We implement a long-horizon static and dynamic portfolio allocation involving a risk-free and a risky asset. This model is calibrated at a quarterly frequency for ten European countries. We also use maximum-likelihood estimates and Bayesian estimates to account for parameter uncertainty. We find...

The paper examines the problem of portfolio selection based on the forecasts of unknown quality in a mean-variance framework. Early work by Treynor and Black (1973) established a relationship between the correlation of forecasts, the number of independent securities available and the Sharpe...

Unknown model parameters, like expected returns, cannot be accurately estimated from short samples. Respective estimation error most likely leads to the portfolio, inconsistent with its target risk/return profile. We investigate the ways of reducing the impact of estimation error on portfolio...

In this paper, the authors describe a simple procedure for blending statistical estimates with expert opinions to produce a forward-looking view of the performance of assets. They discuss the impact of behavioural biases on the views and propose general modelling principles to biases. Standard...

We demonstrate that the parameters controlling skewness and kurtosis in popular equity return models estimated at daily frequency can be obtained almost as precisely as if volatility is observable by simply incorporating the strong information content of realized volatility measures extracted...

A novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive model and...

We study how stock return's predictability and model uncertainty affect a rational buy-and-hold investor's decision to allocate her wealth for different lengths of investment horizons in the UK market. We consider the FTSE All-Share Index as the risky asset, and the UK Treasury bill as the risk...

This paper solves the dynamic asset allocation problem under stock return predictability based on the dividend price ratio with regime shifts and parameter uncertainty in a fully Bayesian framework. Intertemporal hedging demands are simultaneously induced by predictability, regime shifts,...

The purpose of this study is to incorporate some of the influential findings in the forecasting literature in an integrated framework to examine whether a real-time optimizing investor can benefit from the stock market by allocating assets based on a predictive model that only uses industry...

A novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive model and...