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Improving the accuracy: volatility modeling and forecasting using high-frequency data and the variational component

Journal Name:

  • Journal of Industrial Engineering and Management

Publication Year:

  • 2010
DOI: 
doi:10.3926/jiem.2010.v3n1.p199-220

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Abstract: In this study, we predict the daily volatility of the S&P CNX NIFTY market index of India using the basic ‘heterogeneous autoregressive’ (HAR) and its variant. In doing so, we estimated several HAR and Log form of HAR models using different regressor. The different regressors were obtained by extracting the jump and continuous component and the threshold jump and continuous component from the realized volatility. We also tried to investigate whether dividing volatility into simple and threshold jumps and continuous variation yields a substantial improvement in volatility forecasting or not. The results provide the evidence that inclusion of realized bipower variance in the HAR models helps in predicting future volatility.
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  • 1
199-220
  • English

REFERENCES

References: 

Andrew, W. L., & MacKinlay, A. C. (1988). Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test. Review of Financial Studies, 1(1), 41-66. doi:10.1093/rfs/1.1.41
Andersen, T. G., Bollerslev, T., & Meddahi, N. (2005) Correcting the errors: volatility forecast evaluation using high-frequency data and realized volatilities. Econometrica, 73(1), 279-296. doi:10.1111/j.1468-0262.2005.00572.x
Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica, 71(2), 579-625. doi:10.1111/1468-0262.00418
Andersen, T. G., Bollerslev, T., & Diebold, F. X. (2007a). Roughing it up: Including jump components in the measurement, modeling and forecasting of return volatility. Review of Economics and Statistics, 89, 701–720. doi:10.1162/rest.89.4.701
Andersen, T. G., Bollerslev, T., & Dobrev, D. (2007b). No-arbitrage semi-martingale restrictions for continuous time volatility models subject to leverage effects, jumps and iid noise: Theory and testable distributional implications. Journal of Econometrics, 138 (1), 125–180. doi:10.1016/j.jeconom.2006.05.018
Andersen, T. G., Bollerslev, T., Diebold, F. X., & Ebens, H. (2001a). The Distribution of Realized Stock Return Volatility. Journal of Financial Economics, 61, 43-76. doi:10.1016/S0304-405X(01)00055-1
Andersen, T. G., Bollerslev, T., Diebold, F. X., & Labys, P. (2001b). The Distribution of Realized Exchange Rate Volatility. Journal of the American Statistical Association, 96, 42-55. doi:10.1198/016214501750332965
Andersen, T. G., & Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review, 39, 885-905. doi:10.2307/2527343
Andreou, E. & Ghysels, E. (2002). Rolling-sample volatility estimators: some new theoretical, simulation, and empirical results. Journal of Business and Economic Statistics, 20(3), 363-76. doi:10.1198/073500102288618504
Atchison, M. D., Butler, K. C., & Simonds, R. R. (1987). Nonsynchronous Security Trading and Market Index Autocorrelation. Journal of Finance, 42(1), 111-18. doi:10.2307/2328422
Barndorff-Nielsen, O.E., & Shephard, N. (2004). Power and Bi-power Variation with Stochastic Volatility and Jumps. Journal of Financial Econometrics, 2, 1-37. doi:10.1093/jjfinec/nbh001
Barndorff-Nielsen, O.E., & Shephard, N. (2002a). Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models. Journal of the Royal Statistical Society, 64: 253-80.
Barndorff-Nielsen, O.E., & Shephard, N. (2002b). Estimating Quadratic Variation Using Realized Variance. Journal of Applied Econometrics, 17, 457-78. doi:10.1002/jae.691
Barndorff-Nielsen, O.E., & Shephard, N. (2006). Econometrics of Testing for Jumps in Financial Economics Using Bi-power Variation. Journal of Financial Econometrics, 4, 1-30. doi:10.1093/jjfinec/nbi022
Bates, J. M., & Granger, C. W. J. (1969). The Combination of Forecasts. Operation Research Quarterly, 20, 451–468. doi:10.1057/jors.1969.103
Bollerslev, T. & Zhou. H. (2002). Estimating stochastic volatility diffusion using conditional moments of integrated volatility. Journal of Econometrics, 109(1), 33-65. doi:10.1016/S0304-4076(01)00141-5
Bollerslev, T. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 31, 307–327. doi:10.1016/0304-4076(86)90063-1
Bollerslev, T., Kretschmer, U., Pigorsch, C., & Tauchen, G. (2005). A discrete-time model for daily S&P 500 returns and realized variations: Jumps and leverage effects. Journal of Econometrics.
Brooks, C. (1998). Predicting stock index volatility: can market volume help? Journal of Forecasting, 17, 59–80. doi:10.1002/(SICI)1099-131X(199801)17:1<59::AID-FOR676>3.0.CO;2-H
Corsi, F. (2004). A Simple Long Memory Model of Realized Volatility. Manuscript.
Corsi, F., Pirinos, D. & Ren, R. (2009). Threshold Bipower Variation and the Impact of Jumps on Volatility Forecasting. Working paper at Dipartimento di Economia Politica, Università di Siena.
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimator of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1008. doi:10.2307/1912773
Fleming, J., Kirby, C., & Ostdiek, B. (2003). The economic value of volatility timing using‘realized volatility. Journal of Financial Economics, 67, 473-509. doi:10.1016/S0304-405X(02)00259-3
Forsberg, L., & Ghysels, E. (2007). Why Do Absolute Returns Predict Volatility So Well? Journal of Financial Econometrics, 5, 31-67. doi:10.1093/jjfinec/nbl010
Garman, M. B. & Klass, M. J. (1980). On the estimation of Security Price Volatilities from Historical data. Journal of Business, 53, 67-78. doi:10.1086/296072
Ghysels, E., Sinko, A., & Valkanov, R. (2007). MIDAS Regressions: Further Results and New Directions. Econometric Reviews, 26, 53-90. doi:10.1080/07474930600972467
Ghysels, E., Harvey, A., & Renault, E. (1996). “Stochastic Volatility” in Handbook of Statistics: Statistical Methods in Finance, 14, G.S. Maddala and C.R. Rao, eds. Amsterdam: Elsevier Science, 119–91.
Hamilton, J. D., & Lin, G. (1996). Stock Market Volatility and the Business Cycle. Journal of Applied Econometrics, 11(5), 573–93. doi:10.1002/(SICI)1099-1255(199609)11:5<573::AID-JAE413>3.0.CO;2-T
Hamilton, J. D., & Susmel, R. (1994). Autoregressive Conditional Heteroskedasticity and Changes in Regime. Journal of Econometrics, 64(1–2), 307–33. doi:10.1016/0304-4076(94)90067-1
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57, 357–84. doi:10.2307/1912559
Harvey, C. R. (1995). The Risk Exposure of Emerging Equity Markets. World Bank Economic Review, 9, 19–50. doi:10.1093/wber/9.1.19
Huimin, C., Chin-Sheng, H., & Tseng-Chan, T. (2008). Modeling and Forecasting of Realized Volatility Based on High- Frequency Data: Evidence from Taiwan, International Research Journal of Finance and Economics.
Koopman, S. J., Jungbacker, B., & Hol. E. (2005). Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements. Journal of Empirical Finance, 12(3), 445-475. doi:10.1016/j.jempfin.2004.04.009
Lamoureux, C., &Lastrapes, W. (1993). Forecasting Stock-Return Variance: toward an Understanding of Stochastic Implied Volatilities. Revue of Financial Studies, 6(2), 293–326. doi:10.1093/rfs/6.2.293
Maheu, J. M. & McCurdy, T. H. (2002). Nonlinear Features of Realized FX Volatility. The Review of Economics and Statistics, 84(4), 668-681. doi:10.1162/003465302760556486
Mancini, C. (2009). Non-parametric threshold estimation for models with stochastic diffusion coefficient and jumps. Scandinavian Journal of Statistics, 36(2), 270–296. doi:10.1111/j.1467-9469.2008.00622.x
Martens, M., van Dijk, D. J. C., & de Pooter, M. (2004). Modeling and forecasting S&P 500 volatility: Long memory, structural breaks and nonlinearity. Discussion Paper, Tinbergen Institute Pandey, A. (2003). Modeling and Forecasting Volatility in Indian Capital Markets, No 2003-08-03, IIMA Working Papers from Indian Institute of Management Ahmedabad, Research and Publication Department.
Parkinson, M. (1980). The extreme value method for estimating the variance of the rate of return. Journal of Business, 53, 61-65. doi:10.1086/296071
Patton, A. (2006). Volatility Forecast Comparison Using Imperfect Volatility Proxies. Manuscript, London School of Economics
Poon, S., & Granger, C. W. R. (2003). Forecasting Volatility in Financial Markets: A Review. Journal of Economic Literature, 66, 478-539. doi:10.1257/002205103765762743
Reid, D.J. (1968). Combining Three Estimates ff Gross Domestic Product. Economica, 35, 431–444. doi:10.2307/2552350
Tae Hyup, R. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications, 33 (4), 916-922. doi:10.1016/j.eswa.2006.08.001
Vasilellis, G. A. & Meade, N. (1996). Forecasting Volatility for Portfolio Selection. Journal of Business and Financial Accounting, 23(1), 125–143. doi:10.1111/j.1468-5957.1996.tb00407.x

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