A Comparison of Conditional Volatility Estimators for the ISE National 100 Index Returns
AbstractWe compare more than 1000 different volatility models in terms of their fit to the historical ISE-100 Index data and their forecasting performance of the conditional variance in an out-of-sample setting. Exponential GARCH model of Nelson (1991) with “constant mean, t-distribution, one lag moving average term” specification achieves the best overall performance for modeling the ISE-100 return volatility. The t-distribution seems to characterize the distribution of the heavy tailed returns better than the Gaussian distribution or the generalized error distribution. In terms of forecasting performance, the best models are the ones that can accommodate a leverage effect. Results from fitting the selected exponential GARCH model to the historical ISE-100 return data indicates that the return volatility reacts to bad news 24% more than they react to good news as a result of a one standard deviation shock to the returns. As the magnitude of shock increases, the asymmetry becomes larger.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 30510.
Date of creation: 2009
Date of revision:
Publication status: Published in Journal of Economic and Social Research 11.2(2009): pp. 1-29
GARCH; Volatility Models; Istanbul Stock Exchange; ISE-100;
Find related papers by JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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