Model risk of risk models

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 a regulatory approach for restricting debt financing as an amplification mechanism across the financial system. A small stylised model illustrates the trade-off between static and time varying limits on leverage in dampening the financial cycle. The policy section proposes its application to highly leveraged entities and activities across the financial system. Whereas the traditional view on regulation focuses on capital as a buffer against exogenous risks, our approach focuses instead on debt financing, endogenous feedback mechanisms and resource allocation. It explicitly addresses the boundary problem in entity-based financial regulation and provides a motivation for substantially lower levels of leverage -and thereby higher capital buffers -than in the traditional approach.

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“…mainly credit and market risk). Leverage will amplify the impact of asset returns on the private return (Danielsson, 2013):…”

Section: Debt Financing and Amplification Mechanismsmentioning

confidence: 99%

Regulating the financial cycle: An integrated approach with a leverage ratio

Schoenmaker

Wierts

2015

Economics Letters

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“…Although examining the dynamics of riskometer performance is beyond the scope of this article, we note that Danielsson et al. () explore this topic.…”

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confidence: 99%

Can We Prove a Bank Guilty of Creating Systemic Risk? A Minority Report

Danı́elsson¹,

James²,

Valenzuela³

et al. 2016

J of Money Credit Banking

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Because increasing a bank's capital requirement to improve the stability of the financial system imposes costs upon the bank, a regulator should ideally be able to prove beyond a reasonable doubt that banks classified as systemically risky really do create systemic risk before subjecting them to this capital punishment. Evaluating the performance of two leading systemic risk models, we show that estimation error alone prevents the reliable identification of the most systemically risky banks. We conclude that it will be a considerable challenge to develop a riskometer that is sound and reliable enough to provide an adequate foundation for macroprudential policy.JEL codes: G10, G18, G20, G28, G38

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“…Dari berbagai macam metode yang dapat dipilih masing-masing bank, hal yang menjadi masalah dasar pengukuran value at risk adalah model volatilitas yang digunakan. Pemilihan model volatilitas yang tepat dapat mendeskripsikan kerugian terbesar dari nilai pasar (Danielsson et al, 2016) dan sesuai dengan keadaan yang sebenarnya sebagai informasi penyediaan modal minimum bank (Anginer, Demirgüç-Kunt, & Mare, 2018) .…”

Section: Pendahuluanunclassified

“…Banyak pengamat pasar mengindikasikan kebijakan yang berlaku pada bank komersial tentang penyediaan modal minimum bank dapat menghasilkan informasi yang menyimpang (Cohen & Scatigna, 2016). Pada peristiwa tertentu, bank cenderung tidak menghasilkan pengukuran value at risk yang akurat sehingga mengurangi kualitas pengukuran risiko pasar (Anginer et al, 2018) atau menurunkan kualitas dari sistem manajemen risiko (Danielsson et al, 2016).…”

Section: Pendahuluanunclassified

Evaluasi Kualitas Pengungkapan Value at Risk Perbankan Indonesia

Wibowo

Fadhila

2019

Jurnal Ilmiah Akuntansi dan Bisnis

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Market risk measurement of bank investment portfolios is a still problem not only among practitioners, but also among academicians. The accuracy and quality of market risk disclosures are important issues because transparency of the bank risk level encourages market control in the form of market discipline and it also improve the quality of risk management carried out internally by the bank. This research measures the quality of Value at Risk disclosures carried out by Indonesian banks. The accuracy of Value at Risk in this research is measured from the Value at Risk component which contains information of yield volatility of bank trading treasury activities. To measure Value at Risk disclosure, this research runs various methods of Value at Risk measurement. This research shows Historical Simulation is a Value at Risk method that is most widely used by Indonesia banks. The empirical test results show that the Value at Risk parametric method using asymmetric volatility have better quality than the Value at Risk Historical Simulation method. This research shows that Value at Risk as measured by Historical Simulation method contains the least information of bank trading treasury yields. Keywords: value at risk; disclosure; market risk; volatility

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Model risk of risk models

Cited by 116 publications

References 39 publications

Regulating the financial cycle: An integrated approach with a leverage ratio

Regulating the financial cycle: An integrated approach with a leverage ratio

Can We Prove a Bank Guilty of Creating Systemic Risk? A Minority Report

Evaluasi Kualitas Pengungkapan Value at Risk Perbankan Indonesia

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