Technical paper/Value-at-risk (VAR)
Confidence in controlling risk measures
Insurers increasingly use stochastic simulation approaches for estimating risk capital, but numerical errors are rarely measured. A control variate method can improve the accuracy dramatically without increasing the number of simulations.
Assessment of longevity risk under Solvency II
As the implementation of Solvency II looms, the calibration of the standard formula remains a controversial issue as the industry runs the fifth quantitative impact study. But the current design overshoots the one in 200 year confidence level.
Modest means
Credit loss models typically calibrate default separate from loss given default. Here, Jon Frye calibrates simultaneously, using credit loss data. This produces a surprising test result: the credit loss models do not significantly outperform a…
A rotationally invariant technique for rare event simulation
Because of their low probability, including extreme events in Monte Carlo calculations of the value-at-risk of a credit-risky portfolio requires many simulations. Here, Susanne Klöppel, Ranja Reda and Walter Schachermayer demonstrate a geometrically…
Cutting edge: Visualising value-at-risk
Risk transparency is an important yet elusive goal of any risk management process. One challenge is to understand the diversification effects of the portfolio elements. Wentao Zhao and Kevin Kindall introduce a graphical technique based on value-at-risk…
Shortfall: who contributes and how much?
Understanding risk contributions is a key part of successful risk management and portfolio optimisation. Richard Martin extends the discussion from value-at-risk to expected shortfall and shows that saddlepoint approximation preserves the convexity…
Credit spread shocks: how big and how often?
The second half of 2007 saw violent moves in credit spreads. In the fallout, there has been much discussion about how to estimate the probabilities of these severe events, but few conclusions have been obtained beyond the fact that historical data is…
Credit spread shocks: how big and how often?
The second half of 2007 saw violent moves in credit spreads. In the fallout, there has been much discussion about how to estimate the probabilities of these severe events, but few conclusions have been obtained beyond the fact that historical data is…
Error of VAR by overlapping intervals
When overlapping intervals in time series are used, volatility and price changes' percentiles are underestimated. Consequently, value-at-risk is also underestimated. Heng Sun, Izzy Nelken, Guowen Han and Jiping Guo measure the size of this underestimation
Component VAR for a non-normal world
It has become standard to account for non-normality when estimating portfolio value-at-risk, but there are few methods available to calculate the risk contributions of each component in a non-normal portfolio. Brian Peterson and Kris Boudt present a…
Component VAR for a non-normal world
Market Risk
