Forecasting accuracy of stochastic volatility, GARCH and EWMA models under different volatility scenarios
ABSTRACT The forecasting of the volatility of asset returns is a prerequisite for many risk management tasks in finance. The objective here is to identify the volatility scenarios that favour either Generalized Autoregressive Conditional Heteroscedasticity (GARCH) or Stochastic Volatility (SV) models. Scenarios are defined by the persistence of volatility (its robustness to shocks) and the volatility of volatility. A simulation experiment generates return series using both volatility models for a range of volatility scenarios representative of that observed in real assets. Forecasts are generated from SV, GARCH and Exponentially Weighted Moving Average (EWMA) volatility models. SV model forecasts are only noticeably more accurate than GARCH in scenarios with very high volatility of volatility and a stochastic volatility generating process. For scenarios with medium volatility of volatility, there is little penalty for using EWMA regardless of the volatility generating process. A set of return time series selected from FX rates, equity indices, equities and commodities is used to validate the simulation-based results. Broadly speaking, the real series come from the medium volatility of volatility scenarios where EWMA forecasts are reliably accurate. The robust structure of EWMA appears to contribute to its greater forecasting accuracy than more flexible GARCH model.
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ABSTRACT: This paper provides clear-cut evidence that the out-of-sample VaR (value-at-risk) forecasting performance of alternative parametric volatility models, like EGARCH (exponential general autoregressive conditional heteroskedasticity) or GARCH, and Markov regime-switching models, can be considerably improved if they are combined with skewed distributions of asset return innovations. The performance of these models is found to be similar to that of the EVT (extreme value theory) approach. The performance of the latter approach can also be improved if asset return innovations are assumed to be skewed distributed. The performance of the Markov regime-switching model is considerably improved if this model allows for EGARCH effects, for all different volatility regimes considered. Copyright © 2014 John Wiley & Sons, Ltd.Journal of Forecasting 11/2014; 33(7). DOI:10.1002/for.2303 · 0.93 Impact Factor
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ABSTRACT: Financial market volatility is an important input for investment, option pricing, and financial market regulation. The emphasis of this review article is on forecasting instead of modelling; it compares the volatility forecasting findings in 93 papers published and written in the last two decades. Provided in this paper as well are volatility definitions, insights into problematic issues of forecast evaluation, data frequency, extreme values and the measurement of "actual" volatility. We compare volatility forecasting performance of two main approaches; historical volatility models and volatility implied from options. Forecasting results are compared across different asset classes and geographical regions.Journal of Economic Literature 06/2003; 41(2):478-539. DOI:10.1257/002205103765762743 · 9.24 Impact Factor