市场（非）有效性、波动率聚集与非线性依赖的稳健推断新方法
New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence
AI总结：
本文针对金融收益的非线性依赖、异质性和重尾性问题，提出基于绝对收益小次幂的度量及分组t统计量检验的新方法，实现对市场（非）有效性与波动率聚集的稳健推断。
摘要：
许多金融和经济变量（包括金融收益）表现出非线性依赖、异质性和重尾性。这些性质可能使得使用依赖于收益及其平方的样本自相关函数渐近正态性的传统方法，来分析经济和金融市场中的（非）有效性与波动率聚集时出现问题。本文提出了处理上述问题的新方法。我们给出的结果支持使用基于绝对收益（及其带符号版本）的（小）次幂来度量市场（非）有效性和波动率聚集。我们进一步提出了在一般时间序列（包括GARCH型过程）情形下对这些度量进行稳健推断的新方法。这些方法基于稳健的t统计量检验，并给出了其适用性的新结果。在这些方法中，参数估计（例如非线性依赖度量的估计）是对数据分组计算的，推断则基于所得分组估计的t统计量。这在实际金融市场所满足的异质性和依赖假设下产生了有效的稳健推断。数值结果和实证应用证实了所提方法的优势和广泛适用性。
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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