Degiannakis, Stavros and Filis, George and Hassani, Hossein (2015): Forecasting implied volatility indices worldwide: A new approach.
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Abstract
This study provides a new approach for implied volatility indices forecasting. We assess whether non-parametric techniques provide better predictions of implied volatility compared to standard forecasting models, such as AFRIMA and HAR. A combination of Singular Spectrum Analysis (SSA) and Holt-Winters (HW) model is applied on eight implied volatility indices for the period from February, 2001 to July, 2013. The findings confirm that the SSA-HW provides statistically superior one trading day and ten trading days ahead implied volatility forecasts world widely. Model-averaged forecasts suggest that the forecasting accuracy is further enhanced, for the ten-days ahead, when the SSA-HW is combined with an ARI(1,1) model. Additionally, the trading game reveals that the SSA-HW and the ARI-SSA-HW are able to generate significant average positive net daily returns in the out-of-sample period. The results are important for option pricing, portfolio management, value-at-risk and economic policy.
Item Type: | MPRA Paper |
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Original Title: | Forecasting implied volatility indices worldwide: A new approach |
Language: | English |
Keywords: | Implied Volatility, Volatility Forecasting, Singular Spectrum Analysis, ARFIMA, HAR, Holt-Winters, Model Confidence Set, Combined Forecasts. |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets |
Item ID: | 72084 |
Depositing User: | George Filis |
Date Deposited: | 19 Jun 2016 17:45 |
Last Modified: | 30 Sep 2019 16:35 |
References: | Andersen, T. G., Bollerslev, T., Diebold, F. X., and Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579-625. Andersen, T. G., Bollerslev, T., and Meddahi, N. (2005). Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities. Econometrica, 73(1), 279-296. Andersen, T. G., Bollerslev, T., and Diebold, F. X. (2007). Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility. Review of Economics and Statistics, 89, 701–720. Angelidis, T., Benos, A. and Degiannakis, S. (2004). The Use of GARCH Models in VaR Estimation, Statistical Methodology, 1(2), 105-128. Angelidis, T., and Degiannakis, S. (2008). Volatility forecasting: Intra-day versus inter-day models. Journal of International Financial Markets, Institutions and Money, 18(5), 449-465. Baillie, R.T. (1996). Long Memory Processes and Fractional Integration in Econometrics. Journal of Econometrics, 73, 5-59. Barunik, J., Krehlik, T., & Vacha, L. (2016). Modeling and forecasting exchange rate volatility in time-frequency domain. European Journal of Operational Research, 251(1), 329-340. Beckers, S. (1981). Standard deviations implied in options prices as predictors of future stock price variability. Journal of Banking and Finance, 5, 363– 381. Beneki, C., Eeckels, B., and Leon, C. (2012). Signal Extraction and Forecasting of the UK Tourism Income Time Series: A Singular Spectrum Analysis Approach. Journal of Forecasting, 31(5), 391-400. Blair, B.J., Poon, S-H and Taylor S.J. (2001). Forecasting S&P100 Volatility: The Incremental Information Content of Implied Volatilities and High-Frequency Index Returns. Journal of Econometrics, 105, 5-26. Bollerslev, T., Engle, R. F. and Nelson, D. (1994). ARCH models, in Handbook of Econometrics, Vol. 4 (Eds) R. F. Engle and D. McFadden, Elsevier Science, Amsterdam, 2959–3038. Busch, T., Christensen, B. J., and Nielsen, M. Ø. (2011). The role of implied volatility in forecasting future realized volatility and jumps in foreign exchange, stock, and bond markets. Journal of Econometrics, 160, 48–57. Charles, A. (2010). The day-of-the-week effects on the volatility: The role of the asymmetry. European Journal of Operational Research, 202(1), 143-152. Chiras, D.P., and Manaster, S. (1978). The information content of options prices and a test of market efficiency. Journal of Financial Economics, 6, 213–234. Christensen, B.J., and Prabhala, N.R. (1998). The relation between implied and realised volatility. Journal of Financial Economics, 50, 125– 150. Christodoulakis, G. A. (2007). Common volatility and correlation clustering in asset returns. European Journal of Operational Research, 182(3), 1263-1284. Corsi, F. (2009). A Simple Approximate Long-Memory Model of Realized Volatility. Journal of Financial Econometrics, 7(2), 174-196. Corsi, F., Kretschmer, U., Mittnik, S. and Pigorsch, C. (2005). The Volatility of Realised Volatility. Center for Financial Studies, Working Paper, 33 Degiannakis, S. (2004). Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model. Applied Financial Economics, 14, 1333–1342. Degiannakis, S. (2008a). Forecasting VIX. Journal of Money, Investment and Banking, 4, 5-19. Degiannakis, S. (2008b). ARFIMAX and ARFIMAX- TARCH Realized Volatility Modeling. Journal of Applied Statistics, 35(10), 1169-1180. Degiannakis, S., Livada, A. and Panas, E. (2008). Rolling-sampled parameters of ARCH and Levy-stable models. Journal of Applied Economics, 40(23), 3051-3067. Deo, R., Hurvich, C., and Lu, Y. (2006). Forecasting realized volatility using a long-memory stochastic volatility model: estimation, prediction and seasonal adjustment. Journal of Econometrics, 131(1), 29-58. Doornik, J.A. and Ooms, M. (2006). A Package for Estimating, Forecasting and Simulating Arfima Models: Arfima Package 1.04 for Ox. Working Paper, Nuffield College, Oxford. Engle, R.F., Hong, C.H., Kane, A. and Noh, J. (1993). Arbitrage Valuation of Variance Forecasts with Simulated Options, Advances in Futures and Options Research, 6, 393-415. Favero, C. and Aiolfi, M. (2005). Model uncertainty, thick modelling and the predictability of stock returns, Journal of Forecasting, 24, 233-254. Fernandes, M., Medeiros, M. C., and Scharth, M. (2014). Modeling and predicting the CBOE market volatility index. Journal of Banking & Finance, 40, 1-10. Fleming, J. (1998). The quality of market volatility forecast implied by S&P 100 index option prices. Journal of Empirical Finance, 5, 317– 345. Fleming, J., Ostdiek, B. and Whaley, R.E. (1995). Predicting Stock Market Volatility: A New Measure. Journal of Futures Markets, 15, 265-302. Frijns, B., Tallau, C., and Tourani‐Rad, A. (2010). The information content of implied volatility: evidence from Australia. Journal of Futures Markets, 30(2), 134-155. Fuertes, A. M., Izzeldin, M., & Kalotychou, E. (2009). On forecasting daily stock volatility: The role of intraday information and market conditions. International Journal of Forecasting, 25(2), 259-281. Ghodsi, M., Hassani, H., Sanei, S., and Hicks, Y. (2009). The use of noise information for detection of temporomandibular disorder. Biomedical Signal Processing and Control, 4(2), 79-85. Giot, P. (2003). The information content of implied volatility in agricultural commodity markets. Journal of Futures Markets, 23, 441–454. Granger, C.W.J. and Joyeux, R. (1980). An Introduction to Long Memory Time Series Models and Fractional Differencing. Journal of Time Series Analysis, 1, 15-39. Hansen, P.R., 2005. A test for superior predictive ability. Journal of Business and Economic Statistics, 23, 365–380. Hansen, P.R., and Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1,1)? Journal of Applied Econometrics, 20(7), 873-889. Hansen, P.R., Lunde, A. and Nason, J.M. (2011). The model confidence set, Econometrica, 79, 456–497. Hassani, H., Heravi, S., and Zhigljavsky, A. (2009). Forecasting European Industrial Production with Singular Spectrum Analysis. International Journal of Forecasting, 25(1), 103-118. Hassani, H., and Zhigljavsky, A. (2009). Singular spectrum analysis: methodology and application to economics data. Journal of System Science and Complexity, 223, 372–394. Hassani, H., Heravi, S., and Zhigljavsky, A. (2013). Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis. Journal of Forecasting, 32(5), 395-408. Holt, C. C. (2004). Forecasting trends and seasonals by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10. Hyndman, R. J., Athanasopoulos, G., Razbash, S., Schmidt, D., Zhou, Z., Khan, Y., and Bergmeir, C. (2013). Package forecast: Forecasting functions for time series and linear models. Available via: http://cran.r-project.org/web/packages/forecast/forecast.pdf Jung, Y. C. (2015). A portfolio insurance strategy for volatility index (VIX) futures. The Quarterly Review of Economics and Finance, in press. Koopman, S.J., Jungbacker, B., and Hol, E. (2005). Forecasting daily variability of the S&P100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance, 12(3), 445–475. Mincer, J. and Zarnowitz, V. (1969). The Evaluation of Economic Forecasts. In (ed.) Mincer. J., Economic Forecasts and Expectations, National Bureau of Economic Research, , New York, 3-46. Müller, U.A., Dacorogna, M.M., Davé, R.D., Olsen, R.B., Pictet, O.V. and VonWeizsäcker, J.E. (1997). Volatilities of Different Time Resolutions – Analyzing the Dynamics of Market Components. Journal of Empirical Finance, 4, 213-239. Samuels, J.D. and Sekkel, R.M. (2013). Forecasting with Many Models: Model Confidence Sets and Forecast Combination. Working Paper, Bank of Canada. Sanei, S., Ghodsi, M., and Hassani, H. (2011). An Adaptive Singular Spectrum Analysis Approach to Murmur Detection from Heart Sounds. Medical Engineering and Physics, 33(3), 362-367. Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics, 6, 461-464. Sevi, B. (2014). Forecasting the Volatility of Crude Oil Futures using Intraday Data. European Journal of Operational Research, 235, 643-659. Simon, D. P. (2003). The Nasdaq volatility index during and after the bubble. The Journal of Derivatives, 11, 9–24. Thomakos, D. D., Wang, T., and Wille, L. T. (2002). Modeling daily realized futures volatility with singular spectrum analysis. Physica A: Statistical Mechanics and its Applications, 312(3), 505-519. Timmermann, A. (2006). Forecast combinations. Handbook of Economic Forecasting, 1, 135-196. Vautard, R., Yiou, P., and Ghil, M. (1992). Singular-spectrum analysis: A toolkit for short, noisy chaotic signals. Physica D: Nonlinear Phenomena, 58(1), 95-126. Xekalaki, E. and Degiannakis, S. (2010). ARCH models for financial applications. Wiley and Sons, New York. Yu, J. (2012). A semiparametric stochastic volatility model. Journal of Econometrics, 167(2), 473-482. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/72084 |
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