Interest rate model risk: an overview

Gibson, Rajna; Lhabitant, François‐Serge; Pistre, Nathalie; Talay, Denis

doi:10.21314/jor.1999.009

Cited by 17 publications

(7 citation statements)

References 16 publications

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“…This type of model risk has perhaps most frequently been discussed in the literature; see for instance Gibson et al (1999), Talay and Zheng (2002) and Bossy et al (2000). Misspecification risk is associated with incorrect model specification.…”

Section: Introductionmentioning

confidence: 98%

Model risk and capital reserves

Kerkhof¹,

Melenberg

Schumacher

2010

Journal of Banking & Finance

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Section: Introductionmentioning

confidence: 98%

Model risk and capital reserves

Kerkhof¹,

Melenberg

Schumacher

2010

Journal of Banking & Finance

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“…As the first model risk, the one of estimation indeed occurs in every estimation process; it is the risk associated with inaccurate estimations of parameters, due to the estimator quality and/or limited sample of data (past and/or future), and/or noise in the data. This estimation risk is the most discussed in the literature (see for instance Gibson et al, 1999; and Talay and Zheng, 2002). Pritsker (1997) is one of the first to discuss the estimation risk for VaR in the identically and independently distributed return setting (see also, more recently, Inui and Kijima, 2005, with the same setting with the Expected Shortfall).…”

Section: About Literature On Model Riskmentioning

confidence: 99%

Learning by Failing: A Simple VaR Buffer

Boucher¹,

Maillet²

2013

Financial Market

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International audienceWe study in this article the problem of model risk in VaR computations and document a procedure for correcting the bias due to specification and estimation errors. This practical method consists of “learning from model mistakes”, since it dynamically relies on an adjustment of the VaR estimates – based on a back-testing framework – such as the frequency of past VaR exceptions always matches the expected probability. We finally show that integrating the model risk into the VaR computations implies a substantial minimum correction to the order of 10–40% of VaR levels

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“…While Jorion (1996) analyzes the sample error related to Value-at-Risk estimates, Gibson et al (1999) provide an overview for different sources of model risk of interest rate risk. Berkowitz and O'Brien (2002) empirically backtest the performance of internal Value-at-Risk models at commercial banks in order to draw conclusions about related estimation risk, whereas Talay and Zhang (2002) use a game theoretic framework for the analysis of model risk.…”

Section: Introductionmentioning

confidence: 99%

The role of model risk in extreme value theory for capital adequacy

Scheule¹,

Kellner²,

Rösch³

2016

JOR

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In the recent literature, methods from extreme value theory (EVT) have frequently been applied for the estimation of tail risk measures. While previous analyses show that EVT methods often lead to accurate estimates for risk measures, a potential drawback lies in high standard errors of point estimates of these methods as only a fraction of the data set is used. Thus, the aim of this paper is to comprehensively study the impact of model risk on EVT methods when determining the Value-at-Risk and Expected Shortfall. We distinguish between first order effects of model risk, which consist of misspecification and estimation risk, and second order effects of model risk which refer to the dispersion of risk measure estimates. We show that EVT methods are less prone to first order effects of model risk, however, they exhibit a higher sensitivity towards second order effects of model risk. We find that this can lead to severe Value-at-Risk and Expected Shortfall underestimations and should be reflected in regulatory capital models.

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Interest rate model risk: an overview

Cited by 17 publications

References 16 publications

Model risk and capital reserves

Model risk and capital reserves

Learning by Failing: A Simple VaR Buffer

The role of model risk in extreme value theory for capital adequacy

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