Alexander Stremme

International Dynamic Asset Allocation and Return Predictability

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 Optimal Use of Return Predictability: An Empirical Study

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.

Asset Pricing Anomalies and Time-Varying Betas: A New Specification Test for Conditional Factor Models

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. We draw three main conclusions from our empirical findings. First, optimal scaling clearly improves the performance of asset pricing models, to the point where several of the scaled models are capable of explaining asset pricing anomalies. However, even the optimally scaled models fall short of being true conditional asset pricing models in that they fail to price actively managed portfolios correctly. Second, there is significant time-variation in factor loadings and hence risk premia, which plays a significant role in asset pricing. Moreover, the optimal factor loadings display a high degree of non-linearity in the conditioning variables, suggesting that the linear scaling prevalent in the literature is sub-optimal and does not capture the inter-temporal pattern of risk premia. Third, skewness and kurtosis do matter in the conditional setting, while adding little to unconditional performance.

Exploiting Predictability in International Anomalies

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.