Abstract: In an effort to bolster soundness standards in banking, the 2006 international regulatory agreement of Basel II requires globally active banks to include operational risk in estimating regulatory and economic capital to be held against major types of risk. This paper discusses practical issues faced by a bank in designing and implementing an operational risk capital model. Focusing on the use of the Loss Distribution Approach (LDA) in the context of the Basel Advanced Measurement Approach (AMA), pertinent topics of future research are suggested.

Abstract: In this paper, in order to model breach of contract risk, we design and value a bundled option that is composed of contract abandonment and price renegotiation. We numerically show that the bundled option is more valuable for the contract than either of the options, i.e., contract abandonment and price renegotiation, in isolation. This value increases monotonically as the spot price becomes more volatile. The value of the bundled option is less than the sum of the individual option values, hence showing the sub-additive property. We demonstrate that in the presence of high spot price volatility, the bundled option is more valuable when renegotiation date is selected to be closer to the half-life of the contract. We also show that early contract abandonment probability goes down in the presence of renegotiation option. We conclude that the commodity supplier should negotiate a supply chain contract with flexible options at the design stage with the buyer, obtaining contract abandonment and price renegotiation options –as a bundled option—in order to enhance the supply contract value and reduce the breach of contract risk.

Abstract: The topic of operational risk has gained increasing attention in both academic research and in practice. We discuss means to quantify operational risk with specific focus on manufacturing companies. In line with the view of depicting operations of a company using material, financial and information flows, we extend the idea of overlaying the three flows with risk flow to assess operational risk. We demonstrate the application of the risk flow concept by discussing a case study with a consumer goods company. We implemented the model using discrete-event and Monte Carlo simulation techniques. Results from the simulation are evaluated to show how specific parameter changes affect the level of operational risk exposure for this company. Introduction The number of major incidences and catastrophic events affecting global business operations is on the rise. The impact of recent volcano eruption in Iceland, earthquakes around the world, the BP oil spill and financial crisis is making headlines but companies may never know the true extent of the loss. These events reinforce the need for companies to consider operational risk in a more formal manner and act strategically to minimize the negative impact of these and other types of disruptions. Having a better view of operational risks can allow a company to act proactively in many cases to come out unscathed in fact such a capability can be converted into a competitive advantage. Quantification and measurement is an integral part of managing operational risk. The topic of operational risk is very central to the financial industry due to the immediate and very direct impact of the bankruptcy of a financial institution on the economy and businesses. Not surprisingly, therefore, it has attracted a lot of attention from regulators, academics and practitioners alike. Targeted efforts have been made in researching operational risk especially since the Basel II guidelines on its assessment and the building of capital reserves came out in 2001 [1]. But the breadth of the catastrophic disasters mentioned above raises an important question: Is the domain of operational risk measurement too narrowly focused on financial institutions and their risk exposures? Clearly, assessing operational risk exposure is necessary in non-financial companies as well. To this end, we propose a method to quantify operational risk for any organization including non-financial companies. From this point forward, we will use risk and operational risk interchangeably and discuss it in the context of a manufacturing environment. A fundamental issue in studying operational risk is a lack of uniform understanding of its meaning among academics and practitioners. Operational risk has been defined in a variety of ways in the literature so for the purpose of this research, we will adopt the definition proposed by the Basel Committee to define operational risk “as the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events.” [1]. It should be noted that although developed for financial institutions and referring to specific risk elements, this definition is suitable for other industries as well. For more definitions and the historical development of operational risk perception we refer to Cruz [2] and Moosa [3] for extended background information to the topic. In this paper, we will discuss the findings of a project that was completed in 2008/2009 in collaboration between the authors 1 and a Fortune 100 Consumer Packaged Goods company with global footprint, referred to as Company X, the sponsors of the research. Since there are no legislative instruments in place to guide non-financial institutions to build capital reserves for operational risk, Company X, like most other businesses, was focused on understanding the impact of various risks on its overall performance. Indeed, the negative impact on business performance can be directly or indirectly converted into financial terms to gauge the level of risk exposure. We modeled the supply network of Company X using a simulation software package and studied its behavior under different risk scenarios. The rest of the paper is organized as follows. First, we discuss the state of the art with regard to operational risk and its quantification. We then compare and analyze different approaches to operational risk. Next, we propose our model for assessing operational risk, including the introduction to the concept of risk flows and the risk assessment process. A case study is presented to demonstrate the application of the model, followed by a discussion of the results, along with the strategic implications. Conclusions are presented to discuss limitations and potential future research directions. 1 The first two authors were key members of the extended team that worked on this project. Literature Review Many researchers have addressed the topic of operational risk in their work. Different quantification approaches have been proposed and applied. In this section, we will discuss some of the quantification methods available for operational risk and position this paper among the current literature. A majority of the existing literature addresses operational risk of financial institutions with a strong focus on banks. Indeed, insurance companies have also been discussed [4]. Literature not only covers different quantification approaches outlined here [5-10], but also provides background to operational risk such as definitions, categorization and cyclicality [3, 6, 11-15]. The different quantification approaches can be divided into top-down and bottom-up approaches [16]. Top-down approaches use aggregated figures, often derived from financial statements or publicly available information. Little attention is given to the actual sources of risk, limiting the use of these approaches in operational risk management [6, 17]. But the simplicity of implementation has attributed to its popularity. Key among the top-down approaches are the singleand multi-indicator models which assume a correlation between an indicator such as profit and the operational risk exposure. The Basel Committee has also included indicator based quantification methods in their guidelines [1]. Multi-factor regression models use publicly available figures to measure company performance and relate this to input factors of the performance. The residual term is believed to describe operational risk. The CAPM approach is mentioned here only for completeness but its practical relevance and the underlying assumptions limit its validity. Scenario analysis and stress testing are also classified as a quantification approach, but their limitations with regards to expressing risk exposure are obvious. Bottom-up models assess the risk exposure by identifying risk factors at a lower level and aggregating risk to derive the overall level of operational risk. This can be further divided into process-based models and statistical models. Process-based models portray the chain of reaction from event to actual loss. These include Causal models [16, 18, 19], Bayesian models [8, 20], Reliability theory [3, 21] and System Dynamics approach [11]. Statistical models include the value-at-risk approach and the extreme value theory. These are based on the historical loss distribution data. Lambrigger et al. [7] have combined internal and external data with expert opinions using a Bayesian inference method to estimate parameters of frequency and the severity distribution for a Loss Distribution Approach. It should be noted that the above mentioned approaches primarily focus on financial institutions and do not address the specific challenges of risk quantification for manufacturing companies. As mentioned previously, our objective is to propose a general approach to risk quantification that can be applied to non-financial companies as well.

Abstract: The fallout from the financial crisis has illustrated that many sources of systemic risk were triggered or at least propagated by vulnerabilities in operational risk management (ORM), which has not kept pace with financial innovation, and an excessive focus of regulation on prudential requirements without recognition of substantial operational risk in market-based liquidity transformation. At the same time, institutions are at different stages of systems development and show considerable dispersion in ORM practices while falling short of integrating operational risk as a horizontal process. This is troubling in light of continued regulatory shortcomings. The following article highlights the increased importance of operational risk amid greater systemic risk concerns and reviews the current situation of ORM. In conclusion, it provides some suggestions on the future development ORM – both from an organizational and industry perspective.

Abstract: Operational risk quantification requires dealing with data sets which often present extreme values which have a tremendous impact on capital computations (VaR). In order to take into account these effects we use extreme value distributions to model the tail of the loss distribution function. We focus on the Generalized Pareto Distribution (GPD) and use an extension of the Peak-over-threshold method to estimate the threshold above which the GPD is fitted. This one will be approximated using a Bootstrap method and the EM algorithm is used to estimate the parameters of the distribution fitted below the threshold. We show the impact of the estimation procedure on the computation of the capital requirement - through the VaR - considering other estimation methods used in extreme value theory. Our work points also the importance of the building's choice of the information set by the regulators to compute the capital requirement and we exhibit some incoherence with the actual rules. Particularly, we highlight a problem arising from the granularity which has recently been mentioned by the Basel Committee for Banking Supervision.