Four-dimensional variational data assimilation (4D-Var) combines the information from a time-sequence of observations with the model dynamics and a background state to produce an analysis. In this paper, a new mathematical insight into the behaviour of 4D-Var is gained from an extension of concepts that are used to assess the qualitative information content of observations in satellite retrieva...

The age of stratospheric air is computed with a trajectory model, using ECMWF ERA-40 3-D-Var and operational 4-D-Var winds. The sensitivity to the forecast period and assimilation technique are studied, and the results are compared with observations and with results from a chemistry transport model that uses the same data sets. A large 5 number of backward trajectories is started in the stratos...

Cointegration has different theoretical implications for forecasting. Several empirical studies have compared the out of sample forecasting performance of cointegrted VECMs against unrestricted VARs in levels and in differences. The results of these studies have been generally mixed and inconclusive. This paper provides a comprehensive review over the subject, and also examines the effects...

Plasmodium falciparum infection during pregnancy leads to abortions, stillbirth, low birth weight, and maternal mortality. Infected erythrocytes (IEs) accumulate in the placenta by adhering to chondroitin sulfate A (CSA) via var2CSA protein exposed on the P. falciparum IE membrane. Plasmodium berghei IE infection in pregnant BALB/c mice is a model for severe placental malaria (PM). Here, we des...

Nowadays the most extensively used risk measure by financial institution is known as Value-at-Risk (VaR), which is defined as the maximum expected loss on an investment over a specified horizon at a given confidence level. To evaluate the accuracy and quality of the out-of-sample VaR forecast (backtesting procedures) is an important issue in practice. The purpose of this paper is to quantify th...

In accordance with Basel Capital Accords, the Capital Requirements (CR) for market risk exposure of banks is a nonlinear function of Value-at-Risk (VaR). Importantly, the CR is calculated based on a bank’s actual portfolio, i.e. the portfolio represented by its current holdings. To tackle mean-VaR portfolio optimization within the actual portfolio framework (APF), we propose a novel mean-VaR op...

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...

Value at risk (VaR) is the standard measure of market risk used by financial institutions. Interpreting the VaR as the quantile of future portfolio values conditional on current information, the conditional autoregressive value at risk (CAViaR) model specifies the evolution of the quantile over time using an autoregressive process and estimates the parameters with regression quantiles. Utilizin...

This paper proposes the mean-dynamic VaR multi-period portfolio selection model with the transaction costs and the constraints on trade volumes. The Bat algorithm is applied to solve the multi-period mean-dynamic VaR model. Numerical results show that the Bat algorithm is effective and feasible to solve multi-period portfolio selection problems.

We extend the vector autoregression (VAR) based expectations hypothesis test of term structure, considered in Bekaert & Hodrick (2001) using recent developments in bootstrap literature. Modifications include the use of wild bootstrap to allow for conditional heteroskedasticity in the VAR residuals without imposing strict parameterization, endogeneous model selection procedure in the bootstrap r...