Volatility Forecast in FX Markets using Evolutionary Computing and Heuristic Technique

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@InProceedings{Chinthalapati:2012:CIFEr,
  author =       "V. L. Raju Chinthalapati",
  title =        "Volatility Forecast in FX Markets using Evolutionary
                 Computing and Heuristic Technique",
  booktitle =    "IEEE Computational Intelligence for Financial
                 Engineering and Economic (CIFEr 2012)",
  year =         "2012",
  editor =       "Robert Golan",
  address =      "New York, USA",
  month =        "29-30 " # mar,
  organisation = "IEEE Computational Intelligence Society",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming,
                 autoregressive processes, economic forecasting, foreign
                 exchange trading, learning (artificial intelligence),
                 time series, 5-day annualised volatility forecasting,
                 EUR-USD, EWMA, FX markets, GARCH(1,1), GBP-USD, GP,
                 USD-JPY, evolutionary computing, exponentially weighted
                 moving average, financial asset volatility, heuristic
                 techniques, machine learning applications,
                 mean-reversion, optimisation, time-series, volatility
                 autocorrelation, volatility forecast, Biological system
                 modelling, Correlation, Forecasting, Sociology,
                 Standards",
  URL =          "http://eprints.pascal-network.org/archive/00008630/",
  DOI =          "doi:10.1109/CIFEr.2012.6327813",
  size =         "8 page",
  abstract =     "A financial asset's volatility exhibits key
                 characteristics, 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.",
  type =         "Conference or Workshop Item; PeerReviewed",
  bibsource =    "OAI-PMH server at eprints.pascal-network.org",
  oai =          "oai:eprints.pascal-network.org:8630",
  notes =        "http://www.ieee-cifer.org/program.html Also known as
                 \cite{6327813}",
}

Genetic Programming entries for V L Raju Chinthalapati

Citations