The role of the trading volume in explaining the volatility persistence: evidence from Austrian, Belgian and French stock market

• Autorzy: Chocholatá M.
• Języki publikacji: EN

Abstrakty

EN

This paper deals with the role of the trading volume in explaining the volatility persistence for the daily data (both stock indices and individual stocks) from the Austrian, Belgian and French stock market. The main aim was to investigate the volatility – trading volume relation to find out if there are differences between stock indices and individual stocks concerning the reduction of the volatility persistence after inclusion of the trading volume into the conditional volatility equation. Unlike many other studies, our analysis in general didn’t confirm that the inclusion of the trading volume into the conditional volatility equation made the volatility persistence negligible. Furthermore there was also no difference observed concerning the results for index return series and individual stocks.

Słowa kluczowe

EN

trading volume; volatility persistence; GARCH; EGARCH

PL

obrót woluminami; ulotność stałości; model GARCH; EGARCH

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Metody Informatyki Stosowanej
• Rocznik: 2011
• Tom: nr 4
• Strony: 5--17
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Chocholatá M.

• University of Economics in Bratislava, Faculty of Economic Informatics

Bibliografia

• [1] Ané T. – Ureche-Rangau L. Does Trading Volume Really Explain Stock Returns Volatility? Journal of International Financial Markets, Institutions and Money, Vol. 18, No. 3 (July), 2008, p. 216-235.
• [2] Arago V. – Nieto L. Heteroskedasticity in the returns of the main world stock exchange indices: volume versus GARCH effects. Journal of International Financial Markets, Institutions and Money 15, 2005, p. 271-284.
• [3] Bollerslev T. Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics 31, 1986, No. 3.
• [4] Clark, P.K. A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices. Econometrica, 41, 1973, p. 135-156.
• [5] Engle R.F. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica 50, 1982, No. 4.
• [6] Franses P.H. – van Dijk D. Non-Linear Time Series Models in Empirical Finance. Cambridge, Cambridge University Press 2000. 14 Michaela Chocholatá
• [7] Giorgioni G. - Holden K. - Hančlová J. A review of the current methods of econometric modelling. In: Proceedings of 26th International Conference - Mathematical Methods in Economics 2008, 1. ed. Liberec: Technická univerzita Liberec, 2008. p. 140-148.
• [8] Girard E. – Biswas R. Trading Volume and Market Volatility: Developed versus Emerging Stock Markets. The Financial Review 42, 2007, p. 429 – 459.
• [9] Glosten L.R.- Jagannathan R. – Runkle D. Relationship between the expected value and the volatility of the nominal excess return on stocks. Journal of Finance 48, 1993, p.1779-1801.
• [10] Gursoy G. – Yuksel A. – Yuksel A. Trading volume and stock market volatility: evidence from emerging stock markets. Investment Management and Financial Innovations, Vol. 5, Issue 4, 2008, p. 200 – 210.
• [11] Kumar B. – Singh P. – Pandey A. The Dynamic Relationship between Price and Trading Volume: Evidence from Indian Stock Market. W.P. No. 2009-12-04. Available at: http://www.esocialsciences.com/data/articles/Document12512010400.2685358.pdf
• [12] Lamoureux C. – Lastrapes N. Heteroscedasticity in stock return data: volume versus GARCH Effects. The Journal of Finance, Vol. XLV (March 1990), No. 1, p. 221-229.
• [13] Miyakoshi T. ARCH versus Information-Based Variances: Evidence from the Tokyo Stock Market. Japan and the World Economy 14, 2002, p. 215-231.
• [14] Miyakoshi T. News and Asian Emerging Markets. Review of Pacific Basin Financial Markets and Policies. Vol. 9, No. 3, 2006, p. 359-384.
• [15] Naliniprava T. The Empirical Relationship between Trading Volumes & Stock Return Volatility in Indian Stock Market. European Journal of Economics, Finance and Administrative Sciences 24, 2010, p. 59 – 77.
• [16] Nelson D. Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59, 1991, p. 347-370.
• [17] Noryati A. Impact of Automated Trading in the Crude Palm Oil Futures Market on its Underlying Spot Market. International Research Journal of Finance and Economics 36, 2010. Available at: http://www.eurojournals.com/finance.htm.
• [18] Omran M.F. – Mckenzie E. Heteroscedasticity in stock returns data revisited: volume versus GARCH effects. Applied Financial Economics 10, 2000, p. 553 – 560.
• [19] Pekár J. - Brezina I. - Čičková Z. Model support for construction of regional models. In: Regional disparities in Central and Eastern Europe : theoretical models and empirical analyses. Bratislava : EKONÓM. 2010, p. 191-195.
• [20] Poon S-H. – Granger C.W.J.: Forecasting Volatility in Financial Markets: A Review. Journal of Economic Literature, Vol. XLI (June 2003), p. 478 – 539.
• [21] Rachev S.T. et al. Financial Econometrics. From Basics to Advanced Modeling Techniques. John Wiley&Sons, Hoboken, New Jersey, 2007.
• [22] Sharma J.L. – Mougoue M. – Kamath R. Heteroscedasticity in stock market indicator return data: volume versus GARCH effects. Applied Financial Economics, Vol. 6, 1996, p. 337-342.
• [23] Zakoian J.M. Threshold Heteroskedastic Models. Journal of Economic Dynamics and Control, 18, 1994, p. 931-955.
• [24] http://finance.yahoo.com
• [25] http://en.indices.cc/indices/details/atx/composition/ (valid to May 31, 2011)
• [26] http://www.euronext.com/trader/indicescomposition/composition-4411-ENBE0389555039. html?selectedMep=3 (valid to May 31, 2011)
• [27] http://www.euronext.com/trader/indicescomposition/composition-4411-ENFR0003500008. html?selectedMep=1 (valid to May 31, 2011)