论文检索

非平稳波动环境下股票收益可预测性的实时监测AI 解读

Stock return predictability, should it exist, is likely to be episodic in nature. In order to exploit such pockets of predictability it is essential that they are rapidly detected, in real-time, as the nascent predictive regime emerges. This will typically entail the repeated (sequential) application of one-shot end-of-sample predictability statistics, updated as new data become available. Consequently, in addition to dealing with the usual inference problems caused by…

时变可预测性的稳健推断：Sieve-IVX 方法AI 解读

Predictive regression models are often used to evaluate the predictive capability of economic fundamentals on bond and equity returns. Inferential procedures in these regressions typically employ parameter constancy or piecewise constancy in slope coefficients. Such formulations are prone to misspecification, more especially during periods of disturbance or evolution in prevailing economic and financial conditions, which can lead to size distortion and spurious evidence of…

动态系统性风险测度的多元推断AI 解读

This paper provides statistical inference for marginal expected shortfall (MES) and delta conditional value-at-risk (ΔCoVaR) measures, which are semiparametrically estimated by a two-step procedure in a multivariate GARCH-type framework. We establish the asymptotic properties of corresponding estimators and illustrate how the estimation uncertainty can be decomposed into dynamic univariate marginal and time-varying dependence components. Our methodology reveals good finite…

单一积分型尾部形状参数下极端 VaR 与 ES 的联合动态AI 解读

We propose a robust semi-parametric framework for persistent time-varying extreme tail behavior, including extreme Value-at-Risk (VaR) and Expected Shortfall (ES). The framework builds on Extreme Value Theory and uses a conditional version of the Generalized Pareto Distribution (GPD) for peaks-over-threshold (POT) dynamics. Unlike earlier approaches, our model (i) has unit root-like, that is, integrated autoregressive dynamics for the GPD tail shape, and (ii) re-scales POTs…

跨时间平滑方差：自适应随机波动率AI 解读

We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with Dynamic Shrinkage Processes (DSP) in log-variances. Unlike the classical Stochastic Volatility (SV) or GARCH-type models with restrictive parametric stationarity assumptions, our proposed Adaptive Stochastic Volatility (ASV) model provides smooth yet dynamically adaptive estimates of evolving volatility and its uncertainty. We…

基于条件异方差识别日度油价冲击及其不确定性AI 解读

This paper develops a daily structural VAR for identifying oil price shocks from conditional heteroskedasticity and quantifies the transmission of their timevarying uncertainty. Identification exploits volatility clustering and spillovers in a structural GARCH specification and the complete identified set is derived. We derive covariance impulse response functions and introduce a time-varying forecast error variance decomposition (TV-FEVD) that allows for a decomposition of…

有限状态马尔可夫链近似：一种隐马尔可夫方法AI 解读

This paper proposes a novel finite-state Markov chain approximation method for Markov processes with continuous support, providing both an optimal grid and a transition probability matrix. The method can be used for multivariate processes and processes with time-varying components. It is based on minimizing the information loss between a Hidden Markov Model and the true data-generating process. We provide sufficient conditions under which this information loss can be made…

共同基金 alpha 的异质可预测性：稀疏聚类 GMM 方法AI 解读

Mutual fund managers' skills, measured by risk-adjusted alphas, are predictable using fund characteristics identified by machine learning (e.g., Kaniel et al., 2023, JFE; DeMiguel et al., 2023, JFE). However, alpha's predictive power varies across funds and periods, with most funds exhibiting negligible alphas. To model this heterogeneous predictability, this paper introduces Sparse Clustering GMM, a nonparametric approach for uncovering latent fund groups. SCGMM clusters…

不预设平稳性的 GARCH(1,1) 过程变化检测AI 解读

This article develops a new test to detect changes in generalized autoregressive conditionally heteroscedastic (GARCH(1,1)) processes without imposing a stationary assumption. Specifically, the procedure tests the null hypothesis of a GARCH process with constant parameters, either in (strictly) stationary or explosive regimes, against the alternative hypothesis of parameter changes. We derive the limiting distribution of the test statistics and establish their asymptotic…

动态因子相关性AI 解读

ABSTRACT We introduce a dynamic factor correlation model whose core methodological innovation is a variation‐free parametrization of dynamic factor loadings, inspired by the generalized Fisher transformation. The model accommodates time‐varying correlations, heterogeneous heavy tails, and dependent idiosyncratic shocks. Applied to a Small Universe of 12 assets and a Large Universe of 323 stocks, the factor structure induces a sparse idiosyncratic correlation matrix with…

动态矩阵因子模型：协变量驱动的时变载荷AI 解读

This paper proposes a covariate-driven matrix factor model in which the row and column loading matrices evolve as nonparametric functions of an observable time-varying covariate sequence. This flexible specification accommodates dynamic factor loadings driven by the temporal characteristics of the covariate. We develop a nonparametric estimation procedure that combines kernel smoothing with principal component analysis (PCA). Under high-dimensional asymptotics, we establish…

非线性模型时变参数的局部多项式估计AI 解读

We develop a novel asymptotic theory for local polynomial extremum estimators of time-varying parameters in a broad class of nonlinear time-series models, including discrete-valued ones. We show that the proposed estimators are asymptotically normally distributed under weak conditions. We also provide a precise characterization of the leading bias term due to smoothing. We demonstrate the usefulness of our general results by establishing primitive conditions for local…

分位数中的区制特定股票收益可预测性AI 解读

This article captures the underlying regime switching mechanism in the prediction of stock returns via a predictive quantile regression with multiple thresholds. The predictability of each predictor is allowed to switch from one regime to another according to the value of a threshold variable and could vary across quantiles, and the predictors could possess heterogeneous degrees of persistence. Statistical learning techniques, including a sequential estimation procedure, an…

动态状态空间 HAR 模型AI 解读

The Heterogeneous AutoRegressive model for the logs of Realised Volatility (HARL) has established itself as the benchmark specification for modelling and forecasting return volatility, owing to its parsimony and ability to capture the strong persistence typically observed in RV. To address potential concerns such as measurement errors, nonlinearities, and non-spherical residuals, numerous variants of the baseline HARL model have been developed in the literature. This paper…

结构突变、模型不确定性与因子选择AI 解读

This paper addresses the non-standard problem of detecting multiple structural breaks when the data-generating model in each regime is uncertain, with an application to factor selection in empirical asset pricing. Detection is based on the marginal likelihood of break points, obtained by a novel integration over all possible pairings of models across regimes, from all possible models within regimes. The optimal break points maximize this marginal likelihood. Applying this…

具有动态条件依赖的图形 Copula GARCH 模型AI 解读

Modeling returns on large portfolios is a challenging problem as the number of parameters in the covariance matrix grows as the square of the size of the portfolio. Traditional correlation models, for example, the dynamic conditional correlation (DCC)-GARCH model, often ignore the nonlinear dependencies in the tail of the return distribution. In this paper, we aim to develop a framework to model the nonlinear dependencies dynamically, namely the graphical copula GARCH…

协变量增强的累积和泡沫监测程序AI 解读

We explore how information from covariates can be incorporated into the CUSUM-based real-time monitoring procedure for explosive asset price bubbles developed in Homm and Breitung (2012, Journal of Financial Econometrics 10, 198–231). Where dynamic covariates are present in the data generating process (DGP), the false positive rate (FPR) of the basic CUSUM procedure, which is based on the assumption that prices follow a univariate DGP, under the null of no explosivity will…

异方差误差线性模型中多个变点的置信区间AI 解读

We consider the problem of estimating and deriving confidence intervals for change points in linear models with heteroscedastic errors. A CUSUM process-based estimator is proposed, and we establish its asymptotic properties when the linear regression model exhibits change points in both the regression parameters and the distribution of the errors. This theory motivates the construction of confidence sets for multiple change points by refining preliminary change point…

主特征值与主特征向量的检验AI 解读

We establish central limit theorems for principal eigenvalues and eigenvectors under divergent spiked covariance models, and develop three two-sample tests for testing (1) the equality of principal eigenvalues, (2) the stability of eigenvalue proportions, and (3) the invariance of principal eigenvectors. One important application of our results is to understand structural breaks in large factor models. That is, when a structural break is suspected or detected, these tests…

数据丰富环境中的时变完全子集平均AI 解读

This study proposes two novel time-varying model-averaging methods for time-varying parameter regression models. When the number of predictors is small, we propose a novel time-varying complete subset-averaging (TVCSA) procedure, where the optimal time-varying subset size is obtained by minimizing the local leave- h -out cross-validation criterion. The TVCSA method is asymptotically optimal for achieving the lowest possible local mean squared error. When the number of…

高频价格停滞因子与波动率估计AI 解读

In this article, we propose a price staleness factor model that accounts for pervasive market friction across assets and incorporates relevant covariates. Using large-panel high-frequency data, we derive the maximum likelihood estimators of the regression coefficients, the nonstationary factors, and their loading parameters. These estimators recover the time-varying price staleness probabilities. We develop asymptotic theory in which both the dimension d and the sampling…

得分驱动模型时变参数的一致性、分布收敛与最优性AI 解读

Observation-driven models for time series have a long history in statistics and econometrics, but are typically studied under the assumption of correct dynamic specification. We develop an in-fill asymptotic framework to study the limiting behavior of the estimated (i.e., filtered) time-varying parameter paths obtained with such models in (severely) mis-specified settings. We show that despite such mis-specification, the filtered paths, particularly those from the class of…

因子结构的自适应残差检验AI 解读

This article addresses the critical issue of testing static approximate factor models against unspecified alternatives by proposing an adaptive residual-based test. Our methodology is versatile, encompassing alternatives such as factor models with nonlinear factor structures, conditional factor models, and factor models with structural breaks. We first establish the asymptotic properties of a so-called fixed bandwidth-based test, showing its asymptotic normality and power…

对跳跃、微观结构噪声与异步观测稳健的高频方差分析AI 解读

This paper develops the necessary methodology for high frequency ANOVA, focusing on the estimations of idiosyncratic volatility and averaged R-Squared. As a nonlinear and complicated functional of the spot covariance matrix, idiosyncratic volatility estimation poses significant challenges, particularly with jumps, microstructure noise, and asynchronous observation times. Averaged R-Squared, a newly introduced quantity in high frequency econometrics, provides a measure of…