Evaluation of forecast optimality in economics and finance has almost exclusively been conducted under the assumption of mean squared error loss. Under this loss function optimal forecasts should be unbiased and forecast errors should be serially uncorrelated at the single period horizon with...

A common problem in out-of-sample prediction is that there are potentially many relevant predictors that individually have only weak explanatory power. We propose bootstrap aggregation of pre-test predictors (or bagging for short) as a means of constructing forecasts from multiple regression...

In real time forecasting, the sample is usually split into an estimation period of R observations and a prediction period of P observations, where T=R+P. Parameters are often estimated in a recursive manner, initially using R observations, then R+1 observations and so on until T-1 observations...

Recently financial econometricians have shifted their attention from point and interval forecasts to density forecasts mainly to address the issue of the huge loss of information that results from depicting portfolio risk by a measure of dispersion alone. One of the major problems in this area...

In this paper we investigate portfolio coskewness using a quadratic market model as return generating process. It is shown that portfolios of small (large) firms have negative (positive) coskewness with market. An asset pricing model including coskewness is tested through the restrictions it...

This paper examines the long-run dynamics and the cyclical structure of the US stock market using fractional integration techniques. We implement a version of the tests of Robinson (1994a), which enables one to consider unit (or fractional) roots both at the zero (long-run) and at the cyclical...

Recently financial econometricians have shifted their attention from point and interval forecasts to density forecasts mainly to address the issue of the huge loss of information that results from depicting portfolio risk by a measure of dispersion alone. One of the major problems in this area...

Under the squared error loss, the optimal forecast is the conditional mean, and the one-step forecast error is a martingale difference (MD). The one-step forecast error forms the conditional moment condition obtained from the loss derivative with respect to the forecast. Similarly, under a...