Quantitative analysis
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
Pay attention to interest
Masterclass – with JP Morgan
On the dependence of equity and asset returns
Asset returns play an important role in credit risk modelling. Here, Roy Mashal, Marco Naldiand Assaf Zeevi investigate the co-dependence behaviour of asset returns semiparamatrically.They find that the Student-t copula outperforms the normal copula as…
Modelling venture capital funds
Modelling venture capital funds is a challenge and has become more important due to recentand upcoming securitisation deals, the need for efficient portfolio management, and Basel II.Here, Thomas Meyer and Tom Weidig summarise the issues both industry…
VAR: history or simulation?
Greg Lambadiaris, Louiza Papadopoulou, George Skiadopoulos and Yiannis Zoulis assess the performance of historical and Monte Carlo simulation in calculating VAR, using data from the Greek stock and bond market. They find that while historical simulation…
Crossing the frontier
Portfolio risk management
Selling risk at a premium
Option strategies
Hedging using forward rate bias
Foreign exchange
Insurance optional
Asset/liability management
VAR for fund managers
Investment management
Collateral damage
Credit risk
Analysing counterparty risk
In an attempt to improve on existing regulatory approaches to derivatives counterparty creditrisk, Eduardo Canabarro, Evan Picoult and Tom Wilde present a new method based on expectedpositive exposure (EPE).
Creating an op risk loss-collection framework
To meet the Basel II advanced measurement requirements and improve op risk management, firms must establish robust loss databases. Ulrich Anders and Jürgen Platz of Dresdner Bank in Frankfurt outline such a framework.
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…
Analysing counterparty risk
In an attempt to improve on existing regulatory approaches to derivatives counterparty creditrisk, Eduardo Canabarro, Evan Picoult and Tom Wilde present a new method based on expectedpositive exposure (EPE). Using a one-factor conditional independence…
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…
On the slide
market trends
Both sides of the fence: a statistical and regulatory view of electricity risk
Ernst Eberlein and Gerhard Stahl analyse price series of 25 energy spot rates simultaneously using Lévy models. This model class allows the capture of stochastic behaviour of these financial instruments. The implications of this analysis will form the…
Unexpected recovery risk
For credit portfolio managers, the priority is to properly incorporate recovery rates into existingmodels. Here, Michael Pykhtin improves upon earlier approaches, allowing recovery rates todepend on the idiosyncratic part of a borrower's asset return, in…
Ultimate recoveries
Measuring recovery using the ultimate rate observed at emergence from bankruptcy may be conceptually desirable, but modelling it is difficult.
A false sense of security
Credit portfolio models often assume that recovery rates are independent of default probabilities. Here, Jon Frye presents empirical evidence showing that such assumptions are wrong. Using US historical default data, he shows that not only are recovery…
Ultimate recoveries
Measuring recovery using the ultimate rate observed at emergence from bankruptcy may be conceptually desirable, but modelling it is difficult. Craig Friedman and Sven Sandow tackle the problem by maximising the creditor’s utility function, constructed…
Probing granularity
Basel II