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异步性与价格陈旧并存时的高频协方差稳健估计AI 解读

The Epps effect refers to the empirical decline in measured asset dependence as the sampling frequency increases. Existing studies have shown that market microstructure frictions, including asynchronous trading and price staleness, can contribute to this downward bias in high-frequency covariance estimation. In this study, we propose a novel estimator of high-frequency covariance that is robust to the concurrent presence of asynchronicity and price staleness. We establish…

离散时间与连续时间仿射期权定价模型之间的关系AI 解读

This article studies the weak convergence of discrete-time generalized autoregressive conditional heteroskedasticity (GARCH) and stochastic volatility (SV) option pricing models that allow for fat tails, multi-component volatilities, and non-monotonic pricing kernels. We introduce a general affine framework that enables the simultaneous derivation of new diffusion limits for Gaussian and inverse Gaussian GARCH models based on distributional invariant parametric convergence…

多因子线性资产定价模型 GMM 估计中的效率与定价精度权衡AI 解读

Even though a multi-factor linear asset pricing model can be equivalently represented in a Beta or in a stochastic discount factor (SDF) form, its inferential efficiency and pricing accuracy features may differ when estimated by the generalized method of moments (GMM), both in small and in large samples. We derive the analytical asymptotic variance of the Beta and the SDF estimators under GMM and find that the SDF approach is likely to be less efficient but to yield more…

面向重尾数据的高维切片逆 Kendall 秩相关估计AI 解读

Sufficient dimension reduction is an important branch of statistical learning, which performs an optimal projection of covariates under a supervised learning framework. Most of the existing sufficient dimension reduction algorithms require strict restrictions on the distribution of models, making them sensitive to heavy-tailed predictors and outliers. In this article, we propose a novel method called Sliced Inverse Kendall’s tau Estimation (SIKE) designed for analyzing…

半参数广义帕累托模型中的尾部期望分位数估计AI 解读

Expectiles have received increasing attention as a market risk measure that is both coherent and elicitable. They are defined as a least squares analog to quantiles. Their estimation from heavy-tailed loss data in an extreme value framework is an important and difficult problem, especially when the target tail expectile is beyond the range of the data. This problem has been studied only fairly recently, using solely the Weissman extrapolation method. The competing…

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…

即期相关过程日内变动的非参数检验AI 解读

The association between log-price increments of exchange-traded equities, as measured by their spot correlation estimated from high-frequency data, exhibits a pronounced upwardsloping and almost piecewise linear relationship at the intraday horizon. There is notably lower—on average less positive—correlation in the morning than in the afternoon. We develop a nonparametric testing procedure to detect such variation in a correlation process. The test statistic has a known…

资产定价模型中 Alpha 的一般随机化检验AI 解读

We propose a methodology to test for the null hypothesis that the alphas of a panel of asset returns are jointly equal to zero in a linear factor pricing model with observable and tradable factors — that is, the null of “zero alpha”. The test is based on equation-by-equation estimation, using a randomized version of the estimated alphas, which only requires rates of convergence. The distinct features of the proposed methodology are that it does not require the estimation of…

一族相干风险度量：在险价值的无限加权平均AI 解读

Value-at-Risk (VaR) remains one of the most widely used risk measures in finance due to its simplicity, interpretability, and regulatory prominence, particularly within the Basel framework. However, VaR is not a coherent risk measure, as it generally fails to satisfy the subadditivity property. This paper demonstrates that, although finite weighted combinations of VaR-type functionals do not necessarily yield coherent risk measures, appropriately constructed infinite…

从旋转不变性到标量不变性：提升得分驱动因子模型的可识别性AI 解读

We show that, for a certain class of scaling matrices including the inverse square-root of the conditional Fisher information, score-driven factor models are identifiable up to a multiplicative scalar constant under very mild restrictions. This result has no analogue in parameter-driven models, as it exploits the different structure of the score-driven factor dynamics. Consequently, score-driven factor models overcome the issue of rotational invariance that typically affects…

线性因子模型的 Alpha 检验：投资组合方法AI 解读

This paper develops a test for evaluating linear factor models in high-dimensional settings. The proposed Maximum Random Sparse Portfolio (MRSP) test generates L random sparse portfolios, each obtained through K random draws, and then selects the hardest-to-price portfolio to assess factor model performance. By evaluating multiple randomly generated sparse portfolios, the MRSP test retains a portfolio-based economic interpretation while avoiding reliance on a single…

基于正态混合准极大似然的双重自回归模型稳健估计AI 解读

This paper investigates the estimation of the double autoregressive (DAR) model in the presence of skewed and heavy-tailed innovations. We propose a novel Normal Mixture Quasi-Maximum Likelihood Estimation (NM-QMLE) method to address the limitations of conventional quasi-maximum likelihood estimation (QMLE) under non-Gaussian conditions. By incorporating a normal mixture distribution into the quasi-likelihood framework, NM-QMLE effectively captures both heavy-tailed behavior…

多重门槛回归的 Lasso 后推断及收益可预测性应用AI 解读

This article revisits the multiple threshold regression model, wherein coefficient parameters shift across regimes in accordance with the value of a threshold variable. We focus on developing a post-Lasso inference framework to resolve the selection uncertainty—particularly pronounced when threshold effects are weak—intrinsic to the group Lasso-based procedure that jointly estimates the number and locations of multiple thresholds. This objective is accomplished by…

识别失败与模型设定正确性的协调检验AI 解读

We develop a specification test for GMM and Minimum Distance (MD) inference that is: (i) robust to weak identification; (ii) easy to implement and broadly applicable; and (iii) has meaningful power. Our specification test is based on a conditional inference approach: information about the existence of strongly identified directions in the parameter space is exploited to define a data-dependent critical value, which, in turn, increases the power of the resulting specification…

可观测因子与潜在因子AI 解读

We test for temporal stability in local linear projection coefficients of observable factors on latent ones embedded in a high-dimensional vector obeying a linear factor model. The proposed test explores the fact that, under the null hypothesis, residuals from global linear projections of observable factors on latent ones, computed over a fixed time interval via Principal Component Analysis (PCA), should also be locally uncorrelated with the PCA factors. The test is fully…

设定 LASSO 与灵活的特征型资产定价模型AI 解读

This paper studies a partially linear semiparametric additive model that allows for interactions among variables in high-dimensional settings. We propose the Specification LASSO (S-LASSO), a two-step method that integrates LASSO and Adaptive Group LASSO to simultaneously perform variable selection and model specification. Theoretically, we establish that S-LASSO possesses oracle properties in terms of selection consistency and asymptotic normality. Monte Carlo simulations…

用多元分数布朗运动建模与预测已实现波动率AI 解读

A multivariate fractional Brownian motion (mfBm) with component-wise Hurst exponents is used to model and forecast realized volatility (RV). We investigate the interplay between correlation coefficients and Hurst exponents and propose a novel method to estimate model parameters, establishing its consistency and asymptotic normality. Additionally, we develop a time-reversibility test, which is typically not rejected by RV data. When the data generating process is a…

用于企业生存预测的高维删失 MIDAS 逻辑回归AI 解读

This paper addresses the challenge of forecasting corporate distress, a problem marked by three key statistical hurdles: (i) right censoring, (ii) high-dimensional predictors, and (iii) mixed-frequency data. To overcome these complexities, we introduce a novel high-dimensional censored MIDAS (Mixed Data Sampling) logistic regression. Our approach handles censoring through inverse probability weighting and achieves accurate estimation with numerous mixed-frequency predictors…

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利用高阶多元累积量估计弱因子AI 解读

When factors are weak, covariance-based factor analysis methods tend to exhibit poor performance. To address this issue in the case of non-Gaussian data, we propose a new method called Higher-order multi-cumulant Factor Analysis (HFA). HFA estimates factors and factor loadings via the eigenvalue decomposition of the product of a higher-order multi-cumulant matrix and its transpose. We derive the asymptotic properties of HFA under a weak factor model where non-Gaussianity…

高维环境中的涵盖检验与递进检验AI 解读

This paper introduces the Subseries-based Cauchy Combination Test (SCT), a novel procedure for testing encompassing relationships or model validity using identification conditions formulated as multiple moment restrictions. SCT applies to weakly or short-range-dependent data and eliminates the need to estimate high-dimensional covariance matrices. Unlike Wald- or J-type tests, it remains reliable in both low- and high-dimensional settings. The test is asymptotically unbiased…

自回归逆Wishart多元随机波动模型及其因子扩展AI 解读

ABSTRACT Despite its conceptual appeal, the autoregressive inverse‐Wishart (AIW) multivariate stochastic volatility model has been hindered by inefficient sampling methods. The existing samplers for the latent covariance matrix severely suffer from the curse of dimensionality and only work when the dimension is very low. In this paper, we introduce a new proposal for the latent covariance matrix in the AIW model, which demonstrates far better scalability. We introduce…

宏观审慎政策的尾部传导：叙事识别证据AI 解读

ABSTRACT We estimate the causal effects of macroprudential policies on the entire distribution of GDP growth for advanced European economies using a narrative‐identification strategy in a quantile‐regression framework. While macroprudential policy has near‐zero effects on the center of the GDP‐growth distribution, tighter policy brings benefits by reducing the variance of future growth, significantly boosting the left tail while simultaneously reducing the right. Assessing a…

广义自回归得分树与森林AI 解读

We propose methods to improve the forecasts from generalized autoregressive score (GAS) models (Creal, et al., 2013; Harvey, 2013) by localizing their parameters using decision trees and random forests. These methods avoid the curse of dimensionality faced by kernel-based approaches, and allow one to draw on information from multiple state variables simultaneously. We apply the new models to four distinct empirical analyses, and in all applications the proposed new methods…