论文检索

多元旋转 ARCH 模型AI 解读

This paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to fit them using a BEKK-type parameterization of the time-varying covariance whose long-run covariance is the identity matrix. This yields the rotated BEKK (RBEKK) model. The extension to DCC-type parameterizations is given,…

基于高频数据的多元波动率HEAVY模型AI 解读

SUMMARY This paper introduces a new class of multivariate volatility models that utilizes high‐frequency data. We discuss the models' dynamics and highlight their differences from multivariate generalized autoregressive conditional heteroskedasticity (GARCH) models. We also discuss their covariance targeting specification and provide closed‐form formulas for multi‐step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model…