摘要翻译：
包括财务收益在内的许多金融和经济变量表现出非线性依赖性、异质性和强尾性。这些性质使得用传统的方法来分析经济和金融市场中的（非）效率和波动性聚类问题，这些方法依赖于样本的渐近正态性、收益的自相关函数及其平方。本文提出了解决上述问题的新途径。我们提供了激励使用基于绝对收益（小）幂的市场（非）效率和波动性聚类测度及其签名版本的结果。在一般时间序列（包括GARCH型过程）的情况下，我们进一步提供了对测度的鲁棒推理的新方法。这些方法是基于稳健的$T-$统计检验的，并给出了它们适用性的新结果。在这些方法中，为数据组计算参数估计（例如，非线性相关性度量的估计），并且推断是基于得到的组估计中的$T-$统计量。这导致了在现实世界金融市场中满足的异质性和依赖性假设下的有效鲁棒推理。数值结果和经验应用证实了所提方法的优越性和广泛的适用性。
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英文标题：
《New Approaches to Robust Inference on Market (Non-)Efficiency,
  Volatility Clustering and Nonlinear Dependence》
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作者：
Rustam Ibragimov and Rasmus Pedersen and Anton Skrobotov
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  Many financial and economic variables, including financial returns, exhibit nonlinear dependence, heterogeneity and heavy-tailedness. These properties may make problematic the analysis of (non-)efficiency and volatility clustering in economic and financial markets using traditional approaches that appeal to asymptotic normality of sample autocorrelation functions of returns and their squares.   This paper presents new approaches to deal with the above problems. We provide the results that motivate the use of measures of market (non-)efficiency and volatility clustering based on (small) powers of absolute returns and their signed versions.   We further provide new approaches to robust inference on the measures in the case of general time series, including GARCH-type processes. The approaches are based on robust $t-$statistics tests and new results on their applicability are presented. In the approaches, parameter estimates (e.g., estimates of measures of nonlinear dependence) are computed for groups of data, and the inference is based on $t-$statistics in the resulting group estimates. This results in valid robust inference under heterogeneity and dependence assumptions satisfied in real-world financial markets. Numerical results and empirical applications confirm the advantages and wide applicability of the proposed approaches.
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PDF链接：
https://arxiv.org/pdf/2006.01212