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Volatility Forecast in FX Markets using Evolutionary Computing and Heuristic Techniques

Volatility Forecast in FX Markets using Evolutionary Computing and Heuristic Techniques

Chinthalapati, V L Raju (2012) Volatility Forecast in FX Markets using Evolutionary Computing and Heuristic Techniques. In: Computational Intelligence for Financial Engineering & Economics (CIFEr), 2012 IEEE Conference. IEEE. ISBN 9781467318020 (doi:10.1109/CIFEr.2012.6327813)

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Abstract

Afinancialasset’svolatilityexhibitskeycharacteristics, such as mean-reversion and high autocorrelation [1], [2]. Empirical evidence suggests that this volatility autocorrelation exponentially decays (or exhibits long-range memory) [3]. We employ Genetic Programming (GP) for volatility forecasting because of its ability to detect patterns such as the conditional mean and conditional variance of a time-series. Genetic Programming is typically applied to optimisation, searching, and machine learning applications like classification, prediction etc. From our experiments, we see that Genetic Programming is a good competitor to the standard forecasting techniques like GARCH(1,1), Moving Average (MA), Exponentially Weighted Moving Average (EWMA). However it is not a silver bullet: we observe that different forecasting methods would perform better in different market conditions. In addition to Genetic Programming, we consider a heuristic technique that employs a series of standard forecasting methods and dynamically opts for the most appropriate technique at a given time. Using a heuristic technique, we try to identify the best forecasting method that would perform better than the rest of the methods in the near out-of-sample horizon. Our work introduces a preliminary framework for forecasting 5-day annualised volatility in GBP/USD, USD/JPY, and EUR/USD.

Item Type: Conference Proceedings
Title of Proceedings: Computational Intelligence for Financial Engineering & Economics (CIFEr), 2012 IEEE Conference
Additional Information: 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 29-30 March 2012, New York, NY US
Uncontrolled Keywords: Genetic Programming, FX markets and Volatility forecast
Faculty / Department / Research Group: Faculty of Business > Department of Accounting & Finance
Last Modified: 07 Aug 2018 10:19
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/13356

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