FORECASTING VOLATILITY FOR PORTFOLIO SELECTION
比较了时间序列模型（如GARCH）和期权隐含波动率在三个月投资期内的预测表现，发现隐含波动率预测更准，但两者结合的预测效果显著优于单一方法。
The volatility of an asset is a primary input to the portfolio selection problem. Information about volatility is available from two sources, namely the share market and the option market. This paper examines the forecasting performance, over a three month investment horizon, of time series forecasts (from the share market) and option based implied volatilities. Three time series models, including GARCH, are used and twenty four implied volatility estimation models are employed. Using a data set of twelve UK companies, it is demonstrated that implied volatilities produce better individual forecasts than time series. However, more remarkably, forecasts combining implied volatilies and time series estimates significantly outperform both component forecasts.