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关于德国Ostap Okhrin博士的讲座通知

2016-06-19

各位老师、同学:

大家好!6月19日(周六)上午8:30---11:00,德国洪堡大学最年轻教授,数学奇才Ostap Okhrin 博士及其夫人Iryna Okhrin 博士来我院作报告,地点:明商 0105 。欢迎师生踊跃参加。

报告人

Prof. Ostap Okhrin; Dr. Iryna Okhrin

工作单位

Ladislaus von Bortkiewicz Chair of Statistics

C.A.S.E. Centre for Applied Statistics and Economics

School of Economics and Business Administration

Humboldt-Universität zu Berlin

报告题目

Prof. Ostap Okhrin: On the structure and estimation of hierarchical Archimedean copulas

Dr. Iryna Okhrin: Online Surveillance of Volatility Forecasting Models

时间

6月19日(周六)8:30---11:00,两个报告分别为8:30-9:30 和 10:00-11:00,中间提问交流和休息30分钟。

地点

明商 0105

内容摘要

报告一:On the structure and estimation of hierarchical Archimedean copulas

Abstract: In this paper we provide a methodology for estimating multivariate distributions defined through hierarchical Archimedean copulas. In general the true structure of the hierarchy is unknown. We develop a computationally efficient technique for grouping the data. For this purpose we introduce a hierarchical estimation procedure of the parameters and provide their asymptotic analysis. We consider both parametric and nonparametric estimation of the marginal distributions. The simulation study shows the effectiveness of the grouping procedure in the sense of structure selection. The methodology turns out to be very useful in order to model the distribution of asset returns in practical applications.

报告二:Online Surveillance of Volatility Forecasting Models

Abstract: A valid statistical model is crucial for proper volatility forecasting. This paper suggests a sequential procedure for monitoring validity of the actual volatility model. A simple state space representation serves for reflecting dynamics of the actual volatility, whereas realized volatility or bipower variation measures refer to the observation equation. Monitoring the stochastic behavior of the resulting forecasting errors allows to decide whether the chosen representation remains correctly specified. A signal indicates that the assumed volatility model may be no more valid. Our approach is illustrated by an empirical study based on selected U.S. market stocks.