Even though stock returns are not highly autocorrelated, their persistent expected returns, even if they are small, impact the cross-sectional estimations of asset pricing models and tests. We find that estimations that do not utilize conditioning information about the nature of persistent expected returns efficiently are biased. The bias is more severe for assets with larger and more persistent expected returns, for larger cross-section of assets and in shorter time-series. Our results, further, show that the estimation of the asset pricing models and tests that uses conditional information efficiently provides more accurate results in the presence of persistent expected returns.

This paper examines the link between risk and institutional quality, an unresolved issue in finance. Our hypothesis is that institutions affect risk through extreme events and less through volatility. We focus on relative tail risk with an original approach that is able to estimate historical tail risk with greater precision. Using international stock market data, we show that tail risk is stable over time, unlike volatility. We find that tail risk captures the relation between risk and institutional quality better than volatility. Better governance substantially reduces the probability of extreme events.

We examine the after-cost out-of-sample performance of the unconditional mean–variance (UMV) strategy in the presence of conditioning information (Ferson and Siegel (2001)) using portfolios of U.K. equity closed-end funds. We find that the performance of the UMV strategy significantly improves when using lagged information variables with the highest persistence (first-order autocorrelation) levels and reduces turnover. This strategy is able to outperform alternative dynamic trading strategies and performs well across different subperiods. At low levels of trading costs, the UMV strategy is able to deliver significant value added to investors.

We construct long–short factor mimicking portfolios that capture the hedging pressure risk premium of commodity futures. We consider single sorts based on the open interests of hedgers or speculators, as well as double sorts based on both positions. The long–short hedging pressure portfolios are priced cross-sectionally and present Sharpe ratios that systematically exceed those of long-only benchmarks. Further tests show that the hedging pressure risk premiums rise with the volatility of commodity futures markets and that the predictive power of hedging pressure over cross-sectional commodity futures returns is different from the previously documented forecasting power of past returns and the slope of the term structure.

In this paper we study the economic value and statistical significance of asset return predictability, based on a wide range of commonly used predictive variables. We assess the performance of dynamic, unconditionally efficient strategies, first studied by Hansen and Richard (1987) and Ferson and Siegel (2001), using a test that has both an intuitive economic interpretation and known statistical properties. We find that using the lagged term spread, credit spread, and inflation significantly improves the risk-return trade-off. Our strategies consistently outperform efficient buy-and-hold strategies, both in and out of sample, and they also incur lower transactions costs than traditional conditionally efficient strategies.

We find persistent predictors known to bias predictive regressions for stock returns to matter also for asset pricing models. For example, deciding whether hedge funds offer an expansion of the investment opportunity set depends, among other things, on the persistence levels of predictors used to create managed returns. Using simulations to disentangle the effects of persistence from predictability, we find highly persistent predictors to bias asset pricing models and tests even if managed portfolios and conditioning information are used optimally. Our framework enables us to construct tests that are robust in the presence of persistent predictors, and we find it to be more difficult to construct such robust tests for linear than for the non-linear ways of utilizing conditioning information.

In this article we construct and investigate the performance of elementary trading strategies that allow an investor to time between equities and commodities. Our strategies appear to capture time-varying risk premiums in the equity and commodity markets, enabling them to successfully time the market, outperforming the benchmark index as well as buy-and-hold and trend-based strategies.

The presence of time varying investment opportunity sets has been documented in the context of international asset allocation, and the economic value associated with these is a topic of lively debate in the academic literature. This paper constructs simple, real-time dynamic international asset allocation strategies based on daily data that exploit the return predictability arising from time varying market integration. Our timing strategies outperform the major (US, UK, Japanese and German) country indices and related portfolios, particularly in down markets. The strategies appear to capture much of the economic value of the return predictability implied by market integration and have many of the characteristics of successful timing strategies.

The authors construct real-time trading strategies based on the dynamic theories of Cootner [1960], Stoll [1979], and Hirshleifer [1990]. These strategies are constructed using the aggregate positions of hedgers. For a sample of 10 liquid commodities they find broad support for these dynamic theories. The active long flat strategies outperform buy and hold strategies, even during a commodity bull market, suggesting that these actively managed strategies are better investments than passive indexes. The results illustrate the importance of being able to capture “phases of backwardation” even during a commodity bull market.

In this paper we study the economic value of predicting the equity risk premium using market variables that reflect the positions of traders in futures and derivatives market. The economic value is ascertained by studying the performance of market timing strategies that use the positions of commercial hedgers and small speculators as predictive variables. Our market timing strategies have high positive Sharpe ratios over the 1999-2007 period compared to a Sharpe ratio of almost zero for the market index. They avoid losses during major downturns and have significant positive alphas, in contrast to timing strategies based on business cycle variables which under-perform the index over this period. The predictive ability seems to originate from a response to changes in fundamentals ahead of the market for large hedgers and from herding among small speculators. Overall these results indicate that futures market variables could play an important and economically significant role in predicting the equity risk premium.

This paper approaches the central questions of the identification and the price of risk in an international asset pricing context. We construct and use factor mimicking portfolios to obtain factor loadings for testing unconditional and conditional pricing. We use a new measure of specification error for conditional models. The dynamic stochastic discount factor which explicitly admits skewness and kurtosis factors in the aggregate global market portfolio significantly expands the factor frontier more than the effect achieved by adding the Fama-French factors to the return on the world market portfolio. A cubic SDF augmented by country-specific inflation and inflation skewness with time-varying risk premiums that are functions of global predictive variables is the best performing model overall for pricing the size, book-to-market and momentum portfolios in the U.S., U.K. and Japan. The country-specific risks are significantly priced, suggesting that the financial markets in these countries may be partially-segmented.

We construct simple timing strategies for the anomaly portfolios based on the lagged return on the market. These strategies have similar or higher Sharpe ratios than the corresponding anomaly portfolio, with lower volatility, and remain profitable for relatively high levels of transaction costs. They have positive, often significant, alphas with respect to factor models that explain the returns on the anomaly portfolios well. These alphas are accounted for by adding an upside risk factor. Our results indicate that much of the high return or upside of the anomaly portfolios may be compensation for bearing downside risk and that the return to these portfolios is correlated with the state of the market.

This paper introduces an international asset pricing model with time-varying risk premia. It augments the two factor model which has the return on the world index and trade weighted exchange rates as factors, with skewness and kurtosis factors. This leads to a stochastic discount factor that is non-linear and has time-varying factor loadings that are functions of global variables. We test this model on market indices, size and momentum sorted portfolios that are formed from stocks listed in G8 countries, as well as country-neutral size, book-to-market and momentum portfolios. Overall, the model is capable of pricing almost all sets of base assets unconditionally using only global predictive variables. It also explains much of the cross sectional variation of the country, size and momentum portfolios, and also achieves much of the substantial size and momentum premiums. The role of time-varying risk premiums that are functions of global variables is crucial to the performance of the model, particularly in the case of the exchange rate factor.

The failure of the static-beta CAPM to explain the cross-section of returns on portfolios sorted on firm size, book-to-market ratio, momentum, and even portfolios sorted on past CAPM betas, is well documented. In this paper we show that the model's performance dramatically improves when portfolio betas are allowed to be time-varying functions of (lagged) business cycle variables. We use an approach based on Hansen and Richard (1987) to construct a candidate stochastic discount factor (SDF), using the excess return on the market portfolio as the single factor, scaled by a time-varying coe±cient. The result is a model in which the conditional factor risk premium is a non-linear function of the business cycle variables. We assess the performance of our model by computing the R2 of the cross-sectional regression of realized on model-implied expected returns, as for example in Jagannathan and Wang (1996). While this is not a formal test of the model's ability to price the assets correctly, it does provide an informative summary statistic that allows us to compare the performance of our scaled model with that of the static version, and also to compare our findings to those of other similar studies.

This paper focuses on the use of market variables that exploit the linkages between spot, futures and derivatives markets, as opposed to the business cycle indicators employed in most of the earlier studies. Spot and futures market linkages are exploited by using commercial and non reportable hedging pressure as the predictive variables while the linkages between the derivatives and spot markets are exploited using the VIX index, a proxy for implied volatility. Using the S&P 500 and gold as our base assets, we study the performance of these variables by examining both the out of sample performance of unconditionally efficient portfolios based on our predictive variables as well as their in-sample performance using a statistical test. Our trading strategies can successfully time the market and avoid losses during the bursting of the 'dot.com' bubble in the second half of 2000, as well as during the bull run that followed. The in-sample results confirm our out of sample experiments with p-values of less than 1% in all cases. The predictive variables on their own do not perform nearly as well, indicating that it is linkages between these markets that is important for market timing. The VIX provides a signal to change the weight on the market while hedging pressure indicates the direction. We construct variables that combine both of these features and find that these variables provide the clearest signals for successful market timing.

We construct unconditionally efficient asset allocation strategies that ex- ploit return predictability of international size and momentum portfolios. The strategies achieve comparable returns to these investment assets while exhibit- ing much lower volatility. They largely avoid major losses by successfully tim- ing these assets. The strategies utilizing the MSCI world index and the term spread as predictive variables achieve better performance than those without exploiting return predictability. The optimal strategies perform better than conditionally efficient strategies due the conservative response of the optimal portfolio weight to extreme realizations of the predictive variables, thus leading to lower volatility.

Stochastic discount factor bounds provide a useful diagnostic tool for testing asset pricing models by specifying a lower bound on the variance of any admissible discount factor. In this paper, we provide a unified derivation of such bounds in the presence of conditioning information, which allows us to compare their theoretical and empirical properties. We find that, while the location of the ‘unconditionally efficient (UE)’ bounds of Ferson and Siegel (2003) is statistically indistinguishable from the (theoretically) optimal bounds of Gallant, Hansen, and Tauchen (1990) (GHT), the former exhibit better sampling properties. We demonstrate that the difference in sampling variability of the UE and GHT bounds is due to the different behavior of the efficient return weights underlying their construction. JEL Classification: G11, G12

In this paper, we develop a new measure of specification error, and thus derive new statistical tests, for conditional factor models, i.e. models in which the factor loadings (and hence risk premia) are allowed to be time-varying. Our test exploits the close links between the stochastic discount factor framework and mean-variance efficiency. We show that a given set of factors is a true conditional asset pricing model if and only if the efficient frontiers spanned by the traded assets and the factor-mimicking portfolios, respectively, intersect. In fact, we show that our test is proportional to the difference in squared Sharpe ratios of these two frontiers.

Over the past few years, commodity prices have experienced the biggest boom in half a century. In this paper we investigate whether it is possible by active asset management to take advantage of the unique risk-return characteristics of commodities, while avoiding their excessive volatility. We show that observing (and learning from) the actions of different groups of market participants enables an active asset manager to successfully 'time' the commodities market. We focus on the information contained in the Commitment of Traders report, published by the CFTC. This report summarizes the size and direction of the positions taken by different types of traders in different markets. Our findings indicate that there is indeed significant informational content in this report, which can be exploited by an active portfolio manager. Our dynamically managed strategies exhibit superior out-of-sample performance, achieving Sharpe ratios in excess of 1.0 and annualized alphas relative to the S&P 500 of around 15%.