TOWARDS A FRAMEWORK FOR
OPERATIONAL RISK IN THE BANKING
SECTOR
Chapter 1
M. Hoohlo1
Prof. Dr. E. Schaling2
Dr. T. Mthanti2
1 Faculty
of Law,Commerce and Management
University of the Witwatersrand
2 Supervisors:
Graduate School of Business
University of the Witwatersrand
PhD Defence Panel, 2017
Outline
Introduction
Background of Operational Risk (OpRisk)
What is OpRisk value-at-risk (VaR)?
Problem and Argument
Statement of the Problem
Conceptual Framework and Empirical Approach
Framework
Context of the Study
Concepts
Outline
Introduction
Background of Operational Risk (OpRisk)
What is OpRisk value-at-risk (VaR)?
Problem and Argument
Statement of the Problem
Conceptual Framework and Empirical Approach
Framework
Context of the Study
Concepts
Why OpRisk
Key focus of risk management:
I
Expensive.
I
I
I
I
% age of regulatory capital (RC) due to loss driven by OpRisk
Multi-billion dollar settlements by Financial Institutions (FI’s)
to authorities
Shortcomings of existing frameworks
Important.
I
I
I
Relevant to the basel committee’s revision of it’s OpRisk RC
capital framework
To continue to develop what appears fit to improve within
OpRisk models
Stimulate active discussions beyond primitive approaches of
managing OpRisk
What are the important steps towards completing the post crisis
reforms?
Outline
Introduction
Background of Operational Risk (OpRisk)
What is OpRisk value-at-risk (VaR)?
Problem and Argument
Statement of the Problem
Conceptual Framework and Empirical Approach
Framework
Context of the Study
Concepts
Purpose of the Study
I
Developing a Loss Distribution Approach (LDA) for OpRisk
RC in VaR is useful.
I
I
how bad can things get?
Pushing the boundary by running this against credibility theory
which says..
Purpose of the Study
I
Developing a Loss Distribution Approach (LDA) for OpRisk
RC in VaR is useful.
I
I
I
how bad can things get?
Pushing the boundary by running this against credibility theory
which says..
Asserting the use of Artificial Neural Networks (ANN’s) with
statistical theory.
I
I
I
Taking into account of features of ANN’s
People’s risk aversion should better be factored in
Expect the predictability of my method to be better
Purpose of the Study
I
Developing a Loss Distribution Approach (LDA) for OpRisk
RC in VaR is useful.
I
I
I
Asserting the use of Artificial Neural Networks (ANN’s) with
statistical theory.
I
I
I
I
how bad can things get?
Pushing the boundary by running this against credibility theory
which says..
Taking into account of features of ANN’s
People’s risk aversion should better be factored in
Expect the predictability of my method to be better
Pushing the boundary for OpRisk RC by means of two strands
of literature: Integrating ANN’s and VaR is something new.
Purpose of the Study
I
Developing a Loss Distribution Approach (LDA) for OpRisk
RC in VaR is useful.
I
I
I
how bad can things get?
Pushing the boundary by running this against credibility theory
which says..
Asserting the use of Artificial Neural Networks (ANN’s) with
statistical theory.
I
I
I
Taking into account of features of ANN’s
People’s risk aversion should better be factored in
Expect the predictability of my method to be better
I
Pushing the boundary for OpRisk RC by means of two strands
of literature: Integrating ANN’s and VaR is something new.
I
Use empirical data to test this idea in a way that is falsifyable.
Purpose of the Study
I
Developing a Loss Distribution Approach (LDA) for OpRisk
RC in VaR is useful.
I
I
I
how bad can things get?
Pushing the boundary by running this against credibility theory
which says..
Asserting the use of Artificial Neural Networks (ANN’s) with
statistical theory.
I
I
I
Taking into account of features of ANN’s
People’s risk aversion should better be factored in
Expect the predictability of my method to be better
I
Pushing the boundary for OpRisk RC by means of two strands
of literature: Integrating ANN’s and VaR is something new.
I
Use empirical data to test this idea in a way that is falsifyable.
I
Then hopefully.
Purpose of the Study
I
Developing a Loss Distribution Approach (LDA) for OpRisk
RC in VaR is useful.
I
I
I
how bad can things get?
Pushing the boundary by running this against credibility theory
which says..
Asserting the use of Artificial Neural Networks (ANN’s) with
statistical theory.
I
I
I
Taking into account of features of ANN’s
People’s risk aversion should better be factored in
Expect the predictability of my method to be better
I
Pushing the boundary for OpRisk RC by means of two strands
of literature: Integrating ANN’s and VaR is something new.
I
Use empirical data to test this idea in a way that is falsifyable.
I
Then hopefully. An empirical contribution will follow.
Outline
Introduction
Background of Operational Risk (OpRisk)
What is OpRisk value-at-risk (VaR)?
Problem and Argument
Statement of the Problem
Conceptual Framework and Empirical Approach
Framework
Context of the Study
Concepts
Problem Statement
The LDA Integration Method
Should OpRisk managements focus shift towards improving on
what they see fit within existing AMA frameworks? or abandon the
adoption of innovative internal measurement approaches in
exchange for being able to use a simple SMA formula across the
whole industry.
Definition
OpRisk is defined as “the risk of loss resulting from inadequate
or failed internal processes, people and systems, and from
external events. This definition includes legal risk, but
excludes strategic and reputational risk.” .
Example
Capacity for dealing with the human side of risk management - we
are testing for this inherent bias.
Outline
Introduction
Background of Operational Risk (OpRisk)
What is OpRisk value-at-risk (VaR)?
Problem and Argument
Statement of the Problem
Conceptual Framework and Empirical Approach
Framework
Context of the Study
Concepts
Prospect Theory & LDA framework
Mathematical Framework
* π(p)ν(x) + π(q)ν(y )
* where π(·) is a decision weight and ν(·) a number reflecting
the subjective value of the outcome.
Theorem (Compound loss distribution function)
Gϑ(t) (x) =
P∞
k?
n,k=0,1 pk (n)FX (x)
pk (0)
x >0
x =0
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PT is helpful for thinking about financial phenomena
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Captures greater sensitivity to losses than to gains
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Transform probabilities with a weighting function π(·)
(1)
Outline
Introduction
Background of Operational Risk (OpRisk)
What is OpRisk value-at-risk (VaR)?
Problem and Argument
Statement of the Problem
Conceptual Framework and Empirical Approach
Framework
Context of the Study
Concepts
Human error example
Internal historical loss data
I
Perform a better modelling method of the LDA using a better
approximation technique for an optimal mix of internal and
external data with no known solution to date.
Outline
Introduction
Background of Operational Risk (OpRisk)
What is OpRisk value-at-risk (VaR)?
Problem and Argument
Statement of the Problem
Conceptual Framework and Empirical Approach
Framework
Context of the Study
Concepts
Bullet Points
Empirical Approach
I
Operational loss data from 4 big banks
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Decipher a pattern and fit a distribution based on actual event
data
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Perform an approximation of the LDA (analytical) solution
using a better approximation technique
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Assess how it forecasts compared to credibility theory
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Then we expect over-compensation for OpRisk RC, fitting
normal behavioural patterns around individuals psycological
make up which is consistent with risk aversion
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This is a testable proposition via empirical data
Summary
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Propose a new theoretical framework based on Prospect
Theory to improve the confidence in risk modelling of OpRisk
RC.
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For finance applications probability weighting may be the
most useful element to reduce modelling errors.
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Design an ANN based LDA model to predict whether loss
aversion increases after past losses.
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Outlook
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We can gain an understanding of how past losses affect risk
attitudes.
Probability weighting can significantly increase/decrease the
OpRisk RC requirement.