Modelling the Relationships between Duration and Volatility in Asset Prices

F. Chan, J. Petchey
{"title":"Modelling the Relationships between Duration and Volatility in Asset Prices","authors":"F. Chan, J. Petchey","doi":"10.2139/ssrn.2559726","DOIUrl":null,"url":null,"abstract":"This paper proposes a new model that captures the interaction between duration and magnitude of changes in asset prices, and thus provides a convenient framework to test statistically the existence of such relationship. The model is flexible and contains various well known models as special cases, including, the Exponential Generalised Autoregressive Heteroskedasticity (EGARCH) model of Nelson (1991) and the Logarithmic Conditional Duration (Log-ACD) model of Bauwens and Giot (2000). Despite having the EGARCH model as a special case, the objective of the model is not trying to model conditional duration and conditional volatility jointly. As shown in Ghysels and Jasiak (1998), modelling conditional duration and volatility jointly is technically challenging. This is due to the fact that volatility is defined over a regular sampling frequency but duration is defined over irregular time intervals. Given GARCH model is not generally closed under temporal aggregation, this creates a challenging modelling problem. The aim of this paper is to avoid this challenge by not modelling the conditional volatility, but instead, model the dynamics in the magnitudes of price change. The paper argues that since volatility is a function of the magnitudes of price change, testing the relationship between duration and the magnitude of price change provides an indirect test on the relationship between duration and volatility. The paper also obtains theoretical results for the Quasi-Maximum Likelihood Estimator (QMLE) for the proposed model. Specifically, sufficient conditions for consistency and asymptotic normality are derived under mild assumptions. Monte Carlo experiments also provide further support of the theoretical results and demonstrate that the QMLE has reasonably good finite sample performance. The paper then applies the model to nine different assets from three different asset classes, namely two exchange rate, two commodities and five stocks. The two currencies are Australia/US and British Pound/US exchange rates; the two commodities are Gold and Silver and the five stocks are BHP, Rio Tinto, CBS, ANZ and Apple. The sample spans from 4 January 2010 to 30 December 2011 with an average of 100,000 observations.","PeriodicalId":422957,"journal":{"name":"2015 Financial Markets & Corporate Governance Conference (Archive)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 Financial Markets & Corporate Governance Conference (Archive)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.2559726","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This paper proposes a new model that captures the interaction between duration and magnitude of changes in asset prices, and thus provides a convenient framework to test statistically the existence of such relationship. The model is flexible and contains various well known models as special cases, including, the Exponential Generalised Autoregressive Heteroskedasticity (EGARCH) model of Nelson (1991) and the Logarithmic Conditional Duration (Log-ACD) model of Bauwens and Giot (2000). Despite having the EGARCH model as a special case, the objective of the model is not trying to model conditional duration and conditional volatility jointly. As shown in Ghysels and Jasiak (1998), modelling conditional duration and volatility jointly is technically challenging. This is due to the fact that volatility is defined over a regular sampling frequency but duration is defined over irregular time intervals. Given GARCH model is not generally closed under temporal aggregation, this creates a challenging modelling problem. The aim of this paper is to avoid this challenge by not modelling the conditional volatility, but instead, model the dynamics in the magnitudes of price change. The paper argues that since volatility is a function of the magnitudes of price change, testing the relationship between duration and the magnitude of price change provides an indirect test on the relationship between duration and volatility. The paper also obtains theoretical results for the Quasi-Maximum Likelihood Estimator (QMLE) for the proposed model. Specifically, sufficient conditions for consistency and asymptotic normality are derived under mild assumptions. Monte Carlo experiments also provide further support of the theoretical results and demonstrate that the QMLE has reasonably good finite sample performance. The paper then applies the model to nine different assets from three different asset classes, namely two exchange rate, two commodities and five stocks. The two currencies are Australia/US and British Pound/US exchange rates; the two commodities are Gold and Silver and the five stocks are BHP, Rio Tinto, CBS, ANZ and Apple. The sample spans from 4 January 2010 to 30 December 2011 with an average of 100,000 observations.
资产价格的持续时间与波动之间的关系建模
本文提出了一个新的模型,该模型捕捉了资产价格变化的持续时间和幅度之间的相互作用,从而提供了一个方便的框架来检验这种关系的存在。该模型是灵活的,包含各种众所周知的模型作为特殊情况,包括Nelson(1991)的指数广义自回归异方差(EGARCH)模型和Bauwens和Giot(2000)的对数条件持续时间(Log-ACD)模型。尽管EGARCH模型是一个特例,但该模型的目标并不是试图将条件持续时间和条件波动率联合建模。正如Ghysels和Jasiak(1998)所示,将条件持续时间和波动性联合建模在技术上具有挑战性。这是由于波动性是在一个规则的采样频率上定义的,而持续时间是在不规则的时间间隔上定义的。鉴于GARCH模型在时间聚合下通常不是封闭的,这就产生了一个具有挑战性的建模问题。本文的目的是通过不模拟条件波动来避免这一挑战,而是模拟价格变化幅度的动态。本文认为,由于波动率是价格变化幅度的函数,检验持续时间与价格变化幅度之间的关系提供了对持续时间与波动率之间关系的间接检验。本文还得到了该模型的拟极大似然估计的理论结果。具体地说,在温和的假设下,得到了一致性和渐近正态性的充分条件。蒙特卡罗实验也为理论结果提供了进一步的支持,并证明了QMLE具有相当好的有限样本性能。然后,本文将该模型应用于来自三种不同资产类别的九种不同资产,即两种汇率,两种商品和五种股票。这两种货币分别是澳元兑美元汇率和英镑兑美元汇率;这两种商品是黄金和白银,这五只股票是必和必拓、力拓、哥伦比亚广播公司、澳新银行和苹果。样本时间跨度为2010年1月4日至2011年12月30日,平均有10万个观测值。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信