Currency Options Volatility Forecasting with Shift-Invariant Wavelet Transform and Neural Networks
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.
KeywordsOption Price Stochastic Volatility Forecast Performance Normalize Mean Square Error Volatility Forecast
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