英文标题：
《Quantile-Frequency Analysis and Spectral Divergence Metrics for
  Diagnostic Checks of Time Series With Nonlinear Dynamics》
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作者：
Ta-Hsin Li
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最新提交年份：
2019
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英文摘要：
  Nonlinear dynamic volatility has been observed in many financial time series. The recently proposed quantile periodogram offers an alternative way to examine this phenomena in the frequency domain. The quantile periodogram is constructed from trigonometric quantile regression of time series data at different frequencies and quantile levels. It is a useful tool for quantile-frequency analysis (QFA) of nonlinear serial dependence. This paper introduces a number of spectral divergence metrics based on the quantile periodogram for diagnostic checks of financial time series models and model-based discriminant analysis. The parametric bootstrapping technique is employed to compute the $p$-values of the metrics. The usefulness of the proposed method is demonstrated empirically by a case study using the daily log returns of the S\\&P 500 index over three periods of time together with their GARCH-type models. The results show that the QFA method is able to provide additional insights into the goodness of fit of these financial time series models that may have been missed by conventional tests. The results also show that the QFA method offers a more informative way of discriminant analysis for detecting regime changes in time series.
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中文摘要：
许多金融时间序列都存在非线性动态波动。最近提出的分位数周期图提供了另一种在频域检查这种现象的方法。分位数周期图由时间序列数据在不同频率和分位数水平上的三角分位数回归构造而成。它是非线性序列相关分位数频率分析（QFA）的一个有用工具。本文介绍了一些基于分位数周期图的谱散度度量，用于金融时间序列模型的诊断检查和基于模型的判别分析。参数自举技术用于计算度量的$p$-值。通过使用标准普尔500指数在三个时期内的日对数收益率及其GARCH型模型进行案例研究，实证证明了该方法的有效性。结果表明，QFA方法能够为这些金融时间序列模型的拟合优度提供额外的见解，而传统测试可能会遗漏这些模型。结果还表明，QFA方法为检测时间序列中的状态变化提供了一种信息更丰富的判别分析方法。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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