A Haar-Fisz technique for locally stationary volatility estimation

• 成果类型: Article
• 署名作者: Fryzlewicz, Piotr; Sapatinas, Theofanis; Rao, Suhasini Subba
• 署名单位: University of Bristol; University of Cyprus
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/93.3.687
• 发表日期: 2006
• 页码: 687704
• 关键词: financial time-series; nonstationarities; spectrum; models

摘要

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.