2 VAR models 4 2.1 Analytical results for VAR models . . . . . . . . . . . . . . . . . . . . 4 2.1.1 The Diffuse Prior . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 The Natural Conjugate Prior . . . . . . . . . . . . . . . . . . . 5 2.1.3 The Minnesota Prior . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Estimation of VARs using the Gibbs sampler . . . . . . . . . . . . . 6...

This paper assesses the forecast performance of a set of VAR models under a growing number of restrictions. With a maximum forecast horizon of 12 years, we show that the farther the horizon is, the more structured and restricted VAR models have to be to produce accurate forecasts. Indeed, unrestricted VAR models, not subjected to integration or cointegration, are poor forecasters for both short...

Identified vector autoregressive (VAR) models have become widely used on time series data in recent years, but finite sample inference for such models remains a challenge. In this study, we propose a conjugate prior for Bayesian analysis of normalized VAR models. Under the prior, themarginal posterior of VAR parameters involved in identification can be either derived in closed form or simulated...

Value-at-Risk (VaR) is widely used as a tool for measuring the market risk of asset portfolios. However, alternative VaR implementations are known to yield fairly different VaR forecasts. Hence, every use of VaR requires choosing amongst alternative forecasting models. This paper undertakes two case studies in model selection, for the S&P 500 index and India’s NSE-50 index, at the 95% and 99% l...

The purpose of this study is to contrast the forecasting performance of two non-linear models, a regime-switching vector autoregressive model (RS-VAR) and a recurrent neural network (RNN), to that of a linear benchmark VAR model. Our specific forecasting experiment is UK inflation and we utilize monthly data from 1969-2003. The RS-VAR and the RNN perform approximately on par over both monthly a...

This study extends the existing forecasting accuracy debate in the tourism literature by examining the forecasting performance of various vector autoregressive (VAR) models. In particular, this study seeks to ascertain whether the introduction of the Bayesian restrictions (priors) to the unrestricted VAR process would lead to an improvement in forecasting performance in terms of achieving a hig...

Multivariate time-series modeling and forecasting constitutes an important problem with numerous applications. In this work, we consider multivariate continuous time series modeling from aviation, where the data consists of multiple sensor measurements from real world flights. While traditional approaches such as VAR (vector auto-regressive) models have been widely used for aviation time series...

The purpose of this study is estimation of daily Value at Risk (VaR) for total index of Tehran Stock Exchange using parametric, nonparametric and semi-parametric approaches. Conditional and unconditional coverage backtesting are used for evaluating the accuracy of calculated VaR and also to compare the performance of mentioned approaches. In most cases, based on backtesting statistics Results, ...