Market risk computation for nonlinear portfolios

Studer, Gerold

doi:10.21314/jor.1999.016

Cited by 36 publications

(17 citation statements)

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“…This correspondence of the value-at-risk to a worst-case problem was already shown by Studer (1997Studer ( , 1999, who denotes M as the trust region. The supremum in (3.11) corresponds to problem (3.8) and is the kind of worst-case problem that we study in this paper.…”

Section: Calculations On the Safe-sidesupporting

confidence: 58%

“…The regulatory frameworks of both Solvency II and Basel II require to base the risk measurement on the value-at-risk approach. Studer (1997Studer ( , 1999 showed that there is a strong relationship between the valueat-risk and the stress scenario concept, proving that certain worst-case scenarios yield conservative bounds for the value-at-risk. The strong link between stress testing and the risk measure value-at-risk was also emphasized by Berkowitz (2000).…”

Section: Introductionmentioning

confidence: 99%

“…One of the first systematic, probabilistic modeling frameworks of stress testing was given by Studer (1997Studer ( , 1999, who defined stress scenarios as scenarios that maximize the loss of a market portfolio with respect to some trust region. By assuming that the risk factors have a multivariate normal distribution, his trust regions have an ellipsoidal shape.…”

Section: Introductionmentioning

confidence: 99%

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Safe-Side Scenarios for Financial and Biometrical Risk

Christiansen

Steffensen

2013

ASTIN Bull.

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Premium settlement and calculation of reserves and capital requirements are typically based on worst- or just bad-case assumptions on interest rates, mortality rates, and other transition rates between states defined according to the insurance benefits. If interest and transition rates are chosen independently from each other, the worst choice, i.e. the combination of interest rates and transition rates that maximizes the reserve, can be found by dynamic programming. Here, we generalize this idea by choosing the interest and transition rates from a set that allows for mutual dependence. In general, finding the worst case is much more complicated in this situation, but we characterize a set of relatively tractable problems and present a series of examples from this set. Our approach with mutual dependence is relevant e.g. for internal models in Solvency II.

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Section: Calculations On the Safe-sidesupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Safe-Side Scenarios for Financial and Biometrical Risk

Christiansen

Steffensen

2013

ASTIN Bull.

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“…The stress scenario is simulated in the line of Studer [10] and Breuer and Krenn [11], who employed the Mahalanobis distance as a mathematical tool to choose stress scenarios [1]. Additionally, the condi- are not too arbitrary numbers to be accepted the same as a risk measure.…”

Section: Empirical Findingsmentioning

confidence: 99%

Value-at-Risk Based on Time-Varying Risk Tolerance Level

Majumder¹

2018

TEL

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The conventional judgement-based method for fixing the risk tolerance level in the Value-at-Risk (VaR) model might be a suboptimal method, because the procedure induces the possibility of bias in risk measurement. Conversely, a superior risk management practice might be one, where input parameters are determined by a quantitative process which is "non-subjective to the risk modeller's preferences". Based on this insight, we have improved on the VaR model. Our model allows time variation of the risk tolerance level and so is suitable for scenario-wise risk analysis.

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“…Our approach also differs from the Monte Carlo simulation method proposed in the literature on scenario selection. A number of papers (e.g., Studer (1997Studer ( , 1999; Breuer et al (2009); Breuer et al (2010); and Flood and Korenko (2010)) propose statistical criteria for the selection of stress scenarios among those that are generated by a Monte Carlo simulation of underlining risk factors. While this simulation method allows modelers to generate a large number of scenarios, in contrast to our approach, it does not take into account plausible extreme scenarios that scenario experts may be particularly interested.…”

Section: Introductionmentioning

confidence: 99%