Technical paper/Value-at-risk (VAR)
Evaluating credit risk models using loss density forecasts
The evaluation of credit portfolio risk models is an important issue for both banks and regulators. It is impeded by the scarcity of credit events, long forecasthorizons, and data limitations. To make efficient use of available information, the…
VAR: history or simulation?
Greg Lambadiaris, Louiza Papadopoulou, George Skiadopoulos and Yiannis Zoulis assess theperformance of historical and Monte Carlo simulation in calculating VAR, using data from theGreek stock and bond market. They find that while historical simulation…
Project risk: improving Monte Carlo value-at-risk
Cashflows from projects and other structured deals can be as complicated as we are willing to allow, but the complexities of Monte Carlo project modelling need not complicate value-at-risk calculation. Here, Andrew Klinger imports least-squares valuation…
Risk management based on stochastic volatility
Risk management approaches that do not incorporate randomly changing volatility tend to under- or overestimate the risk, depending on current market conditions. We show how some popular stochastic volatility models in combination with the hyperbolic…
Extreme forex moves
What is the appropriate statistical description of tail risk in a market portfolio? In the context offoreign exchange, Peter Blum and Michel Dacorogna address this problem using extreme valuetheory. Using 20 years of data, they estimate parameters for an…
Unsystematic credit risk
Although Basel has shifted its treatment of unsystematic credit risk from the first, capital rules pillar (where it was called the 'granularity adjustment') to the second, supervisory pillar of the forthcoming Accord, this issue is of great practical…
Unsystematic credit risk
Although Basel has shifted its treatment of unsystematic credit risk from the first, capital rules pillar (where it was called the ‘granularity adjustment’) to the second, supervisory pillar of the forthcoming Accord, this issue is of great practical…
Fallacies about the effects of market risk management systems
This paper takes another look at allegations that risk management systems have contributed to increased volatility in financial markets, with the particular example of the summer of 1998. The paper also provides new evidence on the potential effect of…
A bootstrap back-test
Back-testing
VAR you can rely on
Analytical and simulation-based methods often appear as rivals, but many real world problems are best served by judicious combinations of both approaches. In a first of a pair of computationally themed papers, Rabi De and Tanya Tamarchenko present a…
Risk and probability measures
Although its drawbacks are well known, VAR has become institutionalised as the market risk measure of choice among trading firms and regulators. Now there is a growing feeling that a reappraisal is overdue, exemplified here by Phelim Boyle, Tak Kuen Siu…
The maturity effect on credit risk capital
In a mark-to-market approach to credit risk capital, ratings or spread volatility has the effect of making longer-maturity loans more capital-intensive. This is incorporated in the current Basel II proposals via a maturity adjustment factor. Arguing that…
Testing assumptions
In calculating value-at-risk forecasts for trading portfolios, distributional assumptions are asimportant as the choice of risk factors, but it is not easy to determine the source of errorwhen rejected forecasts occur. Here, Jeremy Berkowitz develops a…
Honour your contribution
What is the best method for determining the risk contribution of a component in a portfolio? An exploration of the pros and cons of three important methods, showing that none dominates the others.
Analytical approach to credit risk modelling
The increasing popularity of VAR-based credit portfolio risk models has led to a growing recognition that Monte Carlo techniques are inadequate for economic capital calculations. Here, Michael Pykhtin and Ashish Dev present a new analytical alternative…
Style-based value-at-risk for UK equities
Risk measures