.png){kind=icon}
返回
Modeling maxima with autoregressive conditional Frechet model
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2018.07.004.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper introduces a novel dynamic generalized extreme value (GEV) framework for modeling the time-varying behavior of maxima in financial time series. Specifically, an autoregressive conditional Frechet (AcF) model is proposed in which the maxima are modeled by a Frechet distribution with time-varying scale parameter (volatility) and shape parameter (tail index) conditioned on past information. The AcF provides a direct and accurate modeling of the time-varying behavior of maxima and furthermore offers a new angle to study the tail risk dynamics in financial markets. Probabilistic properties of AcF are studied, and a maximum likelihood estimator is used for model estimation, with its statistical properties investigated. Simulations show the flexibility of AcF and confirm the reliability of its estimators. Two real data examples on cross-sectional stock returns and high-frequency foreign exchange returns are used to demonstrate the AcF modeling approach, where significant improvement over the static GEV has been observed for market tail risk monitoring and conditional VaR estimation. Empirical result of AcF is consistent with the findings of the dynamic peak-over-threshold (POT) literature that the tail index of financial markets varies through time. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Extreme value theory
Dynamic modeling
Financial risk management
Value at risk
High-frequency data
Generalized extreme value distribution
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
5.3K
被引数:
3.0W
机构
引用论文
.png){kind=icon}
分享收藏Langmuir-Blodgett films as single-layer analogs of known organic/inorganic solid-state materials
PREAI
分享收藏
查看更多内容