Currency Options Volatility Forecasting with Shift-Invariant Wavelet Transform and Neural Networks

  • Fan-Yong Liu
  • Fan-Xin Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


This paper describes four currency options volatility forecasting models. These models are based on shift-invariant wavelet transform and neural networks techniques. The à trous algorithm is used to realize the shift-invariant wavelet transform. Wavelets provide a decomposition of the volatility in a nonlinear feature space. Neural networks are used to infer future volatility from the feature space. The individual wavelet domain forecasts are recombined by different techniques to form the accurate overall forecast. The proposed models have been tested with the USD/Yen options volatility market data. Experimental results show that wavelet prediction scheme has the best forecasting performance on testing dataset among four models, with regards to the least error values. Therefore, wavelet prediction scheme outperforms the other three models and avoids effectively over-fitting problems.


Option Price Stochastic Volatility Forecast Performance Normalize Mean Square Error Volatility Forecast 
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  1. 1.
    Zapart, C.: Stochastic Volatility Options Pricing with Wavelets and Artificial Neural Networks. Quantitative Finance 2(6), 487–495 (2002)MathSciNetGoogle Scholar
  2. 2.
    Bai-Ling, Z., Zhao-Yang, D.: An Adaptive Neural-Wavelet Model for Short Term Load Forecasting. Electric Power Systems Research 59, 121–129 (2001)CrossRefGoogle Scholar
  3. 3.
    Beylkin, G., Satio, N.: Wavelets, their Autocorrelation Functions and Multiresolution Representation of Signals. IEEE Trans. Signal Processing 7, 147–164 (1997)Google Scholar
  4. 4.
    Satio, N., Beylkin, G.: Multiresolution Representations Using the Auto-correlation Functions of Compactly Supported Wavelets. IEEE Trans. Signal Processing 41(12), 3584–3590 (1993)CrossRefGoogle Scholar
  5. 5.
    Coifman, R.R., Donoho, D.L.: Translation-Invariant De-noising. In: Antoniades, A., Oppenheim, G. (eds.) Wavelets and Statistics, pp. 125–150. Springer, Heidelberg (1995)Google Scholar
  6. 6.
    Sari-Sarraf, H., Brzakovic, D.: A shift-invariant discrete wavelet transform. IEEE Trans. Signal Processing 45(10), 2621–2630 (1997)CrossRefGoogle Scholar
  7. 7.
    Aussem, A., Campbell, J., Murtagh, F.: Wavelet-based Feature Extraction and Decomposition Strategies for Financial Forecasting. Journal of Computational Intelligence in Finance 6, 5–12 (1998)Google Scholar
  8. 8.
    MacKay, D.J.C.: Bayesian Non-linear Modelling for the Energy Prediction Competition. ASHRAE Transcations 4, 448–472 (1994)Google Scholar
  9. 9.
    MacKay, D.J.C.: Bayesian Non-linear Modelling for the Prediction Competition. ASHRAE Transcations 100, 1053–1062 (1994)Google Scholar
  10. 10.
    Makridakis, S., Wheelwright, S.C., Hyndman, R.J.: Forecasting, Methods and Applications, 3rd edn. Wiley, New York (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fan-Yong Liu
    • 1
  • Fan-Xin Liu
    • 2
  1. 1.Financial Engineering Research CenterSouth China University of TechnologyWushanP.R. China
  2. 2.Department of PhysicsHuazhong University of Science and TechnologyWuhanP.R. China

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