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Activity Number: 365
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract #319384 View Presentation
Title: ATM: Autoregressive Tail-Index Model for Financial Time Series
Author(s): Zifeng Zhao* and Zhengjun Zhang and Rong Chen
Companies: University of Wisconsin - Madison and University of Wisconsin - Madison and Rutgers University
Keywords: Extreme value theory ; Dynamic modeling ; Financial time series ; Value at risk ; High-frequency data

Classical generalized extreme value (GEV) model has been widely used in financial risk management. In this paper, we extend the static GEV modeling to dynamic modeling by introducing an autoregressive evolution scheme on the three parameters of GEV. Speci cally, an autoregressive tail model (ATM) is proposed to better study tail behavior changes of time series. Probabilistic properties of the model is studied and an ML estimator is proposed for model estimation. Asymptotic properties of MLE is derived and finite sample performance is illustrated by simulations. To demonstrate the superior performance of dynamic GEV modeling, we apply the proposed modeling scheme to cross-sectional maxima of negative daily log-returns for components of S&P100 and to daily maxima of 3-minute negative log-returns for high frequency trading of foreign exchanges (JPY v.s. USD).

Authors who are presenting talks have a * after their name.

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