Realised Volatility Forecasting: A Genetic Programming Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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