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Volatility in the stock market: ANN versus parametric models

  • Rita Laura D’EcclesiaEmail author
  • Daniele ClementiEmail author
S.I.: Recent Developments in Financial Modeling and Risk Management
  • 22 Downloads

Abstract

Forecasting and adequately measuring equity returns volatility is crucial for portfolio selection and trading strategies. Implied volatility is often considered to be informationally superior to the realized volatility. When available, implied volatility is largely used by practitioners and investors to forecast future volatility. To this extent we want to identify the best approach to track equity returns implied volatility using parametric and ANN approaches. Using daily equity prices and stock market indices traded on major international Exchanges we estimate time varying volatility using the E-GARCH approach, the Heston model and a novel ANN framework to replicate the corresponding implied volatility. Overall the ANN approach results the most accurate to track the equity returns implied volatility.

Keywords

Conditional volatility GARCH models Heston model ANN Implied volatility 

JEL

G14 C22 

Notes

References

  1. Alexander, C. (2009). Practical financial econometrics. Hoboken: Wiley.Google Scholar
  2. Alexander, C., & Lazar, E. (2006). Normal mixture GARCH (1,1): Applications to exchange rate modelling. Journal of Applied Econometrics,21(3), 307–336.Google Scholar
  3. Andersen, L. B. G. (2008). Simple and efficient simulation of the Heston stochastic volatility model. Journal of Computational Finance, 11(3), 1–42.Google Scholar
  4. Bai, J., & Perron, P. (1998). Estimating and testing linear models with multiple structural changes. Econometrica,66(1), 47–78.Google Scholar
  5. Bhattacharya, A. K. (1987). Option expirations and treasury bond futures prices. The Journal of Future Markets,7(1), 49–64.Google Scholar
  6. Black F. (1976) Studies of stock market volatility changes. In Proceedings of the American Statistical Association, business and economic statistics section (pp. 177–181).Google Scholar
  7. Blair, B., Poon, S. H., & Taylor S. J. (2000). Forecasting S&P 100 volatility: The incremental information content of implied volatilities and high frequency index returns. Lancaster University Management School, Accounting and Finance Working Paper No. 99/014.Google Scholar
  8. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics,31(3), 307–327.Google Scholar
  9. Bollerslev, T. (1987). A conditionally Heteroskedastic time series model for speculative prices and rate of returns. The Review of Economics and Statistics,69(3), 542–547.Google Scholar
  10. Canina, L., & Figlewski, S. (1993). The informational content of implied volatility. Review of Financial Studies,6, 659–681.Google Scholar
  11. Capelle-Blancard, G., & Chaudhury, M. (2003). Do market and contract designs matter? Evidence from the CAC 40 index options market. McGill Finance Research Centre Working Paper & SSRN.Google Scholar
  12. Chaudhuri, T. D., & Ghosh, I. (2015). Forecasting volatility in Indian stock market using artificial neural network with multiple inputs and outputs. International Journal of Computer Applications,120(8), 7–15.Google Scholar
  13. Chernov, M. (2007). On the role of risk premia in volatility forecasting. Journal of Business & Economic Statistics,25(4), 411–426.Google Scholar
  14. Christensen, B. J., Hansen, C. S., & Prabhala, N. R. (2001) The telescoping overlap problem in options data. Working paper, University of Aarhus and University of Maryland, AFA 2002 Atlanta Meetings.Google Scholar
  15. Christensen, B. J., & Prabhala, N. R. (1998). The relation between implied and realized volatility. Journal of Financial Economics,50(2), 125–150.Google Scholar
  16. Christie, A. A. (1982). The stochastic behavior of common stock variances: Value, leverage and interest rate effects. Journal of Financial Economics,10(4), 407–432.Google Scholar
  17. Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics,7(2), 174–196.Google Scholar
  18. Day, T. E., & Lewis, C. M. (1992). Stock market volatility and the information content of stock index options. Journal of Econometrics,52(1–2), 267–287.Google Scholar
  19. Donaldson, R. G., & Kamstra, M. (1997). An artificial neural network-GARCH model for international stock return volatility. Journal of Empirical Finance,4(1), 17–46.Google Scholar
  20. Dumas, B., Fleming, J., & Whaley, R. E. (1998). Implied volatility functions: Empirical tests. The Journal of Finance,53(6), 2059–2106.Google Scholar
  21. Easley, D., O’Hara, M., & Srinivas, P. S. (1998). Option volume and stock prices: Evidence on where informed traders trade. The Journal of Finance,53(2), 431–465.Google Scholar
  22. Ederington, L. H., & Guan, W. (2002). Measuring implied volatility: Is an average better? Which average? The Journal of Futures Markets,22(9), 811–837.Google Scholar
  23. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation. Econometrica,50(4), 987–1007.Google Scholar
  24. Engle, R. F., Ghysels, E., & Sohn, B. (2013). Stock market volatility and macroeconomic fundamentals. Review of Economics and Statistics,95(3), 776–797.Google Scholar
  25. Foresee, D. F., & Hagan, M. T. (1997) Gauss–Newton approximation to Bayesian learning. In Proceedings of the international joint conference on neural networks.Google Scholar
  26. Goyal, A., & Saretto, A. (2009). Cross-section of option returns and volatility. Journal of Financial Economics,94(2), 310–326.Google Scholar
  27. Heston, S. L. (1993). Closed-form solution for options with stochastic volatility with applications to bond and currency options. The Review of Financial Studies,6(2), 327–343.Google Scholar
  28. Hsu, D. A. (1984). The behavior of stock returns: Is it stationary of evolutionary? The Journal of Financial and Quantitative Analysis,19(1), 11–28.Google Scholar
  29. Jorion, P. (1995). Predicting volatility in the foreign exchange market. The Journal of Finance,50(2), 507–528.Google Scholar
  30. Kaminska, I., & Roberts-Sklar, M. (2018). Volatility in equity markets and monetary policy rate uncertainty. Journal of Empirical Finance,45, 68–83.Google Scholar
  31. Lamoureux, C. G., & Lastrapes, W. D. (1993). Forecasting stock-return variance: Toward an understanding of stochastic implied volatilities. The Review of Financial Studies,6(2), 293–326.Google Scholar
  32. Manaster, S., & Rendleman, R. J. (1982). Option prices as predictors of equilibrium stock prices. The Journal of Finance,37(4), 1043–1057.Google Scholar
  33. Miranda, F., & Burgess, N. (1997). Modelling market volatilities: The neural network perspective. The European Journal of Finance,3(2), 137–157.Google Scholar
  34. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica,59(2), 347–370.Google Scholar
  35. Pong, S., Shackleton, M. B., Taylor, S. J., & Xu, X. (2004). Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models. Journal of Banking & Finance,28(10), 2541–2563.Google Scholar
  36. Poteshman, A. M. (2001). Underreaction, overreaction, and increasing misreaction to information in the options market. The Journal of Finance,56(3), 851–876.Google Scholar
  37. Stărică, C., & Granger, C. (2005). Nonstationarities in stock returns. Review of Economics and Statistics,87(3), 503–522.Google Scholar
  38. Stein, J. C. (1989). Efficient capital markets, inefficient firms: A model of myopic corporate behavior. The Quarterly Journal of Economics,104(4), 655–669.Google Scholar
  39. Szakmary, A., Ors, E., Kyoung, J. K., & Davidson, W. N. (2003). The predictive power of implied volatility: Evidence from 35 futures markets. Journal of Banking & Finance,27(11), 2151–2175.Google Scholar
  40. Vejendla, A., & Enke, D. (2013). Evaluation of GARCH, RNN and FNN models for forecasting volatility in the financial markets. The IUP Journal of Financial Risk Management,10(1), 41–49.Google Scholar
  41. White, H. (1988). Economic prediction using neural networks: The case of IBM daily stock returns. ‎Oakland: Department of Economics, University of California.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Sapienza University of RomeRomeItaly

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