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This paper compares two types of volatility models for returns, ARCH-type and stochastic volatility (SV) models, both from a theoretical and an empirical point of view. In particular a GARCH(1,1) model, an EGARCH(1,1) model and a log-normal AR(1) stochastic volatility model are considered. The three models are estimated on UK stock data: a series of the British equity index FTSE100 is used to estimate the relevant parameters. Diagnostic tests are implemented to evaluate how well the models fit the data. The models are used to obtain daily volatility forecasts and these volatilities are used to estimate the “VaR” on a simple one-unit position on FTSE100. The VaR accuracy is tested by means of a backtest. While the results do not lead to a straightforward preference between GARCH(1,1) and SV, the EGARCH shows the best performance. [ABSTRACT FROM AUTHOR]

The paper examines the medium-term forecasting ability of several alternative models of currency volatility. The data period covers more than eight years of daily observations, January 1991 to March 1999, for the spot exchange rate, 1- and 3-month volatility of the DEM/JPY, GBP/DEM, GBP/USD, USD/CHF, USD/DEM and USD/JPY. Comparing with the results of 'pure' time series models, the reported work investigates whether market implied volatility data can add value in terms of medium-term forecasting accuracy. This is done using data directly available from the marketplace in order to avoid the potential biases arising from 'backing out' volatility from a specific option pricing model. On the basis of the over 34 000 out-of-sample forecasts produced, evidence tends to indicate that, although no single volatility model emerges as an overall winner in terms of forecasting accuracy, the 'mixed' models incorporating market data for currency volatility perform best most of the time. [ABSTRACT FROM AUTHOR]