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Forecasting High-Frequency Financial Data Volatility Via Nonparametric Algorithms: Evidence From Taiwan'S Financial Markets

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  • WO-CHIANG LEE

    (Department of Finance and Banking, Aletheia University, 32, Chen Li Street, Tamsui, Taipei County, 251, Taiwan, ROC)

Abstract

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.

Suggested Citation

  • Wo-Chiang Lee, 2006. "Forecasting High-Frequency Financial Data Volatility Via Nonparametric Algorithms: Evidence From Taiwan'S Financial Markets," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 2(03), pages 345-359.
    • Handle: RePEc:wsi:nmncxx:v:02:y:2006:i:03:n:s1793005706000543
    DOI: 10.1142/S1793005706000543
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