This survey reviews the large and growing literature on the use of pair-copula constructions (PCCs) in financial applications. Using a PCC, multivariate data that exhibit complex patterns of dependence can be modeled using bivariate copulae as simple building blocks. Hence, this model represents...

This survey paper discusses the Cointegrated Vector AutoRegressive (CVAR) methodology and how it has evolved over the past 30 years. It describes major steps in the econometric development, discusses problems to be solved when confronting theory with the data, and, as a solution, proposes a...

The standard Vector Error Correction Model (VECM) approach to investigating the underlying dynamics of economic variables assumes a constant co-integration space. This paper relaxes this assumption by implementing a regime switching VECM that allows for shifts in both the drift and the long-run...

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...

A novel class of dimension reduction methods is combined with a stochastic multi-factor panel regression-based state-space model in order to model the dynamics of yield curves whilst incorporating regression factors. This is achieved via Probabilistic Principal Component Analysis (PPCA) in which...

Financial asset returns are known to be conditionally heteroskedastic and generally non-normally distributed, fat-tailed and often skewed. These features must be taken into account to produce accurate forecasts of Value-at-Risk (VaR). We provide a comprehensive look at the problem by considering...

This paper develops a Bayesian vector autoregressive model(BVAR) for the leader of the Portuguese car market to forecast the market share. The model includes five marketing decision variables.The Bayesian prior is selected on the basis of the accuracy of the out-of-sample forecasts. We find that...

An information matrix of a parametric model being singular at a certain true value of a parameter vector is irregular. The maximum likelihood estimator in the irregular case usually has a rate of convergence slower than the Ín-rate in a regular case. We propose to estimate such models by the...