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Discrete Dynamics in Nature and Society
Volume 2014, Article ID 964654, 10 pages
http://dx.doi.org/10.1155/2014/964654
Research Article

Forecasting Return Volatility of the CSI 300 Index Using the Stochastic Volatility Model with Continuous Volatility and Jumps

School of Business, Central South University, Changsha, Hunan 410083, China

Received 13 April 2014; Accepted 12 June 2014; Published 9 July 2014

Academic Editor: Chuangxia Huang

Copyright © 2014 Xu Gong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. F. Corsi, “A simple approximate long-memory model of realized volatility,” Journal of Financial Econometrics, vol. 7, no. 2, pp. 174–196, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. F. Wen and X. Yang, “Skewness of return distribution and coefficient of risk premium,” Journal of Systems Science & Complexity, vol. 22, no. 3, pp. 360–371, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. J. Liu, C. Ma, and F. Wen, “An Actuarial approach to option pricing under O-U process and stochastic interest rates,” in Proceedings of the International Joint Conference on Computational Sciences and Optimization (CSO '09), pp. 549–553, IEEE, Sanya, China, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. T. G. Andersen, T. Bollerslev, and X. Huang, “A reduced form framework for modeling volatility of speculative prices based on realized variation measures,” Journal of Econometrics, vol. 160, no. 1, pp. 176–189, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. T. G. Andersen, D. Dobrev, and E. Schaumburg, “Jump-robust volatility estimation using nearest neighbor truncation,” Journal of Econometrics, vol. 169, no. 1, pp. 75–93, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. Z. Dai, D. Li, and F. Wen, “Robust conditional value-at-risk optimization for asymmetrically distributed asset returns,” Pacific Journal of Optimization, vol. 8, no. 3, pp. 429–445, 2012. View at Google Scholar · View at MathSciNet · View at Scopus
  7. F. Wen, Z. Li, C. Xie, and D. Shaw, “Study on the fractal and chaotic features of the Shanghai composite index,” Fractals, vol. 20, no. 2, pp. 133–140, 2012. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  8. T. Bollerslev, D. Osterrieder, N. Sizova, and G. Tauchen, “Risk and return: long-run relations, fractional cointegration, and return predictability,” Journal of Financial Economics, vol. 108, no. 2, pp. 409–424, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Bollerslev, V. Todorov, and S. Z. Li, “Jump tails, extreme dependencies, and the distribution of stock returns,” Journal of Econometrics, vol. 172, no. 2, pp. 307–324, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. J. Liu, L. Yan, and C. Ma, “Pricing options and convertible bonds based on an actuarial approach,” Mathematical Problems in Engineering, vol. 2013, Article ID 676148, 9 pages, 2013. View at Publisher · View at Google Scholar
  11. J. Liu, M. Tao, C. Ma, and F. Wen, “Utility indifference pricing of convertible bonds,” International Journal of Information Technology & Decision Making, vol. 13, no. 2, pp. 429–444, 2014. View at Google Scholar
  12. J. Liu, J. Xiao, L. Yan, and F. Wen, “Valuing catastrophe bonds involving credit risks,” Mathematical Problems in Engineering, vol. 2014, Article ID 563086, 6 pages, 2014. View at Publisher · View at Google Scholar
  13. R. F. Engle, “Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation,” Econometrica, vol. 50, no. 4, pp. 987–1007, 1982. View at Publisher · View at Google Scholar · View at MathSciNet
  14. T. Bollerslev, “Generalized autoregressive conditional heteroskedasticity,” Journal of Econometrics, vol. 31, no. 3, pp. 307–327, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. J. S. Taylor, Modeling Financial Time Series, John Wiley & Sons, Chichester, UK, 1986.
  16. J. Danielsson, “Stochastic volatility in asset prices estimation with simulated maximum likelihood,” Journal of Econometrics, vol. 64, no. 1-2, pp. 375–400, 1994. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  17. F. C. Wang, L. X. Jiang, and G. Li, “Estimating volatility of Chinese stock market by stochastic volatility model,” Journal of Management Sciences in China, vol. 4, pp. 63–72, 2003. View at Google Scholar
  18. S. Kim, N. Shephard, and S. Chib, “Stochastic volatility: likelihood inference and comparison with ARCH models,” Review of Economic Studies, vol. 65, no. 3, pp. 361–393, 1998. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Yu, “Forecasting volatility in the New Zealand stock market,” Applied Financial Economics, vol. 12, no. 3, pp. 193–202, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. P. Sadorsky, “A comparison of some alternative volatility forecasting models for risk management,” in Proceedings of the 2nd IASTED International Conference: Financial Engineering and Applications, 2004.
  21. C. Pederzoli, “Stochastic volatility and GARCH: a comparison based on UK stock data,” The European Journal of Finance, vol. 12, no. 1, pp. 41–59, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Wei, “Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?” Physica A: Statistical Mechanics and its Applications, vol. 391, no. 22, pp. 5546–5556, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. S. J. Koopman, B. Jungbacker, and E. Hol, “Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements,” Journal of Empirical Finance, vol. 12, no. 3, pp. 445–475, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. T. G. Andersen and T. Bollerslev, “Answering the critics: yes, ARCH models do provide good volatility forecasts,” International Economic Review, vol. 4, pp. 885–905, 1998. View at Google Scholar
  25. J. Geweke, G. Koop, and H. Dijk, The Oxford Handbook of Bayesian Econometrics, Oxford University Press, 2011.
  26. E. Jacquier and S. Miller, “The information content of realized volatility,” Working Paper, HEC Montreal, 2013. View at Google Scholar
  27. O. E. Barndorff-Nielsen and N. Shephard, “Power and bipower variation with stochastic volatility and jumps,” Journal of Financial Econometrics, vol. 2, pp. 1–37, 2004. View at Google Scholar
  28. O. E. Barndorff-Nielsen and N. Shephard, “Econometrics of testing for jumps in financial economics using bipower variation,” Journal of Financial Econometrics, vol. 4, pp. 1–30, 2006. View at Google Scholar
  29. P. R. Hansen, “A test for superior predictive ability,” Journal of Business & Economic Statistics, vol. 23, no. 4, pp. 365–380, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  30. M. Martens, “Measuring and forecasting S&P 500 index-futures volatility using high-frequency data,” Journal of Futures Markets, vol. 22, no. 6, pp. 497–518, 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. T. G. Andersen, T. Bollerslev, and F. X. Diebold, “Roughing it up: including jump components in the measurement, modeling, and forecasting of return volatility,” The Review of Economics and Statistics, vol. 89, no. 4, pp. 701–720, 2007. View at Publisher · View at Google Scholar · View at Scopus
  32. X. Huang and G. Tauchen, “The relative contribution of jumps to total price variance,” Journal of Financial Econometrics, vol. 3, no. 4, pp. 456–499, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. C. Huang, X. Gong, X. Chen, and F. Wen, “Measuring and forecasting volatility in Chinese stock market using HAR-CJ-M model,” Abstract and Applied Analysis, vol. 2013, Article ID 143194, 13 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. E. Ruiz, “Quasi-maximum likelihood estimation of stochastic volatility models,” Journal of Econometrics, vol. 63, no. 1, pp. 289–306, 1994. View at Publisher · View at Google Scholar · View at Scopus
  35. E. Jacquier, N. G. Polson, and P. E. Rossi, “Bayesian analysis of stochastic volatility models,” Journal of Business and Economic Statistics, vol. 12, no. 4, pp. 371–417, 1994. View at Publisher · View at Google Scholar · View at Scopus
  36. T. G. Andersen and B. E. Sørensen, “GMM estimation of a stochastic volatility model: a Monte Carlo study,” Journal of Business and Economic Statistics, vol. 14, no. 3, pp. 328–352, 1996. View at Google Scholar · View at Scopus
  37. T. Watanabe, “A non-linear filtering approach to stochastic volatility models with an application to daily stock returns,” Journal of Applied Econometrics, vol. 14, no. 2, pp. 101–121, 1999. View at Publisher · View at Google Scholar · View at Scopus
  38. J. Durbin and S. J. Koopman, Time Series Analysis by State Space Methods, vol. 38, Oxford University Press, Oxford, UK, 2nd edition, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  39. L. Bauwens and M. Lubrano, “Bayesian inference on GARCH models using Gibbs sampler,” Econometrics Journal, vol. 1, pp. 23–46, 1998. View at Google Scholar
  40. D. J. Spiegelhalter, N. G. Best, B. P. Carlin, and A. van der Linde, “Bayesian measures of model complexity and fit,” Journal of the Royal Statistical Society B, vol. 64, no. 4, pp. 583–639, 2002. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  41. A. P. Dempster, “The direct use of likelihood for significance testing,” in Proceedings of Conference on Foundational Questions in Statistical Inference, pp. 335–352, Department of Theoretical Statistics, University of Aarhus, 1974. View at MathSciNet
  42. T. Bollerslev, R. F. Engle, and D. Nelson, “ARCH models,” in Handbook of Econometrics, D. L. McFadden, Ed., vol. 4, pp. 2961–3038, Elsevier Science B.V., Amsterdam, The Netherlands, 1994. View at Google Scholar
  43. P. R. Hansen and A. Lunde, “A forecast comparison of volatility models: does anything beat a GARCH (1, 1)?” Journal of Applied Econometrics, vol. 20, no. 7, pp. 873–889, 2005. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  44. G. M. Martin, A. Reidy, and J. Wright, “Does the option market produce superior forecasts of noise-corrected volatility measures?” Journal of Applied Econometrics, vol. 24, no. 1, pp. 77–104, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  45. Y. Wang and C. Wu, “Forecasting energy market volatility using GARCH models: can multivariate models beat univariate models?” Energy Economics, vol. 34, no. 6, pp. 2167–2181, 2012. View at Publisher · View at Google Scholar · View at Scopus
  46. J. C. Hung, T. W. Lou, Y. H. Wang, and J. Lee, “Evaluating and improving GARCH-based volatility forecasts with range-based estimators,” Applied Economics, vol. 45, no. 28, pp. 4041–4049, 2013. View at Publisher · View at Google Scholar · View at Scopus
  47. H. White, “A reality check for data snooping,” Econometrica, vol. 68, no. 5, pp. 1097–1126, 2000. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus