Technical paper/Value-at-risk (VAR)
Sensible and efficient capital allocation for credit portfolios
Michael Kalkbrener, Hans Lotter and Ludger Overbeck construct a new approach to economiccapital allocation, showing that three axioms uniquely determine a capital allocation scheme,and, more importantly, that any allocation satisfying the axioms is…
Understanding the expected loss debate
The final draft of the new global Accord on bank regulatory capital – Basel II – has been delayed. A critical and unresolved issue is whether banks should include expected losses in their measure of credit risk. The IMF's Paul Kupiec reports on efforts…
Using the grouped t-copula
Student-t copula models are popular, but can be over-simplistic when used to describe credit portfolios where the risk factors are numerous or dissimilar. Here, Stéphane Daul, Enrico De Giorgi, Filip Lindskog and Alexander McNeil construct a new,…
Operational and market risks of a regulated power utility
Victor Dvortsov and Ken Dragoon present an analytical method for including market and operational risks when estimating utility portfolio value-at-risk.
Operational and market risks of a regulated power utility
Victor Dvortsov and Ken Dragoon present an analytical method for including market and operational risks when estimating utility portfolio value-at-risk
Crossing the frontier
Portfolio risk management
VAR for fund managers
Investment management
Correlation stress testing for value-at-risk
The correlation matrix is of vital importance for value-at-risk (VAR) modelsin the financial industry. Risk managers are often interested in stressing a subsetof market factors within large-scale risk systems containing hundreds ofmarket variables…
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…