Traditionally, the volatility of daily returns in financial markets is modeled autoregressively
using a time-series of lagged information. These autoregressive models exploit
stylised empirical properties of volatility such as strong persistence, mean reversion and
asymmetric dependence on lagged returns. While these methods can produce good forecasts,
the approach is in essence atheoretical as it provides no insight into the nature of
the causal factors and how they affect volatility. Many plausible explanatory variables relating
market conditions and volatility have been identified in various studies but despite
the volume of research, we lack a clear theoretical framework that links these factors together.
This setting of a theory-weak environment suggests a useful role for powerful
model induction methodologies such as Genetic Programming (GP). 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 (waiting time between trades) and implied volatility. The
forecasting performance from the evolved GP models is found to be significantly better
than those numbers of benchmark forecasting models drawn from the finance literature,
namely, the heterogeneous autoregressive (HAR) model, the generalized autoregressive
conditional heteroscedasticity (GARCH) model, and a stepwise linear regression model
(SR). Given the practical importance of improved forecasting performance for realised
volatility this result is of significance for practitioners in financial markets.
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