Cross Hedging with Single Stock Futures

This study evaluates the efficiency of cross hedging with the new single stock futures (SSF) contracts recently introduced in the United States. We use matched sample estimation techniques to select SSF contracts that will reduce the basis risk of crossing hedging and will yield the most efficient hedging portfolio. Employing multivariate matching techniques with cross-sectional matching characteristics, we can improve hedging efficiency while at the same time overcoming the contingency of the correlation between spot and futures prices on the sample period and length. Overall, we find that the best hedging performance is achieved through a portfolio that is hedged with market index futures and a SSF matched by both historical return correlation and cross-sectional matching characteristics. We also find it preferable to retain the chosen SSF contracts for the whole out-of-sample period but to re-estimate the optimal hedge ratio for each rolling window.

Transaction fees and optimal rebalancing in the growth-optimal portfolio

The growth-optimal portfolio optimization strategy pioneered by Kelly is based on constant portfolio rebalancing which makes it sensitive to transaction fees. We examine the effect of fees on an example of a risky asset with a binary return distribution and show that the fees may give rise to an optimal period of portfolio rebalancing. The optimal period is found analytically in the case of lognormal returns. This result is consequently generalized and numerically verified for broad return distributions and returns generated by a GARCH process. Finally we study the case when investment is rebalanced only partially and show that this strategy can improve the investment long-term growth rate more than optimization of the rebalancing period.Comment: 17 pages, 7 figure

Optimal portfolio choice under regime switching, skew and kurtosis preferences

This paper proposes a new tractable approach to solving multi-period asset allocation problems. We assume that investor preferences are defined over moments of the terminal wealth distribution such as its skew and kurtosis. Time-variations in investment opportunities are driven by a regime switching process that can capture bull and bear states. We develop analytical methods that only require solving a small set of difference equations and thus are very convenient to use. These methods are applied to a simple portfolio selection problem involving choosing between a stock index and a risk-free asset in the presence of bull and bear states in the return distribution. If the market is in a bear state, investors increase allocations to stocks the longer their time horizon. Conversely, in bull markets it is optimal for investors to decrease allocations to stocks the longer their investment horizon.Assets (Accounting)

Hedge fund return predictability; To combine forecasts or combine information?

While the majority of the predictability literature has been devoted to the predictability of traditional asset classes, the literature on the predictability of hedge fund returns is quite scanty. We focus on assessing the out-of-sample predictability of hedge fund strategies by employing an extensive list of predictors. Aiming at reducing uncertainty risk associated with a single predictor model, we first engage into combining the individual forecasts. We consider various combining methods ranging from simple averaging schemes to more sophisticated ones, such as discounting forecast errors, cluster combining and principal components combining. Our second approach combines information of the predictors and applies kitchen sink, bootstrap aggregating (bagging), lasso, ridge and elastic net specifications. Our statistical and economic evaluation findings point to the superiority of simple combination methods. We also provide evidence on the use of hedge fund return forecasts for hedge fund risk measurement and portfolio allocation. Dynamically constructing portfolios based on the combination forecasts of hedge funds returns leads to considerably improved portfolio performance

On the importance of clean accounting measures for the tests of stock market efficiency

Tests of the semi-strong form of the efficient market hypothesis (EMH) typically use earnings and book value of equity as benchmarks of fundamental value. Accounting earnings, however, are contaminated by noise due to their transient component and book value of equity tends to be biased downwards due to accounting conservatism. We investigate whether controlling for these effects impacts on the implications concerning the information efficiency of the Swedish stock market. We conclude that relevant adjustments increase both the magnitude and the consistency of the value premium earned on a contrarian investment strategy that buys (shorts) stocks with low (high) relative market valuation. The existence of the value premium cannot be explained by common risk proxies or transaction costs argument. Using cleaner accounting proxies thus strengthens the evidence on the imperfect efficiency of the Swedish stock market.market efficiency, investment, contrarian strategy, transitory earnings, accounting conservatism, Sweden, Scandinavia

Multistep Predictions for Multivariate GARCH Models: Closed Form Solution and the Value for Portfolio Management

The missing wage rigidity in general equilibrium models of efficiency wages is an artifact of the external wage reference perspective conventionally adopted by the literature. Efficiency wage models based on an internal wage reference perspective are capable of generating strong wage rigidity. We propose a structural model of efficiency wages that is broadly consistent with the reported evidence on fairness in labor relations and rent-sharing. Our model provides a robust explanation for wage rigidity and procyclical effort. It also rationalizes reciprocal behavior by workers and the observation that firm productivity is a significant predictor of wage setting.multivariate GARCH models; volatility forecasts; portfolio optimization; minimum variance portfolio