An Analysis of the Performance of Genetic Programming for Realised Volatility Forecasting

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@Article{yin:2016:jaiscr,

• author = "Zheng Yin and Conall O'Sullivan and Anthony Brabazon",
• title = "An Analysis of the Performance of Genetic Programming for Realised Volatility Forecasting",
• journal = "Journal of Artificial Intelligence and Soft Computing Research",
• year = "2016",
• volume = "6",
• number = "3",
• pages = "155--172",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Realised Volatility, High Frequency Data",
• ISSN = "2083-2567",
• URL = "https://www.degruyter.com/view/j/jaiscr.2016.6.issue-3/jaiscr-2016-0012/jaiscr-2016-0012.xml?format=INT",
• DOI = "doi:10.1515/jaiscr-2016-0012",
• abstract = "Traditionally, the volatility of daily returns in financial markets is modelled 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 a theoretical 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.",

}

Genetic Programming entries for Zheng Yin Conall O'Sullivan Anthony Brabazon

Citations