This paper formulates dynamic density functions, based upon skewed-t and similar representations, to model and forecast electricity price spreads between different hours of the day. This supports an optimal day ahead storage and discharge schedule, and thereby facilitates a bidding strategy for...

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based on the forecast of the conditional variances, covariances and correlations of financial returns. Although the decisions are based on forecasts covariance matrix little is known...

This chapter presents a unified set of estimation methods for fitting a rich array of models describing dynamic relationships within a longitudinal data setting. The discussion surveys approaches for characterizing the micro dynamics of continuous dependent variables both over time and across...

Financial analysts typically estimate volatilities and correlations from monthly or higher frequency returns when determining the optimal composition of a portfolio. Although it is widely acknowledged that these measures are not necessarily stationary across samples, most analysts assume...

The Sharpe ratio is the most widely used metric for comparing performance across investment managers and strategies, and the information ratio is as commonly used to evaluate performance relative to a benchmark. Although it is widely recognized that non-linearities arising from the inclusion of...

We propose a method for forecasting individual outcomes and estimating random effects in linear panel data models and value-added models when the panel has a short time dimension. The method is robust, trivial to implement and requires minimal assumptions. The idea is to take a weighted average...

We consider two ways to aggregate expert opinions using simple averages: averaging probabilities and averaging quantiles. We examine analytical properties of these forecasts and compare their ability to harness the wisdom of the crowd. In terms of location, the two average forecasts have the...

Most Difference-in-Difference (DD) papers rely on many years of data and focus on serially correlated outcomes. Yet almost all these papers ignore the bias in the estimated standard errors that serial correlation introduces. This is especially troubling because the independent variable of...