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新冠疫情期间的股息暂停与现金流：动态计量模型AI 解读

Firms suspended dividend payments in unprecedented numbers in response to the outbreak of the Covid-19 pandemic. We develop a multivariate dynamic econometric model that allows dividend suspensions to affect the conditional mean, volatility, and jump probability of growth in daily industry-level dividends and demonstrate how the parameters of this model can be estimated using Bayesian Gibbs sampling methods. We find considerable heterogeneity across industries in the…

预测模型之间的条件轮换AI 解读

We establish conditions under which forecasting performance can be improved by rotating between a set of underlying forecasts whose predictive accuracy is tracked using a set of time-varying monitoring instruments. We characterize the properties that the monitoring instruments must possess to be useful for identifying, at each point in time, the best forecast and show that these reflect both the accuracy of the predictors used by the underlying forecasting models and the…

风险溢价消失了吗？AI 解读

We apply a new methodology for identifying pervasive and discrete changes (“breaks”) in cross-sectional risk premia. Size, value, and investment risk premia have fallen off to the point where they are insignificantly different from zero at the end of the sample period. The market risk premium has also declined systematically over time but remains significant and positive as do the momentum and profitability risk premium. We construct a new instability risk factor from…

经济约束下的股票收益预测AI 解读

We propose a new approach to imposing economic constraints on time series forecasts of the equity premium. Economic constraints are used to modify the posterior distribution of the parameters of the predictive return regression in a way that better allows the model to learn from the data. We consider two types of constraints: non-negative equity premia and bounds on the conditional Sharpe ratio, the latter of which incorporates time-varying volatility in the predictive…

完全子集回归AI 解读

This paper proposes a new method for combining forecasts based on complete subset regressions. For a given set of potential predictor variables we combine forecasts from all possible linear regression models that keep the number of predictors fixed. We explore how the choice of model complexity, as measured by the number of included predictor variables, can be used to trade off the bias and variance of the forecast errors, generating a setup akin to the efficient frontier…

结构断点下的股票收益可预测性与资产配置AI 解读

This paper adopts a new approach that accounts for breaks to the parameters of return prediction models both in the historical estimation period and at future points. Empirically, we find evidence of multiple breaks in return prediction models based on the dividend yield or a short interest rate. Our analysis suggests that model instability is a very important source of investment risk for buy-and-hold investors with long horizons and that breaks can lead to a negative slope…

ABSTRACT We apply a new bootstrap statistical technique to examine the performance of the U.S. open‐end, domestic equity mutual fund industry over the 1975 to 2002 period. A bootstrap approach is necessary because the cross section of mutual fund alphas has a complex nonnormal distribution due to heterogeneous risk‐taking by funds as well as nonnormalities in individual fund alpha distributions. Our bootstrap approach uncovers findings that differ from many past studies.…

不同计量设定下风险的期限结构AI 解读

This paper characterizes the term structure of risk measures such as value at risk (VaR) and expected shortfall under different econometric approaches including multivariate regime switching, GARCH-in-mean models with Student-t errors, two-component GARCH models and a nonparametric bootstrap. We show how to derive the risk measures for each of these models and document large variations in term structures across econometric specifications. An out-of-sample forecasting…

股票与债券收益联合分布非线性动态的计量模型AI 解读

Abstract This paper considers a variety of econometric models for the joint distribution of US stock and bond returns in the presence of regime switching dynamics. While simple two‐ or three‐state models capture the univariate dynamics in bond and stock returns, a more complicated four‐state model with regimes characterized as crash, slow growth, bull and recovery states is required to capture their joint distribution. The transition probability matrix of this model has a…

股票价格的持续期依赖AI 解读

This article studies time series dependence in the direction of stock prices by modeling the (instantaneous) probability that a bull or bear market terminates as a function of its age and a set of underlying state variables, such as interest rates. A random walk model is rejected both for bull and bear markets. Although it fits the data better, a generalized autoregressive conditional heteroscedasticity model is also found to be inconsistent with the very long bull markets…

结构突变、不完全信息与股票价格AI 解读

This article presents empirical evidence on the existence of structural breaks in the fundamentals process underlying U.S. stock prices. I develop an asset-pricing model that represents breaks in the context of a Markov switching process with an expanding set of nonrecurring states. Different hypotheses on how investors form expectations about future dividends after a break are proposed and analyzed. A model in which investors do not have full information about the…

时变贴现因子现值模型的协整检验AI 解读

Abstract The paper analyses the impact of persistence and volatility in the discount rate in present‐value models on cointegration tests in levels and in logarithms. In simulations we find that the probability of not rejecting the null of no cointegration depends on the persistence of the discount rate process and can be very high when the expected returns process is highly persistent. In contrast, the cointegration tests are very robust with respect to the level of…