英文标题：
《A changepoint approach for the identification of financial extreme
regimes》
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作者：
Chiara Lattanzi and Manuele Leonelli
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最新提交年份：
2019
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英文摘要：
Inference over tails is usually performed by fitting an appropriate limiting distribution over observations that exceed a fixed threshold. However, the choice of such threshold is critical and can affect the inferential results. Extreme value mixture models have been defined to estimate the threshold using the full dataset and to give accurate tail estimates. Such models assume that the tail behavior is constant for all observations. However, the extreme behavior of financial returns often changes considerably in time and such changes occur by sudden shocks of the market. Here we extend the extreme value mixture model class to formally take into account distributional extreme changepoints, by allowing for the presence of regime-dependent parameters modelling the tail of the distribution. This extension formally uses the full dataset to both estimate the thresholds and the extreme changepoint locations, giving uncertainty measures for both quantities. Estimation of functions of interest in extreme value analyses is performed via MCMC algorithms. Our approach is evaluated through a series of simulations, applied to real data sets and assessed against competing approaches. Evidence demonstrates that the inclusion of different extreme regimes outperforms both static and dynamic competing approaches in financial applications.
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中文摘要：
对于超过固定阈值的观测值，通常通过拟合适当的极限分布来进行尾部推断。然而，这种阈值的选择是至关重要的，并且会影响推理结果。定义了极值混合模型，以使用完整数据集估计阈值，并给出准确的尾部估计。此类模型假设所有观测值的尾部行为都是恒定的。然而，财务回报的极端行为往往会随着时间发生显著变化，这种变化是由市场的突然冲击引起的。在这里，我们扩展了极值混合模型类，通过允许存在对分布尾部建模的状态相关参数，正式考虑了分布极值变化点。该扩展正式使用完整的数据集来估计阈值和极端变化点位置，为这两个数量提供不确定性度量。通过MCMC算法对极值分析中的相关函数进行估计。我们的方法通过一系列仿真进行评估，应用于真实数据集，并与其他方法进行比较。证据表明，在金融应用中，包含不同极端制度的方法优于静态和动态竞争方法。
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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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