English, Article edition: FORECASTING HIGH-FREQUENCY FINANCIAL DATA VOLATILITY VIA NONPARAMETRIC ALGORITHMS: EVIDENCE FROM TAIWAN'S FINANCIAL MARKETS WO-CHIANG LEE

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Bookmark: http://trove.nla.gov.au/version/133077
Physical Description
  • article
Language
  • English

Edition details

Title
  • FORECASTING HIGH-FREQUENCY FINANCIAL DATA VOLATILITY VIA NONPARAMETRIC ALGORITHMS: EVIDENCE FROM TAIWAN'S FINANCIAL MARKETS
Author
  • WO-CHIANG LEE
Physical Description
  • article
Notes
  • This paper uses two computational intelligence algorithms, namely, artificial neural networks (ANN) and genetic programming (GP), for forecasting the volatility of high-frequency TAIEX financial data with four different horizons and compares the out-sample forecasting performance with the GARCH(1,1), EGRACH(1,1) and GJR-GARCH(1,1) models. Based on intraday integrated volatility, the mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Theil's U and the VaR backtest are used as performance indexes. Our empirical results reveal that the GP and ANN perform reasonably well in forecasting out-sample volatility compared to other parametric volatility forecasting models for most of the performance indexes. Our results also suggest that nonparametric computational intelligence algorithms are powerful for modeling the volatility of high-frequency intraday financial data.
  • Integrated volatility, genetic programming, artificial neural networks
  • RePEc:wsi:nmncxx:v:02:y:2006:i:03:p:345-359
Language
  • English
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