Modeling Nonstationary and Leptokurtic Financial Time Series

Ying Chen, Vladimir Spokoiny

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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 parameters are aggregated in a locally adaptive way. The procedure is fully automatic and the parameters 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.

Original languageEnglish
Pages (from-to)703-728
Number of pages26
JournalEconometric Theory
Volume31
Issue number4
DOIs
Publication statusPublished - 30 Sep 2014
Externally publishedYes

Fingerprint

Dive into the research topics of 'Modeling Nonstationary and Leptokurtic Financial Time Series'. Together they form a unique fingerprint.

Cite this