On forecasting stock options volatility: evidence from London international financial futures and options exchange
AbstractOptions’ volatility forecasting represented, in the last decades, a very interesting and frequent domain of research in financial econometrics due to its importance in option pricing, portfolio selection, risk management and other financial activities. The aim of this study is to realize a comparative analysis of the performances obtained by several forecast models in forecasting stock options volatility. For this, we consider the volatility of the 4 most traded options at Euronext London International Financial Futures and Options Stock Exchange (Euronext.Liffe) in the period 2009-2010. When analyzing and forecasting these stock options we use the period January 2009-May 2011; using this base period, we determine the models that describe better the evolution of the volatility. Based on these models we realize forecasts that are finally compared with the real values recorded in the next 10 trading days. In relation with the differences that appear, we determine the forecast errors and by these we identify the best models and the ones that generate the biggest errors.
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Bibliographic InfoArticle provided by Faculty of Economics, Tibiscus University in Timisoara in its journal Anale. Seria Stiinte Economice. Timisoara.
Volume (Year): XVIII (2012)
Issue (Month): (May)
options; volatility; forecast; EWMA; GARCH class models;
Find related papers by JEL classification:
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
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- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001.
"Modeling and Forecasting Realized Volatility,"
Center for Financial Institutions Working Papers
01-01, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Anderson, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Labys, Paul, 2002. "Modeling and Forecasting Realized Volatility," Working Papers 02-12, Duke University, Department of Economics.
- Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2001. "Modeling and Forecasting Realized Volatility," NBER Working Papers 8160, National Bureau of Economic Research, Inc.
- Taylor, Stephen J., 1987. "Forecasting the volatility of currency exchange rates," International Journal of Forecasting, Elsevier, vol. 3(1), pages 159-170.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- Beckers, Stan, 1981. "Standard deviations implied in option prices as predictors of future stock price variability," Journal of Banking & Finance, Elsevier, vol. 5(3), pages 363-381, September.
- Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
- Black, Fischer & Scholes, Myron S, 1973. "The Pricing of Options and Corporate Liabilities," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 637-54, May-June.
- Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
- Mills,Terence C. & Markellos,Raphael N., 2008. "The Econometric Modelling of Financial Time Series," Cambridge Books, Cambridge University Press, number 9780521710091, April.
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