BOUSALAM, Issam and HAMZAOUI, Moustapha and ZOUHAYR, Otman (2016): Forecasting Daily Stock Volatility Using GARCH-CJ Type Models with Continuous and Jump Variation.
Preview |
PDF
MPRA_paper_69636.pdf Download (478kB) | Preview |
Abstract
In this paper we decompose the realized volatility of the GARCH-RV model into continuous sample path variation and discontinuous jump variation to provide a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. By using 5-minute high-frequency data of MASI Index in Morocco for the period (January 15, 2010 - January 29, 2016), we estimate parameters of the constructed GARCH and EGARCH-type models (namely, GARCH, GARCH-RV, GARCH-CJ, EGARCH, EGARCH-RV, and EGARCH-CJ) and evaluate their predictive power to forecast future volatility. The results show that the realized volatility and the continuous sample path variation have certain predictive power for future volatility while the discontinuous jump variation contains relatively less information for forecasting volatility. More interestingly, the findings show that the GARCH-CJ-type models have stronger predictive power for future volatility than the other two types of models. These results have a major contribution in financial practices such as financial derivatives pricing, capital asset pricing, and risk measures.
Item Type: | MPRA Paper |
---|---|
Original Title: | Forecasting Daily Stock Volatility Using GARCH-CJ Type Models with Continuous and Jump Variation |
English Title: | Forecasting Daily Stock Volatility Using GARCH-CJ Type Models with Continuous and Jump Variation |
Language: | English |
Keywords: | GARCH-CJ; Jumps variation; Realized volatility; MASI Index; Morocco. |
Subjects: | C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes F - International Economics > F3 - International Finance > F37 - International Finance Forecasting and Simulation: Models and Applications F - International Economics > F4 - Macroeconomic Aspects of International Trade and Finance > F47 - Forecasting and Simulation: Models and Applications G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation |
Item ID: | 69636 |
Depositing User: | Issam BOUSALAM |
Date Deposited: | 22 Feb 2016 07:22 |
Last Modified: | 27 Sep 2019 08:04 |
References: | Andersen, T. G. and Bollerslev, T. (1998). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International economic review, pages 885–905. 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. The Review of Economics and Statistics, 89(4):701–720. Andersen, T. G., Dobrev, D., and Schaumburg, E. (2012). Jump-robust volatility estimation using nearest neighbor truncation. Journal of Econometrics, 169(1):75–93. Barndorff-Nielsen, O. E. and Shephard, N. (2006). Econometrics of testing for jumps in financial economics using bipower variation. Journal of financial Econometrics, 4(1):1–30. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3):307–327. Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, page nbp001. Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation. Econometrica: Journal of the Econometric Society, pages 987–1007. Frijns, B., Lehnert, T., and Zwinkels, R. C. (2011). Modeling structural changes in the volatility process. Journal of Empirical Finance, 18(3):522–532. Fuertes, A.-M., Izzeldin, M., and Kalotychou, E. (2009). On forecasting daily stock volatility: The role of intraday information and market conditions. International Journal of Forecasting, 25(2):259–281. Huang, C., Gong, X., Chen, X., and Wen, F. (2013). Measuring and forecasting volatility in Chinese stock market using har-cj-m model. In Abstract and Applied Analysis, volume 2013. Hindawi Publishing Corporation. Koopman, S. J., Jungbacker, B., and 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. Martens, M. (2002). Measuring and forecasting s&p 500 index-futures volatility using high-frequency data. Journal of Futures Markets, 22(6):497–518. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the Econometric Society, pages 347–370. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/69636 |