Modelling and forecasting volatility of East Asian Newly Industrialized Countries and Japan stock markets with non-linear models
AbstractThis paper explores the forecasting performances of several non-linear models, namely GARCH, EGARCH, APARCH used with three distributions, namely the Gaussian normal, the Student-t and Generalized Error Distribution (GED). In order to evaluate the performance of the competing models we used the standard loss functions that is the Root Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error and the Theil Inequality Coefficient. Our result show that the asymmetric GARCH family models are generally the best for forecasting NICs indices. We also find that both Root Mean Squared Error and Mean Absolute Error forecast statistic measures tend to choose models that were estimated assuming the normal distribution, while the other two remaining forecast measures privilege models with t-student and GED distribution.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 19851.
Date of creation: Jan 2010
Date of revision:
GARCH; Volatility forecasting; forecast evaluation.;
Find related papers by JEL classification:
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2010-01-16 (All new papers)
- NEP-FMK-2010-01-16 (Financial Markets)
- NEP-FOR-2010-01-16 (Forecasting)
- NEP-SEA-2010-01-16 (South East Asia)
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