Value-at-Risk (VaR) is increasingly used in portfolio risk measurement, risk capital allocation and performance attribution. Financial risk managers are therefore rightfully concerned with the precision of typical VaR techniques. The purpose of this paper is to assess the precision of common dynamic models and to quantify the magnitude of the estimation error by constructing confidence interval...

Beginning in 1998, U.S. commercial banks may determine their regulatory capital requirements for financial market risk exposure using value-at-risk (VaR) models. Currently, regulators have available three hypothesis-testing methods for evaluating the accuracy of VaR models: the binomial, interval forecast and distribution forecast methods. Given the low power often exhibited by their correspond...

This paper compares the out-of-sample forecasting accuracy of five classes of time series models for market shares of the six most important Portuguese car market competitors over different horizons. As representative time series models I employ a random walk with drift (Naive), a univariate ARIMA, a near-VAR and a general BVAR. The out-of-sample forecasts are also compared against forecasts ge...

This paper applies the hybrid dynamic general-equilibrium, vector autoregressive (DGE-VAR) model developed by Ireland (1999) to Canadian time series. It presents the first Canadian evidence that a hybrid DGE-VAR model may have better out-of-sample forecasting accuracy than a simple, structure-free VAR model. The evidence suggests that estimated DGE models have the potential to add good forecast...

One of the implications of the creation of Basel Committee on Banking Supervision was the implementation of Value-at-Risk (VaR) as the standard tool for measuring market risk. Thereby the correct specification of parametric VaR models became of crucial importance in order to provide accurate and reliable risk measures. If the underlying risk model is not correctly specified, VaR estimates under...

Recently, a significant share of the empirical analysis on the impact of public capital on regional growth has used multivariate time-series frameworks based on vector auto regressive (VAR) models. Nevertheless, not as much attention has been dedicated to the analysis of the long-run determinants of regional growth processes using multi-region panel data and applying panel integration and co-in...

We propose a mixed-effects vector auto-regressive (ME-VAR) model for studying brain effective connectivity. One common approach to investigating inter-regional associations in brain activity is the multivariate auto-regressive (VAR) model. The standard VAR model unrealistically assumes the connectivity structure to be identical across all participants in a study and therefore, could yield misle...

VAR models [13] are a type of multi-equation model that linearly describe the simultaneous interactions and behaviour among a group of variables using only their own past. More specifically, a VAR is a model of simultaneous equations formed by a system of equations in which the contemporary values of model variables do not appear in any explanatory variable in the equations. The set of explanat...

VAR analysis is a widespread method of quantitatively analyzing macro-economic issues. In this paper we examine the use of "hybrid" VAR models that retain the short-run features of a VAR but are designed to reproduce selected characteristics of calibrated models that are frequently used for the simulation of policy actions. The calibrated model we use is the McKibbin Sachs Global (MSG2) model o...

Unrestricted reduced form vector autoregressive (VAR) models have become a dominant research strategy in empirical macroeconomics since Sims (1980) critique of traditional macroeconometric modeling. They are however subjected to the curse of dimensionality. In this paper we propose general-to-specific reductions of VAR models and consider computer-automated model selection algorithms embodied i...