This paper examines different multivariate models to evaluate what are the main determinants when doing VaR forecasts for a portfolio of assets. To achieve this goal, we unify past multivariate models by using a general copula framework and we propose many new extensions. We differentiate the...

In this paper, we investigate the out-of-sample forecasting ability of a genetic program to approach the dynamic evolution of the Yen/US$ and Pound Sterling/US$ exchange rates, and verify whether the method can beat the random walk model. Later on, we use the predicted values to generate a...

Recent work has found that, without the benefit of hindsight, it can prove difficult for policy-makers to pin down accurately the current position of the output gap; real-time estimates are unreliable. However, attention primarily has focused on output gap point estimates alone. But point...

Data in which each observation is a curve occur in many applied problems. This paper explores prediction in time series in which the data is generated by a curve-valued autoregression process. It develops a novel technique, the predictive factor decomposition, for estimation of the...

This paper documents that inflation forecasts of the Federal Reserve systematically under-predicted inflation before Volker's appointment as Chairman and systematically over-predicted it afterward. It also documents that, under quadratic loss, commercial forecasts have information not contained...

In this paper we analyse the effect of model uncertainty on the wealth and utility outcomes of an investment decision. We compute optimal portfolio weights for domestic and foreign assets and using these weights we construct end investment horizon wealth and utility ratios. Model uncertainty is...

We emphasize using our solutions to the problems of omitted variables, measurement errors, and unknown functional forms to improve model specification, and to estimate the mean square error of an empirical best linear unbiased predictor of an individual drawing of the dependent variable of an...

This paper introduces model uncertainty into a simple Lucas-type monetary model. Inflation depends on agents' expectations and a vector of exogenous random variables. Following (Branch and Evans 2004) agents are assumed to underparameterize their forecasting models. A Misspecification...

The mixture of two already known soft computing technics, like Genetic Algorithms and Neural Networks (NN) in Financial modeling, takes a new approach in the search for the best variables involving an Econometric model using a Neural Network. This new approach helps to recognice the importance...

This paper investigates the precision of multivariate models of the output gap and considers their implications for the formulation of macroeconomic policy. Multivariate models identify the gap by including information from structural economic relationships, such as Okun's Law, the Phillips...