Stochastic Volatility Models for Asset Returns with Leverage, Skewness and Heavy-Tails via Scale Mixture

IF 0.9 Q3 BUSINESS, FINANCE Quarterly Journal of Finance Pub Date : 2014-11-21 DOI:10.1142/S2010139214500116
Jing-Zhi Huang, Li Xu
{"title":"Stochastic Volatility Models for Asset Returns with Leverage, Skewness and Heavy-Tails via Scale Mixture","authors":"Jing-Zhi Huang, Li Xu","doi":"10.1142/S2010139214500116","DOIUrl":null,"url":null,"abstract":"We propose and estimate a new class of equity return models that incorporate scale mixtures of the skew-normal distribution for the error distribution into the standard stochastic volatility framework. The main advantage of our models is that they can simultaneously accommodate the skewness, heavy-tailedness, and leverage effect of equity index returns observed in the data. The proposed models are flexible and parsimonious, and include many asymmetrically heavy-tailed error distributions — such as skew-t and skew-slash distributions — as special cases. We estimate a variety of specifications of our models using the Bayesian Markov Chain Monte Carlo method, with data on daily returns of the S&P 500 index over 1987–2009. We find that the proposed models outperform existing ones of index returns.","PeriodicalId":45339,"journal":{"name":"Quarterly Journal of Finance","volume":"23 1","pages":"1450011"},"PeriodicalIF":0.9000,"publicationDate":"2014-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Quarterly Journal of Finance","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1142/S2010139214500116","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 2

Abstract

We propose and estimate a new class of equity return models that incorporate scale mixtures of the skew-normal distribution for the error distribution into the standard stochastic volatility framework. The main advantage of our models is that they can simultaneously accommodate the skewness, heavy-tailedness, and leverage effect of equity index returns observed in the data. The proposed models are flexible and parsimonious, and include many asymmetrically heavy-tailed error distributions — such as skew-t and skew-slash distributions — as special cases. We estimate a variety of specifications of our models using the Bayesian Markov Chain Monte Carlo method, with data on daily returns of the S&P 500 index over 1987–2009. We find that the proposed models outperform existing ones of index returns.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于规模混合的杠杆、偏态和重尾资产收益随机波动模型
我们提出并估计了一类新的股票收益模型,该模型将误差分布的偏态-正态分布的规模混合纳入标准随机波动率框架。我们的模型的主要优点是,它们可以同时适应数据中观察到的股票指数回报的偏度、重尾性和杠杆效应。所提出的模型是灵活和简洁的,并包括许多非对称的重尾误差分布-如斜t和斜斜线分布-作为特殊情况。我们使用贝叶斯马尔可夫链蒙特卡罗方法估计模型的各种规格,并使用1987-2009年标准普尔500指数的日收益数据。我们发现所提出的模型优于现有的指数回报模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Quarterly Journal of Finance
Quarterly Journal of Finance BUSINESS, FINANCE-
CiteScore
1.10
自引率
0.00%
发文量
0
期刊介绍: The Quarterly Journal of Finance publishes high-quality papers in all areas of finance, including corporate finance, asset pricing, financial econometrics, international finance, macro-finance, behavioral finance, banking and financial intermediation, capital markets, risk management and insurance, derivatives, quantitative finance, corporate governance and compensation, investments and entrepreneurial finance.
期刊最新文献
Trust and Lending: An Experimental Study Non-Cognitive Skills at the Time of COVID-19: An Experiment with Professional Traders and Students Managing Climate Change Risks: Sea-Level Rise and Mergers and Acquisitions The Impact of Role Models on Women’s Self-Selection into Competitive Environments Futures Replication and the Law of One Futures Price
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1