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Improving the Forecasting Power of Volatility Models

  • Ahmed Bensaida

    ()

    (University of Monastir Sidi Massaoud)

Volatility models have been extensively used in risk modeling especially GARCH models under the normal distribution. Although they generate highly significant coefficient estimates, these models are known to have poor forecasting power. It is therefore interesting to develop a different approach of risk modeling to improve forecasting results. By using the generalized t-distribution in modeling the changes in the distribution of stock index returns, the results show a significant improvement in the forecasting power. Moreover, Monte Carlo simulations have confirmed that the index returns are better explained by ARCH-type models.

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Article provided by Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences in its journal International Journal of Academic Research in Accounting, Finance and Management Sciences.

Volume (Year): 2 (2012)
Issue (Month): 3 (July)
Pages: 51-64

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Handle: RePEc:hur:ijaraf:v:2:y:2012:i:3:p:51-64
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  1. George Marsaglia & Wai Wan Tsang, . "The Ziggurat Method for Generating Random Variables," Journal of Statistical Software, American Statistical Association, vol. 5(i08).
  2. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-47, August.
  3. Adrian R. Pagan & G. William Schwert, 1989. "Alternative Models For Conditional Stock Volatility," NBER Working Papers 2955, National Bureau of Economic Research, Inc.
  4. Venus Khim-Sen Liew & Terence Tai-leung Chong, 2005. "Autoregressive Lag Length Selection Criteria in the Presence of ARCH Errors," Economics Bulletin, AccessEcon, vol. 3(19), pages 1-5.
  5. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
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