We derive discrete markov chain approximations for continuous state equilibrium term structure models. The states and transition probabilities of the markov chain are chosen effciently according to a quadrature rule as in Tauchen and Hussey (1991). Quadrature provides a simple yet method which...

The type function of an agent, in a type space, associates with each state a probability distribution on the type space. Thus, a type function can be considered as a Markov chain on the state space. A common prior for the space turns out to be a probability distribution which is invariant under...

Value-at-Risk (VaR) determines the probability of a portfolio of assets losing a certain amount in a given time period due to adverse market conditions with a particular level of confidence. Value-at-Risk has received considerable attention from financial economists and financial practitioners...

This article is devoted to the study cashflow maps used in the computation of value-at-risk (VaR). Properties and characteristics of the approaches found in the literature are presented and two new approaches are introduced. The goal of this paper is to study the quality of these maps. This is...

In this paper coherent risk measures and other currently used risk measures, notably Value-at-Risk (VaR), are studied from the perspective of the theory of coherent imprecise previsions. We introduce the notion of coherent risk measure defined on an arbitrary set of risks, showing that it can be...

This note describes the problem arising from using a currency basket in the computation of value-at-risk. This applies mainly when the basket is used as base currency. A solution based on the modification of the historical time series is proposed. The solution is easy to implement and doesn't...

We measure the loss potential of Hedge Funds by combining three market risk measures: VaR, Draw-Down and Time Under-The-Water. Calculations are carried out considering three different frameworks regarding Hedge Fund returns: i) Normality and time-independence, ii) Non-normality and time-...

This paper deals with the issue of calculating daily Value-at-Risk (VaR) measures within an environment of thin trading. Our approach focuses on fixed income portfolios with low frequency of transactions in which the missing data problem makes VaR measures difficult to calculate. We propose and...

We study a source of bias in value-at-risk estimates that has not previously been recognized. Because value-at-risk estimates are based on past data, a trader will often have a good understanding of the errors in the value-at-risk estimate, and it will be possible for her to choose portfolios...