Forecasting Stock Return Volatility Using the Realized Garch Model and an Artificial Neural Network

Volatility forecasting is required for risk management, asset allocation, option pricing, and financial market trading. It can be done by using various time series forecasting techniques and Artificial Neural Networks (ANN).

Keywords

The current research focuses on the modeling and forecasting of stock market indices using high-frequency data. A recent high-frequency volatility model is called the Realized GARCH (RGARCH) model, where the key feature is an equation that relates the realized measure to the conditional variance of returns. RGARCH then takes into account the asymmetry of effects due to shocks.

References

1. Ait-Sahalia Y., Mancini L. Out of Sample Forecasts of Quadratic Variation. Journal of Econometrics, 2008, vol. 147, no. 1, pp. 17-33. DOI:10.1016/j.jeconom.2008.09.015
2. Arneri'c J., Sestanovi'c T., Teai J. Neural Network Approach in Forecasting Realized Variance Using High-Frequency Data. Business Systems Research Journal, 2018, vol. 9, no. 2, pp. 18-34. DOI:10.2478/bsrj-2018-0016
3. Barndorff-Nielsen O.E., Shephard N. Econometric Analysis of Realized Volatility and Its Use in Estimating Stochastic Volatility Models. Journal of the Royal Statistical Society, 2002, vol. 64, no. 2, pp. 253-280. DOI:10.1111/1467-9868.00336
4. Barndorff-Nielsen O.E., Shephard N. Power and Bipower Variation with Stochastic Volatility and Jumps. Journal of Financial Econometrics, 2004, vol. 2, no. 1, pp. 1-48. DOI:10.1093/jjfinec/nbh001
5. Boudt K., Boudt P., Brian G., Croux C. Estimation and Decomposition of Downside Risk for Portfolios with Non-Normal Returns. Journal of Risk, 2008, vol. 11, no. 2, pp. 79-103. DOI:10.2139/ssrn.1024151
6. Corsi F. A Simple Long Memory Model of Realized Volatility. Journal of Financial Econometrics, 2009, vol. 7, no. 2, pp. 174-196. DOI: 10.1093/jjfinec/nbp001
7. Vortelinos D.I. Forecasting Realized Volatility: HAR Against Principal Components Combining Neural Networks and GARCH. Research in International Business and Finance, 2017, vol. 39, pp. 824-839. DOI:10.1016/j.ribaf.2015.01.004
8. Engle R.F. Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 1982, vol. 50, pp. 987-1007. DOI:10.2307/1912773
9. Eryilmaz F.M. Modelling Stock Market Volatility: The Case of Bist-100. International Journal of Commerce and Finance, 2015, vol. 2, no. 1, pp. 37-53.
10. Fausett L. Fundamentals of Neural Network. Hoboken, Prentice-Hall, 1994.
11. Granger C. Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 1969, vol. 37, no. 3, pp. 424-438. DOI:10.2307/1912791
12. Funahashi K. On the Approximate Realization of Continuous Mappings by Neural Networks. Neural Networks, 1989, vol. 2, no. 3, pp. 183-192. DOI:10.1016/0893-6080(89)90003-8
13. Hornik K., Stinchcombe M., White H. Universal Approximation of an Unknown Mapping and its Derivatives Using Multilayer Feedforward Networks. Neural Networks, 1990, vol. 3, no. 5, pp. 551-560. DOI:10.1016/0893-6080(90)90005-6
14. Hansen P.R., Zhuo Huang, Shek H.H. Realized GARCH: a Joint Model for Returns and Realized Measures of Volatility. Journal of Applied Econometrics, 2012, vol. 27, no. 6, pp. 877-906. DOI:10.1002/jae.1234
15. Huang Shih-Feng, Chiang Hsin-Han, Lin Yu-Jun. A Network Autoregressive Model with GARCH Effects and its Applications. The Public Library of Science One, 2021, vol. 16, no. 7, article ID: e0255422. DOI:10.1371/journal.pone.0255422
16. Lee S.S., Mykland P.A. Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics. Review of Financial Studies, 2008, vol. 21, no. 6, pp. 2535-2563. DOI:10.2139/ssrn.686372
17. Xunfa Lu, Danfeng Que, Guangxi Cao. Volatility Forecast Based on the Hybrid Artificial Neural Network and GARCH-type Models. Procedia Computer Science, 2016, vol. 91, pp. 1044-1049. DOI:10.1016/j.procs.2016.07.145
18. Nelson D.B. Conditional Heteroskedasticity in Asset Returns: a New Approach. Econometrica, 1991, vol. 59, no. 2, pp. 347-370. DOI:10.2307/2938260
19. Bollerslev T. Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 1986, vol. 31, no. 3, pp. 307-327. DOI: 10.1016/0304-4076(86)90063-1
20. Andersen T.G., Bollerslev T., Diebold F.X., Ebens H. The Distribution of Realized Stock Return Volatility. Journal of Financial Economics, 2001, vol. 61, no. 1, pp. 43-76. DOI:10.1016/S0304-405X(01)00055-1
21. Zhang Peter, Patuwo Eddy, Hu Michael. Forecasting With Artificial Neural Networks: The State of the Art. International Journal of Forecasting, 1998, vol. 14, no. 1, pp. 35-62. DOI:10.1016/S0169-2070(97)00044-7