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资产定价因子模型检验：样本外视角AI 解读

We develop the Sample Splitting Alpha (SSA) test, a formal out-of-sample (OOS) test for evaluating whether an asset pricing factor model satisfies the zero-alpha restriction in high-dimensional settings. The test uses sample splitting to separate portfolio weight estimation from model evaluation and ridge regularization to stabilize the portfolio problem when the number of test assets is large. We show that each single split SSA statistic is asymptotically Gaussian under…

两步分位数回归中 CoVaR 推断的渐近理论AI 解读

Measuring systemic risk has been an important topic in recent research on risk management. Adrian and Brunnermeier (2016) pioneered the CoVaR measure for systemic risk, which is defined as the conditional quantile of system loss given some state predictors and one stress event of an individual loss at a certain risk level. They proposed to estimate CoVaR by a two-quantile-regression approach, which is simple and easy to implement in practice, but without stating or…

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…

无条件异方差下温和爆炸性自回归模型的推断AI 解读

Mildly explosive autoregressions have been extensively employed in recent theoretical and applied econometric work to model the phenomenon of asset market bubbles. An important issue in this context concerns the construction of confidence intervals for the autoregressive parameter that represents the degree of explosiveness. Existing studies rely on intervals that are justified only under conditional homoskedasticity/heteroskedasticity. This paper studies the problem of…

线性随机贴现因子模型中无关因子的误设稳健自助 t 检验AI 解读

This paper examines the applicability of the bootstrap approach to test for irrelevant risk factors that are potentially useless in misspecified linear stochastic discount factor (SDF) models. In the literature, the misspecification-robust inference with useless factors is known to give rise to nonstandard limiting distributions bounded stochastically to compute critical values. We show how and to what extent the wild bootstrap yields a more accurate approximation of the…

潜在因子与可观测因子的空间张成检验AI 解读

Factor analysis is a widely used tool to summarize high dimensional panel data via a small dimensional set of latent factors. Many applications in finance and macroeconomics, are often focused on observable factors with an economic interpretation. The objective of this paper is to provide a test to answer a question which naturally comes up in discussions regarding latent versus observable factors: do latent and observable factors span the same space? We derive asymptotic…

通过相关性的多重检验正则化股票收益协方差矩阵AI 解读

This paper develops a large-scale inference approach for the regularization of stock return covariance matrices. The framework allows for the presence of heavy tails and multivariate GARCH-type effects of unknown form among the stock returns. The approach involves simultaneous testing of all pairwise correlations, followed by setting non-statistically significant elements to zero. This adaptive thresholding is achieved through sign-based Monte Carlo resampling within…

动态系统性风险度量的统计推断AI 解读

Systemic risk measures (SRM) quantify the risk of a system induced by the possible distress of any of its components. Applications in economics and finance are numerous. We define a general dynamic framework for the risk factors, allowing us to obtain explicit expressions of the corresponding dynamic SRM. We deduce an easy-to-implement statistical approach which, based on semi-parametric assumptions, reduces to estimating univariate location-scale models and to computing…

缺乏小阶有限矩的 GARCH-MIDAS 模型推断AI 解读

In GARCH-mixed-data sampling models, the volatility is decomposed into the product of two factors which are often interpreted as “short-run” (high-frequency) and “long-run” (low-frequency) components. While two-component volatility models are widely used in applied works, some of their theoretical properties remain unexplored. We show that the strictly stationary solutions of such models do not admit any small-order finite moment, contrary to classical GARCH. It is shown…

状态空间模型的分块相关伪边际采样器AI 解读

Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly.Our article develops an efficient PMMH method that scales up better to higher dimensional state vectors than previous approaches.The improvement is achieved by the following innovations.First, a novel block version of PMMH that works with multiple particle filters is proposed.Second, the trimmed mean of the unbiased…

预测分位数回归的统一推断AI 解读

The asymptotic behavior of quantile regression inference becomes dramatically different when it involves a persistent predictor with zero or nonzero intercept. Distinguishing various properties of a predictor is empirically challenging. In this article, we develop a unified predictability test for quantile regression regardless of the presence of intercept and persistence of a predictor. The developed test is a novel combination of data splitting, weighted inference, and a…

干扰参数位于边界时的 GARCH 设定检验AI 解读

This article develops tests for the correct specification of the conditional variance function in GARCH models when the true parameter may lie on the boundary of the parameter space. The test statistics considered are of Kolmogorov-Smirnov and Cramér-von Mises type, and are based on empirical processes marked by centered squared residuals. The limiting distributions of the test statistics depend on unknown nuisance parameters in a nontrivial way, making the tests difficult…

条件风险价值的残差自助法AI 解读

A fixed-design residual bootstrap method is proposed for the two-step estimator of Francq and Zakoïan(2015) associated with the conditional Value-at-Risk. The bootstrap’s consistency is proven for a general class of volatility models and intervals are constructed for the conditional Value-at-Risk. A simulation study reveals that the equal-tailed percentile bootstrap interval tends to fall short of its nominal value. In contrast, the reversed-tails bootstrap interval yields…

系统性风险暴露张成空间的变化AI 解读

We develop a test for deciding whether the linear spaces spanned by the factor exposures of a large cross‐section of assets toward latent systematic risk factors at two distinct points in time are the same. The test uses a panel of asset returns in local windows around the two time points. The asymptotic setup is of joint type: the number of assets and the number of return observations per asset increase asymptotically while the length of both time windows shrinks. We…

收益可预测性 IVX 推断方法的扩展AI 解读

The contribution of this paper is threefold. First, we demonstrate that, provided either a suitable bootstrap implementation is employed or heteroskedasticity-consistent standard errors are used, the IVX-based predictability tests of Kostakis et al. (2015) retain asymptotically valid inference under the null hypothesis under considerably weaker assumptions on the innovations than are required by Kostakis et al. (2015). Second, under the same assumptions, we develop…

基于极端依赖的时间序列模型设定检验AI 解读

We propose a specification test for conditional location–scale models based on extremal dependence properties of the standardized residuals. We do so comparing the left-over serial extremal dependence—as measured by the pre-asymptotic tail copula—with that arising under serial independence at different lags. Our main theoretical results show that the proposed Portmanteau-type test statistics have nuisance parameter-free asymptotic limits. The test statistics are easy to…

条件风险价值与条件预期损失的同时置信带AI 解读

Conditional value-at-risk (CVaR) and conditional expected shortfall (CES) are widely adopted risk measures which help monitor potential tail risk while adapting to evolving market information. In this paper, we propose an approach to constructing simultaneous confidence bands (SCBs) for tail risk as measured by CVaR and CES, with the confidence bands uniformly valid for a set of tail levels. We consider one-sided tail risk (downside or upside tail risk) as well as relative…

含外生协变量的因子 copula 模型估计与推断AI 解读

A factor copula model is proposed in which factors are either simulable or estimable from exogenous information. Point estimation and inference are based on a simulated methods of moments (SMM) approach with non-overlapping simulation draws. Consistency and limiting normality of the estimator is established and the validity of bootstrap standard errors is shown. Doing so, previous results from the literature are verified under low-level conditions imposed on the individual…

动态条件分布模型的自助法设定检验AI 解读

This paper proposes bootstrap based tests for the specification of a given parametric conditional distribution in autoregressive time series with GARCH-type disturbances. The tests are based on an estimated residual empirical process and are implemented by parametric bootstrap. We show that the proposed tests are asymptotically valid, consistent, and have nontrivial asymptotic power against a large proportion of local alternatives. Our approach relies on non-primitive…

时变系数模型的设定检验AI 解读

Time-varying coefficient models have been widely used to characterize changing relationships among economic and financial variables. The existing literature usually specifies the time-varying coefficient vector as a stationary stochastic process, a deterministic function of time, and a unit root process, respectively. In this paper, we propose two tests to distinguish these three specifications. Both test statistics follow asymptotic normal distributions under the respective…

时间序列模型两阶段准极大似然估计的 Bootstrap 推断AI 解读

This article provides results on the validity of bootstrap inference methods for two-stage quasi-maximum likelihood estimation involving time series data, such as those used for multivariate volatility models or copula-based models. Existing approaches require the researcher to compute and combine many first- and second-order derivatives, which can be difficult to do and is susceptible to error. Bootstrap methods are simpler to apply, allowing the substitution of capital…

共同基金绩效的自助法分析AI 解读

We study bootstrap methods for fund performance evaluation. We first show that two prominent bootstrap tests have biased test sizes in a large cross-section with short time series and lack test power to detect skilled funds when a substantial number of unskilled funds are present. We then develop the theory for a valid bootstrap Hotelling’s T-squared test for zero alpha. We apply the proposed bootstrap test in a practical two-step procedure to identify skilled funds. Our…

非平稳时间序列的泛函估计与变化检测AI 解读

Tests for structural breaks in time series should ideally be sensitive to breaks in the parameter of interest, while being robust to nuisance changes. Statistical analysis thus needs to allow for some form of nonstationarity under the null hypothesis of no change. In this paper, estimators for integrated parameters of locally stationary time series are constructed and a corresponding functional central limit theorem is established, enabling change-point inference for a broad…

非参数高频估计量的推断及金融贝塔时变性的应用AI 解读

We consider the problem of conducting inference on nonparametric high-frequency estimators without knowing their asymptotic variances. We prove that a multivariate subsampling method achieves this goal under general conditions that were not previously available in the literature. By construction, the subsampling method delivers estimates of the variance-covariance matrices that are always positive semidefinite. Our simulation study indicates that the subsampling method is…