In this paper I have used copula functions to forecast the Value-at-Risk (VaR) of an equally weighted portfolio comprising a small cap stock index and a large cap stock index for the oil and gas industry. The following empirical questions have been analyzed: (i) are there nonnormalities in the...

Although survey-based point predictions have been found to outperform successful forecasting models, corresponding variance forecasts are frequently diagnosed as heavily distorted. Professional forecasters who report inconspicuously low ex ante variances often produce squared forecast errors...

Commodity prices co-move, but the strength of this co-movement changes over time due to structural factors, like changing energy intensity in production and consumption as well as changing composition of underlying shocks. This paper explores whether econometric models that exploit this...

The paper presents an incomplete competition model (ICM), where inflation is determined jointly with unit labour cost growth. The ICM is estimated on data for the Euro area and evaluated against existing models, i.e. the implicit inflation equation of the Area Wide model (AWM) - cf. Fagan, Henry...

Many questions of economic interest in structural VAR analysis involve estimates of multiple impulse response functions. Other questions relate to the shape of a given impulse response function. Answering these questions requires joint inference about sets of structural impulse responses,...

We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a horizon of ten trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly or biweekly...

In this work the P* model is used to analyze and forecast the inflation rate in the economy of Puerto Rico. This model is based on two essential points: the first one is to identify the inflationary potential of an economic system through the estimation of the price level to which the inflation...

Density forecast combinations are examined in real-time using the log score to compare five methods: fixed weights, static and dynamic prediction pools, as well as Bayesian and dynamic model averaging. Since real-time data involves one vintage per time period and are subject to revisions, the...

This research aims at exploring whether simple trading strategies developed using state-ofthe-art Machine Learning (ML) algorithms can guarantee more than the risk-free rate of return or not. For this purpose, the direction of S&P 500 Index returns on every 6th day (SPYRETDIR6) and magnitude of...

We consider forecast combination and, indirectly, model selection for VAR models when there is uncertainty about which variables to include in the model in addition to the forecast variables. The key di erence from traditional Bayesian variable selection is that we also allow for uncertainty...