Realised Volatility Forecasting: A Genetic Programming Approach
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{yin:rvfagpa:cec2015,
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author = "Zheng Yin and Anthony Brabazon and
Conall O'Sullivan and Michael O'Neill",
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title = "Realised Volatility Forecasting: A Genetic Programming
Approach",
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booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary
Computation (CEC 2015)",
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editor = "Yadahiko Murata",
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pages = "3305--3311",
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year = "2015",
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address = "Sendai, Japan",
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month = "25-28 " # may,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC.2015.7257303",
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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).",
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notes = "1030 hrs 15196 CEC2015",
- }
Genetic Programming entries for
Zheng Yin
Anthony Brabazon
Conall O'Sullivan
Michael O'Neill
Citations