We propose a new approach to predictive density modeling that allows for MIDAS effects in both the first and second moments of the outcome and develop Gibbs sampling methods for Bayesian estimation in the presence of stochastic volatility dynamics. When applied to quarterly U.S. GDP growth data,...

This paper presents new models for aggregate UK data on mortgage possessions (foreclosures) and mortgage arrears (payment delinquencies). The innovations include the treatment of difficult to observe variations in loan quality and shifts in forbearance policy by lenders, by common latent...

We propose a method to produce density forecasts of the term structure of government bond yields that accounts for (i) the possible mispecification of an underlying Gaussian Affine Term Structure Model (GATSM) and (ii) the time varying volatility of interest rates. For this, we derive a Bayesian...

Our objective in this paper is to examine whether one can use option-implied information to improve mean-variance portfolio selection with a large number of stocks, and to document which aspects of option-implied information are most useful for improving the out-of-sample performance of...

Studies of bond return predictability find a puzzling disparity between strong statistical evidence of return predictability and the failure to convert return forecasts into economic gains. We show that resolving this puzzle requires accounting for important features of bond return models such...

This paper considers the problem of model uncertainty in the case of multi-asset volatility models and discusses the use of model averaging techniques as a way of dealing with the risk of inadvertently using false models in portfolio management. Evaluation of volatility models is then considered...

Recent financial research has provided evidence on the predictability of asset returns. In this Paper we consider the results contained in Pesaran-Timmerman (1995), which provided evidence on predictability of excess returns in the US stock market over the sample 1959-92. We show that the...

We develop a tactical asset allocation strategy that incorporates the effects of macroeconomic variables. The joint distribution of financial asset returns and the macroeconomic variables is modelled using a VAR with an M-GARCH error structure. As a result the portfolio frontier is time varying...

In this paper, we show how an investor can incorporate uncertainty about expected returns when choosing a mean-variance optimal portfolio. In contrast to the Bayesian approach to estimation error, where there is only a single prior and the investor is neutral to uncertainty, we consider the case...

In this paper, we compare the out-of-sample performance of the rule allocating 1/N to each of the N available assets to several static and dynamic models of optimal asset-allocation for ten datasets. We devote particular attention to models the literature has proposed to account for estimation...