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系统性与共同风险驱动的边际期望副作用AI 解读

System-wide stress events (SWSE) can occur in social, economic, financial, environmental, medical, and many other systems. An SWSE can cause severe damage to some or all members in the system. Understanding and measuring the marginal expected side-effect (MES) for each member caused by an SWSE can lead to better risk management and decision-making. This paper defines an SWSE as a scenario in which either a representative systematic index or the index of the worst-performing…

金融时间序列截面最大值的自回归条件 GB2 动态建模AI 解读

This article introduces the “autoregressive conditional generalized beta distribution of the second kind” (AcGB2) to model the dynamic cross-sectional maxima of multivariate financial times series. The temporal dependence of the resulting univariate maxima time series is characterized by the parameter dynamics of the standard GB2 distribution, which offer versatility in approximating various distributions, including heavy-tailed distributions, through parameter adjustments.…

采用极端阈值惩罚的双向截断线性回归AI 解读

This article introduces a new type of linear regression model with regularization. Each predictor is conditionally truncated through the presence of unknown thresholds. The new model, called the two-way truncated linear regression model (TWT-LR), is not only viewed as a nonlinear generalization of a linear model but is also a much more flexible model with greatly enhanced interpretability and applicability. The TWT-LR model performs classifications through thresholds similar…

以 copula 连接单变量 D-vine 的多元时间序列建模AI 解读

This article proposes a novel multivariate time series model named copula-linked univariate D-vines (CuDvine), which enables the simultaneous copula-based modeling of both temporal and cross-sectional dependence for multivariate time series. To construct CuDvine, we first build a semiparametric univariate D-vine time series model (uDvine) based on a D-vine. The uDvine generalizes the existing first-order copula-based Markov chain models to Markov chains of an…

带正则化的最大线性回归模型AI 解读

Motivated by the newly developed max-linear competing copula factor models and max-stable nonlinear time series models, we propose a new class of max-linear regression models to take advantages of easy interpretable features embedded in linear regression models. It can be seen that linear relation is a special case of max-linear relation. We develop an EM algorithm based maximum likelihood estimation procedure. The consistency and asymptotics of the estimators for parameters…

离散结果 Copula 回归模型的非参数估计AI 解读

Multivariate discrete outcomes are common in a wide range of areas including insurance, finance, and biology. When the interplay between outcomes is significant, quantifying dependencies among interrelated variables is of great importance. Due to their ability to accommodate dependence flexibly, copulas are being applied increasingly. Yet, the application of copulas on discrete data is still in its infancy; one of the biggest barriers is the nonuniqueness of copulas, calling…

用自回归条件Fréchet模型刻画极大值AI 解读

This paper introduces a novel dynamic generalized extreme value (GEV) framework for modeling the time-varying behavior of maxima in financial time series. Specifically, an autoregressive conditional Fréchet (AcF) model is proposed in which the maxima are modeled by a Fréchet distribution with time-varying scale parameter (volatility) and shape parameter (tail index) conditioned on past information. The AcF provides a direct and accurate modeling of the time-varying behavior…

高频金融数据的随机尾指数模型与贝叶斯分析AI 解读

This paper proposes a new dynamic model called Stochastic Tail Index (STI) model to analyze time-varying tail index for financial asset using high frequency return data. Bayesian tools are developed to estimate the model, make related inferences, and perform model selection. To construct efficient posterior sampler for the STI model by an approximation approach, a new algorithm called ALSO (Auxiliary Least Squares Optimization) is introduced, which can quickly make…

多元时间序列的半参数动态 max-copula 模型AI 解读

Summary The paper presents a novel non-linear framework for the construction of flexible multivariate dependence structure (i.e. copulas) from existing copulas based on a straightforward ‘pairwise max-’rule. The newly constructed max-copula has a closed form and has strong interpretability. Compared with the classical ‘linear symmetric’ mixture copula, the max-copula can be viewed as a ‘non-linear asymmetric’ framework. It is capable of modelling asymmetric dependence and…

最大线性竞争因子模型AI 解读

Models incorporating “latent” variables have been commonplace in financial, social, and behavioral sciences. Factor model, the most popular latent model, explains the continuous observed variables in a smaller set of latent variables (factors) in a matter of linear relationship. However, complex data often simultaneously display asymmetric dependence, asymptotic dependence, and positive (negative) dependence between random variables, which linearity and Gaussian…

Copula 结构的 M4 过程及其在高频金融数据中的应用AI 解读

Statistical applications of classical parametric max-stable processes are still sparse mostly due to lack of (1) efficiency of statistical estimation of many parameters in the processes, (2) flexibility of concurrently modeling asymptotic independence and asymptotic dependence among variables, and (3) capability of fitting real data directly. This paper studies a more flexible model, i.e. a class of copula structured M4 (multivariate maxima and moving maxima) processes, and…

刻画非对称性与非线性的广义相关度量AI 解读

Applicability of Pearson's correlation as a measure of explained variance is by now well understood. One of its limitations is that it does not account for asymmetry in explained variance. Aiming to develop broad applicable correlation measures, we study a pair of generalized measures of correlation (GMC) that deals with asymmetries in explained variances, and linear or nonlinear relations between random variables. We present examples under which the paired measures are…

商相关：基于样本的 Pearson 相关替代指标AI 解读

The quotient correlation is defined here as an alternative to Pearson’s correlation that is more intuitive and flexible in cases where the tail behavior of data is important. It measures nonlinear dependence where the regular correlation coefficient is generally not applicable. One of its most useful features is a test statistic that has high power when testing nonlinear dependence in cases where the Fisher’s Z-transformation test may fail to reach a right conclusion. Unlike…