The Forecasting Performances of Volatility Models in Emerging Stock Markets: Is a Generalization Really Possible?

In almost all stages of forecasting volatility, certain subjective decisions need to be made. Despite of an enormous literature in the area, these subjectivities are hindrances to reaching an overall conclusion on the performances of the models. In order to find out outperforming model in general no...

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Bibliographic Details
Published in:Journal of applied finance and banking 2013-01, Vol.3 (2), p.49
Main Authors: Iltuzer, Zeynep, Tas, Oktay
Format: Article
Language:English
Subjects:
Emerging markets
Forecasting
Parameter estimation
Securities markets
Standard deviation
Statistics
Stochastic models
Stock exchanges
Studies
Volatility
Online Access:Get full text
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Summary:In almost all stages of forecasting volatility, certain subjective decisions need to be made. Despite of an enormous literature in the area, these subjectivities are hindrances to reaching an overall conclusion on the performances of the models. In order to find out outperforming model in general not just in the contexts of studies, volatility models should be evaluated in many markets with the same methodology consisting both simple and complex models at different forecast horizon. With this motivation, the purpose of the paper is to search for the possibility of the generalization that one of the competing model outperforms no matter what the market is by analyzing 19 emerging stock market volatilities at 8 different forecast horizons with models grouped into three main categories: Simple models (Random Walk, Historical Mean, Moving Average, EWMA), GARCH family models (GARCH, GRJ-GARCH, GARCH, APARCH, NAGARCH, FIGARCH) and Stochastic Volatility model. The evaluation of the forecasts based on the recent developments in statistics, i.e. Reality Check (RC), Superior Predictive Ability (SPA) and Model Confidence Set (MCS), not only the rank of the error statistics. The scope and the methodology of the study enable us to reach a general conclusion on model performances and their over prediction and under prediction tendencies. [PUBLICATION ASBTRACT]
ISSN:1792-6580
1792-6599