This chapter reviews the literature on the econometric relationship between DSGE and VAR models from the point of view of estimation and model validation. The mapping between DSGE and VAR models is broken down into three stages: 1) from DSGE to statespace model; 2) from state-space model to VAR(1); 3) from VAR(1) to nite order VAR. The focus is on discussing what can go wrong at each step of th...

In this paper, we briefly review the basics of copula theory and the problem of estimating Value at Risk (VaR) of portfolio composed by several assets. We present two VaR estimation models in which each return series is assumed to follow AR(1)-GARCH(1, 1) model and the innovations are simultaneously generated using Gaussian copula and Student t copula. The presented models are applied to estima...

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, ...

This paper aims to estimate the Value-at-Risk (VaR) using GARCH type models with improved return distribution. Value at Risk (VaR) is an essential benchmark for measuring the risk of financial markets quantitatively. The parametric method, historical simulation, and Monte Carlo simulation have been proposed in several financial mathematics and engineering studies to calculate VaR, that each of ...

While the importance of Granger-causal (G-causal) relationships for learning vector autoregressive models (VARs) is widely acknowledged, the state-of-theart VAR methods do not address the problem of discovering the underlying Gcausality structure in a principled manner. VAR models can be restricted if such restrictions are supported by a strong domain theory (e.g. economics), but without such s...

In this talk we propose a family of robust estimate for vector autoregressive models. These estimates are MM-estimates containing an additional mechanism that prevents the propagation of outliers through subsequent periods. This mechanism is based on an adaptive procedure for computing the innovation residuals. When there are no outliers, the residuals are computed using the standard procedure ...

Over the last few years, there has been a growing interest in DSGE modelling for predicting macroeconomic ‡uctuations and conducting quantitative policy analysis. Hybrid DSGE models have become popular for dealing with some of the DSGE misspeci…cations as they are able to solve the tradeo¤ between theoretical coherence and empirical …t. However, these models are still linear and they do not con...

Investment funds in China started in 1991. After 20 years of development, the mutual fund industry is now offering a rich product line for investors. At present, individual investors hold about 90% of the mutual fund with more than 90,000,000 fund accounts. Mutual fund purchasing has become the preferred way of managing money for urban residents in China. This paper study on risk assessment met...

Duration dependent Markov-switching VAR (from now on DDMSVAR) models are time series models with data generating process consisting in a mixture of two VAR processes, which switches according to a two-state Markov chain with transition probabilities depending on how long the process has been in a state. Interesting applications of this class of models have been carried out in business cycle ana...