Operational risk dependencies

Kelliher, Patrick; Acharyya, Madhusudan; Couper, A.; Maguire, Elizabeth M.; Nicholas, Peter; Pang, Natalie; Smerald, C.; Stevenson, David S.; Sullivan, Jim; Teggin, P.

doi:10.1017/s1357321720000033

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…Under LDA and SBA, operational losses are generally modelled by risk type, so there is a need to aggregate these allowing for diversification between operational risk types and possibly with non-operational risks. The IFoA Operational Risk Working Party has produced a paper on dependencies (Kelliher et al, 2020) which gives a good overview of the issues surrounding operational risk aggregation but validators should consider the following:…”

Section: Aggregation and Allocationmentioning

confidence: 99%

Operational Risk Working Party - Validating Operational Risk Models

Kelliher,

Acharyya,

Couper

et al. 2024

Br. Actuar. J.

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Operational Risk is one of the most difficult risks to model. It is a large and diverse category covering anything from cyber losses to mis-selling fines; and from processing errors to HR issues. Data is usually lacking, particularly for low frequency, high impact losses, and consequently there can be a heavy reliance on expert judgement. This paper seeks to help actuaries and other risk professionals tasked with the challenge of validating models of operational risks. It covers the loss distribution and scenario-based approaches most commonly used to model operational risks, as well as Bayesian Networks. It aims to give a comprehensive yet practical guide to how one may validate each of these and provide assurance that the model is appropriate for a firm’s operational risk profile.

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Section: Aggregation and Allocationmentioning

confidence: 99%

Operational Risk Working Party - Validating Operational Risk Models

Kelliher,

Acharyya,

Couper

et al. 2024

Br. Actuar. J.

View full text Add to dashboard Cite

show abstract

“…The Gaussian copula is based on the multivariate normal distribution, while the Student-t copula is based on the multivariate t-distribution. The Gaussian copula is superior to the Student-t copula, which is more complex to implement and requires the assumption of degrees of freedom in addition to the correlation matrix [7]. This research modeled the stocks using an Elliptical copulas, namely Gaussian copula.…”

Section: Introductionmentioning

confidence: 99%

Value at Risk Analysis on Blue Chip Stocks Portfolio With Gaussian Copula

Ardhitha,

Sulistianingsih,

Satyahadewi

2023

BAREKENG: J. Math. & App.

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Value at Risk (VaR) is a risk measurement tool to calculate the estimated maximum investment loss with a certain confidence level and period. VaR calculations using financial data are often not normally distributed, so the copula method is used, which is flexible on the assumption of normality on stock return data. Previous research discussed Gaussian copula using stocks from the telecommunications sector. In this research, using Gaussian copula on Blue Chip stocks. Blue Chip stocks have a good reputation and have a stable growth rate so they have a lower risk. Therefore, the research objective is to analyze the VaR portfolio of Blue Chip stock with Gaussian copula. This research uses the daily stock closing prices of BBNI and BBTN from November 2, 2020 to October 27, 2022. The analysis results suggested that a VaR portfolio using Gaussian copula with a confidence level of 90%, 95%, and 99%, respectively are 2.24%, 2.88%, and 4.02%. The value shows the percentage of investment risk that may be obtained in the next one-day period. This result also indicates that the higher the confidence level, the greater the VaR.

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“Dependencies between risks” by Patrick Kelliher

2022

Br. Actuar. J.

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This discussion relates to the paper Kelliher et al. (2020) which was presented at the IFoA sessional event held on Monday 7 March 2022.The Moderator (Mr P. D. G. Tompkins, F.I.A.): Welcome to this sessional meeting of the Institute and Faculty of Actuaries (IFoA). I am Peter Tompkins. I am a member of the IFoA Council. This is a sessional meeting which is intended to give the opportunity both for Patrick (Kelliher) to present and discuss his paper on "Dependencies between risks," and for people to make contributions to the paper. This can include a discussion about elements of the paper. It does not have to be in the form of questions to be answered by Patrick (Kelliher), but if you want to comment on a particular aspect of the paper, please feel free to do that.You can let me know if there are aspects you want to discuss so I can determine when best to call you when the time comes for discussion.So, without further ado, let me introduce Patrick Kelliher. Patrick (Kelliher) is a Fellow of the IFoA with more than 30 years of experience, predominantly in the life insurance field and specialises in risk management. First, he was doing that with Scottish Widows and later as head of market risk and asset liability management for Aegon UK before starting up his own business, Crystal Risk Consulting, 10 years ago. He is a Chartered Enterprise Risk Actuary and a member of several of our profession's risk management working parties and has produced papers and articles on a variety of risk topics, including risk classification, operational risk, liquidity risk, and the differences between banking and life insurance risks.Mr P. O. J. Kelliher, F.I.A.: Thank you Peter (Tompkins) and good morning, everybody. I am from a life insurance background. My focus was on Solvency II and meeting the internal model requirements of Solvency II regulations, and in particular, the need to identity the key variables driving dependencies and also the lack of diversification you might see under extreme scenarios.My research is split into two parts. The first one concerns the dependency between market and credit risks. We often have enough data to calculate correlations empirically. The key point I would like to stress about this paper is that empirical correlations are only a starting point. There are many issues with empirical correlations. They provide evidence of the dependency between risks, but they say little about why that dependency exists. I think they are intrinsically backward looking, so they might miss changes in interactions between risks. Also, they sometimes have a problem where risks are normally uncorrelated but may be correlated in stressed conditions. Sometimes, if you are not careful, the empirical correlation measures can simply average out the two states and end up with an unsatisfactory representation of each of them.There are a lot of issues with empirical correlations and for that reason we need an expert judgement overlay to find a suitable answer. This is the key thrust of my paper. In terms of expert overlay, I c...

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Operational risk dependencies

Cited by 4 publications

References 4 publications

Operational Risk Working Party - Validating Operational Risk Models

Operational Risk Working Party - Validating Operational Risk Models

Value at Risk Analysis on Blue Chip Stocks Portfolio With Gaussian Copula

“Dependencies between risks” by Patrick Kelliher

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