Frontiers of risk management, volume II key issues and solutions

Frontiers of Risk Management Frontiers of Risk Management Key Issues and Solutions Volume II Edited by Dennis Cox Frontiers of Risk Management: Key Issues and Solutions, Volume I Copyright © Business Expert Press, LLC, 2018 All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations, not to exceed 400 words, without the prior permission of the publisher First published in 2018 by Business Expert Press, LLC 222 East 46th Street, New York, NY 10017 www.businessexpertpress.com ISBN-13: 978-1-94709-848-0 (paperback) ISBN-13: 978-1-94709-849-7 (e-book) Business Expert Press Finance and Financial Management ​Collection Collection ISSN: 2331-0049 (print) Collection ISSN: 2331-0057 (electronic) Cover and interior design by Exeter Premedia Services Private Ltd., Chennai, India First edition: 2018 10 Printed in the United States of America Abstract Frontiers of Risk Management was developed as a text to look at how risk management would develop in the light of Basel II With an objective of being 10 years ahead of its time, the contributors have actually had even greater foresight What is clear is that risk management still faces the same challenges as it did 10 years ago With a series of experts considering financial services risk management in each of its key areas, this book enables the reader to appreciate a practitioner’s view of the challenges that are faced in practice identifying where appropriate suitable opportunities As editor, I have only made changes in the interests of changing regulations but generally have enabled the original text to remain unaltered since it remains as valid today as when originally published Keywords Basel II, credit risk, enterprise risk management, insurance risk, loss data, market risk, operational risk, outsourcing, risk appetite, risk management Contents Chapter The Use of Credit Rating Agencies and Their Impact on the IRB Approach Markus Krebsz, Gary van Vuuren, and Krishnan Ramadurai Part I Operational Risk Chapter Frontiers of Operational Risk Management Ralph Nash and Ioanna Panayiotidou Chapter The Issues Relating to the Use of Operational Loss Data—Internal and External David Breden Chapter Stress Testing and Risk Management Stuart Burns Chapter Money Laundering Deterrence: The Challenge of Applying a Risk-Based Approach David Blackmore Chapter Outsourcing and Risk Management Nina Sodha Part II Other Risk Chapter Developments Within IT and Online Banking Dilip Krishna Chapter Risk Management and Financial Control Angela Caldara Chapter The Risks of Outsourcing Roger Bach Chapter 10Insurance and Risk Management Anthony Smith and Dennis Cox Chapter 11Developments in Pension Fund Risk Paul Sweeting Bibliography Index CHAPTER The Use of Credit Rating Agencies and Their Impact on the IRB Approach Markus Krebsz, Gary van Vuuren, and Krishnan Ramadurai Fitch Ratings Introduction: The IRB Approach—Cornerstone of Basel II This chapter was originally drafted when Basel II was new Basel III in its various manifestations does not make any major change to Basel II in this regard IFRS requiring a general provision for any facility to be introduced essentially builds upon the IRB framework discussed in this chapter which remains as valid today as it was when originally drafted The IRB approach is a cornerstone in the Basel II capital framework and a critical innovation in the regulatory capital treatment of credit risk Indeed, much of the work of the Committee since June 1999 has focused on building and refining the IRB framework, including the form and calibration of the capital formulas, the operational standards and risk management practices that qualifying banks must follow, and the treatment of different types of assets and business activities While this represents a new path in banking regulation, however, the concepts and elements underlying the IRB approach are based largely on the credit risk measurement techniques that are used increasingly by larger, more sophisticated banks in their economic models The IRB approach is, at heart, a credit risk model—but one that is designed by regulators to meet their prudential objectives The building blocks of the IRB capital requirements are the statistical measures of an individual asset that reflect its credit risk, including: probability of default (PD), or the likelihood that the ​borrower defaults over a specified time horizon; loss given default (LGD), or the amount of losses the bank expects to incur on each defaulted asset; remaining maturity (M), given that an instrument with a ​longer tenor has a greater likelihood of experiencing an adverse credit event; and exposure at default (EAD), which, for example, reflects the forecast amount that a borrower will draw on a commitment or other type of credit facility Under the most sophisticated or advanced version of the IRB approach, banks are permitted to calculate their capital requirements using their own internal estimates of these variables (PD, LGD, M, and EAD), derived from both historical data and specific information about each asset More specifically, these internal bank estimates are converted or translated into a capital charge for each asset through a predetermined supervisory formula Essentially, banks provide the inputs and Basel II provides the mathematics As a credit risk model, the IRB formula has been designed to generate the minimum amount of capital that, in the minds of regulators, is needed to cover the economic losses for a portfolio of assets Therefore, the amount of required capital is based on a statistical distribution of potential losses for a credit portfolio and is measured over a given period and within a specified confidence level The IRB formula is calculated based on a 99.9 percent confidence level and a one-year horizon, which essentially means that there is a 99.9 percent probability that the minimum amount of regulatory capital held by the bank will cover its economic losses over the next year In other words, there is a one in 1,000 chance that the bank’s losses would wipe out its capital base, if equal to the regulatory minimum The economic losses covered by the final IRB capital charges represent the bank’s UL (unexpected losses), as distinguished from losses that the bank can reasonably anticipate will occur, or EL (expected losses) Banks that are able to estimate EL typically cover this exposure through either reserves or pricing In statistical terms, the EL is represented by the amount of loss equal to the mean of the distribution, while UL is the difference between this mean loss and the potential loss represented by the assumed confidence interval of 99.9 percent As seen in Exhibit 1.1, the credit risk on an asset, reflected both in the UL and the EL, increases as the default probability increases Likewise, the level of credit risk also increases with higher loss severities, longer maturities, and larger exposures at default Exhibit 1.1 Corporates Source: Fitch Ratings In addition (see Exhibit 1.1), EL contributes a relatively small proportion of the capital charge for high-quality (or low-PD) borrowers, but an increasingly greater proportion as the borrowers move down the credit quality spectrum For example, for a loan to a very strong (or low-PD) borrower, the bank anticipates that the asset will perform well and is unlikely to experience credit-related problems Therefore, any severe credit deterioration or loss that might occur on the loan to the borrower would differ from the bank’s expectation and, thus, be explained primarily by UL By contrast, for a loan to a weaker (or high-PD) borrower, the probability of some credit loss is much greater, enabling the bank to build this expectation of loss into its pricing and reserving strategies Therefore, at the lower end of the credit quality spectrum, EL is a larger component of the credit risk facing the bank than at the higher end of the quality spectrum Of course, the amount of economic loss that an asset might incur depends on the type or structure of the asset For example, is the exposure to a major corporation or to an individual borrower? Is it secured by collateral? How does the borrower generate funds for repaying the bank? What is the typical life or tenor of the asset? How is its value affected by market downturns? Different credit products can behave quite differently, given, for example, their contractual features, cash-flow patterns, and sensitivity to economic conditions Basel II recognizes the importance of product type in explaining an asset’s credit profile and provides a unique regulatory capital formula for each of the major asset classes including corporates, banks, commercial real estate (CRE), and retail Critical Elements of IRB A critical element of the IRB framework and a key driver of the capital charges are the assumptions around correlation and the correlation values used in the formulas Basel II does not recognize full credit risk modeling and does not permit banks to generate their own internal estimates of correlation in light of both the technical challenges involved in reliably deriving and validating these estimates for specific asset classes and the desire for tractability In generating a portfolio view of the amount of capital needed to cover a bank’s credit risk, Basel II captures correlation through a single, systematic risk factor More specifically, the IRB framework is based on an asymptotic, single-risk factor model, with the assumption that changes in asset values are all correlated with changes in a single, systematic risk factor While not defined under Basel II, this systematic risk factor could represent general economic conditions or other financial market forces that broadly affect the performance of all companies In summary, a low correlation implies that borrowers largely experience credit problems independently of each other due to unique problems faced by particular borrowers On the other hand, higher asset correlations indicate that credit difficulties occur simultaneously among borrowers in response to a systematic risk factor, such as general economic conditions Correlation Assumptions Under Basel II, the degree to which an asset is correlated to broader market events depends, in certain cases, on the underlying credit quality of the borrower Based on an empirical study conducted by the Committee, the performance of higher-quality assets tends to be more sensitive to— and more correlated with—market events Although this finding might at first seem counterintuitive, it is consistent with financial theory that states that a larger proportion of economic loss on high-quality exposures is driven by systematic risk By contrast, the economic loss on lower-quality exposures is driven mainly by idiosyncratic, or company-specific, factors and relatively less so by systematic risk This reasoning suggests that the performance of lower-quality assets tends to be less correlated with market events and, therefore, the biggest driver of credit risk is the high-PD value of the borrower or, more broadly, the lower intrinsic credit quality of the borrower The IRB approach distinguishes between three types of retail assets—credit cards (known formally as qualifying revolving retail exposures [QRRE]), residential mortgages, and consumer lending (classified under other retail) Basel II has calibrated the three retail capital curves to reflect the unique loss attributes of each of these different products, as seen in Exhibit 1.2 The IRB formulas for the three retail product types are identical except for the underlying correlation assumption, a key driver of the shape and structure of the capital requirements Additionally, the Basel II charges are sensitive to the underlying LGD estimate, which in practice can vary substantially across the different types of retail assets For example, loss severities tend to be much higher for credit card assets than for residential mortgage lending out the management of the LDI strategy—external auditing of performance against the benchmark is important Interest rate and inflation swap indices are available in some countries and, if the processes described previously are used to find the combination of cash and swaps that gives the lowest predicted tracking error relative to the pension scheme liabilities, then this can be used as a proxy benchmark This means that the manager of the LDI strategy can be measured effectively for each part of the process, with allowance being made for the inability to completely remove all risks A sample attribution analysis is given in Exhibit 11.8 Here, a multi-asset portfolio is being managed against a multi-asset benchmark which, together with a swap overlay, has been designed relative to a benchmark which is a proxy benchmark for the liabilities The choice of multi-asset benchmark and the styles of management within this benchmark would have been chosen so as to best allocate the risk budget between market risk (the multi-asset benchmark decision) and active risk (the allocation away from the benchmark and within asset classes), allowing for the fact that part of the risk budget must be allocated to the difference between the liabilities and the proxy benchmark that cannot be hedged Attribution analysis within the multi-asset portfolio, giving the return due to allocations away from the benchmark and that due to management within each asset class, can be carried out in the same way as would be done for any multi-asset portfolio Exhibit 11.8 LDI attribution analysis Component Code Calculated as Return an assets A Return on multi-asset portfolio B Return on multi-asset benchmark C Return on proxy benchmark D Return on liablities E Performance of assets relative to liabilities F =A−E Unhedgable return difference G =D−E Benchmark decision value added* H =A−B+ C−B Mutli-asset value added I =B−C *includes the efficiency of the swap overlay Source: Author’s own The preceding analysis describes a segregated approach to LDI, where swap overlays and asset allocation strategies are carried out on a scheme-by-scheme basis If swaps are involved, however, then expertise is required in carrying out an LDI strategy and smaller pension schemes might not feel that they have sufficient expertise either to design or to implement the swap overlay Furthermore, unless the swaps traded have notional sizes in the tens of millions of sterling, then the cost of trading and of subsequently rebalancing the swaps may make such an approach uneconomical The alternative is to use a pooled LDI fund These are essentially collateralized swaps, available in both fixed and inflation-linked “flavors,” in which units can be bought as they can for any other fund There are two ways in which a pooled LDI fund can work The first is for each fund to have a fixed maturity date, for funds with initial maturities of, say, 10, 20, and 30 years to be available, and for the maturities of the funds to decrease over time, eventually leading to a redemption payment Such funds could be designated as, say, “2016,” “2026,” and “2036” funds, and they are essentially the same as zerocoupon bonds Another approach is to hold the maturities constant, so a fund initially launched as a 10-year fund would be kept as a 10-year fund by periodically resetting the swaps within each fund Such funds would never mature In both cases, the funds could be used to implement a key rate duration approach to LDI The reducing maturity funds would be most suitable for a scheme where no new benefits were being accrued, so the maturity of the scheme would be reducing year-for-year with the assets; the fixed maturity funds would be most suitable for a scheme where benefits continued to be accrued, so the maturity of the liabilities would be more stable from year to year If the assets underlying the swaps in the pooled LDI funds are low-risk, cash-type assets, then the result of implementing an LDI strategy is potentially to reduce expected return as well as unrewarded risk There are two potential solutions to this The first is for the pooled LDI funds to have assets other than cash-type assets underlying the swaps These might be absolute return funds with return targets in excess of cash, or a range of long-only assets such as equities While this approach solves the problem, it means that a large number of funds needs to be launched in order to satisfy all potential clients The alternative is to use levered LDI funds The standard, pooled LDI fund is unlevered, so £10m notional of swaps has £10m of cash-type assets supporting it within the fund However, a levered fund—for example, with 2x leverage—would have the same £10m notional of swaps, but would hold only £5m of collateral to support it This would mean that a pension scheme with £10m of liabilities could hedge all of its interest rate risk by investing in only £5m of levered LDI funds, and could invest the remaining £5m of assets in a range of return-generating assets It would also mean that a scheme in deficit, without sufficient assets to cover its liabilities, would be able to use a combination of levered and unlevered funds to fully hedge the interest rate risk The various structures are shown in Exhibit 11.9 One complication with levered LDI funds is that the degree of leverage changes as the value of the swaps changes, something that does not happen with the unlevered funds This means that, in practice, a 2x levered fund might operate with leverage between 1.5x and 2.5x Exhibit 11.9 LDI structures Source: Author’s own Other Financial Risks It is worth spending some time discussing increases to pensions in payment and in deferment in some more detail While in many cases these increases are straightforward, in some cases there are caps and floors that make exact hedging using basic nominal and inflation swaps difficult, if not impossible Investment banks will generally be able to provide tailored solutions for very large pension schemes, but what solutions are there for everyone else? A first approximation can be obtained by looking at the expected value of the increase For example, if the expected long-run rate of inflation is percent per annum and pension increases are provided in line with inflation subject to a cap of percent and a floor of percent, then treating the pension increases as purely inflation-linked gives a sensible answer Similarly, if the same increases are provided and the expected long-run rate of inflation is percent, then treat the pension increases as being fixed at percent and hedge accordingly This approach is less satisfactory when the expected rate of inflation is close to the cap or floor In this case, a better approach is to use stochastic modeling and optimization to arrive at the mix of nominal and inflation-linked swaps or securities that best matches the level of increase under investigation Another key financial risk arising from the pension scheme that cannot be hedged is wage inflation Whenever pay increases are provided, accrued pension is increased and this results in an increase in pension scheme liabilities Although wage inflation is a risk, however, it is also something that is under the control of the scheme sponsor The main form of risk mitigation here is for the sponsor to recognize not just the direct effect of wage inflation on corporate profits, but also the indirect effect resulting from the impact on pension scheme liabilities It is also worth noting that although nominal wage inflation has been volatile, real wage inflation— wage inflation net of price inflation—has been much more stable The reason for this is clear: for the vast majority, regular pay increases are decided with reference to the underlying rate of price inflation This means that treating pay inflation as price inflation plus a fixed amount can still result in a close match between assets and liabilities Demographic Risks The main demographic risk for a pension scheme is mortality—or, more correctly, longevity—risk This is the risk that pension scheme members will, on average, live longer than expected, meaning that current valuations will underestimate the true liability However, mortality risk is actually two risks: one risk is that the projections are wrong; however, potentially just as serious a risk is that the projections are correct but that the pension scheme is unlucky This can be thought of as binomial mortality risk Binomial Mortality Risk Binomial mortality risk occurs when the underlying mortality projections for the population or the group as a whole are correct, but random fluctuations are an issue This is particularly a problem for smaller schemes and the problem decreases as the scheme size increases, although it remains a major risk until the number of scheme members is well into the thousands The key to managing binomial mortality risk is simply to increase the number of lives—this risk is a diversifiable risk The number of lives can be increased by taking action such as merging separate pension schemes within a company, a group, or even an industry, or by giving all assets and liabilities to an insurance company The final approach will clearly solve many more problems than just binomial mortality risk, but at a price Mortality Projection Risk Improving longevity does not necessarily cause a problem for pension schemes—providing actuarial valuations make allowance for this improvement Allowance is almost always made for some improvement in longevity, but unfortunately this allowance has often proved to be inadequate Will this improvement continue? It is difficult to say; much of the improvement so far has concentrated on curing illnesses, but the key question in the long run is whether old age itself will remain incurable If so, then there is a finite limit to the possible improvements in longevity; if not, then immortality will provide a challenge for pension schemes, to say the least As Buettner (2002) points out, this distinction forms the two main schools of thought when it comes to long-term longevity projection Whatever the answer is, the fact remains that mortality projection risk is not a diversifiable risk: no matter how large the pension scheme, the risk remains Pension schemes have been aware of longevity risk for many years, but the magnitudes of recent revisions to mortality projections have brought the issue into sharper focus Perhaps this is why capital market solutions for these issues have been explored only relatively recently, first appearing in articles by Blake and Burrows (2001), Milevsky and Promislow (2001) and others Blake and Burrows (2001) are among the first to discuss capital market solutions with their idea of survivor bonds The bonds in their paper are amortizing securities, the payments of which depend on the proportion of a reference population still surviving at the date of payment of each coupon Survivor bonds differ from annuities in that the payments from the bond are not made to the reference population The idea initially sounds appealing When BNP Paribas looked at launching a Blake-andBurrows-style longevity bond with the European Investment Bank, however, the reception could best be described as lukewarm and the bond was withdrawn without being launched Blake et al (2006) give a number of reasons for the bond’s lack of success, the three main reasons being: the duration of the bond was too short for many ​pension schemes; the bond tied the reduction in risk to a nominal bond return; and the basis risk between the proposed reference population (English and Welsh male lives) and age (65 only) and the ​pension scheme populations and ranges of ages would have made the risk premium charged seem unattractive It is possible, however, that the main reason was that it was a bond rather than some other instrument Dowd (2001) suggests a number of alternatives to survivor bonds, among them survivor swaps Here, the population-dependent payments form the floating leg of the swap, with the fixed or preset leg being the expected amount of those payments assessed at the time that the swap is transacted This solves a key problem with the longevity bond in that it separates risk reduction from return generation Given that survivor swaps would be over-the-counter (OTC) products, larger pension schemes (and life assurance companies wanting to hedge mortality rather than longevity) would be able to formulate swap contracts that best suited them This could then lead to a reasonable trade in survivor swaps and, as trade grew, a degree of standardization of swap contracts This might also lead to more success in the development of longevity bonds as, according to Blake et al (2006), one of the constraints on the size of the BNP/EIB issue (€540m) was the lack of capacity for the swap contained within the bond At present, however, survivor swaps are rare and survivor bonds are nonexistent Other Demographic Risks Other demographic risks exist, such as underestimating the proportion of married members, or overestimating the number of members that will leave active membership before retirement, but these are difficult to hedge and are best dealt with through scheme design For example, adopting a “career average revalued earnings” (CARE) structure rather than a final salary structure means that each year of benefit accrued is based on the salary in that year and then revalued to retirement in line with price inflation rather than the individual member’s wage inflation This effectively treats each year’s benefit as being a slice of deferred pension, removing the risk of underestimating early withdrawal It is also worth noting that such demographic risks are generally small in comparison with longevity risk Conclusion Although there are many risks inherent in defined benefit pension schemes, the tools necessary to hedge these risks are improving all the time Even now, the vast majority of these risks can be hedged, and there is no question that many should be It is also worth mentioning that the preceding analysis deals only with the risks relating to accrued benefits, but the future service cost of pension schemes should also be a major source of concern Real and nominal interest rates are lower now than they have been for many years and this, coupled with increasing longevity, has led to the cost of ongoing pension accrual rising sharply Fortunately, this is a risk that can be dealt with relatively easily, through scheme redesign or even the cessation of future accrual Many schemes try to deal with this risk by closure of the pension scheme to new entrants, but this action must be carried out with regard to the level and type of staff turnover If staff turnover is low, then the number of active members will decrease very slowly and no significant change in cost will be seen for some time Even if staff turnover is relatively high, there still might be little effect if the turnover is confined to a particular part of a firm If most of the turnover is within a group of younger employees in a particular department, then there is still likely to be a core of older (and more expensive) employees in the rest of the firm accruing benefits for the foreseeable future Bibliography Bagehot, W (pseudonym of Treynor, J.L.) 1972 “Risk in Corporate Pension Funds.” Financial Analysts Journal 28, no 1, pp 80–84 Black, F July-August 1980 “The Tax Consequences of Long-Run Pension Policy.” Financial Analysts Journal 36, no 4, pp 21–28 Blake, D., and W Burrows 2001 “Survivor Bonds: Helping to Hedge Mortality Risk.” Journal of Risk and Insurance, no 68, pp 339–48 Blake, D., A.J.G Cairns, and K Dowd 2006 “Living With Mortality: Longevity Bonds and Other Mortality-Linked Securities.” Paper Presented to the Institute of Actuaries Buettner, T 2002 “Approaches and Experiences in Projecting Mortality Patterns for the Oldest Age.” Living to 100 and Beyond: Survival at Advanced Ages Symposium, Society of Actuaries Chambers, A.J., A.E Barnes, N Barnes, L.J Beukes, D.E Dyer, P Fulcher, M.H.D Kemp, A.M Lawrence, C.D Tatham, and N.M Winter 2005 “Liability Driven Benchmarks for UK Defined Benefit Pension Schemes.” Paper Presented to the 2005 Actuarial Profession Finance and Investment Conference Cooper, R.W., and T.W Ross 2002 “Pensions: Theories of Underfunding.” Labour Economics 8, no 6, pp 667–89 Dowd, K 2001 “Survivor Bonds: A Comment on Blake and Burrows.” Journal of Risk and Insurance 70, no 2, pp 339–48 Francis, J.R., and S.A Reiter 1987 “Determinants of Corporate Pension Funding Strategy.” Journal of Accounting and Economics 9, no 1, pp 35–59 Graham, B., and D Dodd 1951 Security Analysis New York, NY: McGraw-Hill Hirshleifer, D., and I Welch Fall 2002 “An Economic Approach to the Psychology of Change: Amnesia, Inertia, and Impulsiveness.” Journal of Economics & Management Strategy 11, no 3, pp 379–421 Jensen, M.C May 1986 “Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers.” American Economic Review 76, no 2, pp 323–29 Jin, L., R.C Merton, and Z Bodie July 2006 “Do a Firmís Equity Returns Reflect the Risk of Its Pension Plan?” Journal of Financial Economics 81, no 1, pp 1–26 Milevsky, M.A., and D.S Promislow 2001 “Mortality Derivatives and the Option to Annuitise.” Insurance: Mathematics and Economics 29, no 3, pp 299–318 Sharpe, W.F June 1976 “Corporate Pension Funding Policy.” Journal of Financial Economics 3, no 3, pp 183–93 Tepper, I March 1981 “Taxation and Corporate Pension Policy.” Journal of Finance 36, pp 1–13 Tepper, I., and A.R.P Affleck December 1974 “Pension Plan Liabilities and Corporate Financial Strategies.” Journal of Finance 29, no 5, pp 1549–64 Index Advanced measurement approach (AMA), 26–28 AMA See Advanced measurement approach AML See Anti-money-laundering Anti-money-laundering (AML) documentation cultural challenge, 90 global challenge, 93–94 management challenge, 90–92 philosophical challenge, 89–90 regulatory and reputational challenge, 92–93 going forward, 95–97 overview of, 81–83 risk assessment, 85–86 risk-based approach, 83–84 risk identification, 84 risk mitigation, 86–87 risk monitoring, 87–88 tentative conclusions, 94–95 Assisted build-out (ABO) approach, 116 Basel Committee for Banking Supervision, 39, 42 Basel II challenges historical data and statistical information, 15–16 international markets, jurisdictions and models, 18–20 rating philosophies, 16–17 stress testing, 17–18 Binomial mortality risk, 193 Bond futures, 186 Build-operate-transfer (BOT) approach, 116 Business continuity risks, 125 Capability Maturity Model (CMM), 135 Career average revalued earnings (CARE) structure, 195 Chief Information Officer (CIO), 136–137 Chief Security Officer (CSO), 137 CIO See Chief Information Officer Client-facing attacks, 127 CMM See Capability Maturity Model COBIT See Control Objectives for IT Commercial models, 99–100 Communication, 115–116 Companies Act, 141–142 Completeness level external loss, 58 Concentration risk, 9–12 Consistency level external loss, 58 Contract, in outsourcing, 114–115 Contractual outsourcing risk, 164–165 Control Objectives for IT (COBIT), 135 Convexity matching strategy, 186–187 Credit risk model, stress testing and, 67–68 CSO See Chief Security Officer Cultural challenge, 90 Data governance committees, 137 Data issues, 132–133 Data protection, 106 Data stewards, 137 Defined benefit (DB) pension scheme, 177–178 Defined contribution (DC) pension scheme, 177 Demographic risk binomial mortality risk, 193 mortality projection risk, 193–195 other, 195 Dispute resolution process, 115 Due diligence, 110–111 Duration matching strategy, 186–187 EAD See Exposure at default EL See Expected loss Employment law, 107–108 Expected loss (EL), Exposure at default (EAD), External loss data key factors, 57–59 using data, 59–60 Financial control budget and, 147–149 internal controls, 149 non-specific issues, 149–150 responsibilities of, 140–143 risk issues within, 143–145 risk management within, 140 types of solutions, 147 Financial risks interest rate mismatch, 181–192 bond futures, 186 convexity matching strategy, 186–187 duration matching strategy, 186–187 fixed/real mismatch, 181 LDI attribution analysis, 189–191 liability-driven investment, 182 short-term matching strategy, 183–185 six-bond matching strategy, 188–189 investment mismatch, 179–181 other type, 192–193 in outsourcing, 104–105 Financial Services and Markets Act (FSMA), 82 Financial Services Authority (FSA), 60, 142 Financial strength, 111 Fixed/real mismatch, 181 FMI See Future margin income Fortuitous profit, 50 FSA See Financial Services Authority FSMA See Financial Services and Markets Act Funding risk, 178–179 Future margin income (FMI), 6–7 Global challenge, 93–94 Governance, in outsourcing, 108 Granularity adjustment, 11 Groupthink, 69 Heat mapping, 66 IAS 39, 145–147 Information technology risks business continuity risks, 125 control frameworks, 134–136 developmental state, 123 management and mitigation, 133–134 management roles and responsibilities, 136–137 operational state, 122–123 production rollout and sunsetting, 123 security and privacy risks, 125–127 system characteristics, 124 Insurance policy wording, 173 Insurance risk definition of, 170–171 overview of, 169–170 profit paradigm, 173–176 World Trade Center and, 171–172 Intellectual property, 107 Interest rate mismatch, 181–192 Internal control factors, 61 Internal loss data, 45–46 Internal loss database building, 46 gathering information, 52–53 information to record, 50–52 nature of losses, 49 relevance level losses, 48–49 sources of data, 56–57 threshold level losses, 47–48 using data, 53–56 Internal ratings-based (IRB) framework building blocks of, concentration risk, 9–12 correlation assumptions, 5–9 critical elements of, 4–5 description of, 1–2 economic losses, Investment mismatch, 179–181 ISO 17799, 135 ITIL See IT Infrastructure Library IT Infrastructure Library (ITIL), 135 IT programme managers, 137 JMLSG See Joint Money Laundering Steering Group Joint Money Laundering Steering Group (JMLSG), 82 LDI See Liability-driven investment LGD See Loss given default Liability-driven investment (LDI), 182 London Stock Exchange (LSE), 142 Loss given default (LGD), Loss of control, 162 LSE See London Stock Exchange Management challenge, 90–92 Market risk, 69–70 Maturity, Mortality projection risk, 193–195 Nature of losses, 49 Near miss, 49–50 Offshoring, 101 Open-source software (OSS) risks, 130–131 Operational loss events, 40–41 Operational risk adding values, 31–33 advanced measurement approach, 26–28 characteristics of, 40–42 context dependency, 40–41 in current scenarios, 26–28 focus on, 30–31 growth of, 36–37 inputs, 29–30 overview of, 25–26 portfolio size, 41 regulatory requirements, 42–45 scarce and incomplete data, 41–42 simplicity, 28 specialism vs generalism, 33–36 spurious precision, 28–29 stress testing, 65–67 Outsourcing business issues, 157 commercial models, 99–100 current trends, 102–103 definition of, 151 drivers for, 154–155 early experiences, 153–154 evolution of, 151–153 financial issues, 156–157 future trends, 116–117 level of risk country-specific, 108 data protection, 106 employment law, 107–108 financial, 104–105 intellectual property, 107 partner, 103–104 regulatory, 106 reputation, 105–106 strategic, 105 TUPE, 107 management issues, 158 offshoring, 101 processes, 102 reduction of risk communication, 115–116 contract, 114–115 governance, 108 partner selection, 108–114 software development, 129–130 technology issues, 157–158 Outsourcing risks contractual risk, 164–165 defining what to outsource, 159–160 loss of control, 162 misguided decision making, 158–159 model selection, 160–161 quality and continuous improvement risk, 166 regulatory risk, 162–164 security risk, 165–166 service-level agreements, 166–167 service providers, 161–162 Partner selection, in outsourcing, 108–114 PD See Probability of default Philosophical challenge, 89–90 Phishing, 127 Pillar 3, 12–15 Portfolio size, 41 Probability, 78 Probability of default (PD), Proceeds of Crime Act, 81, 96 QRRE See Qualifying revolving retail exposure Qualifying revolving retail exposure (QRRE), Quality and continuous improvement risk, 166 Rating philosophies, 16–17 Regulatory and reputational challenge, 92–93 Regulatory outsourcing risk, 106, 162–164 Regulatory requirements, 42–45 Relevance level external loss, 58 Relevance level internal loss, 48–49 Reputation, in outsourcing, 105–106 Request for information (RFI), 109 Request for proposal (RFP), 110 Reverse stress testing, 72–73 RFI See See Request for information RFP See Request for proposal Risk correlation, 70–72 Sarbanes-Oxley (SOX) Act, 134 Scaling level external loss, 58–59 Scenario analysis, 60–61, 67 Security administrators, 137 Security and privacy risks, 125–127 Security risk, 165–166 Senior management, 136 Sensitivity analysis, 71 Serious Organised Crime and Police Act, 81, 96 Service-level agreements (SLAs), 166–167 Short-term matching strategy, 183–185 Six-bond matching strategy, 188–189 SLAs See Service-level agreements Society for Worldwide Interbank Financial Telecommunication (SWIFT), 144 Software development outsourcing, 129–130 Specialism vs generalism, 33–36 Standard chartered structure, 73 Strategic risk, in outsourcing, 105 Stress testing Basel II challenges, 17–18 credit risk, 67–68 definition of, 65 market risk, 69–70 nature of, 76–77 operational loss, 60–61 operational risk, 65–67 preventable disasters, 69 probability and, 78 reasons for, 73–74 reverse, 72–73 risk correlation, 70–72 standard chartered structure, 73 time horizon of, 77 Stress-testing programme, 74–76 SWIFT See Society for Worldwide Interbank Financial Telecommunication System development risks, 128–129 System incapacitation, 126–127 System owners, 137 System replacement risks, 131–132 Taxation authorities, 143 Terrorism Act, 81 Third party management, 33 Threshold level internal loss, 47–48 Time horizon, 77 TUPE, 107 UL See Unexpected loss Unexpected loss (UL), Wage inflation, 192 Web-jacking, 127 World Trade Center, 171–172 OTHER TITLES IN OUR FINANCE AND FINANCIAL MANAGEMENT COLLECTION John A Doukas, Old Dominion University, Editor • Rethinking Risk Management: Critically Examining Old Ideas and New Concepts by Rick Nason • Towards a Safer World of Banking: Bank Regulation After the Subprime Crisis by T.T Ram • • • • • • Mohan Escape from the Central Bank Trap: How to Escape From the $20 Trillion Monetary Expansion Unharmed by Daniel Lacalle Tips & Tricks for Excel-Based Financial Modeling: A Must for Engineers & Financial Analysts, Volume I by M A Mian Tips & Tricks for Excel-Based Financial Modeling: A Must for Engineers & Financial Analysts, Volume II by M A Mian The Anti-Bubbles: Opportunities Heading into Lehman Squared and Gold’s Perfect Storm by Diego Parrilla Risk and Win!: A Simple Guide to Managing Risks in Small and Medium-Sized Organizations by John Harvey Murray Essentials of Enterprise Risk Management: Practical Concepts of ERM for General Managers by Rick Nason and Leslie Fleming Announcing the Business Expert Press Digital Library Concise e-books business students need for classroom and research This book can also be purchased in an e-book collection by your library as • • • • • a one-time purchase, that is owned forever, allows for simultaneous readers, has no restrictions on printing, and can be downloaded as PDFs from within the library community Our digital library collections are a great solution to beat the rising cost of textbooks E-books can be loaded into their course management systems or onto students’ e-book readers The Business Expert Press digital libraries are very affordable, with no obligation to buy in future years For more information, please visit www.businessexpertpress.com/librarians To set up a trial in the United States, please email sales@businessexpertpress.com .. .Frontiers of Risk Management Frontiers of Risk Management Key Issues and Solutions Volume II Edited by Dennis Cox Frontiers of Risk Management: Key Issues and Solutions, Volume I Copyright... published Keywords Basel II, credit risk, enterprise risk management, insurance risk, loss data, market risk, operational risk, outsourcing, risk appetite, risk management Contents Chapter The Use of. .. States of America Abstract Frontiers of Risk Management was developed as a text to look at how risk management would develop in the light of Basel II With an objective of being 10 years ahead of