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Volatility forecasting of financial time series using wavelet based exponential generalized autoregressive conditional heteroscedasticity model

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  • Siti Aisyah Mohammed
  • Mohd Aftar Abu Bakar
  • Noratiqah Mohd Ariff

Abstract

An improved forecasting model by merging two different computational models in predicting future volatility was proposed. The model integrates wavelet and EGARCH model where the pre-processing activity based on wavelet transform is performed with de-noising technique to eliminate noise in observed signal. The denoised signal is then feed into EGARCH model to forecast the volatility. The predictive capability of the proposed model is compared with the existing EGARCH model. The results show that the hybrid model has increased the accuracy of forecasting future volatility.

Suggested Citation

  • Siti Aisyah Mohammed & Mohd Aftar Abu Bakar & Noratiqah Mohd Ariff, 2020. "Volatility forecasting of financial time series using wavelet based exponential generalized autoregressive conditional heteroscedasticity model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(1), pages 178-188, January.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:1:p:178-188
    DOI: 10.1080/03610926.2018.1535073
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