Operational Risk - Assignment Example

Literature Review. Operational risk has been defined by the Basel Committee as the risk of losses arising from problems from internal controls, systems, people and external events. (Cruz, 2002). A similar definition is provide in the Dictionary of Accounting (1999) as: "operational risk The risk of direct or indirect loss resulting from inadequate or failed internal processes and systems, or from a wide variety of external events". The control of operational risk has been the object of much attention in recent years, for example in the 2004 Basle Two accord concerning the capital adequacy of banks and in the Turnbull Report in the UK. It has also led to changes in the regulation of financial institutions and the requirements for the listing of public companies. (Dictionary of Accounting, 1999). Most of the prior studies on operational risk have focused on estimating operational risk in a financial institution. Frachot et al. (2001) explored the Loss Distribution Approach (LDA) for computing the capital charge of a bank for operational risk where LDA refers to statistical/actuarial methods for modelling the loss distribution. In this framework, the capital charge is calculated using a Value-at-Risk measure. Frachot et al. (2001) show how to compute the aggregate loss distribution by compounding the loss severity distribution and the loss frequency distribution, how to compute the total Capital-at-Risk using copulas, how to control the upper tail of the loss severity distribution with order statistics. Fachot et al. (2001) also compare LDA with the Internal Measurement Approach (IMA) proposed by the Basel Committee on Banking Supervision to calculate regulatory capital for operational risk. The results show that LDA and IMA are bottom - up internal measurement models which are apparently different. (Frachot et al., 2001). As earlier mentioned, much of the Literature has focused on operational risk in financial institutions with a particular emphasis on the Basel II accord and the quantification of operational risks. Kuritzkes (2002) notes that the Basel II Accord definition of operational risk considers only a subset of operational risks. According to Kuritzkes (2002) the term "operational risk" commonly refers to all non-financial risks whereas the Basel II Accord definition considers only a subset of non-financial risks including those resulting from failure of "internal processes, people, or systems or from external events". Mainelli (2002) suggests that operational risk has many pseudo-standard sub-taxonomies, such as people (e.g. workforce disruption, fraud), process (e.g., documentation risk, settlement failure), systems (e.g. failure, security) and external risks (e.g., suppliers, disasters, infrastructure utilities failures). However, day-to-day operational risk management involves decisions about opening times, cleaning standards, rodent controls in dealing rooms, secure electricity supply, security controls and other management decisions not suitable to real-time spreadsheet analysis. (Mainelli, 2002). Mainelli (2002) further suggests that there is a tension between the top-down imposition of a change and the bottom-up nature of these detailed decisions. If the purpose of risk-based capital allocation is to reflect differences across banks in business mix and risk profile, then operational risk measurement will need to be supported bottom-up within individual institutions. (Kuritzkes, 2002) Figure 1: Taxonomy of Operational Risk. Source: Kuritzkes (2002) As shown in figure 1 above risks can be divided into financial and non-financial risk components. The financial risk components include market risk, credit risks and ALM insurance and other risks whereas the nonfinancial risk component includes internal event risk, external event risk and business risk. As the figure indicates, the Basel II Accord only considers a subset (internal event risk and external event risk) as the nonfinancial risk. It fails to take into consideration a very important aspect of the risks - business risks. This may have significant implications for the quantification of risks if using the Basel II Accord's framework to quantify risk. Kuritzkes (2002) suggests that the ability to allocate capital to operational risk will ultimately depend on the ability to measure it. Jobst (2007) suggests three main approaches to measuring operational risk as follows: (1) The volume-based approach, which assumes that operational risk exposure is a function of the type and complexity of business activity, especially in cases when notoriously low margins (such as in transaction processing and payments-system related activities) magnify the impact of operational risk losses. (2) The qualitative self-assessment of operational risk, which is predicated on subjective judgment and prescribes a comprehensive review of various types of errors in all aspects of bank processes with a view to evaluate the likelihood and severity of financial losses from internal failures and potential external shocks. (3) Quantitative techniques, which have been developed by banks primarily for the purpose of assigning economic capital to operational risk exposures in compliance with regulatory capital requirements. Dissertations Muerman and Oktem (2002) note that it is only recently that attention began shifting from credit and market risk towards operational risk. Events resulting from operational risk have been recognised to have devastating effects on the operations of banks. (Muerman and Oktem, 2002). In a study reviewing the different steps of the risk management process with resect to operational risk in the banking industry, Muerman and Okman (2002) propose the "near-miss" concept as an "Advanced Management Approach" for operational risk to be implemented as a supervisory review process under "Pillar Two". Jobst (2007) provide an overview of the current regulatory framework of operational risk under the New Basel Accord with a view to inform a critical debate about the influence of data collection, loss reporting, and model specification on the consistency of risk-sensitive capital rules. The findings suggest that effective operational risk measurement hinges on how the reporting of operational risk losses and the model sensitivity of quantitative methods affect the generation of consistent risk estimates. (Jobst, 2007). As a result, Jobst (2007) recommend more coherent and consistent recommendation of operational risk. In addition to focusing on the banking sector, most studies on operational risk have also focused on the quality of quantitative measurement methods of operational risk (e.g., Makarov, 2006; Degen et al., 2006; Mignola and Ugoccioni, 2006, 2005; Nslehov et al., 2006; Grody et al., 2005; de Fontnouvelle et al., 2004; Moscadelli, 2004; Alexander, 2003; Coleman and Cruz, 1999; Cruz et al., 1998) and on the theoretical models of economic incentives for the management and insurance of operational risk (e.g., (Leippold and Vanini, 2003; Crouhy et al., 2004; Banerjee and Banipal, 2005). On their part, Dutta and Perry (2006) and Currie (2004, 2005) have devoted attention to the modelling of constraints and statistical issues of coherent and consistent operational risk reporting and measurement within and across banks. In addition to operational risk, reputational risk is another risk element. Feistein (2006: 25) suggests that reputation is the "strongest determinant of any corporation's sustainability". According to Murray (2003) is now considered the "single greatest threat to businesses today". Murray (2003) suggests that reputational risk is not being appropriately managed by most companies. While a company can witness a reversal of its stock price towards a positive direction, as well as make changes to its business strategy to move forward, it is very difficult to recover an organisation's reputation if it is "gravely injured". (Feistein, 2006: 25). Therefore, "a risk to reputation is a threat to the survival of the enterprise". (Feistein, 2006: 25). Resnick (2004) suggest that public "missteps" in the loss of confidence and trust among investors, analysts, customers or other stakeholders are understood to be potentially devastating to the long-term survival of a business. Resnick (2004) further argues that even "modicum" suggestions of "cracks in the armor" on key intangible characteristics such as ethics, management talent, perceptions of "prowess" in research and development, as well as the treatment of employees can result in "undercurrents" or questions that will distract executive team from the core mission of building its business and providing its shareholders with an attractive Return on investment (ROI). Murray (2003) argues that following the recommendations of the Turnbull Report into boardroom responsibility for risk management and accountability for intangible assets such as reputation, and the Higgs Review of corporate governance recommending a more active and independent role for non-executive directors (NEDs), NEDs should be appointed as reputation guardians for the corporation - in much the same way as they now sit on audit, nomination and remuneration committees. The Turnbell Report (1999) in addition to defining the responsibilities of NEDs to oversee and ensure proper management and accountability for internal control systems, they also requires NEDs to protect intangible assets such as brands and reputation. Feistein (2006) recommends structured engagement with investors, regulators, activists organisations, communities, and the media. Such engagements provide a means for developing a trained intelligence that enables leaders to anticipate external responses to their actions and depending on the necessity, the engagement delivers a perspective that helps protect the company from the kind of competition-driven excesses that seem to arise so easily in today's pressure market environment. (Feistein, 2006). One can observe that most of the studies on operational risk have focused on the banking sector and have concentrated mostly on the measurement of these risks. This paper is one of the first papers to study operational risk in the retail industry. In addition the paper also studies reputational risk. This is done through the use of a case study ASDA, which in 2005 laid off 1400 workers. The study seeks to understand how this decision affected the reputation of the company and whether there was an increase in the operational as well as the reputational risk of the company. This will be done through the use of questionnaires, interviews and structured questions addressed to the company management as well as to customers on how they perceive this move by the company. Bibliography Cruz M. (2002) modeling, measuring and hedging operational risk. Riskmaths Frachot, Antoine, Georges, Pierre and Roncalli, Thierry (2001). Loss Distribution Approach for Operational Risk. Available at SSRN: http://ssrn.com/abstract=1032523 Dictionary of Accounting (1999). Operational risk"A. Ed. Jonathan Law and Gary Owe. Oxford University Press, 1999. Oxford Reference Online. Turnbull, N. (1999) 'Internal control guidance for directors on the combined code', ICAEW, London, September. Firestein, P. J. (2006). Building and protecting corporate Reputation Strategy & Leadership, VOL. 34 NO. 4 2006 Murray, K. (2003). Reputation - Managing the single greatest risk facing business today. Journal of Communication Management Vol. 8, 2 142-149 # Henry Stewart Publications 1363-254X. Banerjee, S., Banipal, K. (2005), "Managing operational risk: framework for financial institutions", A.B Freeman School of Business, Tulane University, New Orleans, LA, working paper, . Coleman, R., Cruz, M. (1999), "Operational risk measurement and pricing", Derivatives Week, 5f, Vol. 8 No.30, . Crouhy, M., Galai, D., Robert, M. (2004), "Insuring versus self-insuring operational risk: viewpoints of depositors and shareholders", Journal of Derivatives, Winter, pp.51-5. Cruz, M., Coleman, R., Salkin, G. (1998), "Modeling and measuring operational risk", Journal of Risk, Vol. 1 No.1, pp.63-72. Currie, C.V. (2004), "Basel II and operational risk - overview of key concerns", University of Technology, Sydney, School of Finance and Economics Working Paper No. 134, March, . Currie, C.V. (2005), "A test of the strategic effect of Basel II operational risk requirements on banks", University of Technology, Sydney, School of Finance and Economics Working Paper No. 143, September, . de Fontnouvelle, P., Rosengren, E.S., Jordan, J.S. (2004), "Implications of alternative operational risk modeling techniques", SSRN working paper, June, . Degen, M., Embrechts, P., Lambrigger, D.D. (2006), "The quantitative modeling of operational risk: between g-and-h and EVT", ETH Preprint, Zurich, working paper, . Dutta, A., Perry, J. (2006), "A tale of tails: an empirical analysis of loss distribution models for estimating operational risk capital", Working Paper No. 06-13, Federal Reserve Bank of Boston, July, . Grody, A.D., Harmantzis, F.C., Kaple, G.J. (2005), "Operational risk and reference data: exploring costs, capital requirements and risk mitigation", Stevens Institute of Technology, Hoboken, NJ, working paper, November, . Jobst, A.A. (2007), "Operational risk - the sting is still in the tail but the poison depends on the dose", Journal of Operational Risk, Vol. 2 No.2, . Leippold, M., Vanini, P. (2003), "The quantification of operational risk", SSRN working paper, November, . Makarov, M. (2006), "Extreme value theory and high quantile convergence", Journal of Operational Risk, Vol. 1 No.2, . Mignola, G., Ugoccioni, R. (2005), "Tests of extreme value theory", Operational Risk, Vol. 6 No.10, . Mignola, G., Ugoccioni, R. (2006), "Sources of uncertainty in modeling operational risk losses", Journal of Operational Risk, Vol. 1 No.2, . Moscadelli, M. (2004), "The modelling of operational risk: experience with the data collected by the Basel committee", discussion paper, . Nelehov, J., Embrechts, P., Chavez-Demoulin, V. (2006), "Infinite-mean models and the LDA for operational risk", J. Operational Risk, Vol. 1 No.1, pp.3-25. Read More