This paper evaluates VAR models designed to analyse the monetary policy transmission mechanism in the United States by considering three issues: specification, identification, and the effect of the omission of the long-term interest rate. Specification analysis suggests that only VAR models estimated on a single monetary regime feature parameters stability and do not show signs of mis-specifica...

Recent time series methods are applied to the problem of forecasting New Zealand’s real GDP. Model selection is conducted within autoregressive (AR) and vector autoregressive (VAR) classes, allowing for evolution in the form of the models over time. The selections are performed using the Schwarz (1978) BIC and the Phillips-Ploberger (1996) PIC criteria. The forecasts generated by the data-deter...

This paper evaluates the effectiveness of selected volatility models in forecasting Value-at-Risk (VaR) for 1-day and 10-day horizons. The latter is the actual reporting horizon required by the Basel Committee on Banking Supervision, but not considered in existing studies. The autoregressive stochastic volatility (Taylor, 1982) is found to be less effective than simpler ARCH type models such as...

Value at Risk (VaR) has become the industry standard to measure the market risk. However, the selection of the VaR models is controversial. Simulation Results indicate Historical Simulation has significant positive bias, while GARCH (1,1) has has significant negative bias. Also HS adapts structural change slowly but stable, while GARCH adapts structural break rapidly but less stable. Thus the m...

In this paper we examine the usefulness of multivariate semi-parametric GARCH models for portfolio selection under a Value-at-Risk (VaR) constraint. First, we specify and estimate several alternative multivariate GARCH models for daily returns on the S&P 500 and Nasdaq indexes. Examining the within sample VaRs of a set of given portfolios shows that the semi-parametric model performs uniformly ...

This paper proposes a Vector Autoregressive (VAR) model as a new technique for missing feature reconstruction in ASR. We model the spectral features using multiple VAR models. A VAR model predicts missing features as a linear function of a block of feature frames. We also propose two schemes for VAR training and testing. The experiments on AURORA-2 database have validated the modeling methodolo...

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...

In the first part of this paper we address the non-coherence of value-at-risk (VaR) as a risk measure in the context of portfolio credit risk, and highlight some problems which follow from this theoretical deficiency. In particular, a realistic demonstration of the non-subadditivity of VaR is given and the possibly nonsensical consequences of VaR-based portfolio optimisation are shown. The seco...

Bacterial SecA proteins can be categorized by the presence or absence of a variable subdomain (VAR) located within nucleotide-binding domain II of the SecA DEAD motor. Here we show that VAR is dispensable for SecA function, since the VAR deletion mutant secAΔ519-547 displayed a wild-type rate of cellular growth and protein export. Loss or gain of VAR is extremely rare in the history of bacteria...

The Vector Autoregressive (VAR) model, the Error Correction Model (ECM), and the Kalman Filter Model (KFM) are used to forecast UK stock prices. The forecasting performance of the three models is compared using out of sample forecasting. The results show that the forecasting performance of the ECM is better than that of the VAR and the KFM, and that the VAR performs a forecasting better than th...