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误设模型的广义聚合：资产定价应用AI 解读

We propose a generalized aggregation approach for model averaging. The entropy-based optimal criterion is a natural choice for aggregating information from many “globally” misspecified models as it adapts better to the underlying model uncertainty and obtains more robust approximations. Unlike almost all other approaches in the existing literature, we do not require a “reference model,” or a true data generation process contained in the set of models — neither implicitly nor…

Intermediary and downside risk asset pricing theories lay the foundations for spanning the multi-asset return space by a small number of risk factors. Recent studies show strong empirical support for such factors across major asset classes. We revisit these results and show that robust evidence for common factor pricing remains elusive. Importantly, the proposed risk factors do not seem to provide incremental information to the traditional market factor. We argue that most…

This paper is concerned with statistical inference and model evaluation in possibly misspecified and unidentified linear asset pricing models estimated by maximum likelihood. Strikingly, when spurious factors (that is, factors that are uncorrelated with the returns on the test assets) are present, the model exhibits perfect fit, as measured by the squared correlation between the model’s fitted expected returns and the average realized returns. Furthermore, factors that are…

降秩资产定价模型中的虚假推断AI 解读

We study some seemingly anomalous results that arise in possibly misspecified, reduced-rank linear asset-pricing models estimated by the continuously updated generalized method of moments. When a spurious factor (that is, a factor that is uncorrelated with the returns on the test assets) is present, the test for correct model specification has asymptotic power that is equal to the nominal size. In other words, applied researchers will erroneously conclude that the model is…

资产定价模型评估与比较的卡方检验AI 解读

This paper presents a general statistical framework for estimation, testing and comparison of asset pricing models using the unconstrained distance measure of Hansen and Jagannathan (1997). The limiting results cover both linear and nonlinear models that could be correctly specified or misspecified. We propose modified versions of the existing model selection tests and new pivotal specification and model comparison tests with improved finite-sample properties. In addition,…

条件矩限制时间序列模型的局部GMM估计AI 解读

This paper investigates statistical properties of the local generalized method of moments (LGMM) estimator for some time series models defined by conditional moment restrictions. First, we consider Markov processes with possible conditional heteroskedasticity of unknown forms and establish the consistency, asymptotic normality, and semi-parametric efficiency of the LGMM estimator. Second, we undertake a higher-order asymptotic expansion and demonstrate that the LGMM…

通过分解建模金融收益动态AI 解读

While the predictability of excess stock returns is detected by traditional predictive regressions as statistically small, the direction-of-change and volatility of returns exhibit a substantially larger degree of dependence over time. We capitalize on this observation and decompose the returns into a product of sign and absolute value components whose joint distribution is obtained by combining a multiplicative error model for absolute values, a dynamic binary choice model…

Many economic time series are characterized by high persistence which typically requires nonstandard limit theory for inference. This paper proposes a new method for constructing confidence intervals for impulse response functions and half-lives of nearly non-stationary processes. It is based on inverting the acceptance region of the likelihood ratio statistic under a sequence of null hypotheses of possible values for the impulse response or the half-life. This paper shows…