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