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Over the years, many asset pricing studies have employed the sample cross-sectional regression (CSR) R² as a measure of model performance. We derive the asymptotic distribution of this statistic and develop associated model comparison tests, taking into account the impact of model misspecification on the variability of the CSR estimates. We encounter several examples of large R² differences that are not statistically significant. A version of the intertemporal capital asset pricing model (CAPM) exhibits the best overall performance, followed by the Fama-French three-factor model. Interestingly, the performance of prominent consumption CAPMs is sensitive to variations in experimental design.

We formulate a model of utility for a continuous-time framework that captures aversion to ambiguity about both volatility and drift. Corresponding extensions of some basic results in asset pricing theory are presented. First, we derive arbitrage-free pricing rules based on hedging arguments. Because ambiguous volatility implies market incompleteness, hedging arguments determine prices only up to intervals. In order to obtain sharper predictions, we apply the model of utility to a representative agent endowment economy and study equilibrium asset returns. A version of the consumption capital asset pricing model is derived, and the effects of ambiguous volatility are described.

A Bayesian asset pricing test is derived that is easily computed in closed form from the standard F-statistic. Given a set of candidate traded factors, we develop a related test procedure that permits the computation of model probabilities for the collection of all possible pricing models that are based on subsets of the given factors. We find that the recent models of Hou, Xue, and Zhang (2015a, 2015b) and Fama and French (2015, 2016) are dominated by a variety of models that include a momentum factor, along with value and profitability factors that are updated monthly.

This paper shows that low-risk anomalies in the capital asset pricing model and in traditional factor models arise when investors require compensation for coskewness risk. Empirically, we find that option-implied ex ante skewness is strongly related to ex post residual coskewness, which allows us to construct coskewness factor-mimicking portfolios. Controlling for skewness renders the alphas of betting-against-beta and betting-against-volatility insignificant. We also show that the returns of beta- and volatility-sorted portfolios are driven largely by a single principal component, which in turn is explained largely by skewness.

A common approach to comparing asset pricing models involves a competition in pricing test-asset returns. In contrast, we show that for models with traded factors, when the comparison is framed appropriately in terms of success in pricing both the test-asset and factor returns, the extent to which each model is able to price the factors in the other model is what matters for model comparison. Test assets are irrelevant based on several prominent criteria. For models with nontraded factors, test assets are relevant for model comparison insofar as they are needed to identify factor-mimicking portfolio returns.

We document large average excess returns on U.S. equities in anticipation of monetary policy decisions made at scheduled meetings of the Federal Open Market Committee (FOMC) in the past few decades. These pre-FOMC returns have increased over time and account for sizable fractions of total annual realized stock returns. While other major international equity indices experienced similar pre-FOMC returns, we find no such effect in U.S. Treasury securities and money market futures. Other major U.S. macroeconomic news announcements also do not give rise to preannouncement excess equity returns. We discuss challenges in explaining these returns with standard asset pricing theory.

We propose a novel generalized recursive smooth ambiguity model which permits a three-way separation among risk aversion, ambiguity aversion, and intertemporal substitution. We apply this utility model to a consumption-based asset-pricing model in which consumption and dividends follow hidden Markov regime-switching processes. Our calibrated model can match the mean equity premium, the mean risk-free rate, and the volatility of the equity premium observed in the data. In addition, our model can generate a variety of dynamic asset-pricing phenomena, including the procyclical variation of price-dividend ratios, the countercyclical variation of equity premia and equity volatility, the leverage effect, and the mean reversion of excess returns. The key intuition is that an ambiguity-averse agent behaves pessimistically by attaching more weight to the pricing kernel in bad times when his continuation values are low.

Between 1934 and 1974, the Federal Reserve changed the initial margin requirement for the U.S. stock market 22 times. I use this variation to show that investors' leverage constraints affect the pricing of risk. Consistent with earlier theoretical predictions, I find that tighter leverage constraints result in a flatter relation between betas and expected returns. My results provide strong empirical support for the idea that the constraints investors face may help explain the empirical failure of the capital asset pricing model.

This paper shows that the latest generation of asset pricing models with long-run risk exhibit economically significant nonlinearities, and thus the ubiquitous Campbell-Shiller log-linearization can generate large numerical errors. These errors translate in turn to considerable errors in the model predictions, for example, for the magnitude of the equity premium or return predictability. We demonstrate that these nonlinearities arise from the presence of multiple highly persistent processes, which cause the exogenous states to attain values far away from their long-run means with nonnegligible probability. These extreme values have a significant impact on asset price dynamics.

We develop a dynamic asset pricing model in which monetary policy affects the risk premium component of the cost of capital. Risk-tolerant agents (banks) borrow from risk-averse agents (i.e., take deposits) to fund levered investments. Leverage exposes banks to funding risk, which they insure by holding liquidity buffers. By changing the nominal rate the central bank influences the liquidity premium, and hence the cost of taking leverage. Lower nominal rates make liquidity cheaper and raise leverage, resulting in lower risk premia and higher asset prices, volatility, investment, and growth. We analyze forward guidance, a \"Greenspan put,\" and the yield curve.