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多元密度预测正确校准的变量顺序不变检验AI 解读

Summary Established tests for proper calibration of multivariate density forecasts based on Rosenblatt probability integral transforms can be manipulated by changing the order of variables in the forecasting model. We derive order‐invariant tests. The new tests are applicable to densities of arbitrary dimensions and can deal with parameter estimation uncertainty and dynamic misspecification. Monte Carlo simulations show that they often have superior power relative to…

因子Copula模型的结构突变检验AI 解读

We propose new fluctuation tests for detecting structural breaks in factor copula models and analyse the behaviour under the null hypothesis of no change. In the model, the joint copula is given by the copula of random variables which arise from a factor model. This is particularly useful for analysing data with high dimensions. Parameters are estimated with the simulated method of moments (SMM). The discontinuity of the SMM objective function complicates the derivation of a…

利用自激点过程建模多变量极端事件AI 解读

We propose a model that can capture the typical features of multivariate extreme events observed in financial time series, namely, clustering behaviors in magnitudes and arrival times of multivariate extreme events, and time-varying dependence. The model is developed within the framework of the peaks-over-threshold approach in extreme value theory and relies on a Poisson process with self-exciting intensity. We discuss the properties of the model, treat its estimation, and…

动态随机 Copula 模型：估计、推断与应用AI 解读

SUMMARY We propose a new dynamic copula model in which the parameter characterizing dependence follows an autoregressive process. As this model class includes the Gaussian copula with stochastic correlation process, it can be viewed as a generalization of multivariate stochastic volatility models. Despite the complexity of the model, the decoupling of marginals and dependence parameters facilitates estimation. We propose estimation in two steps, where first the parameters of…