We employ a multivariate BEKK GARCH model which allows news to affect conditional volatility in an asymmetric manner. The asymmetric model outperforms the standard symmetric model, implying that efficient financial decision makers should not treat good and bad news as homogenous. We estimate the conditional variance and covariance of the Japanese yen, Swiss franc and British pound vis-à-vis the...

We analyze the time-dependence of exchange rate correlations using a new multivariate GARCH model. This model consists of two parts. First, we transform the exchange rate changes into their principal components and specify univariate GARCH models for all components. Second, we use the inverse of the principal components construction to transform the conditional component moments back into those...

a r t i c l e i n f o JEL classification: C53 G17 Keywords: GARCH Higher conditional moments Approximate predictive distributions Value-at-Risk S&P 500 Treasury bill rate Euro–US dollar exchange rate It is widely accepted that some of the most accurate Value-at-Risk (VaR) estimates are based on an appropriately specified GARCH process. But when the forecast horizon is greater than the frequency...

return and volatility spillovers are important for portfolio selection, asset valuation and market efficiency investigation. using a var-bekk framework model, this paper investigates return and volatility spillover effects between three size-sorted equity indices in tehran stock exchange (tse). although daily return of large stocks leads small stocks (lead-lag effect), there wasn’t any spillove...

Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single representation of instability could mean that combining forecasts from a range of approaches will improve for...

This Paper describes a procedure for constructing theory restricted prior distributions for BVAR models. The Bayes Factor, which is obtained without any additional computational effort, can be used to assess the plausibility of the restrictions imposed on the VAR parameter vector by competing DSGE models. In other words, it is possible to rank the amount of abstraction implied by each DSGE mode...

This paper analyzes the application of the Markov-switching ARCH model (Hamilton and Susmel, 1994) in improving value-at-risk (VaR) forecast. By considering a mixture of normal distributions with varying variances over different time and regimes, we find that the “spurious high persistence” found in the GARCH model is adjusted. Under relative performance and hypothesis-testing evaluations, the ...

Many economic applications call for simultaneous equations VAR modeling. We show that the existing importance sampler can be prohibitively inefficient for this type of models. We develop a Gibbs simulator that works for both simultaneous and recursive VAR models with a much broader range of linear restrictions than those in the existing literature. We show that the required computation is of an...

This research compares partial equilibrium and statistical time-series approaches to hedging. The finance literature stresses the former approach, while the applied economics literature has focused on the latter. We compare the out-of-sample hedging effectiveness of the two approaches when hedging commodity price risk using futures contracts. For various methods of parameter estimation and infe...

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 ...