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随机波动率入均值模型：广义混合采样器的高效分析AI 解读

In this paper we consider the simulation-based Bayesian analysis of stochastic volatility in mean (SVM) models. Extending the highly efficient Markov chain Monte Carlo mixture sampler for the SV model proposed in Kim et al. (1998) and Omori et al. (2007), we develop an accurate approximation of the logarithm of the non-central chi-squared distribution as a mixture of thirty normal distributions . Under this mixture representation, we sample the parameters and latent…

结合已实现波动率与成对已实现相关系数的多元随机波动率模型AI 解读

Although stochastic volatility and GARCH (generalized autoregressive conditional heteroscedasticity) models have successfully described the volatility dynamics of univariate asset returns, extending them to the multivariate models with dynamic correlations has been difficult due to several major problems. First, there are too many parameters to estimate if available data are only daily returns, which results in unstable estimates. One solution to this problem is to…

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随机效应对无条件风险率与生存函数的影响AI 解读

The consequences of introducing random effects have been a major issue in models for survival analysis. A general result is derived to establish the underestimation of the unconditional hazard rate when a random effect is present. Also, the survival time is shown to be underestimated. No distributional assumption regarding the random effect is required.

随机效应对无条件风险率与生存函数的影响AI 解读

The consequences of introducing random effects have been a major issue in models for survival analysis. A general result is derived to establish the underestimation of the unconditional hazard rate when a random effect is present. Also, the survival time is shown to be underestimated. No distributional assumption regarding the random effect is required.