Volatility Forecast in FX Markets using Evolutionary Computing and Heuristic Techniques
A financial asset’s volatility exhibits key character- istics, such as mean-reversion and high autocorrelation , . Empirical evidence suggests that this volatility autocorrelation exponentially decays (or exhibits long-range memory) . We employ Genetic Programming (GP) for volatility forecasting because of its ability to detect patterns such as the conditional mean and conditional variance of a time-series. Genetic Pro- gramming is typically applied to optimisation, searching, and machine learning applications like classification, prediction etc. From our experiments, we see that Genetic Programming is a good competitor to the standard forecasting techniques like GARCH(1,1), Moving Average (MA), Exponentially Weighted Moving Average (EWMA). However it is not a silver bullet: we observe that different forecasting methods would perform better in different market conditions. In addition to Genetic Programming, we consider a heuristic technique that employs a series of standard forecasting methods and dynamically opts for the most appropriate technique at a given time. Using a heuristic technique, we try to identify the best forecasting method that would perform better than the rest of the methods in the near out-of-sample horizon. Our work introduces a pre- liminary framework for forecasting 5-day annualised volatility in GBP/USD, USD/JPY, and EUR/USD.