Realised Volatility Forecasting: A Genetic Programming Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{yin:rvfagpa:cec2015,

• author = "Zheng Yin and Anthony Brabazon and Conall O'Sullivan and Michael O'Neill",
• title = "Realised Volatility Forecasting: A Genetic Programming Approach",
• booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)",
• editor = "Yadahiko Murata",
• pages = "3305--3311",
• year = "2015",
• address = "Sendai, Japan",
• month = "25-28 " # may,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CEC.2015.7257303",
• 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 a theoretical. 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).",
• notes = "1030 hrs 15196 CEC2015",

}

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

Citations