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

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.

Realised volatility and industry momentum returns

“…The main justification for the choice of realised volatility is that it outperforms any alternatives. For example, model-dependent methods, such as autoregressive conditional heteroskedasticity and stochastic volatility models, require many parameter assumptions, whereas realised volatility is a model-free method that reduces estimation errors (Yin et al, 2016), save when the number of observations within the estimation period is very small. In our analysis, this is for the one-month estimation period only, as the other estimation periods exceed three months and contain more than 60 observations.…”

Liquidity effects on oil volatility forecasting: From fintech perspective

“…Volatility forecasting by using Artificial Intelligence (AI) and Genetic Programming (GP) can been witnessed in the literatures (see Yin et al 2016; [ 13 ]; Ding et al 2019 [ 14 ]; Weng et al 2021 [ 15 ]; Mademlis and Dritsakis 2021 [ 16 ]). The goal of the paper is to adopt AI technologies to generate the best model which can comprise trading liquidity effect into volatility forecasting, and can be integrated into the existing fintech systems such as high frequency trading platforms and derivative trading platforms in the hedge fund industry.…”