Jessica A. Wachter

We build a model with inalienable human capital, in which investors finance individuals who can potentially become skilled. Although investment in skill is always optimal, it does not take place in some states of the world, due to moral hazard. In intermediate states of the world, individuals acquire skill; however outside investors and individuals inefficiently share risk. We show that this simple moral hazard problem, combined with risk aversion of individuals and outside investors, amplifies the equity premium, lowers the risk-free rate, and leads to disaster states that fall especially heavily on some agents but not on others. We show that the possibility of disaster states distorts risk prices and affects wealth inequality, even under calibrations in which they never occur in equilibrium. This paper was accepted by Lukas Schmid, finance. Funding: M. Ebrahimian acknowledges funding from the Swedish House of Finance. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.03068 .

Studies of human memory indicate that features of an event evoke memories of prior associated contextual states, which in turn become associated with the current event’s features. This retrieved-context mechanism allows the remote past to influence the present, even as agents gradually update their beliefs about their environment. We apply a version of retrieved-context theory, drawn from the literature on human memory, to explain three types of evidence in the financial economics literature: the role of early life experience in shaping investment choices, occurrence of financial crises, and the effect of fear on asset allocation. These applications suggest a recasting of neoclassical rational expectations in terms of beliefs as governed by principles of human memory.

Empirical studies demonstrate striking patterns in stock returns related to scheduled macroeconomic announcements. A large proportion of the total equity premium is realized on days with macroeconomic announcements. The relation between market betas and expected returns is far stronger on announcement days as compared with nonannouncement days. Finally, these results hold for fixed-income investments as well as for stocks. We present a model in which agents learn the probability of an adverse economic state on announcement days. We show that the model quantitatively accounts for the empirical findings. Evidence from options data provides support for the model’s mechanism.

Contrary to well-known asset pricing models, volatilities implied by equity index options exceed realized stock market volatility and exhibit a pattern known as the volatility skew. We explain both facts using a model that can also account for the mean and volatility of equity returns. Our model assumes a small risk of economic disaster that is calibrated based on international data on large consumption declines. We allow the disaster probability to be stochastic, which turns out to be crucial to the model’s ability both to match equity volatility and to reconcile option prices with macroeconomic data on disasters. This paper was accepted by Lauren Cohen, finance.

Because multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding.

After lying dormant for more than two decades, the rare disaster framework has emerged as a leading contender to explain facts about the aggregate market, interest rates, and financial derivatives. In this article, we survey recent models of disaster risk that provide explanations for the equity premium puzzle, the volatility puzzle, return predictability, and other features of the aggregate stock market. We show how these models can also explain violations of the expectations hypothesis in bond pricing as well as the implied volatility skew in option pricing. We review both modeling techniques and results and consider both endowment and production economies. We show that these models provide a parsimonious and unifying framework for understanding puzzles in asset pricing.

This paper describes estimation methods, based on the generalized method of moments (GMM), applicable in settings where time series have different starting or ending dates. We introduce two estimators that are more efficient asymptotically than standard GMM. We apply these to estimating predictive regressions in international data and show that the use of the full sample affects inference for assets with data available over the full period as well as for assets with data available for a subset of the period. Monte Carlo experiments demonstrate that reductions hold for small-sample standard errors as well as asymptotic ones.

Recent research finds that the stocks that mutual fund managers buy outperform the stocks that they sell (e.g., Chen, Jegadeesh, and Wermers (2000)). We study the nature of this stock-picking ability. We construct measures of trading skill based on how the stocks held and traded by fund managers perform at subsequent corporate earnings announcements. This approach increases the power to detect skilled trading and sheds light on its source. We find that the average fund’s recent buys significantly outperform its recent sells around the next earnings announcement, and that this accounts for a disproportionate fraction of the total abnormal returns to fund trades estimated in prior work. We find that mutual fund trades also forecast earnings surprises. We conclude that mutual fund managers are able to trade profitably in part because they are able to forecast earnings-related fundamentals.

We examine the evidence on stock return predictability in a Bayesian setting that includes uncertainty about both the existence and strength of predictability. We consider an investor who believes that excess stock returns exhibit predictability with prior probability q < 1. In addition, the investor downweights observed predictability by placing a prior distribution on the R2 of the predictability regression. When we apply our analysis to the dividend-price ratio, we find that even investors who are quite skeptical about the existence and strength of predictability sharply modify their views in favor of predictability when confronted by the evidence. We depart from previous model-selection work by treating the regressor as stochastic rather than known we find that this has a large impact on inference about time- varying expected returns.