In this paper we propose an optimisation approach to determining the optimal decay factor in time weighted (BRW) simulation. Testing of BRW simulation with different decay factors and competing VaR models is performed on a sample of nine Mediterranean countries, over a four year period that includes the ongoing financial crisis. After optimisation the BRW simulation is among the best performing...

The use of log-transformed data has become standard in macroeconomic forecasting with VAR models. However, its appropriateness in the context of out-of-sample forecasts has not yet been exposed to a thorough empirical investigation. With the aim of filling this void, a broad sample of VAR models is employed in a multi-country setup and approximately 16 Mio. pseudo-out-of-sample forecasts are ev...

in this paper we compared multivariate garch models toestimate value-at-risk. we used a portfolio of weekly indexesincluding tedpix, klse, xu100 during ten years. to estimatevalue-at-risk, first we estimated ccc, dcc of engle, dcc of tseand tsui, dynamic equi correlation models by oxmetrics. then,optimum lags were estimated by minimizing the information criteria.to estimate var, the models accu...

Article history: Received 10 May 2013 Received in revised form 2 September 2014 Accepted 2 September 2014 Available online 11 September 2014 The study investigates the relative performance of Value-at-Risk (VaR) models using daily share price index data from six different countries across Asia, Europe and the United States for a period of 10 years from January 01, 2000 toDecember 31, 2009. Them...

The variance of a portfolio can be forecasted using a single index model or the covariance matrix of the portfolio. Using univariate and multivariate conditional volatility models, this paper evaluates the performance of the single index and portfolio models in forecasting Value-at-Risk (VaR) thresholds of a portfolio. The LR tests of unconditional coverage, independence and conditional coverag...

We propose a unified machine learning model (UMLM) for two-class classification, regression and outlier (or novelty) detection via a robust optimization approach. The model embraces various machine learning models such as support vector machine-based and minimax probability machine-based classification and regression models. The unified framework makes it possible to compare and contrast existi...

Purpose of the paper The Basel Committee regulations require the estimation of Value-at-Risk at 99% confidence level for a 10-trading-day-ahead forecasting horizon. The paper provides a multivariate modelling framework for multi-period VaR estimates for leptokurtic and asymmetrically distributed real-estate portfolio returns. The purpose of the paper is to estimate accurate 10-day-ahead 99% VaR...

In this paper, we investigate whether incorporating common factors of CPI sub-aggregates into forecasting models increases the accuracy of forecasts of inflation. We extract factors by both static and dynamic factor models and then embed them in ARMA and VAR models. Using quarterly data of Iran’s CPI and its sub-aggregates, the models are estimated over 1990:2 to 2008:2 and out of sample ...