Junshu Jiang, Jordan Richards, Raphaël Huser, David Bolin
{"title":"The Efficient Tail Hypothesis: An Extreme Value Perspective on Market Efficiency","authors":"Junshu Jiang, Jordan Richards, Raphaël Huser, David Bolin","doi":"arxiv-2408.06661","DOIUrl":null,"url":null,"abstract":"In econometrics, the Efficient Market Hypothesis posits that asset prices\nreflect all available information in the market. Several empirical\ninvestigations show that market efficiency drops when it undergoes extreme\nevents. Many models for multivariate extremes focus on positive dependence,\nmaking them unsuitable for studying extremal dependence in financial markets\nwhere data often exhibit both positive and negative extremal dependence. To\nthis end, we construct regular variation models on the entirety of\n$\\mathbb{R}^d$ and develop a bivariate measure for asymmetry in the strength of\nextremal dependence between adjacent orthants. Our directional tail dependence\n(DTD) measure allows us to define the Efficient Tail Hypothesis (ETH) -- an\nanalogue of the Efficient Market Hypothesis -- for the extremal behaviour of\nthe market. Asymptotic results for estimators of DTD are described, and we\ndiscuss testing of the ETH via permutation-based methods and present novel\ntools for visualization. Empirical study of China's futures market leads to a\nrejection of the ETH and we identify potential profitable investment\nopportunities. To promote the research of microstructure in China's derivatives\nmarket, we open-source our high-frequency data, which are being collected\ncontinuously from multiple derivative exchanges.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"45 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.06661","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
In econometrics, the Efficient Market Hypothesis posits that asset prices
reflect all available information in the market. Several empirical
investigations show that market efficiency drops when it undergoes extreme
events. Many models for multivariate extremes focus on positive dependence,
making them unsuitable for studying extremal dependence in financial markets
where data often exhibit both positive and negative extremal dependence. To
this end, we construct regular variation models on the entirety of
$\mathbb{R}^d$ and develop a bivariate measure for asymmetry in the strength of
extremal dependence between adjacent orthants. Our directional tail dependence
(DTD) measure allows us to define the Efficient Tail Hypothesis (ETH) -- an
analogue of the Efficient Market Hypothesis -- for the extremal behaviour of
the market. Asymptotic results for estimators of DTD are described, and we
discuss testing of the ETH via permutation-based methods and present novel
tools for visualization. Empirical study of China's futures market leads to a
rejection of the ETH and we identify potential profitable investment
opportunities. To promote the research of microstructure in China's derivatives
market, we open-source our high-frequency data, which are being collected
continuously from multiple derivative exchanges.
在计量经济学中,有效市场假说(Efficient Market Hypothesis)认为资产价格反映了市场上所有可用的信息。一些实证研究表明,当市场出现极端事件时,市场效率就会下降。许多多元极端模型都侧重于正向依赖性,因此不适合研究金融市场的极端依赖性,因为金融市场的数据往往同时表现出正负极端依赖性。为此,我们构建了整个$\mathbb{R}^d$的正则变异模型,并开发了一种双变量度量相邻正则之间极端依赖强度的不对称性。我们的定向尾部依赖性(DTD)度量使我们能够为市场的极端行为定义有效尾部假说(ETH)--有效市场假说的类似物。我们描述了 DTD 估计数的渐近结果,并讨论了通过基于排列组合的方法对 ETH 进行检验的问题,还介绍了新的可视化工具。通过对中国期货市场的实证研究,我们得出了 ETH 的投射结果,并发现了潜在的有利可图的投资机会。为了促进中国衍生品市场微观结构的研究,我们开源了从多个衍生品交易所持续收集的高频数据。