- 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",
- isbn13 = "978-1-4673-1802-0",
- type = "Conference or Workshop Item; PeerReviewed",
- bibsource = "OAI-PMH server at eprints.pascal-network.org",
- oai = "oai:eprints.pascal-network.org:8630",
- broken = "http://eprints.pascal-network.org/archive/00008630/",
-
DOI = "
doi:10.1109/CIFEr.2012.6327813",
- size = "8 pages",
- 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.",
-
notes = "broken 2022
http://www.ieee-cifer.org/program.html
Also known as \cite{6327813}",
