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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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2083-2567
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An Analysis of the Performance of Genetic Programming for Realised Volatility Forecasting

Zheng Yin
  • School of Business, University College Dublin, Ireland
/ Conall O’Sullivan
  • School of Business, University College Dublin, Ireland
/ Anthony Brabazon
  • School of Business, University College Dublin, Ireland
Published Online: 2016-06-10 | DOI: https://doi.org/10.1515/jaiscr-2016-0012

Abstract

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.

Keywords: Realised Volatility; Genetic Programming; High Frequency Data

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Published Online: 2016-06-10

Published in Print: 2016-07-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research. Volume 6, Issue 3, Pages 155–172, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2016-0012, June 2016

© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

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