Bentes, Sonia R and Menezes, Rui (2012): On the predictive power of implied volatility indexes: A comparative analysis with GARCH forecasted volatility.
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Abstract
This paper examines the behavior of several implied volatility indexes in order to compare them with the volatility forecasts obtained from estimating a GARCH model. Though volatility has always been a prevailing subject of research it has become particularly relevant given the increasingly complexity and uncertainty of stock markets in these days. An important measure to assess the market expectations of the future volatility of the underlying asset is the implied volatility (IV) indexes. Generally, these indexes are calculated based on the prices of out-of-the money put and call options on the underlying asset. Sometimes called the “investor fear gauge”, the IV indexes are a measure of the implied volatility of the underlying index. This study focuses on the implied and GARCH forecasted volatility of some emerging countries and some developed countries. More specifically, it compares the predictive power of the IV indexes with the ones provided by standard volatility models such as the ARCH/GARCH (Autoregressive Conditional Heteroskedasticity Model/ Generalized Autoregressive Conditional Heteroskedasticity Model) type models. Finally, a debate of the results is also provided.
Item Type: | MPRA Paper |
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Original Title: | On the predictive power of implied volatility indexes: A comparative analysis with GARCH forecasted volatility |
Language: | English |
Keywords: | implied volatility; volatility forecasts, GARCH models, volatility indices |
Subjects: | F - International Economics > F3 - International Finance > F37 - International Finance Forecasting and Simulation: Models and Applications C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics |
Item ID: | 42193 |
Depositing User: | Rui Menezes |
Date Deposited: | 25 Oct 2012 10:42 |
Last Modified: | 27 Sep 2019 00:59 |
References: | Agnolucci, P. (2009). Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models, Energy Economics 31, 316-321. Becker, R., Clements, A.E. and White, S.I. (2006). On the informational efficiency of S&P500 implied volatility, The North American Journal of Economics and Finance 17, 139-153. Beckers, S. (1981). Standard deviation implied in options prices as prdictors of future stock price variability, Journal of Banking and Finance 5, 363-381. Blair, J.B., Poon, S.-H. and Taylor, S.J. (2001). Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns, Journal of Econometrics 105, 5-26. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics . 31, 307-327. Bollerslev, T., Chou, R.K. and Kroner, K.F. (1992). ARCH modeling in finance: A review of the theory and empirical evidence, Journal of Econometrics 52, 5-59. Canina, L. and Fliglewsi, S. (1993). The informational content of implied volatility, Review of Financial Studies 6 (3), 659-681. Chen, E.-T, and Clements, A. (2006). S&P 500 implied volatility and monetary policy announcements, Finance Research Letters 4, 227-232. Chiras, D.P. and Manaster, S., (1978). The information content of option prices and a test of market efficiency, Journal of Financial Economics 6 (2/3), 213-234. Christensen, B.J. and Prabhala, N.R. (1998). The relation between implied and realized volatility, Journal of Financial Economics 50, 125-150. Daly, K. (2008). Minireview: Financial volatility: Issues and measuring techniques, Physica A 387, 2377-2393. Day, T. and Lewis, C. (1992). Stock market volatility and the information content of stock index options, Journal of Econometrics 52, 267-287. Engle, R.F. and Rosenberg, J. (2000). Testing the volatility term structure using option hedging criteria, Journal of Derivatives 8, 10-28. Figlewski, S. (1997). Forecasting volatility, Financial Markets Institutions and Instruments 6, 1-88. Fleming, J., Ostdiek, B., and Whaley, R.E. (1995). Predicting stock market volatility: a new measure, Journal of Futures Markets 15, 265-302. Giot, P. (2003). The information content of implied volatility in agricultural commodity markets, The Journal of Future Markets 23, 441-454. Kumar, R. and Shastri, K. (1990). The predictive ability of stock prices implied in option premia, Advances in Futures and Options Research 4 (1), 165-176. Lamoureux, C.G. and Lastrapes, W. (1993). Forecasting stock returns variance: towards understanding stochastic implied volatility, Review of Financial Studies 6, 293-326. Latane, H.A. and Rendleman, Jr. J.R., (1976). Standard deviations of stock price ratios implied in option prices, Journal of Finance 31 (2), 369-381. Martens, M. and Dick, D. (2007). Measuring volatility with realized range, Journal of Econometrics 1438 (2007), 181-207. Nam, S.O, Oh, S.Y., Kim, H.K. and Kim, B.C. (2006). An empirical analysis of price discovery and pricing bias in KOSPI 200 stock index derivatives markets, International Review of Financial Analysis 15, 398-414. Poon, S.H. and Granger, C.W.J. (2003). Forecasting volatility in financial markets: A review, Journal of Economic Literature 41, 478-488. Randolph, W.L., Rubin, B.L. and Cross, E.M. (1990). The response of implied standard deviations to changing market conditions, Advances in Futures and Options Research 4 (1), 265-280. Vrught, E.B. (2009). US and Japanese macroeconomic news and stock market volatility in Asia-Pacific, Pacific-Basin Finance Journal 17, 611-627. Whaley, R.E. (2000). The investor fear gauge, Journal of Portfolio Management 23, 12-26. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/42193 |