题　目：Modeling Nonstationary and Leptokurtic Financial Time Series
报告人：Prof. Chen Ying （新加坡国立大学）
时　间：9月26日（周三），上午10:30
地　点：物理馆512会议室

报告摘要：

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.

个人简介：
1998，B.Sc. in Economics，Renmin University of China
2002，M.A. in Economics and management science，Humboldt university in Berlin
2005，M.Sc. in Statistics，Humboldt university in Berlin and Freie university in Berlin
2007，Ph.D. in Statistics by Prof. Dr. Wolfgang Härdle (Humboldt university in Berlin) and Prof. Dr. Vladimir Spokoiny (Weierstrass Institute for Applied Analysis and Stochastics)
since June 2005, Research assistant in Weierstrass Institute for Applied Analysis and Stochastics Germany
since October 2002, Faculty staff (Wissenschaftliche Mitarbeiterin) at Humboldt university in Berlin
since July 2007, Assistant professor in Department of Statistics and Applied Probability, National University of Singapore