901 −0.418 −0.433 −0.315

31

Table VII

VAR model: Stock Market and GDP

Quarterly changes. ∆ lnGDPt = a+4∑

k=1

bk∆lnGDPt−k +4∑

k=1

ck∆lnSt−k + et.

Countries c1 c2 c3 c4

CANADA 0.034∗∗∗ 0.027∗∗∗ 0.020∗∗ 0.001

MEXICO 0.042∗∗∗ 0.032∗∗ −0.003 0.036∗∗

UNITED.STATES 0.015∗∗ 0.016∗∗ 0.014∗ 0.001

BRAZIL 0.038∗∗ 0.012 0.015 0.009

AUSTRIA 0.018∗∗∗ 0.008 −0.003 0.008

BELGIUM 0.014∗∗ 0.003 −0.001 0.003BULGARIA 0.024 0.011 −0.004 0.024

CZECH.REPUBLIC 0.000 −0.002 −0.015 0.027∗

DENMARK 0.027∗∗∗ 0.023∗∗ −0.002 0.021∗∗

FINLAND 0.019∗∗∗ 0.011∗ 0.018∗∗∗ 0.004

FRANCE 0.010∗∗ 0.006 0.002 0.001

GERMANY 0.009 0.022∗∗ 0.007 0.007GREECE −0.008 0.043∗∗∗ −0.009 −0.019

HUNGARY 0.012 0.030∗∗ 0.018 0.014IRELAND 0.015 0.012 0.008 0.002

ITALY 0.009 0.013∗ 0.018∗∗∗ −0.002

LUXEMBOURG 0.068∗∗∗ −0.013 −0.012 0.025NETHERLANDS 0.024∗∗∗ 0.020∗∗ 0.016∗ 0.007

NORWAY 0.047∗∗∗ 0.024∗ 0.003 0.023

PORTUGAL 0.025∗∗∗ 0.010 0.008 −0.003

ROMANIA 0.026 0.078∗∗ −0.033 0.040

SLOVENIA 0.036∗∗ 0.013 0.005 0.009

SPAIN 0.006 0.024∗∗∗ 0.007 0.008

SWEDEN 0.015∗∗ 0.028∗∗∗ 0.010 −0.006

UNITED.KINGDOM 0.022∗ 0.015 0.020 −0.004

INDIA 0.022 0.043∗∗∗ 0.013 0.031∗

JAPAN 0.003 0.017∗ 0.011 0.023∗∗

KOREA 0.045∗∗∗ −0.000 0.001 −0.003

TURKEY 0.045 −0.027 0.060∗ −0.014

AUSTRALIA 0.013 0.031∗∗∗ 0.010 0.035∗∗∗

ISRAEL 0.025∗ 0.002 0.034∗∗ 0.013

RUSSIAN.FEDERATION 0.127∗∗∗ 0.093∗∗∗ 0.148∗∗∗ 0.069∗∗

Average 0.026 0.019 0.012 0.012

32

Table VIII

VAR model: Stock Market and IP

Monthly changes. ∆ lnSt = a+12∑

k=1

bk∆ ln IPt−k +12∑

k=1

ck∆ lnSt−k + et.

Countries b1 b2 b3 b4 b5 b6 b7 b8 b9 ˆb10 ˆb11 ˆb12

CANADA 0.582∗∗ 0.411 0.188 −0.519∗∗ −0.342 −0.040 0.234 0.114 −0.572∗∗ −0.146 −0.119 0.491∗∗

MEXICO 0.522 0.376 0.046 −0.221 −0.524 −0.638∗ 0.301 −0.097 0.291 −0.598∗ −0.403 0.468

UNITED.STATES 1.189∗∗∗ 0.761∗ −0.472 −0.498 0.362 −0.311 0.369 −0.256 −0.482 −0.503 −0.017 0.253BRAZIL −0.133 0.417 −0.267 −0.644∗∗ −0.326 0.034 −0.159 −0.312 −0.051 0.066 −0.367 −0.450∗

AUSTRIA 0.253 0.134 0.074 0.105 0.131 −0.474∗∗ −0.242 −0.265 −0.157 −0.108 0.366∗ 0.157

BELGIUM 0.035 0.118 −0.020 −0.217∗ −0.240∗∗ −0.226∗∗ −0.285∗∗ −0.207∗ −0.113 −0.040 0.030 0.149BULGARIA 0.162 0.510 0.561 −0.410 0.679 0.190 −0.056 0.117 0.263 −0.142 −0.050 0.320

CZECH.REPUBLIC 0.404∗∗ 0.195 −0.133 0.071 −0.044 −0.337∗ −0.286 −0.046 −0.009 −0.140 −0.243 −0.001DENMARK 0.093 −0.161 −0.103 −0.051 −0.049 0.035 −0.097 −0.132 −0.019 −0.012 −0.059 0.055

FINLAND 0.216 0.078 0.009 −0.356 −0.280 0.048 −0.196 −0.414∗ −0.218 −0.161 −0.253 0.279

FRANCE 0.344 0.271 0.184 0.116 0.120 −0.463∗ −0.349 −0.577∗∗ −0.099 −0.390 0.171 0.404

GERMANY −0.454∗ 0.350 −0.121 0.076 −0.133 −0.183 −0.172 −0.271 −0.077 −0.154 0.063 −0.119GREECE 0.380 0.684∗∗ 0.524∗ 0.929∗∗∗ 0.400 −0.018 −0.015 −0.194 0.087 −0.083 −0.460 0.003

HUNGARY 0.045 0.159 0.110 −0.010 −0.096 −0.365 −0.539∗∗ −0.003 0.038 −0.114 −0.048 −0.546∗∗

IRELAND −0.119 0.098 0.109 −0.018 0.122 −0.044 −0.162 −0.109 −0.045 0.023 −0.006 −0.100

ITALY 0.496∗∗ 0.250 −0.033 −0.233 0.071 −0.088 −0.349 −0.284 −0.067 −0.630∗∗∗ −0.145 0.308

LUXEMBOURG 0.026 −0.097 −0.027 −0.020 0.184∗ 0.109 −0.045 0.028 −0.244∗∗ −0.018 0.006 −0.102NETHERLANDS −0.042 −0.099 −0.183 −0.191 −0.199 −0.150 −0.210 −0.249 −0.153 −0.343∗∗ −0.159 −0.207∗

NORWAY −0.048 −0.011 0.317∗∗ 0.167 0.132 0.027 0.064 0.031 −0.034 −0.018 0.002 0.009PORTUGAL 0.185 0.113 −0.020 −0.104 −0.013 −0.028 0.177 0.003 −0.189 −0.114 −0.030 −0.005

ROMANIA 0.531 1.527∗∗∗ 1.284∗∗ −1.600∗∗∗ −0.288 −0.079 −0.442 −1.182∗∗ −0.522 −1.001∗ −1.060∗ −0.618SLOVENIA −0.130 −0.279 −0.346 0.160 −0.208 −0.139 0.099 0.018 −0.335 0.051 −0.053 0.119

SPAIN 0.609∗∗∗ 0.639∗∗ 0.582∗∗ 0.329 0.200 −0.177 −0.563∗∗ −0.473∗ −0.586∗∗ −0.355 0.048 0.116

SWEDEN −0.031 −0.096 0.087 0.014 −0.036 −0.205 −0.148 −0.309 0.034 −0.568∗∗∗ −0.181 −0.244

UNITED.KINGDOM 0.420 0.285 0.551∗ −0.338 −0.481 −0.606∗∗ 0.078 −0.058 −0.115 −0.436 0.454 0.084INDIA 0.071 0.074 0.413 −0.116 −0.559 0.323 0.587 0.067 −0.332 −0.109 −0.686 −0.818∗∗

JAPAN 0.011 0.218 −0.286∗ −0.075 −0.049 −0.085 −0.099 0.124 0.007 −0.094 −0.208 −0.083

KOREA 0.367 0.345 0.021 −0.019 −0.106 0.176 −0.474∗∗ −0.408∗ 0.031 −0.052 −0.509∗∗ −0.339

(continued)

33

(continued)

b1 b2 b3 b4 b5 b6 b7 b8 b9 ˆb10 ˆb11 ˆb12

TURKEY −0.315 −0.015 0.121 −0.133 −0.303 0.170 −0.014 −0.010 −0.058 −0.157 −0.311 −0.526∗∗

ISRAEL −0.178 −0.041 −0.013 −0.131 0.175 −0.494∗∗ −0.594∗∗ −0.697∗∗∗ 0.090 −0.092 −0.044 0.082

RUSSIAN.FEDERATION−0.173 −0.043 0.788∗ 0.334 −0.214 −0.222 −0.140 −0.483 −0.474 −0.687∗ −1.224∗∗∗ 0.074Average 0.172 0.231 0.127 −0.116 −0.062 −0.138 −0.120 −0.211 −0.133 −0.230 −0.177 −0.025

34

Table IX

VAR model: IP and Stock Market

Monthly changes. ∆ ln IPt = a+12∑

k=1

bk∆ln IPt−k +12∑

k=1

ck∆ lnSt−k + et.

Countries c1 c2 c3 c4 c5 c6 c7 c8 c9 ˆc10 ˆc11 ˆc12

CANADA 0.017 0.018 0.039∗∗∗ 0.009 0.016 0.024∗∗ −0.012 0.030∗∗∗ 0.004 0.024∗∗ −0.000 −0.009

MEXICO 0.021∗∗ 0.010 0.027∗∗∗ 0.020∗∗ 0.010 −0.010 0.004 −0.004 −0.000 −0.005 0.005 0.011

UNITED.STATES 0.005 0.015∗∗ 0.036∗∗∗ 0.017∗∗ 0.004 0.010 0.001 0.005 0.001 0.000 0.010 0.006

BRAZIL 0.034∗ 0.065∗∗∗ 0.020 0.063∗∗∗ 0.023 0.058∗∗∗ 0.018 0.013 0.008 0.035∗ 0.036∗ 0.017

AUSTRIA 0.010 0.017 0.011 0.043∗∗∗ 0.010 0.020 0.003 −0.031∗∗ 0.006 0.012 −0.005 0.027∗

BELGIUM −0.010 0.052∗ 0.008 0.067∗∗ 0.017 0.032 −0.003 0.019 −0.011 0.006 0.018 0.002BULGARIA 0.047∗∗ −0.024 0.062∗∗∗ 0.033 0.019 −0.023 0.053∗∗ −0.029 0.031 −0.003 0.015 0.033

CZECH.REPUBLIC −0.010 0.071∗∗∗ −0.001 0.031 −0.008 −0.013 0.017 −0.025 −0.030 −0.002 0.041∗ 0.038

DENMARK 0.009 −0.007 0.043 0.022 0.036 0.082∗∗∗ 0.026 0.014 0.003 0.030 0.001 0.053∗

FINLAND 0.012 0.013 0.045∗∗ 0.006 0.038∗∗ 0.029 0.009 0.021 0.023 0.012 0.034∗ 0.018FRANCE 0.012 0.016 0.010 0.016 0.007 0.004 −0.007 0.010 0.010 0.017 0.012 −0.003GERMANY 0.036∗∗ 0.025 0.032∗ 0.027 0.031∗ 0.006 −0.012 0.025 −0.005 0.021 −0.008 0.029

GREECE 0.012 −0.008 −0.012 0.007 −0.014 0.035∗∗ −0.004 0.020 0.008 0.017 0.014 −0.034∗∗

HUNGARY 0.022 0.044∗∗ 0.033∗ 0.040∗∗ 0.006 0.019 0.017 0.022 −0.002 0.050∗∗ −0.005 −0.006

IRELAND 0.008 −0.054 0.082 −0.013 −0.024 0.095 −0.009 −0.022 0.003 0.148∗∗ 0.003 0.083

ITALY 0.006 0.030∗∗∗ 0.003 0.018 0.015 0.006 −0.004 0.007 0.009 −0.013 0.002 0.011

LUXEMBOURG 0.025 0.040 0.013 −0.032 0.107∗∗ −0.024 −0.087∗ 0.038 0.021 0.059 0.053 −0.043

NETHERLANDS 0.012 0.053∗∗ 0.036 0.051∗∗ 0.089∗∗∗ 0.050∗∗ 0.004 0.002 0.020 −0.005 −0.036 0.047∗

NORWAY −0.021 0.019 0.060∗∗ −0.004 −0.011 −0.012 0.025 0.042 0.010 0.014 −0.028 −0.019PORTUGAL 0.015 0.030 0.019 0.045 0.008 0.032 −0.004 0.038 0.004 −0.014 −0.023 0.049

ROMANIA −0.011 0.045∗∗∗ 0.018 −0.023 0.016 −0.010 −0.008 0.003 −0.008 0.009 −0.010 0.020

SLOVENIA 0.048 0.130∗∗∗ −0.037 0.011 0.003 0.042 −0.107∗∗∗ 0.039 0.023 0.051 −0.017 0.024

SPAIN 0.009 0.032∗∗ 0.005 0.027∗ 0.016 0.024 0.016 0.011 0.021 0.002 0.021 0.027∗

SWEDEN −0.000 0.027∗ 0.033∗∗ 0.036∗∗ 0.009 0.003 −0.002 0.026∗ 0.011 0.016 −0.007 0.009

UNITED.KINGDOM 0.027∗∗ 0.010 0.030∗∗ 0.034∗∗∗ 0.023∗ 0.011 0.013 0.011 0.010 0.026∗∗ 0.008 0.008INDIA 0.040∗∗∗ 0.012 0.016 0.033∗∗∗ −0.013 −0.006 −0.006 0.029∗∗ 0.008 −0.008 0.019 0.004

JAPAN 0.013 0.028 0.017 0.031∗ 0.031∗ 0.003 −0.003 −0.007 0.026 0.016 0.031∗ −0.001

KOREA 0.039∗∗ 0.036∗∗ 0.018 0.028∗ 0.026∗ 0.004 0.023 0.011 0.020 0.007 0.009 −0.018

(continued)

35

(continued)

c1 c2 c3 c4 c5 c6 c7 c8 c9 ˆc10 ˆc11 ˆc12

TURKEY 0.018 0.069∗∗∗ 0.055∗∗∗ 0.036∗∗ 0.015 −0.064∗∗∗ −0.010 0.018 0.027 0.029 −0.025 −0.026

ISRAEL 0.000 0.023 0.021 0.078∗∗∗ 0.010 0.017 0.008 0.026 0.008 0.017 −0.007 −0.015

RUSSIAN.FEDERATION 0.042∗∗ 0.033∗ 0.004 0.051∗∗∗ −0.004 0.022 −0.010 0.011 0.034∗∗ 0.037∗∗ −0.002 −0.012Average 0.016 0.028 0.024 0.026 0.016 0.015 −0.002 0.012 0.009 0.019 0.005 0.011

36

Table X

ADF/PP tests on residuals of log-levels regressions

Respectively, the first and second columns show results of ADF and PP tests over residuals of ln(stock(t)) =a+ bln(GDPt) + et.

Countries et = ln (St)− a− b ln (GDPt)

ADF Test PP Test

CANADA −2.635(5) −3.284(4)∗

MEXICO −2.525(4) −2.477(4)UNITED.STATES −1.918(5) −2.277(4)BRAZIL −2.678(4) −2.620(3)AUSTRIA −2.805(5) −2.455(4)BELGIUM −3.070(5) −2.404(4)BULGARIA −1.864(3) −1.399(3)

CZECH.REPUBLIC −3.361(4)∗ −3.737(3)∗∗

DENMARK −3.354(5)∗ −4.438(4)∗∗∗

FINLAND −2.204(4) −1.840(4)FRANCE −2.526(5) −2.443(4)GERMANY −2.396(4) −2.326(3)GREECE −3.103(4) −2.111(4)HUNGARY −2.809(4) −2.724(3)IRELAND −2.157(5) −1.977(4)ITALY −2.540(5) −3.090(4)LUXEMBOURG −2.822(4) −2.287(3)NETHERLANDS −2.045(5) −2.273(4)NORWAY −2.019(5) −2.222(4)PORTUGAL −2.784(4) −2.485(3)ROMANIA −2.752(4) −2.019(3)SLOVENIA −1.838(3) −1.517(3)SPAIN −2.175(4) −2.166(4)SWEDEN −2.691(4) −2.894(4)UNITED.KINGDOM −2.046(4) −2.240(4)INDIA −2.291(4) −2.561(3)JAPAN −2.490(5) −2.610(4)KOREA −2.683(4) −2.605(4)

TURKEY −4.170(4)∗∗∗ −4.232(3)∗∗∗

AUSTRALIA −2.398(5) −2.810(4)ISRAEL −1.795(4) −2.143(3)RUSSIAN.FEDERATION −2.138(3) −2.635(3)

37

Table XI

ADF/PP tests on residuals of log-levels regressions

Respectively, the first and second columns show results of ADF and PP tests over residuals of ln(stock(t)) =a+ bln(IPt) + et.

Countries et = ln (St)− a− b ln (IPt)

ADF Test PP Test

CANADA −3.259(7)∗ −2.941(5)MEXICO −1.516(6) −1.847(5)

UNITED.STATES −3.077(7) −3.405(5)∗

BRAZIL −2.698(6) −4.208(4)∗∗∗

AUSTRIA −2.006(7) −1.917(5)BELGIUM −1.694(7) −1.750(5)BULGARIA −2.523(5) −2.766(4)CZECH.REPUBLIC −2.955(6) −2.492(4)DENMARK −2.546(7) −3.999(5)∗∗∗

FINLAND −2.134(6) −2.406(5)

FRANCE −3.182(7)∗ −3.389(5)∗

GERMANY −2.061(6) −2.198(5)

GREECE −3.057(6) −3.762(5)∗∗

HUNGARY −2.388(6) −2.386(5)IRELAND −2.552(5) −2.369(4)ITALY −3.493(7)∗∗ −3.592(5)∗∗

LUXEMBOURG −2.279(6) −2.126(5)NETHERLANDS −1.625(7) −2.378(5)

NORWAY −2.392(7) −3.248(5)∗

PORTUGAL −2.871(6) −3.350(5)∗

ROMANIA −1.305(5) −1.342(4)SLOVENIA −1.553(5) −1.129(4)SPAIN −2.818(6) −3.327(5)∗

SWEDEN −3.914(7)∗∗ −3.541(5)∗∗

UNITED.KINGDOM −2.585(6) −2.978(5)INDIA −3.043(6) −2.807(4)JAPAN −2.727(7) −2.687(5)KOREA −2.661(6) −2.549(5)

TURKEY −3.393(6)∗ −3.750(5)∗∗

ISRAEL −2.191(6) −2.662(4)RUSSIAN.FEDERATION −2.785(5) −2.532(4)

38

Table XII

Johansen test

This table displays the optimal VECM lag length (p) as determined by the SBIC with a VAR as well as the λmax and λtrace test statistics to determine the rank (r)of the Johansen cointegrating matrix (Π).

Stock market & Industrial Production Stock Market & Gross Domestic Product

Countries p λtrace(r = 0) λtrace(r ≤ 1) λmax(r = 0) λmax(r = 1) p λtrace(r = 0) λtrace(r ≤ 1) λmax(r = 0) λmax(r = 1)

CANADA 4 9.503 1.621 7.882 1.621 2 16.627∗ 0.027 16.600∗∗ 0.027MEXICO 2 6.159 2.048 4.111 2.048 2 10.038 1.283 8.755 1.283UNITED.STATES 4 13.541 1.676 11.865 1.676 2 11.795 2.758 9.037 2.758BRAZIL 2 25.132∗∗∗ 0.783 24.348∗∗∗ 0.783 2 10.614 0.003 10.611 0.003AUSTRIA 3 9.813 0.354 9.458 0.354 2 20.109∗∗ 2.034 18.074∗∗ 2.034BELGIUM 3 7.173 0.911 6.262 0.911 2 23.854∗∗∗ 0.996 22.858∗∗∗ 0.996BULGARIA 2 14.639 2.216 12.423 2.216 2 9.861 3.624 6.236 3.624CZECH.REPUBLIC 2 11.720 0.791 10.929 0.791 2 31.034∗∗∗ 3.699 27.335∗∗∗ 3.699DENMARK 2 20.779∗∗ 3.578 17.201∗∗ 3.578 2 28.072∗∗∗ 2.531 25.540∗∗∗ 2.531FINLAND 2 15.103 1.302 13.801∗ 1.302 2 13.471 1.504 11.967 1.504FRANCE 2 7.986 1.675 6.311 1.675 2 16.787∗ 1.786 15.000∗∗ 1.786GERMANY 2 12.441 2.167 10.275 2.167 2 8.444 1.828 6.616 1.828GREECE 2 12.601 0.350 12.251 0.350 2 22.569∗∗ 3.499 19.071∗∗ 3.499HUNGARY 2 17.167∗ 1.358 15.809∗∗ 1.358 2 10.618 1.477 9.141 1.477IRELAND 2 15.077 2.963 12.114 2.963 2 46.958∗∗∗ 1.600 45.357∗∗∗ 1.600ITALY 2 22.805∗∗ 4.072 18.733∗∗ 4.072 2 43.304∗∗∗ 6.057 37.247∗∗∗ 6.057LUXEMBOURG 2 9.221 4.059 5.161 4.059 2 8.322 0.957 7.365 0.957NETHERLANDS 3 12.743 4.864 7.879 4.864 2 36.418∗∗∗ 3.237 33.181∗∗∗ 3.237NORWAY 4 12.309 3.835 8.474 3.835 2 8.074 0.047 8.028 0.047PORTUGAL 3 13.359 1.534 11.825 1.534 2 45.965∗∗∗ 2.053 43.913∗∗∗ 2.053ROMANIA 2 5.624 0.762 4.862 0.762 3 10.946 0.249 10.697 0.249SLOVENIA 2 6.113 0.556 5.557 0.556 2 17.214∗ 1.833 15.382∗∗ 1.833SPAIN 2 9.585 1.630 7.954 1.630 3 16.011∗ 1.478 14.533∗ 1.478SWEDEN 2 24.728∗∗∗ 3.123 21.605∗∗∗ 3.123 2 22.276∗∗ 1.127 21.149∗∗∗ 1.127UNITED.KINGDOM 2 4.340 0.252 4.087 0.252 2 15.516 2.569 12.947∗ 2.569INDIA 2 18.793∗∗ 0.429 18.364∗∗ 0.429 2 11.676 0.010 11.666 0.010JAPAN 2 20.156∗∗ 7.144∗ 13.012∗ 7.144∗ 2 38.158∗∗∗ 7.071∗ 31.088∗∗∗ 7.071∗

(continued)

39

(continued)

Stock market & Industrial Production Stock Market & Gross Domestic Product

Countries p λtrace(r = 0) λtrace(r ≤ 1) λmax(r = 0) λmax(r = 1) p λtrace(r = 0) λtrace(r ≤ 1) λmax(r = 0) λmax(r = 1)

KOREA 2 9.629 1.070 8.558 1.070 2 22.001∗∗ 4.612 17.388∗∗ 4.612TURKEY 2 13.119 0.432 12.687 0.432 2 23.980∗∗∗ 2.748 21.232∗∗∗ 2.748AUSTRALIA 2 14.878 0.010 14.868∗ 0.010ISRAEL 2 11.350 1.092 10.258 1.092 2 6.598 0.077 6.522 0.077RUSSIAN.FEDERATION2 9.283 0.875 8.408 0.875 5 6.467 0.088 6.379 0.088

40

Table XIII

VECM results

This table displays the VECM estimation results of those countries where a cointegrating relationshipis established in Table XII. Standard errors are displayed in parentheses. µ is the error correct termcoefficient estimate and i = 1 denotes the stock price equation and i = 2 denotes the real activity equation.

Error Correction Terms Cointegrating Vector

Countries µ1 µ2 β1 β2

Stock market and Industrial ProductionBRAZIL −0.047 0.036∗∗∗ 1.000 −3.356

(0.029) (0.008)DENMARK 0.002 0.019∗∗∗ 1.000 −4.433

(0.008) (0.005)ITALY −0.027∗∗ 0.010∗∗∗ 1.000 −3.011

(0.012) (0.003)SWEDEN −0.020 0.016∗∗∗ 1.000 −3.197

(0.014) (0.004)INDIA −0.043∗∗ 0.011∗∗∗ 1.000 −1.268

(0.018) (0.003)Stock market and Gross Domestic ProductAUSTRIA −0.085∗∗ 0.007∗∗∗ 1.000 −3.471

(0.033) (0.002)BELGIUM −0.069∗∗ 0.008∗∗∗ 1.000 −2.532

(0.035) (0.002)CZECH.REPUBLIC −0.144∗∗∗ 0.018∗∗∗ 1.000 −4.270

(0.034) (0.005)DENMARK −0.166∗∗∗ 0.020∗∗∗ 1.000 −4.311

(0.047) (0.005)GREECE −0.036 0.021∗∗∗ 1.000 −2.572

(0.036) (0.005)IRELAND −0.054∗ 0.029∗∗∗ 1.000 −1.304

(0.030) (0.004)ITALY −0.084∗∗ 0.014∗∗∗ 1.000 −2.292

(0.034) (0.002)NETHERLANDS −0.032 0.014∗∗∗ 1.000 −2.257

(0.029) (0.003)PORTUGAL −0.083∗ 0.024∗∗∗ 1.000 −2.177

(0.048) (0.004)SWEDEN −0.123∗∗∗ 0.010∗∗∗ 1.000 −2.875

(0.042) (0.003)JAPAN −0.000 0.013∗∗∗ 1.000 −3.580

(0.023) (0.002)KOREA −0.064∗∗ 0.009∗∗∗ 1.000 −1.857

(0.028) (0.003)TURKEY −0.218∗∗∗ 0.061∗∗∗ 1.000 −2.129

(0.058) (0.021)

41

Table XIV

∆ lnGDPt = a+ b∆ lnSt−k + c∆ lnSt−kDt−k + et.

Quarterly changes. Dummy D equal to one during Q3 and Q4 of 2008.

∆ lnGDPt = a+ b∆ lnSt−k + c∆ lnSt−kDt−k + et

k = 0 k = 1 k = 2

Countries b c b c b c

CANADA 0.027∗∗ 0.068∗ 0.035∗∗∗ 0.133∗∗∗ 0.039∗∗∗ 0.089∗∗∗

MEXICO 0.022 0.205∗∗ 0.041∗∗∗ 0.340∗∗∗ 0.030∗ 0.195∗∗

UNITED.STATES 0.011 0.015 0.019∗∗ 0.019 0.019∗∗ 0.066∗∗

BRAZIL 0.023 0.025 0.031∗∗ 0.088∗∗ 0.022 0.006

AUSTRIA 0.005 0.037∗∗ 0.014∗∗ 0.022 0.004 0.030∗

BELGIUM 0.013∗∗ 0.029 0.015∗∗ 0.041∗∗ 0.012∗ 0.006

BULGARIA 0.016 0.001 0.008 0.077∗ 0.011 0.039

CZECH.REPUBLIC 0.006 0.062 −0.011 0.114 −0.003 0.123∗

DENMARK −0.006 0.064∗∗ 0.016 0.104∗∗∗ 0.012 0.116∗∗∗

FINLAND 0.015∗ 0.063 0.020∗∗ 0.140∗∗∗ 0.021∗∗∗ 0.133∗∗∗

FRANCE 0.005 0.027 0.012∗∗ 0.066∗∗ 0.008∗ 0.075∗∗∗

GERMANY 0.010 0.068∗ −0.004 0.186∗∗∗ 0.020∗∗ 0.049

GREECE 0.001 0.050 −0.024 0.133∗∗ 0.064∗∗∗ −0.125∗∗

HUNGARY −0.022 0.027 0.004 0.075 0.026 0.063IRELAND 0.014 0.045 0.020 0.059 0.022 0.032

ITALY −0.004 0.087∗∗ 0.006 0.092∗∗∗ 0.009 0.062∗

LUXEMBOURG 0.039∗ 0.064 0.055∗∗ 0.067 −0.025 0.216∗

NETHERLANDS 0.018∗ −0.010 0.014 0.059∗∗ 0.009 0.100∗∗∗

NORWAY 0.012 0.070 0.031∗∗ 0.170∗∗∗ 0.018 0.121∗∗∗

PORTUGAL 0.022∗∗ 0.010 0.023∗∗ 0.035 0.023∗∗ 0.026

ROMANIA −0.104∗ 0.116 0.080 0.073 0.161∗∗∗ −0.055

SLOVENIA 0.036∗∗ −0.006 0.040∗∗ 0.036 0.033∗ 0.044

SPAIN 0.004 0.093 0.011 0.065 0.022∗∗ 0.121∗

SWEDEN 0.010 0.129∗∗∗ 0.013∗ 0.158∗∗∗ 0.032∗∗∗ 0.012

UNITED.KINGDOM 0.015 0.143∗∗ 0.019 0.098∗ 0.002 0.163∗∗∗

INDIA 0.042∗∗∗ 0.135∗∗ 0.003 0.167∗∗∗ 0.029∗∗ −0.118∗

JAPAN 0.009 0.100∗∗ 0.001 0.113∗∗∗ 0.021∗∗ 0.037

KOREA 0.008 0.177∗∗∗ 0.036∗∗∗ 0.073 0.009 −0.049TURKEY −0.028 0.198 0.042 0.018 −0.033 0.008

AUSTRALIA −0.006 −0.018 0.013 0.031 0.033∗∗∗ 0.101∗∗

ISRAEL 0.009 0.005 0.037∗∗ −0.086∗∗ 0.000 −0.009

RUSSIAN.FEDERATION −0.085 0.121 0.058 0.342∗∗ 0.139∗∗ 0.098Average 0.004 0.069 0.021 0.097 0.025 0.055

42

Table XV

∆ ln IPt = a+ b∆ lnSt−k + c∆ lnSt−kDt−k + et.

Monthly changes. Dummy D equal to one from July to December 2008.

∆ ln IPt = a+ b∆ lnSt−k + c∆ lnSt−kDt−k + et

k = 0 k = 1 k = 2

Countries b c b c b c

CANADA −0.004 0.048 0.025∗∗ 0.083∗ 0.021∗ 0.150∗∗∗

MEXICO 0.013 −0.027 0.018∗ 0.044 0.003 0.152∗∗∗

UNITED.STATES −0.015∗ 0.084∗∗ 0.013 0.049 0.016∗∗ 0.175∗∗∗

BRAZIL 0.022 0.020 0.007 0.245∗∗∗ 0.044∗∗ 0.350∗∗∗

AUSTRIA −0.012 0.118∗∗ 0.005 0.115∗∗ 0.023 0.033

BELGIUM 0.040 −0.003 −0.055 0.234∗∗ 0.037 0.130

BULGARIA 0.039∗ −0.003 0.018 0.111∗∗ 0.011 0.018

CZECH.REPUBLIC −0.023 0.175∗ −0.031 0.293∗∗∗ 0.035 0.210∗∗

DENMARK 0.024 0.039 0.008 0.087 −0.025 0.115

FINLAND 0.001 0.057 0.009 0.310∗∗∗ 0.011 0.377∗∗∗

FRANCE −0.000 0.190∗∗∗ 0.006 0.290∗∗∗ 0.015 0.236∗∗∗

GERMANY 0.046∗∗∗ 0.208∗∗∗ 0.021 0.259∗∗∗ 0.012 0.474∗∗∗

GREECE −0.011 0.116 0.008 0.088 −0.017 0.074

HUNGARY 0.021 0.117 0.002 0.179∗∗ 0.006 0.335∗∗∗

IRELAND 0.001 0.079 0.017 −0.007 −0.006 −0.009

ITALY −0.002 0.182∗∗ 0.013 0.262∗∗∗ 0.021∗ 0.286∗∗∗

LUXEMBOURG 0.027 0.146 −0.035 0.593∗∗∗ 0.007 0.491∗∗

NETHERLANDS 0.024 −0.003 0.006 0.120 0.028 0.104NORWAY 0.040 −0.132 −0.036 0.043 0.024 −0.012PORTUGAL −0.008 0.114 −0.001 0.102 0.018 0.129

ROMANIA −0.002 0.026 −0.030∗∗ 0.161∗∗∗ 0.032∗∗ 0.073∗∗

SLOVENIA −0.008 0.341∗∗∗ 0.012 0.314∗∗∗ 0.025 0.339∗∗∗

SPAIN −0.001 0.175∗ 0.006 0.280∗∗∗ 0.033∗∗ 0.237∗∗

SWEDEN −0.003 0.092 −0.001 0.144∗ 0.027∗ 0.281∗∗∗

UNITED.KINGDOM 0.015 0.097∗∗ 0.001 0.134∗∗∗ −0.006 0.132∗∗∗

INDIA 0.003 0.033 0.029∗∗ 0.039 −0.011 0.124∗∗∗

JAPAN 0.038∗∗ 0.009 0.011 0.230∗∗∗ 0.015 0.373∗∗∗

KOREA 0.018 0.056 0.023 0.483∗∗∗ 0.022 0.437∗∗∗

TURKEY 0.023 0.018 0.016 0.042 0.034 0.387∗∗

ISRAEL −0.005 0.107 0.009 0.014 0.006 0.130RUSSIAN.FEDERATION 0.047∗∗∗ 0.079∗ 0.024∗ 0.174∗∗∗ −0.011 0.175∗∗∗

Average 0.011 0.083 0.004 0.178 0.015 0.210

43

Table XVI

∆ lnGDPt = a+ b∆ lnSt−k + c∆ lnSt−kDt−k + et.

Quarterly changes. Dummy D equal to one from Q3 2007 to Q1 2009.

∆ lnGDPt = a+ b∆ lnSt−k + c∆ lnSt−kDt−k + et

k = 0 k = 1 k = 2

Countries b c b c b c

CANADA 0.026∗∗ 0.068∗∗ 0.035∗∗∗ 0.125∗∗∗ 0.042∗∗∗ 0.063∗

MEXICO 0.016 0.249∗∗∗ 0.040∗∗∗ 0.238∗∗∗ 0.030∗ 0.122

UNITED.STATES 0.006 0.054∗ 0.012 0.073∗∗∗ 0.015∗ 0.081∗∗∗

BRAZIL 0.027∗ −0.005 0.031∗∗ 0.073∗ 0.019 0.026

AUSTRIA 0.005 0.029∗ 0.014∗∗ 0.025 0.002 0.034∗∗

BELGIUM 0.011 0.035∗∗ 0.012∗ 0.046∗∗∗ 0.007 0.031∗

BULGARIA 0.010 0.020 0.017 0.007 0.008 0.030CZECH.REPUBLIC 0.001 0.100 −0.013 0.092 −0.002 0.072DENMARK −0.011 0.087∗∗∗ 0.012 0.115∗∗∗ 0.013 0.092∗∗∗

FINLAND 0.010 0.095∗∗∗ 0.016∗∗ 0.118∗∗∗ 0.020∗∗ 0.086∗∗∗

FRANCE 0.003 0.044∗∗ 0.008∗ 0.084∗∗∗ 0.007 0.054∗∗∗

GERMANY 0.004 0.091∗∗∗ −0.007 0.124∗∗∗ 0.019∗∗ 0.029

GREECE 0.001 0.043 −0.024 0.098∗ 0.065∗∗∗ −0.101∗

HUNGARY −0.025 0.037 −0.000 0.090∗ 0.022 0.074

IRELAND 0.001 0.103∗∗∗ 0.013 0.081∗∗ 0.008 0.099∗∗

ITALY −0.008 0.098∗∗∗ 0.003 0.080∗∗∗ 0.005 0.083∗∗∗

LUXEMBOURG 0.034 0.114 0.049∗∗ 0.124 −0.026 0.153

NETHERLANDS 0.014 0.015 0.008 0.080∗∗∗ 0.008 0.086∗∗∗

NORWAY 0.012 0.060 0.035∗∗ 0.111∗∗∗ 0.020 0.081∗∗

PORTUGAL 0.020∗ 0.022 0.021∗ 0.029 0.024∗∗ 0.002

ROMANIA −0.113∗ 0.138 0.086 0.022 0.172∗∗∗ −0.100

SLOVENIA 0.046∗∗ −0.027 0.045∗∗ 0.014 0.033∗ 0.029

SPAIN 0.001 0.072∗ 0.003 0.126∗∗∗ 0.020∗ 0.078∗

SWEDEN 0.008 0.093∗∗∗ 0.011 0.102∗∗∗ 0.028∗∗∗ 0.075∗∗

UNITED.KINGDOM 0.008 0.146∗∗∗ 0.006 0.177∗∗∗ −0.007 0.179∗∗∗

INDIA 0.052∗∗∗ −0.011 −0.001 0.062∗ 0.019 0.016

JAPAN 0.007 0.075∗∗ −0.002 0.085∗∗∗ 0.015 0.071∗∗

KOREA 0.008 0.139∗∗ 0.034∗∗∗ 0.096∗ 0.007 0.007TURKEY −0.028 0.070 0.042 −0.004 −0.038 0.093

AUSTRALIA −0.006 −0.012 0.013 0.019 0.037∗∗∗ 0.029

ISRAEL 0.006 0.017 0.038∗∗ −0.063∗ 0.000 −0.007

RUSSIAN.FEDERATION −0.085 0.108 0.066 0.267∗ 0.146∗∗ 0.054Average 0.002 0.068 0.019 0.085 0.023 0.054

44

Table XVII

∆ ln IPt = a+ b∆ lnSt−k + c∆ lnSt−kDt−k + et.

Monthly changes. Dummy D equal to one from July 2007 to January 2009.

∆ ln IPt = a+ b∆ lnSt−k + c∆ lnSt−kDt−k + et

k = 0 k = 1 k = 2

Countries b c b c b c

CANADA −0.004 0.041 0.024∗ 0.077∗ 0.019 0.142∗∗∗

MEXICO 0.010 0.037 0.020∗∗ −0.001 0.002 0.118∗∗∗

UNITED.STATES −0.020∗∗ 0.094∗∗∗ 0.011 0.048∗ 0.011 0.157∗∗∗

BRAZIL 0.024 −0.000 0.006 0.177∗∗∗ 0.042∗∗ 0.257∗∗∗

AUSTRIA −0.013 0.100∗∗ 0.008 0.071 0.026 0.009

BELGIUM 0.047 −0.040 −0.053 0.152∗ 0.031 0.123

BULGARIA 0.009 0.103∗∗ 0.027 0.026 0.005 0.031

CZECH.REPUBLIC −0.018 0.059 −0.031 0.177∗∗ 0.025 0.233∗∗∗

DENMARK 0.026 0.009 0.013 0.021 −0.024 0.075

FINLAND 0.001 0.025 0.011 0.154∗∗ 0.006 0.298∗∗∗

FRANCE −0.004 0.136∗∗∗ 0.009 0.072∗ 0.012 0.144∗∗∗

GERMANY 0.042∗∗ 0.137∗∗ 0.017 0.162∗∗∗ 0.014 0.211∗∗∗

GREECE −0.011 0.077 0.005 0.097 −0.017 0.038

HUNGARY 0.015 0.153∗∗ 0.004 0.105 0.004 0.271∗∗∗

IRELAND −0.007 0.087 −0.001 0.063 −0.002 −0.022

ITALY 0.000 0.058 0.012 0.154∗∗∗ 0.017 0.220∗∗∗

LUXEMBOURG 0.031 0.048 −0.025 0.246 −0.004 0.400∗∗∗

NETHERLANDS 0.011 0.078 0.016 0.032 0.024 0.097NORWAY 0.044 −0.118 −0.040 0.064 0.026 −0.025

PORTUGAL −0.005 0.033 0.000 0.038 0.000 0.163∗

ROMANIA −0.004 0.023 −0.029∗ 0.100∗∗∗ 0.033∗∗ 0.045SLOVENIA −0.015 0.232∗∗∗ 0.021 0.171∗∗∗ 0.024 0.214∗∗∗

SPAIN −0.007 0.157∗∗ 0.009 0.082 0.027 0.206∗∗∗

SWEDEN −0.008 0.100∗ −0.002 0.077 0.026∗ 0.156∗∗∗

UNITED.KINGDOM 0.008 0.114∗∗∗ 0.001 0.091∗∗ −0.010 0.109∗∗∗

INDIA 0.001 0.021 0.025∗ 0.033 −0.014 0.068∗∗

JAPAN 0.033∗ 0.053 0.005 0.191∗∗∗ 0.017 0.213∗∗∗

KOREA 0.018 0.025 0.020 0.302∗∗∗ 0.024 0.192∗∗∗

TURKEY 0.030 −0.152 0.012 0.095 0.033 0.193∗

ISRAEL 0.006 −0.040 0.010 −0.004 0.005 0.083

RUSSIAN.FEDERATION 0.048∗∗∗ 0.055 0.026∗ 0.131∗∗∗ −0.011 0.144∗∗∗

Average 0.009 0.055 0.004 0.103 0.012 0.147

45

Table XVIII

VAR-X type model.

Quarterly changes. Dummy D equal to one during Q3 to Q4 2008. ∆ lnGDPt = a+4∑

k=1

bk∆ lnGDPt−k +4∑

k=0

ck∆ lnSt−k +4∑

k=0

dk∆ lnSt−kDt−k + et.

k = 0 k = 1 k = 2 k = 3 k = 4

Countries c d c d c d c d c d

CANADA 0.025∗∗∗ 0.009 0.021∗∗ 0.093∗∗ 0.024∗∗∗ −0.012 0.022∗∗ 0.014 0.006 −0.079∗∗

MEXICO 0.011 0.038 0.036∗∗ 0.226∗ 0.041∗∗ 0.017 −0.004 −0.024 0.039∗∗ −0.009

UNITED.STATES 0.015∗ −0.026 0.014∗ −0.028 0.015∗ 0.033 0.015∗ 0.002 0.003 −0.026

BRAZIL 0.009 −0.049 0.021 0.144∗∗ 0.012 −0.087 0.015 0.042 0.019 −0.129∗∗

AUSTRIA 0.004 0.033∗ 0.013∗∗ −0.010 0.004 0.033 −0.004 −0.002 0.010∗ −0.019

BELGIUM 0.007 0.006 0.008 0.020 0.004 −0.003 0.003 −0.051∗∗∗ 0.003 0.015BULGARIA 0.023 −0.045 0.013 0.070 0.014 −0.009 −0.012 0.018 0.012 0.092

CZECH.REPUBLIC 0.011 −0.005 0.006 0.047 −0.005 0.016 0.003 0.099 0.020 −0.136∗∗

DENMARK 0.004 0.023 0.011 0.039 0.011 0.118∗∗∗ −0.000 −0.015 0.021∗∗ −0.035

FINLAND 0.006 −0.017 0.014∗∗ 0.060 0.009 0.058 0.016∗∗∗ −0.059 0.002 −0.054

FRANCE 0.001 −0.024 0.008 0.055∗ 0.006 0.026 0.001 −0.017 −0.000 −0.004

GERMANY 0.005 0.009 −0.000 0.158∗∗∗ 0.014∗ 0.026 0.007 −0.038 0.005 −0.081∗

GREECE 0.010 −0.065 −0.024∗ 0.210∗∗∗ 0.060∗∗∗ −0.171∗∗∗ −0.016 0.015 −0.018 0.004

HUNGARY −0.021 −0.001 0.004 0.087∗ 0.023 0.028 0.017 0.023 0.006 −0.018IRELAND 0.007 −0.002 0.020 −0.045 0.019 0.019 0.012 −0.054 0.001 0.044

ITALY 0.001 0.059 0.005 −0.005 0.012∗ 0.062 0.019∗∗∗ −0.074∗ −0.001 −0.005

LUXEMBOURG 0.046∗ 0.099 0.066∗∗ 0.057 −0.021 0.185 −0.015 0.180 0.026 −0.046NETHERLANDS 0.017∗ −0.023 0.013 −0.003 0.010 0.091∗∗∗ 0.015∗ −0.040 0.006 −0.005

NORWAY 0.013 0.008 0.032∗∗ 0.101 0.018 0.084 0.002 −0.110 0.014 0.055

PORTUGAL 0.024∗∗∗ −0.048 0.024∗∗ −0.011 0.015 0.002 0.016 −0.052 0.005 −0.016

ROMANIA 0.008 −0.134 −0.001 0.211∗∗ 0.090∗∗ −0.068 0.012 0.028 −0.000 0.038SLOVENIA 0.029 −0.029 0.010 0.046 0.006 0.048 0.003 −0.008 0.000 −0.018

SPAIN 0.006 0.073 0.005 −0.099 0.027∗∗∗ 0.094 0.002 −0.029 0.002 0.035

SWEDEN 0.012∗ −0.002 0.014∗∗ 0.143∗∗ 0.028∗∗∗ −0.082 0.010 −0.011 −0.007 0.004

UNITED.KINGDOM 0.015 0.215∗∗∗ 0.014 −0.163∗∗ 0.007 0.214∗∗∗ 0.023∗ −0.133∗ −0.001 −0.047

INDIA 0.034∗∗∗ 0.109∗ 0.004 0.155∗∗∗ 0.042∗∗∗ −0.123∗∗ −0.014 0.098∗ 0.032∗∗ −0.045

(continued)

46

(continued)

k = 0 k = 1 k = 2 k = 3 k = 4

Countries c d c d c d c d c d

JAPAN 0.012 0.054 −0.004 0.045 0.014 0.002 0.013 −0.033 0.026∗∗ −0.074

KOREA 0.011 0.134∗∗ 0.049∗∗∗ −0.033 0.004 −0.069 −0.007 −0.053 0.002 0.031

TURKEY −0.013 0.192 0.044 0.161 −0.018 0.121 0.063∗ −0.082 −0.021 0.009

AUSTRALIA 0.003 −0.033 0.011 −0.025 0.026∗∗ 0.064 0.011 0.035 0.037∗∗∗ −0.079

ISRAEL 0.018 0.039 0.048∗∗∗ −0.162∗∗∗ 0.009 0.039 0.034∗∗ −0.014 0.014 0.060

RUSSIAN.FEDERATION −0.015 −0.014 0.075∗∗ 0.220∗∗ 0.070∗∗ 0.007 0.085∗∗∗ 0.089 0.017 0.030Average 0.011 0.018 0.018 0.055 0.018 0.024 0.011 −0.008 0.009 −0.016

47

Table XIX

VAR-X type model.

Quarterly changes. Dummy D equal to one during Q3 2007 and Q1 of 2009. ∆ lnGDPt = a+4∑

k=1

bk∆ lnGDPt−k +4∑

k=0

ck∆ lnSt−k +4∑

k=0

dk∆ lnSt−kDt−k + et.

k = 0 k = 1 k = 2 k = 3 k = 4

Countries c d c d c d c d c d

CANADA 0.026∗∗∗ 0.016 0.020∗∗ 0.093∗∗∗ 0.025∗∗∗ −0.011 0.021∗∗ −0.003 0.006 −0.069∗∗

MEXICO 0.012 0.087 0.035∗∗ 0.115 0.041∗∗ 0.028 −0.004 −0.075 0.040∗∗ 0.003

UNITED.STATES 0.010 0.032 0.011 −0.045 0.014∗ 0.105∗∗ 0.016∗∗ −0.098∗ 0.003 0.023

BRAZIL 0.013 −0.068 0.023 0.097∗∗ 0.006 −0.018 0.015 0.033 0.017 −0.112∗∗

AUSTRIA 0.005 0.018 0.012∗∗ 0.011 0.004 0.025 −0.004 0.005 0.010∗ −0.021

BELGIUM 0.005 0.015 0.006 0.045∗∗ 0.003 −0.006 0.003 −0.050∗∗∗ 0.003 0.008

BULGARIA 0.021 0.011 0.025 −0.046 0.011 0.017 −0.004 0.004 0.003 0.105∗∗

CZECH.REPUBLIC 0.002 0.058 0.015 −0.001 0.002 0.020 0.001 0.026 0.016 −0.050

DENMARK 0.002 0.003 0.011 0.081∗ 0.011 0.076 −0.001 −0.010 0.022∗∗ −0.046

FINLAND 0.004 0.011 0.014∗∗ 0.115∗∗ 0.011∗ −0.066 0.017∗∗∗ −0.034 0.004 0.012

FRANCE −0.000 −0.018 0.005 0.104∗∗∗ 0.005 −0.037 0.000 −0.002 0.000 −0.002

GERMANY 0.005 0.055∗ −0.004 0.123∗∗∗ 0.012 −0.000 0.009 −0.082∗∗ 0.007 −0.001GREECE 0.010 −0.024 −0.023 0.177∗∗∗ 0.061∗∗∗ −0.196∗∗∗ −0.016 0.053 −0.018 0.012

HUNGARY −0.020 −0.033 0.001 0.095∗ 0.019 0.026 0.017 0.028 0.008 −0.015

IRELAND −0.004 0.128∗∗ 0.012 −0.119∗ 0.011 0.080 0.010 −0.052 0.002 0.010

ITALY −0.001 0.068∗∗ 0.004 0.003 0.009 0.054 0.021∗∗∗ −0.095∗∗ −0.001 0.024

LUXEMBOURG 0.042 0.116 0.066∗∗ 0.027 −0.016 0.121 −0.019 0.217∗ 0.027 −0.049

NETHERLANDS 0.016∗ −0.050 0.012 0.040 0.008 0.094∗ 0.015∗ −0.073 0.006 0.005

NORWAY 0.012 0.049 0.035∗∗ 0.072∗ 0.017 0.038 0.003 −0.077∗ 0.014 0.048

PORTUGAL 0.020∗∗ 0.036 0.028∗∗∗ −0.012 0.015 −0.027 0.017∗ −0.042 0.006 0.008

ROMANIA 0.032 −0.123 0.009 0.113 0.103∗∗∗ −0.060 0.015 −0.024 −0.004 0.130SLOVENIA 0.033 −0.045 0.021 0.028 0.001 0.044 −0.008 0.011 0.007 −0.013

SPAIN 0.003 −0.009 0.001 0.072 0.025∗∗∗ −0.016 0.001 0.012 0.003 −0.022

SWEDEN 0.012∗ 0.014 0.013∗ 0.065 0.028∗∗∗ 0.003 0.010 −0.067 −0.006 0.039

UNITED.KINGDOM 0.009 0.044 0.001 0.128∗ −0.001 0.139∗ 0.020 −0.142∗∗ 0.001 −0.095

INDIA 0.050∗∗∗ −0.036 0.010 0.044 0.047∗∗∗ −0.004 −0.016 0.119∗∗∗ 0.033∗∗ −0.037

(continued)

48

(continued)

k = 0 k = 1 k = 2 k = 3 k = 4

Countries c d c d c d c d c d

JAPAN 0.011 0.056 −0.005 0.057 0.011 0.031 0.014 −0.054 0.026∗∗ −0.028

KOREA 0.009 0.116∗∗ 0.046∗∗∗ 0.017 0.004 −0.058 −0.005 −0.038 0.004 0.003

TURKEY −0.008 0.017 0.048 −0.012 −0.020 0.107 0.062∗ 0.036 −0.020 0.091

AUSTRALIA 0.004 −0.074 0.014 0.034 0.029∗∗∗ −0.001 0.009 0.063 0.037∗∗∗ −0.073

ISRAEL 0.016 0.010 0.042∗∗ −0.079∗∗ 0.007 −0.022 0.035∗∗ −0.018 0.009 0.038

RUSSIAN.FEDERATION −0.027 0.057 0.080∗∗ 0.142∗∗ 0.065∗∗ 0.082 0.081∗∗∗ 0.093 0.006 0.095Average 0.010 0.017 0.018 0.050 0.018 0.018 0.010 −0.011 0.008 0.001

49

Table XX

VAR-X type model.

Monthly changes. Dummy D equal to one during Q3 to Q4 2008. ∆ ln IPt = a+12∑

k=1

bk∆ ln IPt−k+12∑

k=0

ck∆lnSt−k+12∑

k=0

dk∆ lnSt−kDt−k+et. Four lags are presented

for space considerations, but 12 lags are included in the estimations.

k = 0 k = 1 k = 2 k = 3 k = 4

Countries c d c d c d c d c d

CANADA 0.001 −0.012 0.012 0.030 0.007 0.072 0.026∗∗ 0.105∗ 0.007 −0.024

MEXICO 0.016 −0.092 0.021∗∗ 0.026 0.003 0.042 0.025∗∗ 0.166∗ 0.018∗ −0.053

UNITED.STATES −0.019∗∗∗ 0.212∗∗∗ 0.005 −0.356∗∗∗ 0.011∗ 0.425∗∗∗ 0.028∗∗∗ −0.286∗∗∗ 0.016∗∗ 0.237∗∗∗

BRAZIL 0.031∗ −0.245∗∗∗ 0.026 0.144∗∗ 0.025 0.335∗∗∗ −0.002 0.076 0.064∗∗∗ −0.098AUSTRIA −0.002 0.055 −0.001 0.083 0.009 0.038 0.007 −0.065 0.028∗ 0.157∗∗

BELGIUM 0.062∗∗ −0.101 −0.034 0.114 0.026 0.150 −0.028 0.140 0.022 0.341∗∗∗

BULGARIA −0.004 −0.008 0.058∗∗ 0.043 −0.014 −0.061 0.017 0.263∗∗∗ 0.005 0.028

CZECH.REPUBLIC −0.004 0.100 −0.036 0.286∗∗ 0.071∗∗ 0.050 −0.023 0.304∗∗ 0.017 −0.028DENMARK 0.013 0.071 0.011 0.006 −0.015 0.072 0.028 −0.092 0.013 0.310FINLAND −0.002 −0.082 0.006 0.035 0.001 0.127 0.026∗ 0.690∗∗∗ 0.002 −0.053

FRANCE 0.006 0.108 0.002 0.191∗∗ 0.009 0.133 0.003 0.163 0.009 0.233∗∗

GERMANY 0.014 0.230∗∗ 0.016 −0.076 0.004 0.260∗ 0.019 0.490∗∗∗ 0.034∗∗ 0.027

GREECE 0.002 0.093 0.009 0.084 −0.013 −0.043 −0.027 0.219∗∗ 0.002 −0.030

HUNGARY 0.004 0.105 0.008 0.062 0.014 0.257∗∗∗ 0.019 0.142∗ 0.033∗∗ 0.213∗∗

IRELAND −0.131 0.135 0.027 −0.158 −0.053 −0.071 0.082 0.334 0.026 −0.098

ITALY 0.010 0.108 −0.003 0.108 0.020∗ 0.182∗∗ −0.003 0.167∗ 0.017 0.070

LUXEMBOURG 0.062 −0.075 −0.016 0.508∗∗∗ −0.013 0.610∗∗∗ −0.018 0.730∗∗∗ −0.041 0.035

NETHERLANDS 0.000 −0.047 0.002 −0.000 0.024 0.172 0.028 0.007 0.054∗∗ −0.112

NORWAY 0.050∗ −0.122 −0.026 0.072 0.021 0.022 0.059∗∗ −0.053 0.003 −0.013

PORTUGAL 0.010 0.037 0.011 0.048 0.035 0.105 −0.013 0.327∗∗ 0.017 0.127

ROMANIA 0.005 −0.043 −0.046∗∗∗ 0.143∗∗∗ 0.019 0.114∗∗∗ 0.014 0.034 −0.026 −0.018

SLOVENIA 0.013 0.100 −0.006 −0.217 0.029 0.733∗∗∗ −0.062∗∗ 0.606∗∗∗ 0.011 −0.461∗

SPAIN 0.007 0.150 −0.000 0.151 0.030∗ 0.152 0.010 0.168 0.026∗ −0.143SWEDEN −0.014 0.167 0.002 0.045 0.009 0.238 0.021 0.386 0.033∗∗ −0.097

UNITED.KINGDOM 0.011 0.041 0.016 0.060 0.001 0.127∗∗ 0.009 0.054 0.025∗ 0.148∗∗∗

(continued)

50

(continued)

k = 0 k = 1 k = 2 k = 3 k = 4

Countries c d c d c d c d c d

INDIA 0.007 0.000 0.028∗∗ 0.033 −0.003 0.089 0.004 0.115∗ 0.031∗∗ −0.066

JAPAN 0.017 −0.060 −0.000 0.127 0.014 0.185 −0.002 0.179 0.003 0.457∗∗∗

KOREA 0.017 −0.181∗∗ 0.032∗∗ 0.416∗∗∗ 0.024∗ 0.471∗∗∗ 0.010 0.094 0.033∗∗ −0.162∗

TURKEY −0.007 0.199 0.015 −0.042 0.060∗∗∗ 0.360∗∗ 0.049∗∗∗ 0.054 0.038∗∗ 0.243

ISRAEL 0.018 0.337∗∗ −0.005 −0.395∗∗ 0.043∗ 0.267 0.018 −0.323∗ 0.082∗∗∗ 0.395∗∗

RUSSIAN.FEDERATION 0.011 −0.081∗ −0.011 0.191∗∗∗ −0.026∗ 0.185∗∗∗ 0.007 0.027 0.028∗∗ 0.119∗

Average 0.007 0.035 0.004 0.057 0.012 0.187 0.011 0.169 0.020 0.054

51

Table XXI

VAR-X type model.

Monthly changes. Dummy D equal to one during Q3 2007 to Q1 2009. ∆ ln IPt = a +12∑

k=1

bk∆ ln IPt−k +12∑

k=0

ck∆ lnSt−k +12∑

k=0

dk∆ lnSt−kDt−k + et. Four lags are

presented for space considerations, but 12 lags are included in the estimations.

k = 0 k = 1 k = 2 k = 3 k = 4

Countries c d c d c d c d c d

CANADA 0.001 0.001 0.012 0.042 0.004 0.123∗∗∗ 0.030∗∗ 0.020 0.006 0.010

MEXICO 0.015 −0.014 0.023∗∗ −0.090∗ 0.004 0.086∗ 0.024∗∗ 0.065 0.018∗ 0.050

UNITED.STATES −0.018∗∗∗ 0.070∗∗∗ 0.005 −0.038 0.007 0.101∗∗∗ 0.026∗∗∗ 0.094∗∗∗ 0.016∗∗ −0.071∗∗

BRAZIL 0.036∗∗ −0.080 0.019 0.085 0.023 0.180∗∗∗ 0.004 −0.030 0.060∗∗∗ −0.055AUSTRIA −0.002 0.032 −0.001 0.075 0.015 0.004 0.008 −0.011 0.028∗ 0.088∗

BELGIUM 0.069∗∗ −0.161∗ −0.030 0.052 0.021 0.158 −0.019 −0.002 0.019 0.288∗∗

BULGARIA −0.013 0.060 0.072∗∗∗ −0.030 −0.014 0.043 0.020 0.059 0.027 0.014

CZECH.REPUBLIC 0.005 0.048 −0.037 0.175∗∗ 0.047 0.286∗∗∗ −0.008 0.001 0.006 0.149∗

DENMARK 0.012 0.040 0.013 −0.060 −0.015 −0.003 0.035 0.019 0.009 0.130FINLAND −0.002 −0.196∗∗∗ 0.007 −0.009 −0.002 0.347∗∗∗ 0.022 0.429∗∗∗ 0.005 −0.091

FRANCE 0.000 0.117∗∗∗ 0.005 0.033 0.007 0.149∗∗∗ 0.004 0.079∗ 0.005 0.206∗∗∗

GERMANY 0.021 0.099∗ 0.014 0.157∗∗∗ 0.011 0.123∗∗ 0.010 0.316∗∗∗ 0.025 0.196∗∗∗

GREECE 0.000 0.017 0.007 0.136∗ −0.012 0.015 −0.026 0.082 0.001 0.093

HUNGARY −0.000 0.128∗∗ 0.009 0.064 0.008 0.293∗∗∗ 0.013 0.174∗∗ 0.029∗ 0.121∗

IRELAND −0.147∗ 0.110 0.001 0.009 −0.098 0.102 0.053 0.139 −0.016 0.002

ITALY 0.013 −0.052 −0.001 0.029 0.017 0.155∗∗∗ −0.004 0.155∗∗ 0.013 0.182∗∗∗

LUXEMBOURG 0.031 0.234 0.008 0.097 −0.018 0.555∗∗∗ −0.012 0.316∗∗ −0.076∗ 0.512∗∗∗

NETHERLANDS −0.007 0.085 0.001 −0.052 0.019 0.151∗∗ 0.037 −0.122 0.056∗∗ 0.006

NORWAY 0.050∗ −0.087 −0.033 0.071 0.021 0.037 0.063∗∗ −0.074 0.010 −0.084PORTUGAL 0.014 −0.061 0.020 0.042 0.009 0.131 −0.009 0.101 0.014 0.229∗∗

ROMANIA 0.006 −0.010 −0.056∗∗∗ 0.088∗∗ 0.019 0.073∗∗ 0.019 0.029 −0.028 0.010

SLOVENIA −0.010 0.057 0.021 0.054 0.055 0.282∗∗∗ −0.026 0.032 −0.024 −0.026

SPAIN −0.001 0.138∗∗ 0.002 −0.054 0.025 0.225∗∗∗ 0.007 0.114 0.019 0.159∗∗

SWEDEN −0.017 0.008 0.001 0.037 0.009 0.094 0.020 0.165∗∗ 0.032∗∗ 0.052

UNITED.KINGDOM 0.008 0.036 0.015 0.057 −0.001 0.119∗∗∗ 0.014 −0.000 0.019 0.154∗∗∗

(continued)

52

(continued)

k = 0 k = 1 k = 2 k = 3 k = 4

Countries c d c d c d c d c d

INDIA 0.014 −0.032 0.030∗∗ 0.045 −0.009 0.058∗ 0.010 0.039 0.029∗∗ 0.029

JAPAN 0.018 0.060 −0.003 0.098 0.016 0.084 −0.004 0.233∗∗∗ 0.007 0.131∗∗

KOREA 0.018 0.004 0.028∗ 0.257∗∗∗ 0.026∗ 0.174∗∗∗ 0.008 0.045 0.031∗∗ 0.005

TURKEY −0.001 −0.074 0.020 0.112 0.057∗∗∗ 0.132 0.051∗∗∗ 0.077 0.029 0.253∗∗∗

ISRAEL 0.028 0.012 0.002 −0.086 0.041 −0.023 0.025 0.029 0.074∗∗∗ 0.101

RUSSIAN.FEDERATION 0.017 0.006 −0.009 0.110∗∗∗ −0.031∗∗ 0.147∗∗∗ 0.009 0.060 0.029∗∗ 0.058Average 0.005 0.019 0.005 0.049 0.008 0.142 0.013 0.085 0.015 0.094

53

Figure 1. Stock market - GDP relationship for the US (top graph), Brazil(centered graph) and Turkey (bottom graph). The graphs illustrate the evo-lution of the stock market index (real prices, solid line) and the GDP (realprices, dashed line) from 1980 to 2013.

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54