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In this paper, we develop a novel high-dimensional time-varying coefficient estimation method, based on high-dimensional Itô diffusion processes. To account for high-dimensional time-varying coefficients, we first estimate local (or instantaneous) coefficients using a time-localized Dantzig selection scheme under a sparsity condition, which results in biased local coefficient estimators due to the regularization. To handle the bias, we propose a debiasing scheme, which…

逆协方差金融邻接矩阵识别局部群组的性质AI 解读

In financial applications, we often observe both global and local factors that are modeled by a multi-level factor model. When detecting unknown local group memberships under such a model, employing a covariance matrix as an adjacency matrix for local group memberships is inadequate due to the predominant effect of global factors. Thus, to detect a local group structure more effectively, this study introduces an inverse covariance matrix-based financial adjacency matrix…

动态已实现最小方差投资组合模型AI 解读

This paper introduces a dynamic minimum variance portfolio (MVP) model using nonlinear volatility dynamic models, based on high-frequency financial data. Specifically, we impose an autoregressive dynamic structure on MVP processes, which helps capture the MVP dynamics directly. To evaluate the dynamic MVP model, we estimate the inverse volatility matrix using the constrained $\ell_1$-minimization for inverse matrix estimation (CLIME) and calculate daily realized…

因子隔夜 GARCH–Itô 模型AI 解读

This paper introduces a unified factor overnight GARCH-Itô model for large volatility matrix estimation and prediction. To account for whole-day market dynamics, the proposed model has two different instantaneous factor volatility processes for the open-to-close and close-to-open periods, while each embeds the discrete-time multivariate GARCH model structure. To estimate latent factor volatility, we assume the low rank plus sparse structure and employ nonparametric…

使用稳健已实现积分贝塔估计量的动态已实现贝塔模型AI 解读

This paper introduces a unified parametric modeling approach for time-varying market betas that can accommodate continuous-time diffusion and discrete-time series models based on a continuous-time series regression model to better capture the dynamic evolution of market betas. We call this the dynamic realized beta (DR Beta). We first develop a non-parametric realized integrated beta estimator using high-frequency financial data contaminated by microstructure noises, which…

中美贸易战对股票市场的影响：金融传染视角AI 解读

In this paper, we investigate the effect of the U.S.--China trade war on stock markets from a financial contagion perspective, based on high-frequency financial data. Specifically, to account for risk contagion between the U.S. and China stock markets, we develop a novel jump-diffusion process. For example, we consider three channels for volatility contagion--such as integrated volatility, positive jump variation, and negative jump variation--and each stock market is able to…

已实现 GARCH 模型的条件分位数分析AI 解读

This paper introduces a novel quantile approach to harness the high-frequency information and improve the daily conditional quantile estimation. Specifically, we model the conditional standard deviation as a realized GARCH model and employ conditional standard deviation, realized volatility, realized quantile, and absolute overnight return as innovations in the proposed dynamic quantile models. We devise a two-step estimation procedure to estimate the conditional quantile…