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29
Quantitative models for Operational Risk: Extremes, dependence and aggregation
- Journal of Banking and Finance
, 2006
"... • Basel II (Banking) and Solvency 2 (Insurance) • AMA approach to Operational Risk Our contribution: ..."
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Cited by 42 (7 self)
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• Basel II (Banking) and Solvency 2 (Insurance) • AMA approach to Operational Risk Our contribution:
Ruin theory revisited: stochastic models for operational risk
- RISK MANAGEMENT FOR CENTRAL BANK FOREIGN RESERVES
, 2004
"... The new Basel Capital Accord has opened up a discussion concerning the measurement of operational risk for banks. In our paper we do not take a stand on the issue of whether or not a quantitatively measured risk capital charge for operational risk is desirable; however, given that such measurement ..."
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Cited by 21 (4 self)
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The new Basel Capital Accord has opened up a discussion concerning the measurement of operational risk for banks. In our paper we do not take a stand on the issue of whether or not a quantitatively measured risk capital charge for operational risk is desirable; however, given that such measurement will come about, we review some of the tools which may be useful towards the statistical analysis of operational loss data. We also discuss the relevance of these tools for foreign reserves risk management of central banks.
Bayesian inference, Monte Carlo sampling and operational risk
- Journal of Operational Risk
"... Operational risk is an important quantitative topic as a result of the Basel II regulatory requirements. Operational risk models need to incorporate internal and external loss data observations in combination with expert opinion surveyed from business specialists. Following the Loss Distributional A ..."
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Cited by 10 (7 self)
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Operational risk is an important quantitative topic as a result of the Basel II regulatory requirements. Operational risk models need to incorporate internal and external loss data observations in combination with expert opinion surveyed from business specialists. Following the Loss Distributional Approach, this article considers three aspects of the Bayesian approach to the modeling of operational risk. Firstly we provide an overview of the Bayesian approach to operational risk, before expanding on the current literature through consideration of general families of non-conjugate severity distributions, g-and-h and GB2 distributions. Bayesian model selection is presented as an alternative to popular frequentist tests, such as Kolmogorov-Smirnov or Anderson-Darling. We present a number of examples and develop techniques for parameter estimation for general severity and frequency distribution models from a Bayesian perspective. Finally we introduce and evaluate recently developed stochastic sampling techniques and highlight their application to operational risk through the models developed.
Simulation of the Annual Loss Distribution in Operational Risk via Panjer Recursions and Volterra Integral Equations for Value at Risk and Expected Shortfall Estimation.
"... * – Corresponding Author. Following the Loss Distributional Approach (LDA), this article develops two procedures for simulation of an annual loss distribution for modeling of Operational Risk. First, we provide an overview of the typical compound-process LDA used widely in Operational Risk modeling ..."
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Cited by 10 (3 self)
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* – Corresponding Author. Following the Loss Distributional Approach (LDA), this article develops two procedures for simulation of an annual loss distribution for modeling of Operational Risk. First, we provide an overview of the typical compound-process LDA used widely in Operational Risk modeling, before expanding upon the current literature on evaluation and simulation of annual loss distributions. We present two novel Monte Carlo simulation procedures. In doing so, we make use of Panjer recursions and the Volterra integral equation of the second kind to reformulate the problem of evaluation of the density of a random sum as the calculation of an expectation. We demonstrate the use of importance sampling and trans-dimensional Markov Chain Monte Carlo algorithms to efficiently evaluate this expectation. We further demonstrate their use in the calculation of Value at Risk and
Practical Methods for Measuring and Managing Operational Risk in the Financial Sector: A Clinical Study *
, 2006
"... This paper analyzes the implications of the Advanced Measurement Approach (AMA) for the assessment of operational risk. Through a clinical case study on a matrix of two selected business lines and two event types of a large financial institution, we develop a procedure that addresses the major issue ..."
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Cited by 9 (0 self)
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This paper analyzes the implications of the Advanced Measurement Approach (AMA) for the assessment of operational risk. Through a clinical case study on a matrix of two selected business lines and two event types of a large financial institution, we develop a procedure that addresses the major issues faced by banks in the implementation of the AMA. For each cell, we calibrate two truncated distributions functions, one for “normal ” losses and the other for the “extreme ” losses. In addition, we propose a method to include external data in the framework. We then estimate the impact of operational risk management on bank profitability, through an adapted measure of RAROC. The results suggest that substantial
Applying Robust Methods to Operational Risk Modeling, Working
, 2006
"... We use robust statistical methods to analyze operational loss data. Commonly used classical estimators of model parameters may be sub-optimal under minor departures of data from the model assumptions. Operational loss data are characterized by a very heavy right tail of the loss distribution attribu ..."
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Cited by 6 (0 self)
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We use robust statistical methods to analyze operational loss data. Commonly used classical estimators of model parameters may be sub-optimal under minor departures of data from the model assumptions. Operational loss data are characterized by a very heavy right tail of the loss distribution attributed to several “low frequency/high severity ” events. Classical estimators may produce biased estimates of parameters leading to unreasonably high estimates of mean, variance, and the operational VaR and CVaR measures. The main objective of robust methods is to focus the analysis on the fundamental properties of the bulk of the data, without being distorted by outliers. We argue that further comparison of results obtained under the classical and robust procedures can serve as a basis for the VaR sensitivity analysis and can lead to an understanding of the economic role played by these extreme events. An empirical study with 1980-2002 public operational loss data reveals that the highest 5 % of losses account for up to 76 % of the operational risk capital charge.
Operational Risk Management and Implications for Bank’s Economic Capital – A Case Study.” IES Working Papers 17/2008, http://ies.fsv.cuni.cz/sci/publication/show/ id/3477/lang/cs
, 2008
"... Institut ekonomický ch studií Fakulta sociálních věd ..."
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Cited by 6 (3 self)
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Institut ekonomický ch studií Fakulta sociálních věd
Advanced Extremal Models for Operational Risk
, 2004
"... Managing risk lies at the heart of the financial services industry. Regulatory frame-works, such as Basel II for banking and Solvency 2 for insurance, mandate a focus on operational risk. A fast growing literature exists on the various aspects of operational risk modelling; see the list of reference ..."
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Cited by 2 (0 self)
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Managing risk lies at the heart of the financial services industry. Regulatory frame-works, such as Basel II for banking and Solvency 2 for insurance, mandate a focus on operational risk. A fast growing literature exists on the various aspects of operational risk modelling; see the list of references towards the end of the paper.
New Technical and Normative Challenges for XBRL: Multidimensionality in the COREP Taxonomy
- The International Journal of Digital Accounting Research
, 2005
"... Abstract: The New Basel Capital Agreement, known as Basel II, requires some notable changes in the systems of measurement and control of risks of credit entities and investment firms. It introduces new concepts and requirements. The systems of risk management to which the Framework or Agreement make ..."
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Cited by 1 (0 self)
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Abstract: The New Basel Capital Agreement, known as Basel II, requires some notable changes in the systems of measurement and control of risks of credit entities and investment firms. It introduces new concepts and requirements. The systems of risk management to which the Framework or Agreement makes reference can be implemented in various degrees of sophistication. By measuring the different exposures to risk with greater accuracy, a more advanced system offers such firms and entities the prospect of needing less own funds and using increased financial leverage over secure bases. The national Supervisors, in the various central banks, must approve the systems and instruments established. The new reporting tool that is destined to fulfil this function, for the moment in the context of the European Union only, is the COREP-XBRL Taxonomy. This is based on the mark-up language of the business world, the eXtensible Business Reporting Language, and makes use of the concept of Multidimensionality. This concept is being incorporated into the XBRL specification currently in force, since it is necessary for the structure of the new reporting model that is required.
