Temperature for Asymmetric GARCH Models with Seasonal, Mean Reverting, and Seasonal Volatility Risk

被引：0

作者：

Chuang, Ming-Che ^{[1
]}

Lin, Shih-Kuei ^{[1
]}

Tsai, Chi-Liang ^{[2
]}

机构：

[1] Natl Chengchi Univ, Dept Money & Banking, Taipei 11623, Taiwan

[2] Simon Fraser Univ, Dept Stat & Actuarial Sci, Burnaby, BC V5A 1S6, Canada

来源：

2016 PPH INTERNATIONAL CONFERENCE ON SOCIAL SCIENCE AND ENVIRONMENT (PPH-SSE 2016), VOL 1 | 2016年 / 6卷

关键词：

Daily Average Temperature Index; GARCH; EM algorithm;

D O I：

10.5729/apeoh.vol6.15

中图分类号：

C [社会科学总论];

学科分类号：

03 ; 0303 ;

摘要：

This study uses the daily average temperature index (DAT) for New York city. By GARCH and mean reverting frameworks, we address the behaver of the temperature. Then, the model parameters are estimated by the MLE and the EM algorithm (Expectation-Maximization) and we find that the GARCH model with seasonal risk, mean reverting, and seasonal volatility risks have the better goodness-of-fit for the path of the temperature index.

引用

页码：15 / 20

页数：6

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