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广义自回归条件贝塔：多因子资产定价中的纵向反馈AI 解读

We propose a new class of observation-driven models, Generalized Autoregressive Conditional Betas, which describe the joint dynamics of time-varying slopes in a system of conditionally heteroskedastic simultaneous multiple regressions. The model accommodates large dimensions, parametric longitudinal restrictions, exogenous variables, and the coexistence of constant and time-varying slopes. It also introduces new mechanisms for the transmission of shocks, namely, beta…

方差风险溢价动态：分离风险价格的新模型AI 解读

This paper formulates a new dynamic model for the variance risk premium based on a state space representation of a bivariate system for the observable ex-post realized variance and the ex-ante option implied variance expectation. A regime switching structure accommodates for periods of unusually high volatility, heterogeneous dynamics and changes in the dependence between the latent states. The model allows separating the continuous component of the variance risk premium…

动态条件相关模型的弱扩散极限AI 解读

The properties of dynamic conditional correlation (DCC) models, introduced more than a decade ago, are still not entirely known. This paper fills one of the gaps by deriving weak diffusion limits of a modified version of the classical DCC model. The limiting system of stochastic differential equations is characterized by a diffusion matrix of reduced rank. The degeneracy is due to perfect collinearity between the innovations of the volatility and correlation dynamics. For…

损失函数与多元波动模型预测表现的排序AI 解读

The ranking of multivariate volatility models is inherently problematic because when the unobservable volatility is substituted by a proxy, the ordering implied by a loss function may be biased with respect to the intended one. We point out that the size of the distortion is strictly tied to the level of the accuracy of the volatility proxy. We propose a generalized necessary and sufficient functional form for a class of non-metric distance measures of the Bregman type which…

多元 GARCH 模型的预测准确性AI 解读

SUMMARY This paper addresses the question of the selection of multivariate generalized autoregressive conditional heteroskedastic (GARCH) models in terms of variance matrix forecasting accuracy, with a particular focus on relatively large‐scale problems. We consider 10 assets from the New York Stock Exchange and compare 125 models based 1‐, 5‐ and 20‐day‐ahead conditional variance forecasts over a period of 10 years using the model confidence set (MCS) and the superior…