Mean variance and goal achieving portfolio for discrete-time market with currently observable source of correlations

The paper studies optimal portfolio selection for discrete time market models in mean-variance and goal achieving setting. The optimal strategies are obtained for models with an observed process that causes serial correlations of price changes. The optimal strategies are found to be myopic for the goal-achieving problem and quasi-myopic for the mean variance portfolio

On asymptotic optimality of Merton's myopic portfolio strategies under time discretization

This paper studies the properties of discrete-time stochastic optimal control problems associated with portfolio selection. We investigate if optimal continuous-time strategies can be used effectively for a discrete-time market after a straightforward discretization. We found that Merton's strategy approximates the performance of the optimal strategy in a discrete-time model with sufficiently small time steps

What tames the Celtic tiger? portfolio implications from a multivariate Markov switching model

We use multivariate regime switching vector autoregressive models to characterize the time-varying linkages among the Irish stock market, one of the top world performers of the 1990s, and the US and UK stock markets. We find that two regimes, characterized as bear and bull states, are required to characterize the dynamics of excess equity returns both at the univariate and multivariate level. This implies that the regimes driving the small open economy stock market are largely synchronous with those typical of the major markets. However, despite the existence of a persistent bull state in which the correlations among Irish and UK and US excess returns are low, we find that state comovements involving the three markets are so relevant to reduce the optimal mean variance weight carried by ISEQ stocks to at most one-quarter of the overall equity portfolio. We compute time-varying Sharpe ratios and recursive mean-variance portfolio weights and document that a regime switching framework produces out-of-sample portfolio performance that outperforms simpler models that ignore regimes. These results appear robust to endogenizing the effects of dynamics in spot exchange rates on excess stock returns.Stock exchanges

Managing international portfolios with small capitalization stocks

In the context of an international portfolio diversification problem, we find that small capitalization equity portfolios become riskier in bear markets, i.e. display negative co-skewness with other stock indices and high co-kurtosis. Because of this feature, a power utility investor ought to hold a well-diversified portfolio, despite the high risk premium and Sharpe ratios offered by small capitalization stocks. On the contrary small caps command large optimal weights when the investor ignores variance risk, by incorrectly assuming joint normality of returns. The dominant factor in inducing such shifts in optimal weights is represented by the co-skewness, the predictable, time-varying covariance between returns and volatilities. We calculate that if an investor were to ignore co-skewness and co-kurtosis risk, he would suffer a certainty-equivalent reduction in utility equal to 300 basis points per year under the steady-state distribution for returns. Our results are qualitatively robust when both European and North American small caps are introduced in the analysis. Therefore this paper offers robust evidence that predictable covariances between means and variances of stock returns may have a first order effect on portfolio composition.Investments, Foreign ; Stocks

That Courage is not inconsistent with Caution: Foreign Currency Hedging for Superannuation Funds

Surveys of Australian superannuation funds verify that most international bond holdings, but not equity holdings, are hedged for currency risk. We compare the mean-variance efficiency of this practice with two alternative strategies: a conventional forward hedge; and a selective hedge triggered by the sign of the interest differential. These strategies produce optimal allocations which stochastically dominate the restricted portfolio according to Barrett-Donald (2003) tests. The advantages of alternative hedging strategies remain when the vector of sample mean returns is replaced by forecasts. Selective hedging works best for equities; conventional hedging for bonds. Adding unhedged bonds does not improve outcomescurrency hedging; portfolio allocation; stochastic dominance

Can VAR models capture regime shifts in asset returns? a long-horizon strategic asset allocation perspective

In the empirical portfolio choice literature it is often invoked that through the choice of predictors that may closely track business cycle conditions and market sentiment, simple Vector Autoregressive (VAR) models could produce optimal strategic portfolio allocations that hedge against the bull and bear dynamics typical of financial markets. However, a distinct literature exists that shows that non-linear econometric frameworks, such as Markov switching, are also natural tools to compute optimal portfolios arising from the existence of good and bad market states. In this paper we examine whether and how simple VARs can produce empirical portfolio rules similar to those obtained under a range of multivariate Markov switching models, by studying the effects of expanding both the order of the VAR and the number/selection of predictor variables included. In a typical stock-bond strategic asset allocation problem on US data, we compute the out-of-sample certainty equivalent returns for a wide range of VARs and compare these measures of performance with those typical of non-linear models that account for bull-bear dynamics and characterize the differences in the implied hedging demands for a long-horizon investor with constant relative risk aversion preferences. We conclude that most (if not all) VARs cannot produce portfolio rules, hedging demands, or out-of-sample performances that approximate those obtained from equally simple non-linear frameworks.Econometric models ; Vector autoregression ; Asset pricing ; Rate of return