Improving the Forecasting Power of Volatility Models
AbstractVolatility 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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Bibliographic InfoArticle 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)
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Generalized t; GARCH; forecast; index return;
Find related papers by JEL classification:
- G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C16 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Econometric and Statistical Methods; Specific Distributions
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
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