Statistical Analysis of Financial Volatility by Wavelet Shrinkage

Enrico Capobianco ()
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Enrico Capobianco: Technical University of Denmark

Methodology and Computing in Applied Probability, 1999, vol. 1, issue 4, 423-443

Abstract: Abstract We analyze the Nikkei daily stock index and verify how wavelets can help in identifying, estimating and predicting its volatility features. While we study the conditional mean and variance dynamics, by utilizing statistical parametric inference techniques, we also decompose the observed signal with a data de-noising procedure. We thus investigate how wavelets discriminate among information at different resolution levels and we attempt to understand whether the de-noised data lead to a better identification of the underlying volatility process. We find that the wavelet data pre-processing strategy, by reducing the measurement error of the observed data, is useful for improving the volatility prediction power.

Keywords: wavelet transforms; data de-noising; GARCH estimation; volatility prediction (search for similar items in EconPapers)
Date: 1999
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DOI: 10.1023/A:1010010825105

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