Density forecast combinations are becoming increasingly popular as a means of improving forecast ‘accuracy’, as measured by a scoring rule. In this paper we generalise this literature by letting the combination weights follow more general schemes. Sieve estimation is used to optimise the score of the generalised density combination where the combination weights depend on the variable one is try...

We generalise the Black-Litterman (BL) portfolio management framework to incorporate time-variation in the conditional distribution of returns in the asset allocation process. We evaluate the performance of the dynamic BL model using both standard performance ratios as well as other measures that are designed to capture tail risk in the presence of non-normally distributed asset returns. We fin...

This paper proposes a computationally efficient algorithm for quantifying the impact of interest-rate risk and longevity risk on the distribution of annuity values in the distant future. The algorithm simulates the state variables out to the end of the horizon period and then uses a Taylor series approximation to compute approximate annuity values at the end of that period, thereby avoiding a c...

The methods used in most SAT validity studies cannot be justified by any sample selection assumptions and are uninformative about the source of the SAT’s predictive power. A new omitted variables estimator is proposed; plausibly consistent estimates of the SAT’s contribution to predictions of University of California freshman grade point averages are about 20% smaller than the usual methods imp...

Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a range of approaches will improve for...

We explore two popular approaches to empirical analysis of monetary policy: the New Keynesian and the identified vector autoregression approaches. Stylized models of private behavior coupled with simple rules describing policy behavior characterize New Keynesian work. Vector autoregressions consist of minimally identified dynamic descriptions of private behavior coupled with a detailed rule for...

In this paper we provide analytical, simulation, and empirical evidence on a test of equal economic value from competing predictive models of asset returns. We define economic value using the concept of a performance fee — the amount an investor would be willing to pay to have access to an alternative predictive model that is used to make investment decisions. We establish that this fee can be ...

Modeling financial volatility is an important part of empirical finance. This paper provides a literature review of the most relevant volatility models, with a particular focus on forecasting models. We firstly discuss the empirical foundations of different kinds of volatility. The paper, then, analyses the non-parametric measure of volatility, named realized variance, and its empirical applica...

Four model selection methods are applied to the problem of predicting business cycle turning points: equally-weighted forecasts, Bayesian model averaged forecasts, and two models produced by the machine learning algorithm boosting. The model selection algorithms condition on different economic indicators at different forecast horizons. Models produced by BMA and boosting outperform equally-weig...