Laboratory of Structural Methods of Data Analysis in Predictive
Modeling Moscow Institute of Physics and Technology
Modeling Nonstationary and Leptokurtic Financial Time Series
Financial time series is often assumed to be stationary and has a normal distribution in the literature. Both assumptions are however unrealistic. This paper proposes a new methodology with a focus on volatility estimation that is able to account for nonstationarity and heavy tails simultaneously. In particular, a local exponential smoothing (LES) approach is developed, in which weak estimates with different memory parameter are aggregated in a locally adaptive way. The procedure is fully automatic, the parameter are tuned by a new propagation approach. The extensive and practically oriented numerical results confirm the desired properties of the constructed estimate: it performs stable in a nearly time homogeneous situation and is sensitive to structural shifts. Our main theoretical ``oracle'' result claims that the aggregated estimate performs as good as the best estimate in the considered family. The results are stated under realistic and unrestrictive assumptions on the model.

Авторы: Spokoiny Vladimir , Chen, Y

Дата: 16 ноября 2014

Статус: опубликована

Журнал: Econometric Theory

Год: 2013

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Направления исследований

Structural adaptive inference

Statistical methods

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