Model Risk of Risk Models

Danı́elsson, Jón; James, Kevin R.; Valenzuela, Marcela; Zer, Ilknur

doi:10.2139/ssrn.2425689

Cited by 39 publications

(40 citation statements)

References 41 publications

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“…This creates uncertainty because the true data-generating process is unknown. Boucher, Daníelsson, Kouontchou, and Maillet (2014) and Daníelsson, James, Valenzuela, and Zer (2016) analyze this notion of so-called "model risk" in the context of risk management, and provide tools to quantify the extent to which measures such as VaR are subject to the uncertainty involved in choosing a particular model specification. The point of departure for this paper is the conventional wisdom that each model is an incomplete description of reality.…”

Section: Introductionmentioning

confidence: 99%

Combining density forecasts using focused scoring rules

Opschoor

Dijk

Wel

2017

J of Applied Econometrics

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SummaryWe investigate the added value of combining density forecasts focused on a specific region of support. We develop forecast combination schemes that assign weights to individual predictive densities based on the censored likelihood scoring rule and the continuous ranked probability scoring rule (CRPS) and compare these to weighting schemes based on the log score and the equally weighted scheme. We apply this approach in the context of measuring downside risk in equity markets using recently developed volatility models, including HEAVY, realized GARCH and GAS models, applied to daily returns on the S&P 500, DJIA, FTSE and Nikkei indexes from 2000 until 2013. The results show that combined density forecasts based on optimizing the censored likelihood scoring rule significantly outperform pooling based on equal weights, optimizing the CRPS or log scoring rule. In addition, 99% Value-at-Risk estimates improve when weights are based on the censored likelihood scoring rule.

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Section: Introductionmentioning

confidence: 99%

Combining density forecasts using focused scoring rules

Opschoor

Dijk

Wel

2017

J of Applied Econometrics

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“…In an earlier empirical investigation of U.S. financial firms (Benoit et al (2013)), they note that a one-factor linear model appears to explain most of the variability across four systemic risk measures (MES, SES, SRISK, and ∆CoVaR). Our paper is different in that they stop short of pursuing that intuition further by extracting the common variation across systemic risk measures to obtain a combined ranking that is less affected by model risk and estimation uncertainty (see also Danielson et al, 2015). Moreover, once we obtain our combined ranking, we investigate how systemic importance co-varies with CDS data at the firm level.…”

Section: Introductionmentioning

confidence: 99%

The information in systemic risk rankings

Nucera

Schwaab

Koopman

et al. 2016

Journal of Empirical Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractWe propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic components from a selection of key systemic risk rankings that are recently proposed. We use a sample of 113 listed financial sector firms in the European Union over the period 2002-2013. The implied ranking from the principal components is less volatile than most individual risk rankings and leads to less turnover among the top ranked institutions. We also find that price-based rankings and fundamentals based rankings deviated substantially and for a prolonged time in the period leading up to the financial crisis. We test the adequacy of our newly pooled systemic risk ranking by relating it to credit default swap premia.

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“…The list of papers further developing the model-based approaches or applying them empirically to specific banking sectors is growing rapidly; however, several studies have also emerged recently which cast doubts on the practical usefulness or value added of some of the most popular modelbased approaches (e.g., Berg, 2011, Danielsson et al, 2011, Idier et al, 2012, Löffler and Raupach, 2013, Jäger-Ambrozewicz, 2013, giving support to the premonition in BCBS (2012) that the modelbased approaches may not be robust enough (yet) to be used as a basis for actual regulatory measures.…”

Section: Measurement Of Systemic Importancementioning

confidence: 99%

Capital buffers based on banks’ domestic systemic importance: selected issues

Skořepa

Seidler

2015

Journal of Financial Economic Policy

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Regulators in many countries are currently considering ways to impose domestic systemic importance-based capital requirements on banks. Aiming to assist these considerations, this article discusses a number of issues concerning the calculation of a bank's systemic importance to the domestic banking sector, such as the choice of indicators used and the pros and cons of focusing on an individual or consolidated level. Also, the "equal expected impact" procedure for determining adequate additional capital requirements is presented in detail and some of its properties are discussed. As an illustrative example of the practical use of the procedures presented, systemic importance scores and implied capital buffers are calculated for banks in the Czech Republic. The article also stresses the crucial role of public communication of the motivation for the buffers: regulators should make every effort to explain that the imposition of a non-zero systemic importance-based capital buffer on a bank is not to be interpreted by the markets as a signal that the bank is too big to fail and would therefore be guaranteed a public bail-out if it got into difficulties. AbstraktRegulátoři v mnoha zemích v současnosti zvažují způsoby, jak bankám stanovit dodatečné kapitálové požadavky založené na jejich domácí systémové významnosti. S cílem přispět k těmto úvahám článek rozebírá řadu témat týkajících se výpočtu systémové významnosti banky pro domácí bankovní sektor, jako jsou například volba použitých indikátorů nebo výhody a nevýhody práce s konsolidovanými nebo individuálními daty. Detailně je představen také postup určení adekvátní výše dodatečných kapitálových požadavků vycházející ze zásady "stejného očekávaného dopadu" a jsou popsány některé jeho vlastnosti. Jako ilustrativní příklad praktického použití popsaných postupů jsou vypočteny hodnoty systémové významnosti a z nich plynoucí kapitálové rezervy pro banky v České republice. Článek také zdůrazňuje zásadní roli veřejné komunikace účelu kapitálových rezerv: regulátoři by měli vynaložit veškeré úsilí, aby vysvětlili, že uvalení nenulové kapitálové rezervy založené na systémové významnosti na danou banku by nemělo být účastníky trhu chápáno jako signál, že je tato banka příliš velká, aby padla (too big to fail), a že by tedy měla zaručenou veřejnou pomoc, pokud by se dostala do potíží. JEL Codes:G21, G28.

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Model Risk of Risk Models

Cited by 39 publications

References 41 publications

Combining density forecasts using focused scoring rules

Combining density forecasts using focused scoring rules

The information in systemic risk rankings

Capital buffers based on banks’ domestic systemic importance: selected issues

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