A Haar-Fisz technique for locally stationary volatility estimation

PZ Fryzlewicz, T Sapatinas, ST Subba Rao

Research output: Contribution to journalArticle (Academic Journal)peer-review

35 Citations (Scopus)


We consider a locally stationary model for financial log-returns whereby the returns are independent and the volatility is a piecewise-constant function with jumps of an unknown number and locations, defined on a compact interval to enable a meaningful estimation theory. We demonstrate that the model explains well the common characteristics of log-returns. We propose a new wavelet thresholding algorithm for volatility estimation in this model, in which Haar wavelets are combined with the variance-stabilising Fisz transform. The resulting volatility estimator is mean-square consistent with a near-parametric rate, does not require any pre-estimates, is rapidly computable and is easily implemented. We also discuss important variations on the choice of estimation parameters. We show that our approach both gives a very good fit to selected currency exchange datasets, and achieves accurate long- and short-term volatility forecasts in comparison to the GARCH(1, 1) and moving window techniques.
Translated title of the contributionA Haar-Fisz technique for locally stationary volatility estimation
Original languageEnglish
Pages (from-to)687 - 704
Number of pages18
Volume93 (3)
Publication statusPublished - Sep 2006

Bibliographical note

Publisher: Oxford University Press


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