In this paper we analyze the manner in which the demand generated by dynamic hedging strategies affects the equilibrium price of the underlying asset. We derive an explicit expression for the transformation of market volatility under the impact of such strategies. It turns out that volatility increases and becomes time and price dependent. The strength of these effects however depends not only on the share of total demand that is due to hedging, but also significantly on the heterogeneity of the distribution of hedged payoffs. We finally discuss in what sense hedging strategies derived from the assumption of constant volatility may still be appropriate even though their implementation obviously violates this assumption. Copyright Blackwell Publishers Inc 1997.

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.

No abstract

No abstract

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. Copyright (c) 2010 Blackwell Publishing Ltd.

No abstract

In this paper, we develop a unified framework for the study of mean-variance efficiency and discount factor bounds in the presence of conditioning information. We extend the framework of Hansen and Richard (1987) to obtain new characterizations of the efficient portfolio frontier and variance bounds on discount factors, as functions of the conditioning information. We introduce a covariance-orthogonal representation of the asset return space, which allows us to derive several new results, and provide a portfolio-based interpretation of existing results.Our analysis is inspired by, and extends the recent work of Ferson and Siegel (2001,2002), and Bekaert and Liu (2004). Our results have several important applications in empirical assetpricing, such as the construction of portfolio-based tests of asset pricing models, conditional measures of portfolio performance, and tests of return predictability.JEL Classification: G11, G12

Recent finance research that draws on behavioral psychology suggests that that investors systematically make errors in forming expectations about asset returns, and thus that investor sentiment can have predictive power for asset returns. A number of empirical studies using both market and survey data as proxies for investor sentiment have found support for these theories. In this study we investigate whether investor sentiment as measured by a component of the University of Michigan survey can help improve dynamic asset allocation over and above the improvement achieved based on commonly used business cycle indicators. We find that the addition of sentiment variables to business cycle indicators considerably improves the performance of dynamically managed portfolio strategies, both for a standard markettimer as well as for a momentum-type investor. We find that sentiment-based dynamic trading strategies, even out-of-sample, would not have incurred any significant losses during the October 1987 crash or the collapse of the 'dot.com' bubble in late 2000. In contrast, standard business cycle indicators fail to predict these events, so that investors relying on these variables alone would have incurred significant losses. Interestingly however, strategies based only on sentiment do not perform well at all. It is the interaction between business cycle indicators and sentiment that makes the strategies work. These strategies are 'active alpha' strategies with low betas and high alphas, in contrast to business cycle based strategies which are effectively 'index-trackers' with high betas and considerably lower alphas.