We study an investor’s optimal consumption and portfolio choice problem when he confronts with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor’s aversion to model uncertainty. The investor deals with specification doubts by slanting his beliefs about submodels of returns pessimistically, causing his investment strategy to be more conservative than the Bayesian strategy. This effect is large for high and low values of the predictive variable. Unlike in the Bayesian framework, the hedging demand against model uncertainty may cause the investor’s stock allocations to first decrease sharply and then increase with his prior probability of the IID model, even when the expected stock return under the IID model is lower than under the predictability model. Adopting suboptimal investment strategies by ignoring model uncertainty can lead to sizable welfare costs.

The predictability of the market return and dividend growth is addressed in an equilibrium model with two regimes. A state variable that drives the conditional means of the aggregate consumption and dividend growth rates follows different time-series processes in the two regimes. In linear predictive regressions over 1930 2009, the market return is predictable by the price-dividend ratio with R² 11.7 % if the probability of being in the first regime exceeds 50%; and dividend growth is predictable by the price-dividend ratio with R² 28.3 % if the probability of being in the second regime exceeds 50%. The model-implied state variables perform significantly better at predicting the equity, size, and value premia, the aggregate consumption and dividend growth rates, and the variance of market return than linear regressions with the market price-dividend ratio and risk free rate as predictive variables.

Vila, and all the participants at the UCLA Brown Bag Seminar for comments. Finally, I thank Micah Allred, Cesare Fracassi, Stefan Petry, and all the Finance Ph.D. students at the UCLA Anderson School for their help and comments. All remaining errors are my own responsibility. This version of the paper is preliminary and incomplete. Please do not cite or quote without the consent of the author.

This paper examines the exchange rate predictability stemming from the equilibrium model of international financial adjustment developed by Gourinchas and Rey (2007). Using theoretically motivated predictive variables that measure cyclical external imbalances for country pairs, we assess the ability of this model to forecast out-of-sample four major US dollar exchange rates using criteria of economic pro…tability. The analysis shows that the model delivers tangible economic value to a risk averse investor, who will pay high performance fees to switch from a portfolio strategy based on the random walk benchmark to one that conditions on the structural model. The results are robust to the presence of reasonable transaction costs across various forecasting performance criteria, and they are further enhanced when sensible economic restrictions are imposed on the predictive model.

In 2008 all ECB publications feature a motif taken from the 10 banknote. This paper can be downloaded without charge from

Theory suggests that the risk of infrequent yet extreme events has a large impact on asset prices. Testing models of this hypothesis remains a challenge due to the difficulty of measuring tail risk fluctuations over time. I propose a new measure of time-varying tail risk that is motivated by asset pricing theory and is directly estimable from the cross section of returns. My procedure applies Hill’s (1975) tail risk estimator to the cross section of extreme events each day. It then optimally averages recent cross-sectional Hill estimates to provide conditional tail risk forecasts. Empirically, my measure has strong predictive power for aggregate market returns, outperforming all commonly studied predictor variables. I find that a one standard deviation increase in tail risk forecasts an increase in excess market returns of 4.4 % over the following year. Crosssectionally, stocks that highly positively covary with my tail risk measure earn average annual returns 6.0 % lower than stocks with low tail risk covariation. I show that these results are consistent with predictions from two structural models: i) a long run risks economy with heavy-tailed consumption and dividend growth shocks, and ii) a time-varying rare disaster framework.

The predictability of the market return and dividend and consumption growth is addressed in an equilibrium model with two regimes. A state variable that drives the conditional means of the aggregate consumption and dividend growth rates follows di¤erent time-series processes in the two regimes. In linear predictive regressions over 1930 2009, the market return is predictable by the market-wide price-dividend ratio with R2 11:7 % if the probability of being in the …rst regime exceeds 50%; and dividend growth is predictable by the price-dividend ratio with R2 28:3 % if the probability of being in the second regime exceeds 50%. The model-implied state variables perform signi…cantly better at in-sample and outof-sample prediction of the equity, size, and value premia, aggregate consumption and dividend growth rates, and variance of market return than linear regressions with the price-dividend ratio and risk free rate as predictive variables.

Warther (1995) documents a positive contemporaneous relationship between aggregate flows and stock market returns. On the other hand, variables such as dividend yields predict returns (Fama and French (1989); Campbell and Thompson (2008)) and innovations in macroeconomic variables affect contemporaneous returns (Chen, Roll, and Ross (1986); Flannery and Protopapadikas (2002)). If fund investors are smart, they should incorporate information about expected returns contained in the predictive and macroeconomic variables while making their investment decisions. This should, in turn, create a relationship between aggregate flows and predictive and macroeconomic variables. Using quarterly flow data from 1951Q4 to 2007Q4, we find that both predictive and macroeconomic variables have a relatively small impact on flows and the return-flow relationship is largely independent of these variables. Our results suggest that fund investors are not smart, in that their investment decisions fail to adequately incorporate the information contained in these variables.

We introduce a “bad environment-good environment ” technology for consumption growth in a consumption-based asset pricing model. Using the preference structure from Campbell and Cochrane (1999), the model generates realistic non-Gaussian features of fundamentals while still permitting closed-form solutions for asset prices. The model not only fits standard salient asset prices features including means and volatilities for equity returns and risk free rates, but also generates realistic features of the “risk-neutral ” conditional density of equity returns, including the variance premium.

We take up the challenge of forecasting out-of-sample monthly returns on stock market indices. Recent contributions show that a wide range of popular predictors poorly forecast the US equity premium when the performance is compared to the average predictor. We revisit these findings focusing on three aspects. First, we report results for four major stock markets: US, UK, Germany and Japan. We find similar patterns of forecastability across countries. Second, this paper focuses on the choice of models and, to a lesser extent, on the choice of predictors. This reflects the changing focus that can be found in recent years in the literature. We explore two structural aspects that have attracted attention: structural changes and theory-induced restrictions. In our samples, models incorporating structural breaks fare better than standard regressions and outperform the average the average predictor at longer horizons. Theory-induced restrictions that were proposed in the literature are shown to be rarely binding and hence the performance is similar to standard regressions. One key finding is that the restrictions produce meaningful results when they are combined with models of structural breaks. These joint models actually do add forecasting power, especially at longer horizons. On the contrary, popular standard models underperform in almost all respects. Finally, we evaluate the forecasting performance in terms of both the size of the forecasting error and the quality of sign predictions. One of the key findings of the paper is that the results look more favorable for forecasting models when it comes to forecasting the direction of market movements.