Evaluating Interest Rate Covariance Models Within a Value-at-Risk Framework

Evaluating Interest Rate Covariance Models within a Value-at-Risk Framework

We find that covariance matrix forecasts for an international interest rate portfolio generated by a model that incorporates interest-rate level volatility effects perform best with respect to statistical loss functions. However, within a value-at-risk (VaR) framework, the relative performance of the covariance matrix forecasts depends greatly on the VaR distributional assumption. Simple foreca...

Value at Risk for Interest Rate-Dependent Securities

MARCH 2003 THE JOURNAL OF FIXED INCOME 81 V alue at risk modelers face difficult trade-offs in choosing between the two major VaR methodologies: historical/ Monte Carlo simulations or parametric models. The historical approach uses the recent history of the asset price (or, for derivatives, the price of the underlying), while the parametric approach imposes functional form assumptions upon the ...

Evaluating Value-at-Risk Models with Desk-Level Data

We present new evidence on disaggregated profit and loss (P/L) and value-at-risk (VaR) forecasts obtained from a large international commercial bank. Our data set includes the actual daily P/L generated by four separate business lines within the bank. All four business lines are involved in securities trading and each is observed daily for a period of at least two years. Given this unique data ...

Evaluating Portfolio Value-at-Risk using Semi-Parametric GARCH Models

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 ...

Dynamic Interest Rate and Credit Risk Models