Christophe Boucher, Jón Daníelsson, Patrick Kouontchou et al. · RePEc: Research Papers in Economics
The experience from the global financial crisis has raised serious concerns about the accuracy of standard risk measures as tools for the quantification of extreme downward risk. A key reason for this is that risk measures are subject to model risk due, e.g., to specification and estimation uncertainty. While the authorities would like financial institutions to assess model risk, there is no accepted approach for such computations. We propose a remedy for this by a general framework for the computation of risk measures robust to model risk by empirically adjusting imperfect risk forecasts by outcomes from backtesting, considering the desirable quality of VaR models such as the frequency, independence and magnitude of violations. We also provide a fair comparison between the main risk models using the same metric that corresponds to model risk required corrections.
Cited by 0
Christophe M. Boucher, Jón Danı́elsson, Patrick Kouontchou et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 2014
The experience from the global financial crisis has raised serious concerns about the accuracy of standard risk measures as tools for the quantification of extreme downward risks. A key reason for this is that risk measures are subject to a model risk due, e.g. to specification and estimation uncertainty. While regulators have proposed that financial institutions assess the model risk, there is no accepted approach for computing such a risk. We propose a remedy for this by a general framework for the computation of risk measures robust to model risk by empirically adjusting the imperfect risk forecasts by outcomes from backtesting frameworks, considering the desirable quality of VaR models such as the frequency, independence and magnitude of violations. We also provide a fair comparison between the main risk models using the same metric that corresponds to model risk required corrections.
Cited by 94
Patrick S. Kouontchou Bertr, B. Maillet, We Thank Carol Alex et al. · 2012
Preliminary Version The recent experience from the global financial crisis has raised serious doubts about the accuracy of standard risk measures as a tool to quantify extreme downward risks. Risk measures are hence subject to a “model risk ” due, e.g., to the specification and estimation uncertainty. Therefore, regulators have proposed that financial institutions assess the “model risk ” but, as yet, there is no accepted approach for computing such a risk. We propose a general framework to compute risk measures robust to the model risk, while focusing on the Value-at-Risk (VaR). The proposed procedure aims empirically adjusting the imperfect quantile estimate based on a backtesting framework, assessing the good quality of VaR models such as the frequency, the independence and the magnitude of violations. We also provide a fair comparison between the main risk models using the same metric that corresponds to model risk required corrections.
Cited by 0
Christophe Boucher, Jon Danielsson, Patrick Kouontchou et al. · RePEc: Research Papers in Economics
Forthcoming
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Boucher, Christophe, Danielsson, Jon, Kouontchou, Patrick et al. · SPIRE - Sciences Po Institutional REpository · 2014
Cited by 0
2012
This chapter deals with variety of opinions that have been expressed about the importance of models or their unimportance in managing financial risk. It examines the procedures that ought to be used for risk evaluation and control for models of all types. The chapter provides a more detailed analysis of model review standards, distinguishing among three types of models: those used for valuation and risk measurement of liquid instruments, those used for valuation and risk measurement of illiquid instruments, and those used for making trading decision. Models for liquid instruments are robust and easy to test, since they can constantly be checked against actual liquid market quotes. Model use for illiquid instruments is much more critical than it is for liquid instruments and model validation is also much more challenging. When a model is being used as part of a trading desk's decision-making process, it clearly requires internal model review by the model creators and users.
Cited by 0
Laura Ford (3053886), Kathryn Glass (336092), Deborah A. Williamson (6656496) et al. · Figshare · 2021
Cited by 0Open access
Adam P. Tashman · 2024
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Michel Crouhy, Dan Galai, Robert Mark · Wiley StatsRef: Statistics Reference Online · 2014
Abstract Models are an inevitable feature of modern finance, and model risk is inherent in the use of models. In this article we stress the technical elements of model risk. Models are susceptible to errors: from incorrect assumptions about price dynamics and market interactions, from implementing a model wrongly, from inaccurate estimation of volatilities and correlations and other inputs that are not directly observable and must be forecasted. We also discuss how to mitigate model risk.
Cited by 21
2005
This chapter contains sections titled: Models and Model Risk Sources of Model Risk Quantifying Model Risk Managing Model Risk Conclusions
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Dowd, K. · Durham Research Online (Durham University) · 2012
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Fernanda Muller, Marcelo Righi · SSRN Electronic Journal · 2018
Cited by 1Open access
Jón Danı́elsson, Kevin R. James, Marcela Valenzuela et al. · Journal of Financial Stability · 2016
Cited by 145Open access
Jón Danı́elsson, Kevin R. James, Marcela Valenzuela et al. · Finance and Economics Discussion Series · 2014
This paper evaluates the model risk of models used for forecasting systemic and market risk. Model risk, which is the potential for different models to provide inconsistent outcomes, is shown to be increasing with market uncertainty. During calm periods, the underlying risk forecast models produce similar risk readings; hence, model risk is typically negligible. However, the disagreement between the various candidate models increases significantly during market distress, further frustrating the reliability of risk readings. Finally, particular conclusions on the underlying reasons for the high model risk and the implications for practitioners and policy makers are discussed.
Cited by 0Open access
Ilknur Zer, Jón Danı́elsson, Kevin J. James et al. · Finance and Economics Discussion Series · 2014
This paper evaluates the model risk of models used for forecasting systemic and market risk. Model risk, which is the potential for different models to provide inconsistent outcomes, is shown to be increasing with and caused by market uncertainty. During calm periods, the underlying risk forecast models produce similar risk readings hence, model risk is typically negligible. However, the disagreement between the various candidate models increases significantly during market distress, with a no obvious way to identify which method is the best. Finally, we discuss the main problems in risk forecasting for macro prudential purposes and propose an evaluation criteria for such models.
Cited by 4Open access
Gerhard Girmscheid · 2015
Cited by 2
Saul I. Gass, Michael C. Fu · 2013
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Božić, Velibor · 2022
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Haditya · 2019
Tujuan dari penelitian ini adalah, untuk mengetahui bagaimana risiko audit ditetapkan, bagaimana tingkat materilitas ditentukan, serta bagaimana pengaruh risiko audit terhadap penentuan tingkat risiko.Risiko audit (Audit Risk)- risiko bawaan, risiko pengendalian, dan risiko deteksi - Mungkin dapat kita sebut bahwa didalam risiko audit itu terdapat beberapa komponen risiko, komponenrisiko tersebut meliputi risiko bawaan, risiko pengendalian, dan risiko deteksi. Dalam artikel saya kali ini, saya akan mencoba untuk menjelaskan tentang risiko audit beserta komponen risiko yang menyertainya tersebut, dan juga bagaimana model risiko auditnya, serta hubungan antara risiko audit dan bukti audit.Kata Kunci : Risiko dalam Audit
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Journal of risk management in financial institutions · 2016
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