Laboratory of Structural Methods of Data Analysis in Predictive
Modeling Moscow Institute of Physics and Technology
ENG
Логин:
Пароль:
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

Google scholar:

Направления исследований

Structural adaptive inference

Statistical methods

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