Asymmetric Impact of Innovations on Volatility in the Case of the US and CEEC–3 Markets: EGARCH Based Approach

Joanna Olbryś



The main goal of this study is to investigate the asymmetric impact of innovations on volatility in the case of the US and three biggest emerging CEEC–3 markets, using univariate EGARCH approach. We compare empirical results for both the whole sample from Jan 3, 2007 to Dec 30, 2011, and two equal subsamples: the ‘down market’ period, and the ‘up market’ period. Pronounced negative asymmetry effects are presented in the case of all markets, and are especially strong in the ‘down market’ period, which is closely connected with the 2007 US subprime crisis period.


volatility; asymmetry effect; down and up market; overlapping information set; univariate EGARCH model

Full Text:



Adkins, L. C. (2012), Using Gretl for Principles of Econometrics, 4th Edition, Version 1.03.

Balaban, E., Bayar, A. (2005), Stock Returns and Volatility: Empirical Evidence from Fourteen Countries, Applied Economics Letters, 12, 603–611, DOI:

Baillie, R. T., Bollerslev, T. (1990), A Multivariate Generalized ARCH Approach to Modeling Risk Premia in Forward Foreign Exchange Rate Markets, Journal of International Money and Finance, 9, 309–324, DOI:

Baumöhl, E., Výrost, T. (2010), Stock Market Integration: Granger Causality Testing with Respect to Nonsynchronous Trading Effects, Finance a Uver: Czech Journal of Eco-nomics and Finance, 60(5), 414–425.

Bauwens, L, Laurent, S., Rombouts, J.V.K. (2006), Multivariate GARCH Models: A Survey, Journal of Applied Econometrics, 21, 79–109, DOI:

Bhar, R. (2001), Return and Volatility Dynamics in the Spot and Futures Markets in Australia: an Intervention Analysis in a Bivariate EGARCH–X Framework, Journal of Futures Markets, 21, 833–850, DOI:

Black, F. (1976), Studies of Stock Market Volatility Changes, 1976 Proceedings of the American Statistical Association, Business and Economic Statistics Section, 177–181.

Bollerslev, T., Mikkelsen, H. O. (1996), Modeling and Pricing Long Memory in Stock Market Volatility, Journal of Econometrics, 73, 151–184, DOI:

Bollerslev, T., Wooldridge, J. M. (1992), Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances, Econometric Reviews, 11, 143–179, DOI:

Booth, G. G., Martikainen T., Tse Y. (1997), Price and Volatility Spillovers in Scandinavian Stock Markets, Journal of Banking & Finance, 21, 811–823, DOI:

Braun, P. A., Nelson, D. B., Sunier, A. M. (1995), Good News, Bad News, Volatility, and Betas, The Journal of Finance, 50(5), 1575–1603, DOI:

Büttner, D., Hayo, B. (2012), EMU-Related News and Financial Markets in the Czech Repub-lic, Hungary and Poland, Applied Economics, 44(31), 4037–4053, DOI:

Campbell J.Y., Lo A.W., MacKinlay A.C. (1997), The Econometrics of Financial Markets, Princeton University Press, New Jersey.

Doman, M. (2011), Mikrostruktura giełd papierów wartościowych (Stock Exchange Micro-structure), Poznan University of Economics Press.

Dooley, M., Hutchison, M. (2009), Transmission of the U.S. Subprime Crisis to Emerging Markets: Evidence on the Decoupling – Recoupling Hypothesis, Journal of Interna-tional Money and Finance, 28, 1331–1349, DOI:

Doornik, J. A., Hansen, H. (2008), An Omnibus Test for Univariate and Multivariate Normal-ity, Oxford Bulletin of Economics and Statistics, 70, 927–939, DOI:

Engle, R. F. (ed.) (2000), ARCH. Selected Readings, Oxford University Press.

Eun, C. S., Shim, S. (1989), International Transmission of Stock Market Movements, The Journal of Financial and Quantitative Analysis, 24(2), 241–256, DOI:

Fiszeder, P. (2009), Modele klasy GARCH w empirycznych badaniach finansowych (The Class of GARCH Models in Empirical Finance Research), Torun, Nicolaus Copernicus University Press.

Frank, N., Hesse, H. (2009), Financial Spillovers to Emerging Markets during the Global Financial Crisis, Finance a Uver: Czech Journal of Economics and Finance, 59(6), 507–521.

Hamao, Y., Masulis, R. W., Ng, V. (1990), Correlations in Price Changes and Volatility across International Stock Markets, Review of Financial Studies, 3(2), 281–307, DOI:

Jane, T-D, Ding, C. G. (2009), On the Multivariate EGARCH Model, Applied Economics Letters, 16, 1757–1761, DOI:

Koutmos, G., Booth, G. G. (1995), Asymmetric Volatility Transmission in International Stock Markets, Journal of International Money and Finance, 14(6), 747–762, DOI:

Lee, J., Stewart, G. (2010), Asymmetric Volatility and Volatility Spillovers in Baltic and Nordic Stock Markets, European Journal of Economics, Finance and Administrative Sciences, 25, 136–143.

Ljung, G., Box, G. E. P. (1978), On a Measure of Lack of Fit in Time Series Models, Biometrika, 66, 67–72.

Lucchetti, K., Balietti, S., (2011), The gig package, Version 2.2.

Mun, M., Brooks, R. (2012), The Roles of News and Volatility in Stock Market Correlations during the Global Financial Crisis, Emerging Markets Review, 13, 1–7, DOI:

Nelson, D. B. (1991), Conditional Heteroskedasticity in Asset Returns: A New Approach, Econometrica, 59, 347–370, DOI:

Olbryś, J. (2013), Price and Volatility Spillovers in the Case of Stock Markets Located in Different Time Zones, Emerging Markets Finance & Trade, 49(2), 145–157, DOI:

Reyes, M .G. (2001), Asymmetric Volatility Spillover in the Tokyo Stock Exchange, Journal of Economics and Finance, 25(2), 206–213, DOI:

Scheicher, M. (2001), The Comovements of Stock Markets in Hungary, Poland and the Czech Republic, International Journal of Finance and Economics, 6, 27–39, DOI:

Syczewska, E. M. (2010), Changes of Exchange Rate Behavior During and After Crisis, Quantitative Methods in Economics, WULS Press, 11(1), 145–157.

Syriopoulos, T. (2007), Dynamic Linkages between Emerging European and Developed Stock Markets: Has the EMU any Impact?, International Review of Financial Analysis, 16, 41–60, DOI:

Tsay, R. S. (2010), Analysis of Financial Time Series, John Wiley, New York.

Tse, Y., Wu, C., Young, A. (2003), Asymmetric Information Transmission between a Transi-tion Economy and the U.S. Market: Evidence from the Warsaw Stock Exchange, Global Finance Journal, 14, 319–332, DOI:

ISSN (print) 1234-3862
ISSN (online) 2450-7067

Partnerzy platformy czasopism