Abstract
Forecasting daily returns volatility is crucial in finance. Traditionally, volatility is modelled using a time-series of lagged information only, an approach which is in essence atheoretical. Although the relationship of market conditions and volatility has been studied for decades, we still lack a clear theoretical framework to allow us to forecast volatility, despite having many plausible explanatory variables. This setting of a data-rich but theory-poor environment suggests a useful role for powerful model induction methodologies such as Genetic Programming. This study forecasts one-day ahead realised volatility (RV) using a GP methodology that incorporates information on market conditions including trading volume, number of transactions, bid-ask spread, average trading duration and implied volatility. The forecasting result from GP is found to be significantly better than that of the benchmark model from the traditional finance literature, the heterogeneous autoregressive model (HAR).
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