Talk Titles and Abstracts
Emre Balta (Office of the Comptroller of the Currency (OCC))
The Known, the Unknown, and the Unknowable: Challenges in Validating
AMA Models
Basel II does not specify a particular approach or distributional
assumptions for the AMA-based models for the operational risk
capital charge. This flexibility inherent in AMA creates a broad
range-of-practice that makes comparison/benchmarking of the model
results a significant challenge for the validation teams and the
supervisors. Furthermore, the ultimate object of interest, the
.999 quantile of the aggregate loss distribution over a one-year
time horizon, combined with the heavy-tailed nature of
the operational losses make the process particularly sensitive
to choice of alternative models and underlying assumptions. In
this talk, we focus on the practical challenges faced in the validation
of AMA models, particularly with respect to high tail estimation,
tail dependence, and model uncertainty.
________________________________________________________
Eric Cope
IBM Research, Zurich
Penalized Likelihood Estimators for Truncated Data
We investigate the performance of linearly penalized likelihood
estimators for estimating distributional parameters in the presence
of data truncation. Truncation distorts the likelihood surface
to create instabilities and high variance in the estimation of
these parameters and the penalty terms help in many cases to decrease
estimation error and increase robustness. Approximate methods
are provided for choosing a priori good penalty estimators, which
are shown to perform well in a series of simulation experiments.
The robustness of the methods are explored heuristically using
both simulated as well as real data drawn from an operational
risk context.
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Mathias Degen
Postdoctoral Research Fellow at Cornell University, Ithaca NY
Diversification benefits: a second-order approximation
The quantification of diversification benefits due to aggregation
of risk plays a prominent role in the (regulatory) capital management
of large firms within the financial industry. However, the complexity
of today's risk landscape makes a quantifiable reduction of risk
concentration a challenging task. We discuss some of the issues
that may arise. The theory of second-order regular variation and
second-order subexponentiality provides the ideal methodological
framework to derive second-order approximations for diversification
benefits. As a byproduct, this allows us to analyze the accuracy
of the closed-form OpVaR approximation (Böcker-Klüppelberg).
________________________________________________________
Kabir Dutta
Principal/Senior Consultant in the Insurance Economics and Risk
Management Practice of the Charles River Associate International
On Using Scenario Analysis in The Measurement of Operational
Risk: A Systematic Approach for Data Integration
While scenario analysis is an important tool for the risk measurement,
its use in the measurement of operational risk capital has been
quite arbitrary and often inaccurate. Using a method based on
the change of measure approach used in financial economics for
asset pricing we will show how one can measure operational risk
exposure of an institution using scenario analysis along with
the internal loss event data. We will also show that the proposed
method can be used in many different situations such as in the
calculation of operation risk capital, stress testing, and what-if
assessment for the scenario analysis, among others. Using this
method one could create a catastrophic bond on various segments
of operation risk exposures of an institution.
Please note that the presentation is based on my latest paper
( with David Babbel) available at the following:
http://fic.wharton.upenn.edu/fic/papers/10/p1010htm.htm
________________________________________________________
Joerg Fritscher
Deutsche Bank
Stabilizing the calculation of expected shortfall contributions
using conditional Monte Carlo methods
The computation of important risk measures such as Value-at-Risk
(VaR) or expected shortfall (ESF) contributions using Monte Carlo
(MC) simulation becomes a challenging task when heavy-tailed loss
distributions are involved. In Operational Risk (OR) one is usually
confronted with such types of distributions and thus forced to
use a large number of scenarios to obtain numerically stable estimates
of aggregate risk capital (i.e. VaR). However, the computation
of ESF tail contributions required for the allocation of capital
at divisional level is even more difficult to stabilize, which
makes straightforward MC simulation often impracticable for this
purpose.
Asmussen and Kroese have successfully employed a variance-reducing
methodology for the rare event simulation with heavy tails: conditional
Monte Carlo estimators.
This presentation describes the general technique of conditional
MC simulations as well as its application within the LDA. Furthermore,
the implementation in DB's AMA model for the calculation of contributory
capital for the different cells of our business line/event type
matrix via expected shortfall contributions is introduced. The
perfomance of the Asmussen-Kroese algorithm and plain MC are compared
to demonstrate the superiority of conditional MC.
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Elise Gourier
Swiss Banking Institute, University of Zurich
Operational risk quantification using extreme value theory and
copulas: from theory to practice
In this talk we present an empirical study pointing out several
pitfalls of the standard methodologies for quantifying operational
losses. Firstly, we use Extreme Value Theory to model real heavy-tailed
data. We show that using the Value-at-Risk as a risk measure may
lead to a mis-estimation of the capital requirements. In particular,
we examine the issues of stability and coherence and relate them
to the degree of heavy-tailedness of the data. Secondly, we introduce
dependence between the business lines using Copula Theory. We
show that standard economic thinking about risk diversification
may be inappropriate when infinite-mean distributions are involved,
as it is standard in operational risk.
________________________________________________________
Giulio Mignola
Intesa Sanpaolo
Challenges in measuring operational risks from loss data
Under the Advanced Measurement Approach of the Basel II Accord,
banks are required to measure their total annual operational risk
exposures at the 99.9th percentile of the loss distribution. Meeting
this measurement standard, given the amount of operational loss
data that is currently available from either internal or external
sources is extremely challenging. Furthermore some difficulties
arise in applying the Loss Distribution Approach to computing
operational risk exposures, as well as in validating the capital
models. Finding many of these problems insurmountable, a possible
way forward is to suggest some changes to the regulatory framework
that could, at least partially, circumvent these difficulties.
________________________________________________________
Martin Neil
Queen Mary University, London
Using Hybrid Dynamic Bayesian Networks to model Operational Risk
in Finance
This paper presents recent new ideas on using cause-effect modeling,
in the form of Hybrid Dynamic Bayesian Networks (HDBNs), to estimate
extreme financial losses resulting from operational failures.
The presentation will focus on a particularly important loss process
- rogue trading - with the aim of demonstrating the advantage
of explicit modeling of banking processes and risk culture over
purely statistical models derived from actuarial loss data alone.
Value at Risk is calculated by applying a new state-of-the-art
HDBN algorithm that approximates continuous loss distributions
and aggregates across loss types using a process called dynamic
discretization. We conclude that the statistical properties of
the model have the potential to explain recent large scale loss
events and offer improved means of loss prediction.
________________________________________________________
Tony Peccia
Citi group
CRO for Citibank Canada
Rethinking Basel II for Operational Risk
The capital charge in most financial institutions is largely
determined by actuarial models that provide little insights into
the actual risk factors that drive the operational risks exposure
and remains an abstraction for most business managers. Operational
risk reporting largely consists of reporting losses after the
fact to mostly those that were actively involved in resolving
the loss event, accompanied by an abstract capital amount and
either aggregated RCSA/KRI information or pages and pages of detailed
risk issues that are not actionable to the recipients of the report.
So what to do?
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Beatriz Santa Cruz Blanco
BBVA
Metodologías de riesgo corporativo
Issues in Modelling Tails in Operational Risk
Basel II establishes different methodologies for the measurement
of the operational risk under advanced methods, being most used
by the industry the one that is based on the actuarial models
(Loss Distribution Approach). According to the method above mentioned,
the regulatory capital by operational risk is obtained from the
distribution function of losses, by means of convolution between
the frequency and severity distributions of every couple business
line - risk type, as well as from the correlation with other ones
accounted for by the Financial Institution. We focus on the modeling
of the distribution of severity, in particular, by instance of
outliers which mismatch the adjustment: a) understate capture
of the empirical information of the entity; b) great extrapolation,
providing figures of expected loss and percentile of the loss
distribution out of any economic rationality. We present an alternative
methodology to the traditional one used for modeling outliers.
The method proposed could be a first approach to conduct a technical
assessment of scenarios in which outliers are involved, being
applicable not only to operational risk in financial area, but
to another sectors like energetic and aerospace
________________________________________________________
Anupam Sahay
KeyCorp
Director Risk Models & Operational Risk, Risk Management
Analytic Approximations for Operational Risk Capital
In the loss distribution approach for operational risk capital
modeling, severity and frequency are modeled separately and then
combined to obtain the aggregate loss distribution and capital.
From the point-of-view of analytic approximations, the realm of
modeling can be divided into four quadrants, based on whether
the tail of the severity distribution is light or heavy, and whether
the expected frequency is low or high. We present asymptotic approximations
for operational risk capital that are relevant in these quadrants.
The accuracy of the approximations and their practical usage are
discussed.
________________________________________________________
Alberto Suarez
Universidad Autónoma de Madrid
Robust quantification of the exposure to operational risk:
Bringing economic sense to economic capital
Operational risk is commonly analyzed in terms of the distribution
of aggregate yearly losses. Risk measures can then be defined
as statistics of this distribution that focus on the region of
extreme losses. Assuming independence among the operational risk
events and between the likelihood that they occur and their magnitude
separate models are made for the frequency and for the severity
of the losses. These models are then combined to estimate the
distribution of aggregate losses. While the detailed form of the
frequency distribution does not significantly affect the risk
analysis, the choice of model for the severity often has a significant
impact on operational risk measures. For heavy-tailed data these
measures are dominated by extreme losses, whose probability cannot
be reliably extrapolated from the available data. With limited
empirical evidence, it is difficult to distinguish among alternative
models that produce very different values of the risk measures.
Furthermore, the estimates obtained can be unstable and overly
sensitive to the presence or absence of single extreme events.
Setting a bound on the maximum amount that can be lost in a single
event reduces the dependence on the distributional assumptions
and improves the robustness and stability of the risk measures,
while preserving their sensitivity to changes in the risk profile.
This bound should be determined by an expert on the basis of economic
arguments and validated by the regulator so that it can be used
as a control parameter in the risk analysis.
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