The dynamics of expected returns: evidence from multi-scale time series modelling

Conventional wisdom posits that all the relevant investors' information lies at the highest possible frequency of observation, so that long-run expected returns can be mechanically inferred by a forward aggregation of short-run estimates. We reverse such logic and propose a novel framework to model and extract the dynamics of latent short-term expected returns by coherently combining the lower-frequency information embedded in multiple predictors. We show that the information cascade from low- to high-frequency levels allows to identify long-lasting effects on expected returns that cannot be captured by standard persistent ARMA processes. The empirical analysis demonstrates that the ability of the model to capture simultaneously medium- to long-term fluctuations in the dynamics of expected returns, has first order implications for forecasting and investment decisions

News shocks and asset prices

We study the importance of anticipated shocks (news) for understanding the comovement between macroeconomic quantities and asset prices. We find that four-quarter anticipated investment shocks are an important source of fluctuations for macroeconomic variables: they account for about half of the variance in hours and investment. However, it is the four-quarter anticipated productivity shock that is driving a large fraction of consumption and most of the price-dividend ratio fluctuations. These productivity news are key for the model to reproduce the empirical tendency for stock-market valuations and excess returns to lead the business cycle. Importantly, a model that does not use asset price information in the estimation would downplay the role of productivity news; in this case, the model implies that return moves (almost) completely contemporaneously with the economic activity, counterfactually with the data

Mind the (Convergence) Gap: Bond Predictability Strikes Back!

We show that the difference between the natural rate of interest and the current level of monetary policy stance, which we label Convergence Gap (CG), contains information that is valuable for bond predictability. Adding CG in forecasting regressions of bond excess returns significantly raises the R squared, and restores countercyclical variation in bond risk premia that is otherwise missed by forward rates. Consistent with the argument that CG captures the effect of real imbalances on the path of rates, our factor has predictive ability for real bond excess returns. The importance of the gap remains robust out-of-sample and in countries other than the U.S. Furthermore, its inclusion brings significant economic gains in the context of dynamic conditional asset allocation

Value return predictability across asset classes and commonalities in risk premia

We show that returns to value strategies in individual equities, industries, commodities, currencies, global government bonds, and global stock indexes are predictable in the time series by their respective value spreads. In all these asset classes, expected value returns vary by at least as much as their unconditional level. A single common component of the value spreads captures about two–thirds of value return predictability and the remainder is asset class–specific. We argue that common variation in value premia is consistent with rationally time–varying expected returns, because (i) common value is closely associated with standard proxies for risk premia, such as the dividend yield, intermediary leverage, and illiquidity, and (ii) value premia are globally high in bad times.authorsversionpublishe

The scale of predictability

We introduce a new stylized fact: the hump-shaped behavior of slopes and coefficients of determination as a function of the aggregation horizon when running (forward/backward) predictive regressions of future excess market returns onto past economic uncertainty (as proxied by market variance, consumption variance, or economic policy uncertainty). To justify this finding formally, we propose a novel modeling framework in which predictability is specified as a property of low-frequency components of both excess market returns and economic uncertainty. We dub this property scale-specific predictability. We show that classical predictive systems imply restricted forms of scale-specific predictability. We conclude that for certain predictors, like economic uncertainty, the restrictions imposed by classical predictive systems may be excessively strong

Implications of return predictability for consumption dynamics and asset pricing

Two broad classes of consumption dynamics—long-run risks and rare disasters—have proven successful in explaining the equity premium puzzle when used in conjunction with recursive preferences. We show that bounds a-là Gallant, Hansen, and Tauchen that restrict the volatility of the stochastic discount factor by conditioning on a set of return predictors constitute a useful tool to discriminate between these alternative dynamics. In particular, we document that models that rely on rare disasters meet comfortably the bounds independently of the forecasting horizon and the asset returns used to construct the bounds. However, the specific nature of disasters is a relevant characteristic at the 1-year horizon: disasters that unfold over multiple years are more successful in meeting the predictors-based bounds than one-period disasters. Instead, at the 5-year horizon, the sole presence of disasters—even if one-period and permanent—is sufficient for the model to satisfy the bounds. Finally, the bounds point to multiple volatility components in consumption as a promising dimension for long-run risk models

Bond risk premiums with machine learning

We show that machine learning methods, in particular, extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those obtained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock- and labor-market-related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both channels

Implications of return predictability for consumption dynamics and asset pricing

Two broad classes of consumption dynamics—long-run risks and rare disasters—have proven successful in explaining the equity premium puzzle when used in conjunction with recursive preferences. We show that bounds a-là Gallant, Hansen, and Tauchen that restrict the volatility of the stochastic discount factor by conditioning on a set of return predictors constitute a useful tool to discriminate between these alternative dynamics. In particular, we document that models that rely on rare disasters meet comfortably the bounds independently of the forecasting horizon and the asset returns used to construct the bounds. However, the specific nature of disasters is a relevant characteristic at the 1-year horizon: disasters that unfold over multiple years are more successful in meeting the predictors-based bounds than one-period disasters. Instead, at the 5-year horizon, the sole presence of disasters—even if one-period and permanent—is sufficient for the model to satisfy the bounds. Finally, the bounds point to multiple volatility components in consumption as a promising dimension for long-run risk models