Subject category:
Production and Operations Management
Originally published in:
2018
Revision date: 28-Aug-2018
Length: 9 pages
Data source: Generalised experience
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Abstract
This case considers two divisions of a fictitious bank, the Allied Commercial Bank of Connecticut. One of the divisions concerns retail banking activities (credit cards), whereas the other division concerns investment banking activities (trading). Several of the authors of the case have extensive experience with regard to operational risk management in the financial services industries. The data that is supplied with the case is not real world data. However, the distributional properties of the data mimic distributional properties of real world data that had been collected over the period 2008-2012. The main objective of this case is to draw students’ attention to TQM issues in financial services and the importance of operational risk relative to credit risk and market risk. It provides examples of multifactor linear and nonlinear regressions. It also illustrates the contrast between the Central Limit Theorem (and the normal distribution) and the Extreme Value Theorem (and the Generalized Extreme Value distribution) and exposes the differences between thin-tailed distributions and fat tail distributions. Part I (credit cards) illustrates the importance of the Central Limit Theorem and the normal distribution (a thin-tailed distribution) of Ops Risk losses incurred through credit cards. The students here have to develop a control (Shewhart) chart of the operational losses while taking into account seasonality and inflation trends that skew the data. Part II of the case (FX trading) illustrates the importance of the Extreme Value Theorem and Extreme Value distributions (examples of fat-tailed distributions) with regard to Ops Risk losses incurred in FX trading. The final discussion of the case focuses on comparing losses following thin-tailed distributions (eg, normal distribution) and losses following fat-tailed distributions (eg, lognormal distribution) and their applications in practice.
Subject category:
Production and Operations Management
Originally published in:
2018
Version: 1-Apr-2018
Length: 40 pages
Data source: Generalised experience
Subject category:
Production and Operations Management
Originally published in:
2018
Format:
.zip
Data source: Generalised experience
Subject category:
Production and Operations Management
Originally published in:
2018
About
Abstract
This case considers two divisions of a fictitious bank, the Allied Commercial Bank of Connecticut. One of the divisions concerns retail banking activities (credit cards), whereas the other division concerns investment banking activities (trading). Several of the authors of the case have extensive experience with regard to operational risk management in the financial services industries. The data that is supplied with the case is not real world data. However, the distributional properties of the data mimic distributional properties of real world data that had been collected over the period 2008-2012. The main objective of this case is to draw students’ attention to TQM issues in financial services and the importance of operational risk relative to credit risk and market risk. It provides examples of multifactor linear and nonlinear regressions. It also illustrates the contrast between the Central Limit Theorem (and the normal distribution) and the Extreme Value Theorem (and the Generalized Extreme Value distribution) and exposes the differences between thin-tailed distributions and fat tail distributions. Part I (credit cards) illustrates the importance of the Central Limit Theorem and the normal distribution (a thin-tailed distribution) of Ops Risk losses incurred through credit cards. The students here have to develop a control (Shewhart) chart of the operational losses while taking into account seasonality and inflation trends that skew the data. Part II of the case (FX trading) illustrates the importance of the Extreme Value Theorem and Extreme Value distributions (examples of fat-tailed distributions) with regard to Ops Risk losses incurred in FX trading. The final discussion of the case focuses on comparing losses following thin-tailed distributions (eg, normal distribution) and losses following fat-tailed distributions (eg, lognormal distribution) and their applications in practice.
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Subject category:
Production and Operations Management
Originally published in:
2018
Version: 1-Apr-2018
Length: 40 pages
Data source: Generalised experience
Subject category:
Production and Operations Management
Originally published in:
2018
Format:
.zip
Data source: Generalised experience
Subject category:
Production and Operations Management
Originally published in:
2018