Scenario Analysis in the Measurement of Operational Risk Capital: A Change of Measure Approach

• DOI: http://doi.org/10.1111/j.1539-6975.2012.01506.x
• Date: 01 June 2014
• Author: David F. Babbel,Kabir K. Dutta
• Published date: 01 June 2014

©

DOI: 10.1111/j.1539-6975.2012.01506.x

303

SCENARIO ANALYSIS IN THE MEASUREMENT

OF OPERATIONAL RISK CAPITAL:ACHANGE

OF MEASURE APPROACH

Kabir K. Dutta

David F. Babbel

ABSTRACT

At large financial institutions, operational risk is gaining the same impor-

tance as market and credit risk in the capital calculation. Although scenario

analysis is an important tool for financial risk measurement, its use in the

measurement of operational risk capital has been arbitrary and often inac-

curate. Wepropose a method that combines scenario analysis with historical

loss data. Using the Change of Measure approach, we evaluate the impact

of each scenario on the total estimate of operational risk capital. The method

can be used in stress-testing, what-if assessment for scenario analysis, and

Loss Given Default estimates used in credit evaluations.

Kabir K. Dutta is a Senior Consultant at Charles River Associates in Boston. David F.

Babbel is a Fellow of the Wharton Financial Institutions Center, Professor at the Whar-

ton School of the University of Pennsylvania, and a Senior Advisor to Charles River Asso-

ciates. The authors can be contacted via e-mail: Kabir.Dutta.wg97@Wharton.UPenn.edu and

Babbel@Wharton.UPenn.edu, respectively.We are grateful to David Hoaglin for painstakingly

helping us by editing the article and making many valuable suggestions for improving the sta-

tistical content. We also thank Ravi Reddy for providingseveral valuable insights and for help

with the methodological implementation, Ken Swenson for providing guidance from practical

and implementation points of view at an early stage of this work, Karl Chernak for many

useful suggestions on an earlier draft, and Dave Schramm for valuable help and support at

various stages. We found the suggestions of Paul Embrechts, Marius Hofert, and Ilya Rosen-

feld very useful in improving the style, content, and accuracy of the method. We also thank

seminar participants at the Fields Institute, University of Toronto, American Bankers Associa-

tion, Canadian Bankers Association, and anonymous referees for their valuable comments and

their corrections of errors in earlier versions of article. Any remaining errors are ours. Three

referees from the Journal of Risk and Insurance provided thoughtful comments that led us to

refine and extend our study, and we have incorporated their language into our presentation

in several places. The methodology discussed in this article, particularly in the “Calculation of

Implied Probability Distributions” section, in several paragraphs of the “Economic Evaluation

of Scenarios: The Change of Measure” section, and in the Appendix, is freely available for use

with proper citation.

The Journal of Risk and Insurance, 2013, Vol. 81, No. 2, 303–334

304 THE JOURNAL OF RISK AND INSURANCE

INTRODUCTION

Scenario analysis is an important tool in decision making. It has been used for sev-

eral decades in various disciplines, including management, engineering, defense,

medicine, finance, and economics. Mulvey and Erkan (2003) illustrate modeling of

scenario data for risk management of a property–casualty insurance company.When

properly and systematically used, scenario analysis can reveal many important as-

pects of a situation that would otherwise be missed. Given the current state of an entity,

it tries to navigate situations and events that could impact important characteristics

of the entity in the future. Thus, scenario analysis has two important elements:

1. Evaluation of future possibilities (future states) with respect to a certain character-

istic.

2. Present knowledge (current states) of that characteristic for the entity.

Scenarios must pertain to a meaningful duration of time, for the passage of time will

ultimately render the scenarios obsolete. Also, the current state of an entity and the

environment in which it operates give rise to various possibilities in the future.

In management of market risk, scenarios also play an important role. Many scenarios

on the future state of an asset are actively traded in the market and could be used for

risk management. Derivatives such as call (or put) options on asset prices are linked

to its possible future price. Suppose, for example, that Cisco (CSCO) is trading today

at $23 in the spot (NASDAQ) market. In the option market, we find many different

prices available as future possibilities. Each of these is a scenario for the future state

of CSCO. The price for each option reflects the probability that the market attaches

to CSCO attaining more (or less) than a particular price on (or before) a certain date

in the future. As the market obtains more information, prices of derivatives change

and our knowledge of the future state expands. In the language of asset pricing, more

information on the future state is revealed.

At one time, any risk for a financial institution that was not a market or credit risk

was considered an operational risk. This definition of operational risk made data

collection and measurement of operational risk intractable. To make it useful for

measurement and management, Basel banking regulation narrowed the scope and

definition of operational risk. Under this definition, operational risk is the risk of

loss, whether direct or indirect, to which the Bank is exposed because of inadequate

or failed internal processes or systems, human error, or external events. Operational

risk includes legal and regulatory risk, business process and change risk, fiduciary

or disclosure breaches, technology failure, financial crime, and environmental risk. It

exists in some form in every business and function. Operational risk can cause not

only financial loss, but also regulatory damage to the business’ reputation, assets,

and shareholder value. One may argue that at the core of most of the financial risk,

one may be able to observe an operational risk. The Financial Crisis Inquiry Com-

mission (2011) report identifies many of the risks defined under operational risk as

among the reasons for the recent financial meltdown. Therefore, it is an important

financial risk to consider along with the market and credit risk. By measuring it prop-

erly,an institution will be able to manage and mitigate the risk. Financial institutions

SCENARIO ANALYSIS IN THE MEASUREMENT OF OPERATIONAL RISK CAPITAL 305

safeguard against operational risk exposure by holding capital based on the measure-

ment of operational risk.

Sometimes, a financial institution may not experience operational losses that its peer

institutions have experienced. At other times, an institution may have been lucky.

In spite of a gap in its risk, it did not experience a loss. In addition, an institution

may also be exposed to some inherent operational risks that can result in a significant

loss. All such risk exposures can be better measured and managed through a com-

prehensive scenario analysis. Therefore, scenario analysis should play an important

role in the measurement of operational risk. Banking regulatory requirements stress

the need to use scenario analysis in the determination of operational risk capital.1

Early on, many financial institutions subjected to banking regulatory requirements

adopted scenario analysis as a prime component of their operational risk capital cal-

culations. They allocated substantial time and resources to that effort. However,they

soon encountered many roadblocks. Notable among them was the inability to use

scenario data as a direct input in the internal data-driven model for operational risk

capital. Expressing scenarios in quantitative form and combining their information

with internal loss data (ILD) poses several challenges. Many attempts in that direction

failed miserably, as the combined effect produced unrealistic capital numbers (e.g.,

1,000 times the total value of the firm). Such outcomes were typical. As a result, bank

regulators relaxed some of the requirements for direct use of scenario data. Instead,

they suggested using external loss data (ELD) to replace scenario data as a direct

input to the model. External loss events are historical losses that have occurred in

other institutions. Such losses are often very different from the loss experience of

the institution. In our opinion, that process reduced the importance of scenarios in

measuring operational risk capital. Previously,as well as in current practice, external

loss data were and are used in generating scenarios.

We believe that the attempts to use scenario data directly in capital models have

failed because of incorrect interpretation and implementation of such data. This work

attempts to address and resolve such problems. Because scenarios have been used

successfully in many other disciplines, we think that scenario data should be as

important as any other data that an institution may consider for its risk assessments.

Some may question, justifiably, the quality of scenario data and whether such data

can be believable. We contend that every discipline faces such challenges. As we will

show, the value in scenario data outweighs the inherent weaknesses it may have.

Also, through systematic use, we will be able to enhance the quality of the data.

In this article, we propose a method that combines scenario analysis with historical

loss data. Using the Change of Measure approach, we evaluate the impact of each

scenario on the total estimate of operational risk capital. Our proposed methodol-

ogy overcomes the aforementioned obstacles and offers considerable flexibility. The

major contribution of this work, in our opinion, is in the meaningful interpretation

of scenario data, consistent with the loss experience of an institution, with regard

to both the frequency and severity of the loss. Using this interpretation, we show

how one can effectively use scenario data, together with historical data, to measure

1A basic source on these requirements is Risk-Based Capital Standards: Advanced Capital Adequacy

Framework—Basel II at (http://edocket.access.gpo.gov/2007/pdf/07-5729.pdf).