Risk Dynamics Banking Traineeship Topics

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Language: English UK

CONFIDENTIAL CONFIDENTIAL CONFIDENTIAL

Banking Traineeship Programme

Brussels, 22nd June 2012

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© Risk Dynamics - Confidential

CONFIDENTIALITY Management consulting is a competitive business. We view our methodologies and approaches as proprietary and therefore expect our clients to protect Risk Dynamics interests in our presentations, documents and analyses. Under no circumstances should this material be shared with any third party without the written consent of Risk Dynamics.

Copyright © 2012 Risk Dynamics

22 June 2012 Banking Traineeship Programme 2


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Risk Dynamics Traineeship Programme José Bustillo

2012 Trainiship Proposals 3


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"   Duration : min 50 working days "   Open positions: 2 to 3 students (in parallel) "   Start : As from September but earlier start is possible – could be later

also "   Interested ?

● Send your motivation letter indicating Ó your subject of interest Ó timing

● With your CV ● By July 15th at [email protected]

Risk Dynamics Traineeship Programme Practical details

22 June 2012 4 Banking Traineeship Programme


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Traineeship Topics



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Modelling dependence in Operational Risk

Dr Olga Reznikova



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"   Context ●  What is Operational Risk?

Ó  Basel Committee definition ¦  “Operational risk is a risk of loss resulting from inadequate or fails internal processes, people and

systems, or from external events". Ó  Mature banks can be allowed to use Advance Measurement Approaches (AMA)

¦  Bottom-up approach: Operational Risk losses are modeled at Unit-of-Measure level and aggregated to the group level Operational Loss distribution. Based on this loss distribution banks assess the OpRisk capital level (both regulatory and economic)

●  Why modelling dependence is important in Operational Risk modelling? Ó  In Operational Risk a sound and robust loss aggregation technique is essential

¦  Banks with AMA need to demonstrate that they are able to assess dependence between Units-of-Measure, such that they are allowed to have diversification benefits

Ó  Currently, in most of the firms (banks, actuarial companies, other large financial institutions) a static approach is used to assess the dependence structure between Business Lines/Event type cells (level at which the losses are modeled), e.g. a Gaussian or a Student copula

Ó  It has been observed that for some categories of Operational Risk dependence is not stable over time. Thus, Regulators not only push the firms forward to develop and implement robust aggregation methodologies, but also recommend them to assess the underlying risk factors and their impact on the level of dependence

Modelling dependence in OpRisk

7 2012 Trainiship Proposals


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"   Traineeship objectives and expectations ●  Perform a literature review on the latest research and developments of time-varying

dependence in Operational Risk ●  Conceptually: determine possible underlying drivers for different risk types ●  Applied:

Ó  Modeling of time-varying dependence structure and analysis on the underlying risk drivers Ó  Determine the best tests to assess the quality of the dependence model




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"   Required qualifications ●  Language: Good level of English ●  Soft: some knowledge of R or Matlab ●  Statistics: Econometrics, Time-Series, some knowledge of Copula modeling, of Non-

parametric statistics, bootstrap ●  Risk management: Very basic knowledge on risk management is preferable but not

mandatory

"   Expected outcomes ●  Internal

Ó  Ppt and word report on Literature review Ó  Ppt/word/excel report on Risk drivers Ó  Codes (ideally toolbox) on testing and modeling

●  External Ó  White paper (potentially)




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Scenario analysis in Operational Risk Dr Olga Reznikova



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"   Context ●  What is Scenarios analysis in Operational Risk?

Ó  According to Basel requirements a bank must effectively use four data sources when building an AMA model. The fours sources of data are

¦  Internal Loss Data (ILD) ¦  External Loss Data (ELD) ¦  Scenario Analysis (SA) ¦  Business environment and internal control factors (BEICF)

Ó  Scenario analysis ¦  An attempt to predict different scenarios that contain extreme/catastrophic shocks ¦  Focus on low frequency high severity risk events ¦  Based on expert judgment and is thus a very subjective exercise

●  Why Scenario analysis is important in Operational Risk modelling? Ó  When building an AMA model a firm needs to assess loss distribution of risk categories Ó  Internal loss data are usually sufficient to model the body of the distribution, i.e. high frequency

low severity events, but not the tail Ó  External loss data are very informative, but are not representative of the banks profile and thus

might not be use directly Ó  Through Scenario analysis experts can identify specific to the bank possible extreme events for

each risk category and determine the loss distribution tail

Scenario analysis in OpRisk



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"   Traineeship objectives and expectations ●  Perform a literature review on expert elicitation and experimental design methodologies ●  Implement these methodologies in the context of Operational Risk Scenario Analysis

(potentially, future use in other arias of Risk assessment)




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"   Required qualifications ●  Language: Good level of English ●  Soft: Some knowledge of R or Matlab ●  Statistics: Basic knowledge ●  Risk management: Basic knowledge on banks operations and risk management is

preferable. Knowledge on sociology and/or psychology is an asset.


Ó  Ppt/word/excel report: potential biases of Scenario analysis process in Operational Risk Ó  Ppt/word/excel report: build of experiments to identify the potential biases Ó  Codes: tests and measures to quantify expert assessments





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Measuring Counterparty Credit Risk and Credit Value Adjustments

Dr Jean-Roch Sibille



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"   Context ●  What is Counterparty Credit Risk (CCR)?

Ó  Risk due to a counterparty’s default on its financial obligations Ó  Generalization of credit risk in the context of OTC market

¦  Whenever you enter into a deal or a hedge, you face a credit risk

●  What is Credit Value Adjustment (CVA)? Ó  Price adjustment due to counterparty’s default risk that corresponds to the expected loss on a

contract and that relates to the price of hedging this risk away Ó  It is mandatory to measure CVA and include it in the banks P&L reports (Basel III) Ó  It leads to great challenges for banks in terms of data, methodology, IT, timing, strategy, etc. Ó  It needs to be measured at a netting set level

Measuring Counterparty Credit Risk and CVA

2012

Trainiship Proposals

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"   Context ●  Why CCR is important ?

Ó  It impacts very significantly banks capital requirements and portfolio management Ó  It is a major area for the future and it is currently under major developments Ó  It has dependencies to a lot of issues

¦  Collateral Agreements (CSA and SCSA) ¦  Funding Value Adjustments (FVA) ¦  Default Value Adjustments (DVA) ¦  Counterparties Clearing Houses (CCP) ¦  Wrong-Way Risk (WWR) ¦  Margin Period of Risks ¦  etc.

Measuring Counterparty Credit Risk



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"   Traineeship objectives and expectations ●  Perform a literature review (business + academic) and position clearly the issues ●  Develop a case study on the application of increasingly complex Basel III regulation

requirements Ó  The idea is to take a simplified portfolio and describe how to apply the measurement of

counterparty credit risk following the different possibilities available from the regulation, meaning: risk-neutral vs. real world risk measures and simple regulatory equation vs. more complex internal modeling.

Ó  The key idea behind would be to identify key strengths and weaknesses of these approaches to provide successful guidance to our customers

"   Required qualifications ●  Language: Good level of English ●  Market Finance: Basic knowledge of market products (Hull Book), continuous time

finance and regulation (Basel II and Basel III) ●  Statistics: Basic knowledge of econometrics ●  Risk management: Basic knowledge on banks operations and risk management is

preferable ●  Programming abilities in Matlab or Mathematica is an asset




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Ó  Ppt/word/excel report: Presentation of challenges around CCR Ó  Ppt/word/excel report: Case Study presentation





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Model Risk Assessment Dr Aykut Özsoy



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"   Context ●  Definition of Model

Ó  A ‘model’ is defined as any type of method, system, or approach that processes input data into quantitative estimates

Ó  According to this definition, the input could be qualitative (e.g., expert judgment) but output is necessarily quantitative

●  Definition of Model Risk Ó  ‘Model Risk’ is defined as risk of adverse consequences (e.g., financial loss, poor business or

strategic decisions, reputational damage) from decisions based on incorrect or misused model outputs

●  Assessment of Model Risk Ó  Assessing (via qualitative / quantitative techniques) the uncertainty in model output

"   Why Model Risk assessment is important? ●  Assessing this uncertainty will help financial institutions manage model risk ●  The primary objective of managing model risk is to reduce the potential losses due to

issues with models. These losses can be very substantial

Model Risk Assessment



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"   Traineeship objectives and expectations ●  Perform a literature review on uncertainty assessment methodologies in the literature

(academic / industry), e.g., Data uncertainty engines, Error propagation equations, Expert elicitation, Extended peer review, Inverse modeling, Monte Carlo analysis, scenario analysis, uncertainty matrix

"   Required qualifications ●  Language: Good level of English ●  Statistics: Basic knowledge ●  Risk management: Basic knowledge on banks operations and risk management is

preferable.

Model Risk Assessment



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RVB 96,96,96

RVB 246,152,26

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RVB 247,201,3

RVB 247,76,7

RVB 253,208,141


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Default Risk: TTC vs PIT Dr Aykut Özsoy



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"   Context ●  Default risk: Risk of a counterparty failing to pay back its debt (e.g., loan payment) to the

bank

●  For some purposes, default risk should be measured on any point in the economic cycle ●  For other purposes, default risk should be measured assuming that economic growth

follows the average trend (dashed line)

Default Risk: Through-The-Cycle vs. Point-In-Time



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RVB 96,96,96

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"   Traineeship objectives and expectations ●  Develop a well-documented tool in a preferred language (SAS, MATLAB, R, C++, etc.)

"   Required qualifications ●  Language: Good level of English ●  Statistics: Basic knowledge ●  Computer programming skills ●  Risk management: Basic knowledge on banks operations and risk management is

preferable.

Default Risk: Through-The-Cycle vs. Point-In-Time



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RVB 246,152,26

RVB 116,0,0

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RVB 96,96,96

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Modelling Retail Portfolio Correlation Yannick Bigah



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"   Context ●  Importance of Correlation

Ó  Correlation between obligors within a portfolio is a crucial component for the measurement of the portfolio risk

●  Correlation Estimation Ó  Correlation estimation is often challenging, notably due to the unavailability of empirical

observations Ó  Different approaches have been developed to estimate correlations. However

¦  Most of the models have focused on portfolios of bonds and corporate loans ¦  Less attention has been given to retail loan portfolio

●  Issues Ó  Applicability of the bonds and corporate based developed correlations model to retail loans

¦  Often in practice, these models are equally used for bonds and corporate; and also retail loans although the underlying assumptions and input data (e.g: Equity returns, Asset returns) might be meaningless for retail loans

Modelling Retail Portfolio Correlation



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"   Traineeship objectives and expectations ●  Perform a literature review on the existing approaches for modeling correlations in the

retail portfolios ●  Identify conceptually (i.e. from theoretical perspective) the strengths and weaknesses of

the existing models ●  From the applied perspective

Ó  Either implement the “most strong” model of the existing ones Ó  Or develop and implement alternative models




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RVB 96,96,96

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RVB 247,76,7

RVB 253,208,141


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"   Required qualifications ●  Language: Good level of English ●  Soft: Some knowledge of Matlab, R or SAS ●  Statistics/Econometrics: Basic knowledge ●  Risk management: Basic knowledge on banks operations and risk management


Ó  Report Ó  PowerPoint presentation Ó  Codes

●  External

Ó  White paper (potentially)



Economy & Finance

Risk Dynamics Banking Traineeship Topics