This paper studies the strategic asset allocation for an investor with risky liabilities which are subject to inflation and real interest rate risk and who invests in stocks, government bonds, corporate bonds, T-bills, listed real estate, commodities and hedge funds. Using a vector autoregression for returns, liabilities and macro-economic state variables the paper explores the intertemporal covariance structure of assets and liabilities. We find horizon effects in time diversification, risk diversification, inflation hedge and real interest rate qualities. The covariance structure gives insights into which asset classes have a term structure of risk that is different from that of stocks and bonds. The alternative assets classes add value for long-term investors. Differences in strategic portfolios for asset-only and asset-liability investors are due to differences in the global minimum variance and ARTICLE IN PRESS www.elsevier.com/locate/jedc 0165-1889/$ -see front matter r (R.P.M.M. Hoevenaars).liability hedge portfolio. We find that the benefits of long-term investing are larger when there are liabilities. r

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This study examines whether the short-term variation in the Japanese size and value premium is sufficiently predictable to be exploited by a timing strategy. In the spirit of Pesaran and Timmermann [J. Finance 50 (1995) 1201], we employ a dynamic modeling approach in which we explicitly allow for permutations among the determinants in order to mitigate typical data-snooping biases. Using a base set of candidate regressors, we perform an in-sample estimation of all economically sensible models. Subsequently, a ''best'' model is determined according to a selection criterion. However, whereas most studies use in-sample model selection criteria, we introduce an out-of-sample training period to select our models. We then implement our strategy in a second-stage out-of-sample period: the trading period. All stages re-occur on a monthly basis via a rolling window framework. The results confirm sufficient predictability under lower transaction cost levels. Under high transaction costs scenarios it is more difficult to obtain incremental benefits. D

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SUMMARY We study the effect of parameter uncertainty on the long‐run risk for three asset classes: stocks, bills and bonds. Using a Bayesian vector autoregression with an uninformative prior we find that parameter uncertainty raises the annualized long‐run volatilities of all three asset classes proportionally with the same factor relative to volatilities that are conditional on maximum likelihood parameter estimates. As a result, the horizon effect in optimal asset allocations is much weaker compared to models in which only equity returns are subject to parameter uncertainty. Results are sensitive to alternative informative priors, but generally the term structure of risk for stocks and bonds is relatively flat for investment horizons up to 15 years. Copyright © 2013 John Wiley & Sons, Ltd.