Asymmetry Effects in Volatility on the Major European Stock Markets: the EGARCH Based Approach

Joanna Olbrys; Elzbieta Majewska; Joanna Olbrys; Elzbieta Majewska

doi:10.3934/QFE.2017.4.411

Quantitative Finance and Economics

2017, Volume 1, Issue 4: 411-427. doi: 10.3934/QFE.2017.4.411

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Research article Special Issues

Asymmetry Effects in Volatility on the Major European Stock Markets: the EGARCH Based Approach

Joanna Olbrys ^,,
Elzbieta Majewska

1.
Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland
2.
Faculty of Mathematics and Informatics, University of Bialystok, K. Ciolkowskiego 1M, 15-245 Bialystok, Poland

Received: 15 June 2017 Accepted: 31 October 2017 Published: 13 December 2017

The main goal of this paper is to investigate the asymmetric impact of innovations on volatility in the case of the largest European stock markets in the United Kingdom, France and Germany by using the EGARCH based approach. The sample period begins in January 2003 and ends in December 2016, and it includes the 2007 U.S. subprime crisis. The robustness analysis of empirical results is provided with respect to the whole sample and three adjacent subsamples, each of equal size: 1) the pre-crisis, 2) the Global Financial Crisis (GFC) and 3) the post-crisis periods. The GFC periods are formally detected by using a statistical method of dividing market states into bullish and bearish markets. Moreover, the common trading window procedure is employed to avoid the nonsynchronous trading problem in the group of investigated markets and to get the overlapping information set. We estimate univariate EGARCH models based on daily percentage logarithmic returns of major stock market indexes: FTSE100 (London), CAC40 (Paris), and DAX (Frankfurt). Pronounced negative asymmetry effects in volatility are presented in the case of all markets and are rather robust to the choice of the period. Our findings are consistent with the literature and suggest that the major European stock markets are more sensitive to 'bad' than 'good' news.
- volatility,
- asymmetry effect,
- Global Financial Crisis,
- overlapping information set,
- univariate EGARCH model
Citation: Joanna Olbrys, Elzbieta Majewska. Asymmetry Effects in Volatility on the Major European Stock Markets: the EGARCH Based Approach[J]. Quantitative Finance and Economics, 2017, 1(4): 411-427. doi: 10.3934/QFE.2017.4.411

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Abstract

The main goal of this paper is to investigate the asymmetric impact of innovations on volatility in the case of the largest European stock markets in the United Kingdom, France and Germany by using the EGARCH based approach. The sample period begins in January 2003 and ends in December 2016, and it includes the 2007 U.S. subprime crisis. The robustness analysis of empirical results is provided with respect to the whole sample and three adjacent subsamples, each of equal size: 1) the pre-crisis, 2) the Global Financial Crisis (GFC) and 3) the post-crisis periods. The GFC periods are formally detected by using a statistical method of dividing market states into bullish and bearish markets. Moreover, the common trading window procedure is employed to avoid the nonsynchronous trading problem in the group of investigated markets and to get the overlapping information set. We estimate univariate EGARCH models based on daily percentage logarithmic returns of major stock market indexes: FTSE100 (London), CAC40 (Paris), and DAX (Frankfurt). Pronounced negative asymmetry effects in volatility are presented in the case of all markets and are rather robust to the choice of the period. Our findings are consistent with the literature and suggest that the major European stock markets are more sensitive to 'bad' than 'good' news.

References

[1]	Abbas Q, Khan S, Shah SZA (2013) Volatility Transmission in Regional Asian Stock Markets. Emerg Mark Rev 16: 66–77.
[2]	Adkins LC (2014) Using Gretl for Principles of Econometrics, 4th Edition, Version 1.041.
[3]	Balaban E, Bayar A (2005) Stock Returns and Volatility: Empirical Evidence from Fourteen Countries. App Econ Lett 12: 603–611.
[4]	Baillie RT, Bollerslev T (1990) A Multivariate Generalized ARCH Approach to Modeling Risk Premia in Forward Foreign Exchange Rate Markets. J Int Money Financ 9: 309–324.
[5]	Baumöhl E, Výrost T (2010) Stock Market Integration: Granger Causality Testing with Respect to Nonsynchronous Trading Effects. Financ Uver 60: 414–425.
[6]	Bauwens L, Laurent S, Rombouts JVK (2006) Multivariate GARCH Models: A Survey. J App Econom 21: 79–109.
[7]	Bhar R (2001) Return and Volatility Dynamics in the Spot and Futures Markets in Australia: an Intervention Analysis in a Bivariate EGARCH-X Framework. J Futures Markets 21: 833–850.
[8]	Bhar R, Nikolova B (2009) Return, Volatility Spillovers and Dynamic Correlation in the BRIC Equity Markets: An Analysis Using a Bivariate EGARCH Framework. Glob Financ J 19: 203–218.
[9]	Black F (1976) Studies of Stock Market Volatility Changes. 1976 Proc Am Stat Assoc, Bus Econ Stat Section: 177–181.
[10]	Bollerslev T, Mikkelsen HO (1996) Modeling and Pricing Long Memory in Stock Market Volatility. J Econom 73: 151–184.
[11]	Bollerslev T, Wooldridge JM (1992) Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances. Econom Rev 11: 143–179.
[12]	Booth GG, Martikainen T, Tse Y (1997) Price and Volatility Spillovers in Scandinavian Stock Markets. J Bank Financ 21: 811–823.
[13]	Braun PA, Nelson DB, Sunier AM (1995) Good News, Bad News, Volatility, and Betas. J Financ 50: 1575–1603.
[14]	Bry G, Boschan C (1971) Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, NBER: New York.
[15]	Campbell JY, Lo AW, MacKinlay AC (1997) The Econometrics of Financial Markets, Princeton University Press, New Jersey.
[16]	Curto JD, Tomaz JA, Pinto JC (2009) A New Approach to Bad News Effects on Volatility: The Multiple-Sign-Volume Sensitive Regime EGARCH Model (MSV-EGARCH). Port Econ J 8: 23–36.
[17]	Dedi L, Yavas BF (2016) Return and Volatility Spillovers in Equity Markets: An Investigation Using Various GARCH Methodologies. Cogent Econ Financ 4: 1–18.
[18]	Doornik JA, Hansen H (2008) An Omnibus Test for Univariate and Multivariate Normality. Oxford B Econ Stat 70: 927–939.
[19]	Engle RF (ed.) (2000) ARCH. Selected Readings, Oxford University Press.
[20]	Engle RF, Ng VK (1993) Measuring end Testing the Impact of News on Volatility. J Financ 48: 1749–1778. doi: 10.1111/j.1540-6261.1993.tb05127.x
[21]	Eun CS, Shim S (1989) International Transmission of Stock Market Movements. J Financ Quant Anal 24: 241–256.
[22]	Fabozzi FJ, Francis JC (1977) Stability Tests for Alphas and Betas over Bull and Bear Market Conditions. J Financ 32: 1093–1099.
[23]	Jane TD, Ding CG (2009) On the Multivariate EGARCH Model. Appl Econ Lett 16: 1757–1761.
[24]	Koutmos G, Booth GG (1995) Asymmetric Volatility Transmission in International Stock Markets. J Int Money Financ 14: 747–762.
[25]	Kuttu S (2014) Return and Volatility Dynamics Among Four African Equity Markets: A Multivariate VAR-EGARCH Analysis. Glob Financ J 25: 56–69.
[26]	Lee J, Stewart G (2010) Asymmetric Volatility and Volatility Spillovers in Baltic and Nordic Stock Markets. European J Econ, Financ Adm Sci 25: 136–143.
[27]	Lee JS, Kuo CT, Yen PH (2011) Market States and Initial Returns: Evidence from Taiwanese IPOs. Emerg Mark Financ Tr 47: 6–20.
[28]	Ljung G, Box GEP (1978) On a Measure of Lack of Fit in Time Series Models. Biometrika 66: 67–72.
[29]	Lucchetti K, Balietti S (2011) The gig package, Version 2.2.
[30]	Nelson DB (1991) Conditional Heteroskedasticity in Asset Returns: A New Approach. Econom 59: 347–370.
[31]	Olbrys J (2013a) Price and Volatility Spillovers in the Case of Stock Markets Located in Different Time Zones. Emerg Mark Financ Tr 49: 145–157.
[32]	Olbryś J (2013b) Asymmetric Impact of Innovations on Volatility in the Case of the US and CEEC-3 Markets: EGARCH Based Approach. Dynamic Econom Models 13: 33–50.
[33]	Olbrys J, Majewska E (2014a) Quantitative Identification of Crisis Periods on the Major European Stock Markets. Pensee J 76: 254–260.
[34]	Olbrys J, Majewska E (2014b) On Some Empirical Problems in Financial Databases. Pensee J 76: 2–9.
[35]	Olbryś J, Majewska E (2015) Bear Market Periods During the 2007–2009 Financial Crisis: Direct Evidence from the Visegrad Countries. Acta Oecon 65: 547–565. doi: 10.1556/032.65.2015.4.3
[36]	Pagan AR, Sossounov KA (2003) A Simple Framework for Analysing Bull and Bear Markets. Appl Econ 18: 23–46.
[37]	Reyes MG (2001) Asymmetric Volatility Spillover in the Tokyo Stock Exchange. J Econ Financ 25: 206–213.
[38]	Scheicher M (2001) The Comovements of Stock Markets in Hungary, Poland and the Czech Republic. Int J Financ Econ 6: 27–39.
[39]	Tsay RS (2010) Analysis of Financial Time Series, John Wiley, New York.
[40]	Tse Y, Wu C, Young A (2003) Asymmetric Information Transmission between a Transition Economy and the U.S. Market: Evidence from the Warsaw Stock Exchange. Glob Financ J 14: 319–332.
[41]	Yang SY, Doong SC (2004) Price and Vilatility Spillovers between Stock Prices and Exchange Rates: Empirical Evidence from the G-7 Countries. Int J Bus Econ 3: 139–153.

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