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Volatility Spillovers and Financial Contagion in the CEE Stock Markets

Academy of Economic Studies Doctoral School of Finance and Banking. Volatility Spillovers and Financial Contagion in the CEE Stock Markets. MSc. Student: Țâ n ț aru Mihai Supervisor: Professor PhD. Mois ă Alt ă r. Summary. Introduction Methodology Data description Estimation results

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Volatility Spillovers and Financial Contagion in the CEE Stock Markets

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  1. Academy of Economic Studies Doctoral School of Finance and Banking Volatility Spillovers and Financial Contagion in the CEE Stock Markets MSc. Student: Țânțaru Mihai Supervisor: Professor PhD. Moisă Altăr

  2. Summary • Introduction • Methodology • Data description • Estimation results • Conclusions • References

  3. Introduction • The spread of crises throughout the financial system at the global or regional level has been (loosely) defined as contagion. • Despite the large interest in the subject, there is no generally accepted definition for contagion. • The implications of contagion in the pricing of risk and for financial regulators are of outmost importance. • The methodologies employed in the scientific literature vary with the definitions for contagion: • Spillovers in return and volatility across financial markets – modeled with simple GARCH models in Engle et al. (1988), Hamao et al. (1990), or multivariate GARCH models as in Beirne et al. (2008). • Restrictive definition – change in the cross-market shock transmission mechanism that takes place during crises – study of cross-market correlation coefficients: King and Wadhwani (1990), Forbes and Rigobon (2002), Dungey et al. (2005).

  4. Introduction • In the light of Bekaert, Harvey and Ng (2005), this study adopts the restrictive definition of contagion as “correlation over and above what one could expect from economic fundamentals”. • Motivations of this study: • To develop a model that correctly accounts for the cross-market fundamental linkages, and therefore, gives an accurate description of the cross-market volatility transmission mechanism. • To verify to what extent does the model choice influence contagion test results. • I construct a two-factor spillover model for the CEE stock markets, with global (US) and regional (European) risk loadings: • It distinguishes between regional and global market integration. • It outperforms the one-factor model in modeling cross-market correlations – Bekaert et al. (2008).

  5. Methodology1. The Bivariate Global – Regional Specification • The framework for the joint process of US and EU returns: • with - return vector • - expected mean: lagged information variables, US and EU returns. • - vector of unexpected returns. • - joint conditional variance-covariance process specified by Engle and Kroner’s bivariate BEKK(1,1). • The orthogonalization process to obtain the US and EU idiosyncratic shocks: , with ,

  6. Methodology2. The Univariate Volatility Spillover Model • General model for the return of CEE stock market index i, at time t: • , with • - conditional mean: (lagged) US return or local dividend yield. • - unexpected return composed of global, regional and local idiosyncratic shocks. • The restricted models for the global/regional risk factor exposure or ‘beta’: • Constant ‘beta’ : • Structural ‘beta’ : , with - a trade integration measure as in Bekaert et al. (2005). • Regime-switching ‘beta’: , with - a latent regime variable as in Baele (2005).

  7. Methodology2. The Univariate Volatility Spillover Model • This study employs the flexible ‘beta’ specification as in Baele et al. (2010): where: • - structural economic instrument that reflects time-varying integration measure. • - regime-switching component that reflects temporary fluctuations in financial markets conditions. • The latent regime variable follows a Markov chain process with constant transition probabilities: and .

  8. Methodology2. The Univariate Volatility Spillover Model • When the spillover model for the individual market i: entertains regime-switching component in the market ‘betas’, then: • Case 1: and • Case 2: - GARCH(1,1) variance process. • The estimation of the regime-switching specification is done through the maximization of the sample log-likelihood function:

  9. Methodology3. Variance Ratios and Conditional Correlations • The depicted models are complete with the assumption: of zero correlation between the local idiosyncratic shocks and US/EU specific innovations. • The total conditional variance of market i can be decomposed: • Variance ratios and conditional correlations are given by:

  10. Methodology4. The Contagion Test • An unconditional correlation (over the full sample) does not guarantee that there has not been contagion across some episodes of time. • The following specification is estimated to test for any remaining correlation, separately for each market and through a panel regression: where: • represents a dummy to account for crisis (high volatility) periods in the global/regional equity markets. • Significant , parameters signal contagion.

  11. Data description • All data spans between Jan 2005 – Mar 2010, 262 weekly (Tue) observations. • Equity market data: returns of the S&P500 for the global market, MSCI Europe for the regional market and of the most liquid stock market indices in Romania (BET), Hungary (BUX), Poland (WIG) and Czech Republic (PX) for the CEE markets. • Information variables : CDS prices for CEE 5Y sovereign debt and EUR/CEE currencies exchange rates; (first difference of) US default spread, TED spread, US 10Y Treasury Bond yield, local dividend yields. • Structural data : the sum of imports and exports between an individual country and US/EU divided by the sum of the total imports and exports for that country. • The crisis dummy equals 1 during periods: • the peak of the recent global economic and financial crisis between Sep 2008 and the beginning of May 2009, when VIX volatility index was more than 1 std. dev. above the sample mean; • when both the S&P and MSCI returns were 1 std. dev. below the sample mean.

  12. Estimation Results1.The US and EU joint specification • The BEKK(1,1) model results: • Specificationtests: • The Ljung-Box tests find that no autocorrelation remains in the (squared) standardized residuals of BEKK(1,1) model. • There are significant unidirectional news and volatility spillovers from the US market to the aggregate European equity market.

  13. Estimation Results2. The Dynamic Factor Regime-Switching Models • The orthogonalized US and European residuals are plugged as components in the unexpected returns of the individual CEE indices. • The various specifications of market ‘betas’ are tested for statistical significance: • For all CEE indices, the model with constant ‘betas’ and the model with time-varying structural ‘betas’ are statistically valid. • When the most flexible ‘beta’ specification as proposed by Baele and Inghelbrecht (2010) does not fit the data, less-complex specifications are employed, at least one factor loading involving a regime-switching component. • The specification tests on the models with regime-switching are Ljung-Box tests on the generalized (regime-independent) residuals as in Smith (2007). The Hansen (1992, 1996) standardized LR test is employed for the general validity of the switching hypothesis.

  14. Estimation Results2. The Dynamic Factor Regime-Switching Models • Specification tests • Romania BET index • EU market ‘beta’: • US market ‘beta’:

  15. Poland WIG index Estimation Results2. The Dynamic Factor Regime-Switching Models • Specification tests • EU market ‘beta’: • US market ‘beta’:

  16. Czech Republic PX index Estimation Results2. The Dynamic Factor Regime-Switching Models • Specification tests • EU market ‘beta’: • US market ‘beta’:

  17. Estimation Results2. The Dynamic Factor Regime-Switching Models • Specification tests • Hungary BUX index • EU market ‘beta’: • US market ‘beta’:

  18. Estimation Results2. The Dynamic Factor Regime-Switching Models • Graphs of smoothed probabilities of being in the low volatility regime

  19. Estimation Results2. The Dynamic Factor Regime-Switching Models • The models involving regime-switching are the best-fitted by the measure of Hansen’s test – the null of one state is rejected at 90% confidence level for all the CEE indices. • The generalized residual-based tests find no evidence of linear dependence or ARCH type effects for residuals from RS models. • The switching component pertains only to the US spillover effects for all the CEE markets. • Poland and Czech equity markets are more integrated at the regional level, while US shock spillovers are prevalent for the Romanian and Hungarian equity markets. • The high local volatility states coincide with the peak of the recent global financial crisis in 2008 -2009.

  20. Estimation Results3. Economic determinants of switching between states • The logit regressions for switching states • Dependent variable in the regressions is a binary dummy: equals 1 if smoothed probability of high volatility regime is greater than 50%, 0 otherwise. • The CDS price entertains a positive effect for switching from low to high local volatility state for all markets. • EUR/RON conditional volatility positively influences switching to a high volatility state for the BET index returns. • Higher US default spread and TED spread turn CEE equity markets turbulent.

  21. Estimation Results4. Variance ratios and Conditional Correlations • Average variance ratios and conditional correlations from best-fitted models • Over full sample, US volatility spillovers explain cross-sectional approx. 25% of the variance of CEE indices returns; the average EU variance ratio is 5%. • During crisis periods, volatility spillovers from US market account for about 30% cross-sectional average for the CEE markets volatility, while EU spillover effects only increase to 6% on average. • The increase of conditional correlations during crisis periods is not evidence for contagion, but an effect of the natural interdependence between markets.

  22. Estimation Results5. Contagion Tests – individual markets • Romania • Poland

  23. Estimation Results5. Contagion Tests – individual markets • Czech Republic • Hungary

  24. Estimation Results5. Contagion Tests – CEE markets group • The panel tests of contagion • There is no evidence for contagion to the Romanian and Polish equity markets, regardless of the cross-market linkages model employed. • Contagion from the global or regional level is identified during crisis periods to the Czech and Hungarian equity markets, except for the RS model. • Testing on residuals from regime-switching models gives the same conclusion of no contagion at both individual market levels and to the CEE as a group. • The panel test of contagion indicates excess exposure to the US effects for the CEE equity markets when structural ‘beta’ model is employed.

  25. Conclusions • Shock-spillover from the global level are larger than those from the regional level to the group of CEE equity markets. • The US market volatility is the dominating influence on CEE equity market variation. • Higher risk of local CEE sovereign default, higher currency volatility and worsening financial conditions in the world economy (US) lead to higher CEE stock market volatility. • Contagion tests results depend upon the model of volatility spillovers. • I find no contagion when using the best-fitted models. The results of the study come in line with the findings in the literature on contagion which employs similar methodologies.

  26. References • Baele, L. (2005), “Volatility Spillover Effects in European Equity markets”, Journal of Quantitative Analysis, 40, 373 – 401 • Baele, L. and K. Inghelbrecht (2010), “Time-varying integration, interdependence and contagion”, Journal of International Money and Finance, 1–28 • Beirne, J., G. M. Caporale, M. Schulze-Ghattas, and N. Spagnolo (2008), “Volatility Spillovers and Contagion from Mature to Emerging Stock Markets”, IMF WP/08/286 • Bekaert, G. and C. Harvey (1997), “Emerging equity market volatility”, Journal of Financial Economics, 43, 29 – 77 • Bekaert, G., C. Harvey, and A. Ng (2005), “Market Integration and Contagion”, Journal of Business, 78, 39 – 69 • Bekaert, G., R. Hodrick, and X. Zhang (2008),“International stock return comovements”, ECB WP NO 931 • Bohl, M. T. and D. Serwa (2005), “Financial contagion vulnerability and resistance: A comparison of European stock markets”, Economic Systems, 29, 344 – 362 • Chen, N. and F. Zhang (1997), “Correlations, trades and stock returns of the Pacific-Basin markets”, Pacific-Basin Finance Journal, 5, 559 – 577

  27. References • Dornbusch, R., Y. C. Park, and S. Claessens (2000), “Contagion: Understanding How It Spreads”, The World Bank Research Observer, 15, 179 – 197 • Dungey, M., R. Fry, B. Gonzalez-Hermosillo, and V. L. Martin (2005), “Empirical modelling of contagion: A review of methodologies”, Quantitative Finance, 5, 9 – 24 • Engel, C. and J. Hamilton (1990), “Long Swings in the Dollar: Are They in the Data and Do Markets Know It?”, American Economic Review, 80, 689 – 713 • Engle, R., T. Ito, and W. Lin (1988), “Meteor showers or heat waves? Heteroskedastic intra-daily volatility in the foreign exchange market”, NBER Working Paper No. 2609 • Engle, R. and K. Kroner (1995), “Multivariate Simultaneous Generalized ARCH”, Econometric Theory, 11, 122 – 150 • Forbes, K. J. and R. Rigobon (2002), “No Contagion, Only Interdependence”, The Journal of Finance, 57, 2223 – 2261 • Franses, P. H. and D. van Dijk (2003), “Nonlinear Time Series Models in Empirical Finance”, Cambridge University Press • Hamao, Y., R. Masulis, and V. Ng (1990), “Correlations in Price Changes and Volatility across International Stock Markets”, The Review of Financial Studies, 3, 281 – 307

  28. References • Hamilton, J. D. (1990), “Analysis of Time Series Subject to Changes in Regime”, Journal of Econometrics, 45, 39-70 • Hamilton, J. D. (1994), “Time Series Analysis”, Princeton University Press, NJ Princeton • Hamilton, J. D. (1996), “Specification testing in Markov-Switching time-series models”, Journal of Econometrics, 70, 127 – 157 • Hansen, B. E. (1992), “The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP”, Journal of Applied Econometrics, 7, S61 – S82 • Hansen, B. E. (1996), “Erratum: The Likelihood Ratio Test under Nonstandard Conditions: Testing the Markov Switching Model of GNP”, Journal of Applied Econometrics, 11, 195 – 198 • Karolyi, G. A. (1995), “A Multivariate GARCH Model of International Transmissions of Stock Returns and Volatility: The Case of the United States and Canada”, Journal of Business & Economic Statistics, 13, 11 – 25 • King, M. A. and S. Wadhwani (1990), “Transmission of Volatility between Stock Markets”, The Review of Financial Studies, 3, 5 – 33

  29. References • Li, H. and E. Majerowska (2007), “Testing stock market linkages for Poland and Hungary: A multivariate GARCH approach”, Research in International Business and Finance • Maheu, J.M., and T.H. McCurdy (2000), “Identifying Bull and Bear Markets in Stock Returns”, Journal of Business and Economic Statistics, 18, 100 – 112 • Ng, A. (2000), “Volatility spillover effects from Japan and the US to the Pacific–Basin”, Journal of International Money and Finance, 19, 207 – 233 • Pericoli, M. and M. Sbracia (2001), “A primer on financial contagion”, Banca d’Italia, Temi di discussione, 407/2001 • Perlin, M. (2009), “MS_Regress - A Package for Markov Regime Switching Models in Matlab”, Available at: http://www.mathworks.com/matlabcentral/fileexchange/authors/21596 • Smith, D. R. (2007), “Evaluating Specification Tests for Markov-Switching Time Series Models”, WP available at SSRN: http://ssrn.com/abstract=976385

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