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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…

非共同结构断点AI 解读

We develop a new Bayesian approach to estimate noncommon structural breaks in panel regression models. Any subset of the cross-section may be hit at different times within a break window. Break-specific parameters are learned from the cross-section. They reflect whether (i) breaks hit many or few series and (ii) there is a long or short lag between the first and final series hit by a break. In an empirical application to international stock return predictability, the method…

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…