The Quarterly Review of Economics and Finance 51 (2011) 350– 359
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The Quarterly Review of Economics and Finance
j ourna l ho me pa ge: www.elsev ier .com/ locate /qre f
hould the government directly intervene in stock market during a crisis?
alman Khan ∗, Pierre Batteau1
entre d’Etudes et de Recherche en Gestion d’Aix-Marseille, Institut d’Administration des Entreprises d’Aix-en-Provence, Clos Guiot Puyricard – CS 30063, 13089 Aix en Provenceedex 2, France
r t i c l e i n f o
rticle history:eceived 25 June 2010eceived in revised form 4 July 2011ccepted 10 July 2011vailable online 19 July 2011
a b s t r a c t
Unlike foreign exchange markets where central banks frequently intervene, the governments strive notto intervene in the stock markets since intervention transmit negative signals and carry market-relatedside effects. The main reasons often cited in support of intervention are to bring price stability and torestore investors’ confidence. During the recent economic turmoil, opportunities for the governments tointervene in the stock markets were mainly exploited in emerging and developing countries. We study
EL classification:1432
eywords:overnment intervention
the outcome of the Russian government’s intervention in its major stock market between Septemberand October 2008. This intervention was intended to reverse the sudden and swift declining trend intraded security prices by altering the market’s expectations. By using a combination of event study anda multivariate GARCH model, our findings does not support direct government intervention in the stockmarket during a crisis.
The economic and financial crisis of 2008 has been labeled ance-in-a-century-storm. The sudden downturn took thousands ofusinesses by surprise. Some were fortunate and were bailed out byheir governments; others were liquidated and sold. Governmentsook drastic steps to decelerate impending recessionary pressuresnd revive the national economy. In doing so, several emerging andeveloping countries intervened in their respective capital mar-ets. For instance, the Chinese government initiated both directnd indirect intervention programs to stimulate a stock market thatad lost nearly 70% of its value since October 2008. The programs
ncluded scrapping the stamp duty on stock purchases and buyinghe stocks in order to support the market. Through its Financialervices Authority (FSA), the Russian government suspended trad-ng in mid September and then intervened several times later toeverse the declining trend in its stock markets.
At the same time, developed countries2 continue to uphold
he non-intervention strategy. These countries used indirect formf intervention in the stock markets to alter the investors’xpectations. These indirect interventions included bank bailouts,
egotiated mergers, acquisitions and takeovers (see Appelbaum &ho, 2009).
In most cases, government ad-hoc intervention is based onbstract and subjective analysis. These interventions are subjecto preemptive action criteria and directed towards achieving pricetability. Such intervention prohibits investors from taking invest-ent decisions under extraordinary circumstances. For instance,
fter 9/11, the US stock markets were kept closed only becausehe flow of information was highly correlated with the attacks andould influence investors’ decisions. During such interventions, theovernment attempts to transmit information signals that allowhe investors to make informed decisions while the markets arelosed.
The question of the desirability of direct intervention in thetock market remains part of the broader economic debate. Propo-ents of intervention claim that intervention can avoid swift priceeclines in the stock market, alleviate investors’ anxiety and restoreheir confidence, and clear up the deals, especially the short-sellingy speculators. However, opponents claim that any form of inter-ention can seriously endanger the integrity of the market since thetock market stands as a leading financial indicator of the economy,nd any tampering with it can transmit incorrect signals about thetate of a nation’s economy. This leads to a loss of investors’ con-dence in the economy. More specifically, intervention voids the
asic principles of functioning of stock market which are risk trans-er and price discovery (i.e., influencing securities prices createsnformation inefficiencies in the market which lead the market torice the risks incorrectly).
Fig. 1. Comparative graph of RTS and ACWI indices.
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S. Khan, P. Batteau / The Quarterly Review
Rather than influencing the stock prices by halting or sus-ending the trade, which presumably adds to market volatility,
ndirect intervention seems reasonable since these actions attempto affect the fundamentals of the securities trading in a stock mar-et, thus changing market expectations. However, such actions cane afforded only by few resourceful economies. In contrast, majortock exchanges of the emerging and developing markets3 wit-essed direct intervention in terms of suspension of trade while
n some cases both direct and indirect forms of intervention weremployed. Thus far, the results of such interventions seem to haveeen ambiguous both from the economic and financial perspec-ives.
Practice on intervention insists on the use of a combination ofconomic and risk management tools in order to bring stock markettability and includes lowering interest rates, devaluing currency,educing obligatory deposit margins on banks with the centralank, using stock exchange stabilization fund for price support, andanaging circuit breakers (see Subrahmanyam, 1994).Two often-quoted studies on stock market intervention are
heng, Fung, and Chan (2000) and Su, Yip, and Wong (2002). Bothf these studies examine the Hong Kong government’s direct inter-ention in the Hang Seng stock market during the 1998 Asianrisis that substantially reduced market volatility and reversed theeclining trend of stock prices. These studies give insight into whyovernment intervention in the stock market can sometimes beore influential and result-oriented.
. Russian government intervention in the Russian Tradingndex
On September 15, 2008, the Russian Trading Index (RTS)lunged to its lowest level: 44% from its high of May 2008. Beforeoon the next day, RTS lost another 17% of its value. The Federalinancial Market Service (FFMS-Russian Capital Market Regulatoryuthority) directly intervened4 and suspended all stock market
rading. Both exchanges were kept closed on September 17–19,008. Finance Minister Alexei Kudrin said “There are risks in our sys-em and when there are more shocks from the global crisis, there areore risks in Russia, but they do not have an extraordinary, systemic
ature.” On September 18, Russian President Dmitry Medvedevrdered 500 billion Rubles ($150 billion) of funds to be injectednto the banking sector (see Halpin, 2008).
Between September 19 and October 15, RTS did well despiteome low level volatility. October 6, RTS took a 19% nosedive. TheFMS intervened three times during the day in order to halt trading.he FFMS kept the market suspended until October 11. On October, the Russian President announced an additional $36 billion toddress banks’ liquidity issues. Fig. 1 depicts price based graph ofTS and the World index in the period under study.
The crisis in RTS together with the government intervention andransmission of positive signals to the market necessitate anal-sis at two different levels: On the first level, if the governmentaw illiquidity as an issue, then it should have taken steps such asurchasing the stocks or reducing the margin requirements withrokers, in effect allowing funds to be directed to the stock mar-et. None of this happened, counter to what transpired in the 1998risis in Hong Kong, where the government made a 10-day inter-ention by buying Hang Seng 33 blue chip stocks in order to support
he index. Nevertheless, the government did transmit positive sig-als such as managing the liquidity crisis in the banking sector thathould have positive impact on the RTS returns. For this purpose it
3 These mainly include Russia, India, China, Brazil, Pakistan and Nigeria.4 Intervention news source: www.rts.ru.
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s imperative to investigate the behavior of RTS abnormal returnsuring and after government intervention. This will allow us tossess the financial impact of intervention in the presence of posi-ive signals that the government transmitted during intervention.or this purpose we will use the event study specified in Section 3.
On the second level, it appear that the government interventionn RTS amidst crisis was aimed at protecting it from external shocks,
claim the Russian government adhered to throughout the crisis.his phenomenon can be referred to as contagion effect. A market isegarded as financially contagious if it spreads the shocks to otherarkets and vice versa. For this process to occur the markets are
equired to be integrated. Parallel to investigating contagion effect,e also look at the herd effect in the markets which takes placehen investors across the markets behave in a highly correlatedanner. Together contagion and herd behavior can be explained
s follows: In the early phases of crisis the investors focus on localountry information only (i.e., contagion takes place). As the crisisecomes public information, investors’ decisions tend to convergeesulting in higher correlations (i.e., herding takes place).
Most of the research on contagion effects remained focusedn Asian and Latin American financial crisis (see e.g. Corbacho,arcia-Escribano, & Inchauste, 2003; Rakshit, 2002). Saleem (2008)
dentified a contagion presence before the 1998 crisis in Russianarket. He employed Bivariate GARCH-BEKK to confirm the pres-
nce of shock and volatility spillover between Russia and developeduropean countries. Scheicher (2001) used a GARCH constantonditional correlation model and found that the East Europeanarkets are well integrated into the world markets. Longin and
olnik (1995) explained the changes in the correlation among mar-ets in terms of changes in the conditional covariance. There resultsejected constant in favor of dynamic correlation among the worldquity markets. Li and Majerowskab (2007) used the MGARCH-EKK method to explore the return and volatility linkages betweenhe Eastern European stock markets and the developed Europe and.S. and concluded that these linkages were weak. Chiang, Jeon,nd Li (2007) used GARCH dynamic conditional correlation (DCC)odel to nine Asian daily stock-return data series from 1990 to
003, confirming the contagion effect in these markets. We willmploy multivariate GARCH-DCC model with time-varying corre-ation in Section 4 to analyze the RTS linkages with other world
arkets.The rest of the paper is organized as follows. Section 3 dis-
usses the financial impact of Russian government interventionsing event study methodology. Section 4 discusses the structuralreaks and dynamic conditional correlations among the markets.
. Financial impact of Russian government intervention
The government’s suspension of trading can take varioustructured-information forms. We look at two levels.
.1. The financial impact of Russian government intervention inTS during event
The Russian government twice announced a banking sectorailout package after a major shock to RTS. Such actions appearo act as signaling instruments for the government. The idea is thatovernment temporarily suspends trade and releases new tangi-le information that is expected to affect securities prices. Duringhe intervention period, if it has any effect, this information shouldessen the selling pressure and the market should see a price rever-al almost immediately. However, this would not happen untilhe intervention contains new information (see Su et al., 2002).xplaining further, the price of the asset depends on the investors’xpectations since these determine the value of the asset basedn its future performance. If the government transmits news thateinforces investors’ expectations and improves investors’ confi-ence, then the demand for the asset will increase, resulting in aositive price change. However, if the investors perceive the gov-rnment information negatively then the asset should experience aegative price change. Finally, if the intervention does not containny new information then any ad hoc price changes will dissipateuickly and there will be no permanent price change.
.2. The financial impact of Russian government intervention inTS post event
In line with Cheng et al. (2000), we argue that if the interventionransmits a positive signal to the investors (i.e., change in futureconomic outlook), then the change in price will be expected toast. However, numerous other theories have analyzed differentngles of the foregoing argument. The Efficient Market HypothesesEMH) by Eugene Fama (1970) states that if the information flows unimpeded, and it is immediately reflected in the prices, thenomorrow’s price is solely based on tomorrow’s information and isndependent of today’s, a concept that is closely related to random
alk. Consequently, the EMH implies that although stock pricesre affected by the intervention, they should react to new infor-ation in a timely manner. Consequently, we should not witness
post event drift (i.e., during but not after the intervention period permanent price change should be expected).
In contrast to EMH, the Overreaction Hypothesis (ORH) suggestshat investors tend to overweight recent information and under-eight prior data (see De Bondet & Thaler, 1985). They found that
he overreaction hypothesis predicts a negative price drift: if anynformation caused overreaction in the market, after the interven-ion there should be negative price changes.
Finally, the uncertain information hypothesis (UIH) assumesthat the risk-averse rational investors often set stock prices beforehe full ramifications of the dramatic financial event are known”see Brown, Harlow, & Tinic 1988). After the uncertainty is over,rice changes tend to be positive regardless of the triggering event.
n this case, it should be evident that during the intervention therice change should be negative; after the intervention the pricehange should be positive. For these purposes, we will present thevent study methodology.
.3. Data and event study methodology
For empirical purposes, Russia is a suitable choice as an emerg-ng country because of its status as rising oil-based economy and
nbAR
Fig. 2. Event study (time period).
ecause of the strong linkages with the EU, the US, and the restf the world. Primary data consists of dollar-denominated Rus-ian Trading System (RTS) taken from the DATASTREAM. The RTSndex is a capital-weighted price index of the 50 most liquid stocksrading on RTS stock exchange. All the prices are obtained as dailylosing prices. For market portfolio, MSCI All Countries World IndexACWI) is taken from Morgan Stanley Capital Index (MSCI Bara). Asf June 2010 the MSCI-ACWI consisted of 45 country indices (23eveloped and 22 emerging market country indices).
Samples of direct intervention are divided into two groups:roup1 Events (September 16–19, 2008) and Group 2 Events (Octo-er 6–11, 2008). We combine these groups since the time lengthetween the two is negligible. For estimating the parameters ofarket model, we initially take 191 trading days (January 10, 2007
o June 23, 2008) (Fig. 2). The event window comprises 60 daysefore the event (June 24, 2008 to September 15, 2008) the eventspan from September 16, 2008 until October 11, 2008), while theost event analysis period of 60 days comprises October 13, 2008o January 2, 2009. The days are calibrated as −60, −59 . . . −1 0 1
. . . 59, 60 where 0 refers to the first intervention on September6, 2008 while last day of intervention is day 19 (20 days).
We employ an event study methodology similar to MacKinlay1997) i.e., Market Model (MM) given by,
Rt = Rt − ˇRm,t − ̨ (1)
here ARt represents the abnormal returns at time t, Rt is the real-zed returns, ̨ is a constant, ̌ is the linear regression estimate forn estimation window prior to the event window and Rm,t is thearket return at time t. We calculate the Abnormal Returns (AR)
ased on the pre-event estimated data for the period including there-event, event window and post event. The findings based on sin-le index model can be biased due to model-misspecification (seeoll, 1977). To eliminate specification bias, we also calculate thearket Adjusted Returns (MAR) model in the same manner. MAR
s different from MM in that the regression parameter ̌ in Eq. (1)s set at 1 while the constant ̨ is set at 0.
Furthermore, we introduce a third method of Generalizedutoregressive Conditionally Heteroskedastic Model GARCH(1, 1).ince the residual of usual Ordinary Least Square (OLS) is assumedo be N ∼ (0, 1), the beta can be spurious if the constant vari-nce assumption is violated. The violation tends to occur since theolatility is found to be variable across time. For this purpose wemploy GARCH(1, 1) model similar to Bollerslev (1987). The modelarameters are expressed in the following way:
Rt = �ARt−1 + ∈ t (2)
2t = ˛0 + ˛1 ∈ 2
t−1 + ˛2h2t−1 (3)
here ∈ t∼(
0, h2t
), � is the AR coefficient. ˛1 is the ARCH compo-
ent and ˛2 is the GARCH component.
.4. Empirical results
Referring to the Table 1 (concise form), on Day 0, RTS confronted
egative Abnormal Returns (AR) of magnitude equal to 15.19%ased on the MM model. At Day 3, RTS witnessed large positiveR of 18.36%. During the second intervention period, on Day 14TS faced a negative AR of 19.90% and on Day 17 a positive AR of
S. Khan, P. Batteau / The Quarterly Review of Economics and Finance 51 (2011) 350– 359 353
Table 1Risk-adjusted abnormal returns for RTS.
Days Market model* Adjusted returns (MAR) Market model-GARCH*
msenaicontained no valuable information, therefore resulting in randomprice changes both during and after intervention.
27 −4.44 −96.15 −* Student’s t-test is used such that the mean adjusted return is equal to zero.
5.19%. The intervention ended on Day 19 with RTS going through negative AR of 12.33%. The results are symmetrical across otherodels.For MM model, the during-event sample comprises of a total of
9 observations, of which 7 are positive and 13 are negative ARs. Athe 5% significance level, we find none of the positive AR significanthile only 3 of the negative ARs are significant. In the post event
ample, out of a total of 60 observations, 24 are positive and 36egative ARs. At the 5% level, only 2 positive and 9 negative ARs are
ound to be significant. The results indicate that the interventionould not reverse the negative price trend and had very little effectn post event as well.
Similar results can be drawn from the MAR model. During thevent, no significant positive ARs are found, while 3 out of 13 neg-tive ARs are found to be significant. In post event, 4 out of 25ositive ARs and 7 out of 34 negative ARs are found to be signifi-ant. MAR reaffirms the conclusion drawn from MM model that thentervention effect was negligible in both during and post event.
The MM-GARCH(1, 1) model deviates somewhat from the aboveesults. During intervention, 4 out of 9 positive ARs are found to beignificant while no negative ARs are found to be significant at the% level. In post event, the significant positive ARs stands at 7 out of4 while negative ARs stands at 2 out of 25. The results reveal thatnce conditional volatility is taken into account, the interventionas a positive albeit negligible impact.
Fig. 3 plots the average CAR for RTS based on MM and mar-
et MAR and GARCH model. Both the index fluctuates below 0ine. After the first intervention a sudden rise in AR, smooth forew days, plunged again and second intervention took place. The
M-GARCH(1, 1) takes into consideration the conditional volatil-
−82.83 −0.03 −0.61
ty therefore resulting in lower abnormal returns compared to MMnd MAR. This implies a significant level of conditional volatility inTS.
From an information content point of view, no evidence of per-anent price change was found during the event (i.e., the ARs
tabilize inconsistently and for very short periods during and postvent). The results imply that news of the government bailout hado effect on investors’ expectations. The results tend to favor EMHs against the ORH and UIH. The Russian government interventions clearly a case in which investors perceived that the intervention
Fig. 3. Comparative graph of RTS and ACWI abnormal returns.
354 S. Khan, P. Batteau / The Quarterly Review of Economics and Finance 51 (2011) 350– 359
Table 2Financial impact of intervention.**
AR series Hypothesis* Probability t-test statistics
* H0:1 indicates a rejection of the null hypothesis; H0:0 indicates a failure to reject the null hypothesis (at the 5% significance level).** MM: Market Model; MAR: Market Adjusted Model; MMG: Market Model-Garch(1,1) Model; AR: abnormal returns; CAR: cumulated abnormal returns; RTS: Russian
Trading Index.
rices f
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Table 2 provides the statistical results of RTS ARs and cumulatedRs (CAR). Based on ARs, all models reach the same conclusion that
he government interventions did not have any financial impact.acKinlay (1997) observed that the ARs must be aggregated in
rder to draw overall inferences for the event of interest. There-ore, in case of CAR, the result indicates that the intervention didave financial impact (at 5% level of significance) albeit a negativene. The result is consistent with Over Reaction Hypothesis (ORH).he government intervention caused over reaction in markets andn cumulated bases the negative price drift takes place.
Table 3 provides the market risk component beta (ˇ) for the MModel. The linear relationship implied by ̌ of RTS using market
ndex ACWI in a pre-event, during event and post event period arell found to be significant at the 5% level. During the event period,he ̌ increases from 1.129 (pre-event) to 1.120 and then to 1.553 inost event. The high ̌ during and post event indicates that the RTS
nete
able 3orrelation analysis.
Pre-event periodJune 24,2008–September15, 2008
Beta t-Stat
RTS-ACWI 1.129* 9.460
R-square 0.264
Correlation (RTS-ACWI)a 0.514 (9.460)*
* 5% significant level.a Pearson’s correlation coefficient is signified by r (rho).
or all sample indices.
arket became more sensitive to the changes in the world marketnd, as expected, should perform much worse than the rest of theorld.
Table 3 tabulates the correlation coefficient for RTS-ACWI index.ll the correlation coefficients were found to be significant at
he 5% level based on Pearson’s linear correlation coefficient test.he correlation between RTS-ACWI is weak in pre-event scenarioi.e., 0.51); however, the correlation lowers by a little marginuring the event period. The results imply that the Russian gov-rnment intervention resulted in low level of external shocksffecting RTS market. In other words, closing the market acted as
barrier for transmission of shocks to Russian market, however
egligible. This is perhaps the main reason why the Russian gov-rnment intervened and at times kept its stock market closed ashe world markets continue to slide. The result indicates that gov-rnment can halt the trade and not the flow of information thereby
S. Khan, P. Batteau / The Quarterly Review of Economics and Finance 51 (2011) 350– 359 355
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Sc(1989). We estimate the model based on two states i.e., low, andhigh volatility for each pair of index. These include RTS versusACWI, RTS versus North America, RTS versus Europe. The dates are
Fig. 5. Estimated Mark
ausing temporary fall in intra-market correlation that recoversnce the government stops intervention. However, the drop in cor-elation is negligible. This particular market behavior suggests thathe investors continue to use the information for stock valuationven if the markets are forcefully closed. Once the markets open,he stock prices adjust quickly reflecting the effect of informa-ion during market halt. In the post event scenario, the correlationoefficient rose to 0.697. This can be expected, as in post eventhe government nonintervention in RTS brought the market intolignment with the world markets.
Summarizing the event study, the results indicate that the gov-rnment intervention did have financial impact on the RTS returnsased on CAR. The impact can be categorized as negative along with
ncreased volatility. The government was unsuccessful in turninghe RTS declining trend such that there was no permanent pricehange witnessed and RTS continues to drift negatively. From annformation hypothesis point of view, the government announce-
ents had no impact. This suggests that the crisis was global inature and could not simply be contained by a temporary localolution (government intervention).
. Impact of intervention on dynamic correlation betweenussian and other markets
In this section, we assess the level of integration between RTSnd the other markets, as a prerequisite to verifying the Russianovernment’s claim that the shocks in RTS were in fact due to otherlobal markets.
For these purposes, we employ two additional return seriesi.e., North America and Europe) for comparison of multivariateeturn correlations and volatility spillover effects. The data haseen obtained from DATASTREAM. The daily data spans over Jan-ary 16, 2006 and December 30, 2010 with 1271 observations. Anrea plot of index closing price is given in Fig. 4. The entire sam-le appears to behave in a similar fashion as far back as January006. The differentiating property of RTS is that the peaks andips are quite discernable (i.e., the RTS appears to be compara-ively more volatile market). This should be expected as RTS hasmerging market status.
Before the Russian government intervention in RTS, it is evi-ent that apart from RTS, all the other stock markets were facingrice declines. However, in contrast to others, the Russian govern-ent intervened in RTS in an attempt to slow down or reverse the
tt
ime switching model.
ecline; however, it appears that RTS continued to follow the gen-ral price trend across the world markets between September 16nd October 10, 2008. It appears from Fig. 4 that Russian stock mar-et is integrated with stock markets across the world. We will testhis relationship later in the study.
In order to correctly analyze the contagion and herd effect, weeed to identify various phases over the selected period such as pre-risis, crisis and post-crisis based on well defined structural breaks.or this purpose, we use the Markov Regime Switching ModelingMRSM) technique. As a trivial objective related to MRSM, we canlso find out whether Russian government intervention was in fact
planned intervention or an ad hoc one. To be regarded as a plannedntervention, the government is expected to intervene in a timely
anner i.e., close to the start of crisis.
.1. Structural breaks
Assume that yt is a time series generated as an autoregressionf order � with regime switching variance,5
t = ˛k + ˇkxk,t + ∈ t (4)
here
∈ t∼N(
0, �2St
)
k coefficient for independent variables xk,t where k = ACWI, Northmerica and Europe; �2
StThe variance of the innovation at state St;
t residual vector which follows a normal distribution; ˛k constant;t RTS and St is assumed to be an n-state first order Markov process,aking the values 1, . . ., n with transition probability matrix P = Pi,j,, j = 1, . . ., n where
= Pij[St = j|St = i] with∑n
j=1Pij = 1 for all i (5)
ince St is unobservable, the unknown parameters of the modelan be estimated using the non-linear filter proposed by Hamilton
5 The original model specification also included the mean switching. However,he estimated coefficients were not significant in most of the cases, so we focus onhe variance switching.
3 of Economics and Finance 51 (2011) 350– 359
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56 S. Khan, P. Batteau / The Quarterly Review
xact points obtained from MRSM where a major structural regimehift is detected. The smooth probabilities obtained from MRSM forll three states are given in Fig. 5. The brief results of MRSM areiven in Table 4.
For each pair of index, we split the series into three cate-ories; (i) pre-crisis (January 16, 2006–July 17, 2008) (ii) crisisJuly 18, 2008–August 12, 2009) and (iii) post-crisis (August 13,009–December 30, 2010) based on RTS and ACWI. Fig. 5 depictshe structural breaks in three paired sample indices. The crisis dura-ion is found to be slightly different in case of RTS-North Americand RTS-Europe where the crisis persisted for a few days longer.t is evident that the Russian intervention period was a subset of aroader worldwide crisis. The actual crisis in RTS began at arounduly 18, 2008 as in other markets, almost two months earlier thanhe beginning of intervention. The government intervened in RTSnly when the market experienced a big fall on September 16, 2008.he government interventions on September 16, 2008 and sub-equent interventions can be regarded as purely ad hoc reactiono the financial crisis set off apparently due to Lehman Brothersankruptcy on September 15, 2008.
Table 5 provides the key statistics for the return series of RTS,CWI, North America and Europe. We consider only the pre-crisisnd crisis periods based on MRSM.
The mean return for all markets significantly changed fromre-crisis to crisis (i.e., from positive to negative returns) and thetandard deviation also increased. The Russian market appearso have suffered more since the magnitude of mean and medianeturns are higher compared to other markets. Table 5 suggests thatll markets were leptokurtic and negatively skewed in pre-crisis.s expected, during crisis, the magnitude of the negative-skewness
ncreased considerably with the exception of European markethere skewness was found to be positive.
In the next section we study the dynamic correlations betweenhe RTS and other indices in order to evaluate the shock and volatil-ty spillover.
.2. Dynamic conditional correlation
The (Engle, 2002) GARCH-Dynamic Conditional CorrelationDCC) model is a two-stage estimator of conditional variancesnd correlations. In the first stage, a univariate GARCH models estimated. In the second stage, the univariate variance esti-
ates obtained from the first stage are used as inputs. The DCCodel provides the time varying correlations among the vari-
bles that indicates whether the co-movement of the variablesncrease/decrease over time. This allows us to analyze the hypoth-sis that the sample indices integration level, a condition requiredor effective shock transmission. Following (Engle, 2002), theARCH-DCC model can be formulated as follows.
t |˝t−1∼N(0, DtRtDt) (6)
2t = diag{ωi} + diag{ki}rt−1 ◦ rt−1 + dial{�i}D2
t−1 (7)
t = D−1t rt (8)
t = S(1 − ̨ − ˇ) + ˛(
εt−1ε′t−1
)+ ˇQt−1 (9)
t = diag{Qt}−1Qt−1diag{Qt−1}−1 (10)
here Eq. (8) represents the standardized errors, Qt is the uncondi-
ional correlation matrix of the errors and is the Hadamard productf two matrices of the same size. The Dt = diag
{√hi,t
}and Rt is the
ime varying correlation matrix. The parameters of the GARCH-DCCodel can be estimated using maximum likelihood. If ( ̨ + ̌ < 1) Ta
ble
4M
arko
v
regi
m
Dep
end
ent
RTS
-AC
WI
RTS
-Nor
th
RTS
-Eu
rop
The
˛k
rep
re*
Sign
ifica
n**
In
brac
ke
S. Khan, P. Batteau / The Quarterly Review of Economics and Finance 51 (2011) 350– 359 357
Table 5Key statistics for index returns in two major regimes.
* Significant at 5% level.a H0: no autocorrelation in the series.
hen Eq. (9) is mean reverting and the log likelihood for this esti-ator can be written as:
= −12
T∑
t=1
(n log(2�)) + 2 log(∣∣Dt
∣∣) + log(∣∣Rt
∣∣) + ε′tR
−1T εt (11)
.3. GARCH-DCC results
We estimate the GARCH(1, 1)-DCC model as laid down in Eqs.6)–(11). The results are given in Table 6. The ARCH coefficientsRTS and ˛k and the GARCH coefficients ˇRTS and ˇk are found toe significant at the 5% confidence level where k = ACWI, North
* Parameters significant at 5% confidence level.** Where k = All Countries World Index (ACWI), North America and Europe.
*** LBQ: Ljung Box Q test performed to examine serial correlation at lag 16.
able 7escriptive statistics of GARCH(1, 1)-DCC estimated correlations.a
RTS-ACWI RTS-North America
BC AC-BI BI AI-BC BC AC-BI
Mean 0.54 0.38 0.59 0.56 0.31 0.20
Max 0.66 0.53 0.63 0.63 0.41 0.30
Min 0.36 0.27 0.52 0.52 0.20 0.13
Std. 0.07 0.07 0.03 0.03 0.05 0.05
a BC: before the crisis begins; AC-BI: after crisis begins but before the intervention intervention end but before the crisis ends.
merica and Europe. The constant ωRTS and ωk are significant at% confidence level.
For the entire sample, the coefficient ˛m is significant at 5%onfidence level indicating the lingering effect of standardizedesiduals in the previous period. The coefficient ˇn that indicateshe memory of correlations is significant for all the paired marketst the 5% confidence level. As the value of ˇn close to 1 indicates atrong degree of persistence in the series of correlations Qt, while
sum of ˛m and ˇn close to 1 indicates higher persistence in the
onditional variance.
The shock (ARCH) effects on the dynamic correlations appearo be high in case of ˛RTS compared to ˛k. This means that ahock from RTS affected the other markets more than the other
n RTS; BI: between the intervention starting and ending period; AI-BC: after the
358 S. Khan, P. Batteau / The Quarterly Review of Economics and Finance 51 (2011) 350– 359
ching
mdˇmdl
odsh
iETRttse
ptfebagRt
kciPaafic
dtsRivpk
wTdaicAti(r
aTpigwdm
5
a
Fig. 6. Estimated Markov regime swit
arkets affecting RTS. However the volatility spillover effect on theynamic correlations appears to be high for all sample indices i.e.,RTS and ˇk. This indicates that there is a bivariate volatility trans-ission relationship between RTS and other markets. We plot the
ynamic correlations Qt in Fig. 6. The dashed and dotted straightines indicate the beginning and the end of intervention.
The crisis begins at July 18, 2008 (approx. same time for all pairsf indices) and stays around August 12, 2009. Fig. 5 shows a suddenrop in correlations as soon as the crisis (state 2) begins in the entireample. Then the correlation spikes and continues to increase at aigh rate until the end of the intervention.
During the intervention period, the correlation continues toncrease in all cases. However, the correlation between the RTS andurope drops for a few days during intervention and rises again.his can be attributed to possible European market reaction to theussian government on and off intervention. After the interventionhe correlations continue to be almost at the same levels gained athe beginning of the crisis. According to Fig. 6, there are no evidentigns that the Russian government intervention had any significantffect on the correlation structure.
Fig. 6 also provides good evidence of the contagion and herdinghenomena that we introduced in Section 2. At the beginning ofhe crisis, the US and European investors start withdrawing theirunds from their respective markets and started reinvesting theselsewhere. Such action result in reduction in correlations at theeginning of the crisis. As the investors in the other countries suchs RTS, start taking similar actions, the contagion effect is trig-ered (i.e., the investors in RTS panic and withdraw funds fromTS and other markets). During this process, the correlations dip athe beginning of the crisis demonstrating contagion effect.
The cost of collecting reliable information in uncertain mar-ets (stock market) is likely to be high, especially during therisis period. Therefore, there will be a general tendency for smallnvestors to follow major investors in making investment decisions.ublic information regarding any country will affect all countries,
nd as a result the correlations will increase among the marketst a higher rate during crisis referred to as herd effect. After therst major dip in correlations during crisis (Fig. 6), all the pairedorrelations begin to increase at a higher rate.
afIv
model and GARCH(1, 1)-DCC model.
The descriptive statistics for conditional correlations regardingifferent periods are given in Table 7. For all the paired indices,he mean correlations are moderate in the period before the cri-is begins (the state 2) with weak correlation (≈0.5). In case ofTS-North America, the results indicate that the two are partially
ntegrated. During the period after crisis begins but before the inter-ention in RTS takes place, the mean correlation declines for allaired indices, implying low level of integration among the mar-ets.
During the intervention period, high levels of correlations areitnessed for all the indices compared to the pre-crisis period.
his is because the intervention period is located at the middle ofeep crisis for all markets and witnessing high correlation during
period of heightened volatility is in line with the herd behav-or concept. After the intervention and before the crisis ends, theorrelations among the markets remain high except for RTS-Northmerican markets where the correlation declined in comparison
o the pre-crisis period. The result suggests that Russia is weaklyntegrated with the ACWI, North American and European markets≈0.5). With correlation little higher than 0.5, RTS appears to beelatively more integrated with Europe than with North America.
Absence of perfect correlation (≈1) implies that bivariate shocknd volatility spillover is weak in each of the three pairs of markets.he result indicate that shocks in North American, ACWI and Euro-ean markets do not have the potential to create widespread panic
n Russian market and vice versa. The results suggest that Russianovernment intervention in RTS had no influence on its correlationith the other markets. This in turn implies that halting the tradeoes not necessarily disintegrate the local market from the worldarkets.
. Conclusions
The study investigates the significance of Russian governmentd hoc intervention in its stock markets carried out in September
nd October 2008. We employ different event study methodologiesor analyzing abnormal returns of RTS versus All Countries Worldndex (ACWI) in order to determine whether the government inter-ention along with transmission of positive signals such as bailing
of Ec
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dphAsiRwtt
kmTgkecwg
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A
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H
H
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S. Khan, P. Batteau / The Quarterly Review
ut the banking sector had any financial impact on the RTS returns.he market model and market adjusted return model arrive at theame results: no significant financial impact was detected duringnd after intervention. In contrast, the Market Model-GARCH(1, 1)emonstrated positive but weak effects of government interven-ion.
We use Markov Regime Switching Model (MRSM) to identifyifferent time periods such as pre-crisis, crisis and post-crisis inaired markets in order to study the presence of contagion anderd effect. These paired markets include RTS versus ACWI, Northmerica and European markets. The results suggest that the cri-is in Russia and elsewhere started as early as July 2008 howeverntervention began on 16 September 2008. This signifies that theussian government did not intend to intervene and did so onlyhen the RTS experienced a big fall. The result thus suggests that
he intervention was ad hoc and apparently in response to the crisisriggered due to Lehman Brothers bankruptcy.
In order to analyze the government claim that the global mar-ets are primarily responsible for inducing shock in the Russianarket, we carried out GARCH-DCC analysis within MRSM setup.
he results suggest weak correlations among the RTS and otherlobal stock markets including North American and European mar-ets. The low correlations at the beginning of the crisis (contagionffect) and the rising correlation during the crisis (herd effect) indi-ate that RTS is partially integrated with the world markets. On thehole the study could not find any substantial evidence in favor of
overnment interventions or its claims.
cknowledgements
The authors wish to thank the editor and an anonymous refereeor constructive comments on the earlier draft of this paper. Thesual disclaimer applies.
ppendix A. Supplementary data
Supplementary data associated with this article can be found,n the online version, at doi:10.1016/j.qref.2011.07.003.
S
S
onomics and Finance 51 (2011) 350– 359 359
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