221

Notes

1 Enterprise Risk Management

1 . D.W. Hubbard (2009) The Failure of Risk Management: Why It’ s Broken and How to Fix It. John Wiley & Sons.

2 . H.N. Higgins (2012) ‘Learning internal controls from a fraud case at Bank of China,’ Issues in Accounting Education, 27(4): 1171–1192.

3 . B. Ballou, D.L. Heitger (2005) ‘A building-block approach for implementing COSO’senterprise risk management–integrated framework,’ Management Accounting Quarterly, 6(2): 1–10.

4 . D. Williamson (2007) ‘The COSO ERM framework: A critique from systems theory of management control,’ International Journal of Risk Assessment and Management, 7(8): 1089–1119.

5 . F. Caron, J. Vanthienen, B. Baesens (2013) ‘A comprehensive investigation of the applicability of process mining techniques for enterprise risk management,’ Computers in Industry, 64, 464–475.

6. L. Rittenberg, F. Martens (2012) Enterprise Risk Management: Understanding and Communicating Risk Appetite. COSO.

7 . The Association of Risk Managers (2010) A Structured Approach to Enterprise Risk Management (ERM) and the Requirements of ISO 31000. COSO.

8 . B.M. Bowling, L. Rieger (2005) ‘Success factors for implementing enterprise risk management,’ Bank Accounting and Finance, 18(3): 21–26.

2 Enron

1. G. Ailon (2012) ‘The discursive management of financial risk scandals: the case of Wall Street Journal commentaries on LTCM and Enron,’ Qualitative Sociology, 35: yy251–270.

2 . J.E. Stiglitz (2003) The Roaring Nineties: A New History of the World’s Most ProsperousDecade. W.W. Norton & Co.

3 . L. Fox (2003) Enron: The Rise and Fall. Wiley. 4 . C. Hurt (2014) ‘The duty to manage risk,’ The Journal of Corporate Law, 39(2): w

153–267. 5 . Ailon (2012), op cit.6 . C. Hollingsworth (2012) ‘Risk management in the post-SOX era,’ International Journal

of Auditing, 16: 35–53. gg7 . H.N. Butler, L.E. Ribstein (2006) The Sarbanes–Oxley Debacle: What We’ve Learned;

How to Fix It, AEI.t8 . A. Dey (2010) ‘The chilling effect of Sarbanes–Oxley: a discussion of Sarbanes–Oxley

and corporate risk-taking,’ Journal of Accounting and Economics , 49(1–2), 53–57. 9 . J.D. Piotroski, S. Srinivasan (2008) ‘Regulation and bonding: the Sarbanes–Oxley Act

and the flow of international listings,’ Journal of Accounting Research, 46(2): 383–425. 10 . L. Bargeron, K. Lehn, C. Zutter (2009) ‘Sarbanes–Oxley and corporate risk-taking,’

Journal of Accounting and Economics, 49(1–2): 34–52.

222 Notes

3 Financial Risk Management

1 . R. Lowenstein (2000) When Genius Failed: The Rise and Fall of Long-Term CapitalManagement, Random House. t

2 . H.M. Markowitz (1952) ‘Portfolio selection,’ The Journal of Finance, 17(1): 77–91. 3. W.F. Sharpe (1964) ‘Capital asset prices: a theory of market equilibrium under condi-

tions of risk,’ The Journal of Finance, 19(3): 425–442. 4 . F. Black, M. Scholes (1972) ‘The valuation of option contracts and a test of market

efficiency,’ The Journal of Finance, 27(2): 399–417. 5. G.J. Alexander, A.M. Baptista (2004) ‘A comparison of VaR and CVaR constraints on

portfolio selection with the mean-variance model,’ Management Science , 50(9): 1261–1273; V. Chavez-Demoulin, P. Embreechts, J. Nešlehová (2006) ‘Quantitative models for operational risk: extremes, dependence and aggregation,’ Journal of Banking & Finance, 30: 399–417.

6. J. Von Neumann, O. Morgenstern (1944) Theory of Games and Economic Behaviour,2nd ed. Princeton University Press.

7 . M. Friedman, L.J. Savage (1948) ‘The utility analysis of choices involving risk,’ TheJournal of Political Economy, 56(4): 279–304. yy

8. S.G. Mandis (2013) What Happened to Goldman Sachs: An Insider’ s Story of Organizational Drift and Its Unintended Consequences. Harvard Business Review Press.

9 . N.N. Taleb (2012) Antifragile: Things That Gain from Disorder . Random House. rr10. D.X. Li (2000) ‘On default correlation: a copula approach,’ Journal of Fixed Income,

9(4): 43–54.11. F. Salmon (2009) ‘Recipe for disaster: the formula that killed Wall Street,’ Wired, 17(3).

4 The Real Estate Crash of 2008

1 . C.M. Reinhart, K.S. Rogoff (2008) ‘Is the 2007 Subprime Crisis so different? Aninternational historical comparison,’ American Economic Review , 98(2), 339–344. w

2. G. Cooper (2008) The Origin of Financial Crises: Central Banks, Credit Bubbles and theEfficient Market Fallacy. Vintage Books. yy

3 . N. Dunbar (1999) Investing Money: The Story of Long-Term Capital Management and the Legends Behind It. Wiley; R. Lowenstein (2000) When Genius Failed: The Rise and Fallof Long-Term Capital Management. Random House.

4 . B. Cohen (1997) The Edge of Chaos: Financial Booms, Bubbles, Crashes and Chaos. John Wiley & Sons, Ltd.

5. A.S. Blinder (2013) After the Music Stopped: The Financial Crisis, the Response, and the Work Ahead. The Penguin Press.

6. L. Laeven, F. Valencia (2010) ‘Resolution of banking crises: the good, the bad, and the ugly,’ IMF Working Paper WP/10/146.

7 . J. Taylor (2009) Getting Off Track: How Government Actions and Interventions Caused,Prolonged, and Worsened the Financial Crisis. Hoover Press; B. Keys, T. Mukherjee, A.Seru, V. Vig (2010) ‘Did securitization lead to lax screening? Evidence from subprimeloans,’ Quarterly Journal of Economics, 125, 307–362.

8 . G. Gorton (2008) ‘The panic of 2007,’ NBER Working Paper No. 14358; M.Brunnermeier (2009) ‘Deciphering the liquidity and credit crunch 2007–2008,’ Journal of Economic Perspectives, 23, 77–100; G. Dell’Arriccia, L. Laeven, D. Igan(2008) ‘Credit booms and lending standards: evidence from the subprime mortgage market,’ IMF Working Paper 08/106.

Notes 223

9 . F.B. Wiseman (2013) Some Financial History Worth Reading: A Look at Credit, Real Estate, Investment Bubbles & Scams, and Global Economic Superpowers. Abcor Publishers.

10 . R. Boyd (2011) Fatal Risk: A Cautionary Tale of AIG’s Corporate Suicide. Wiley.11 . S. Patterson (2010) The Quants: How a New Breed of Math Whizzes Conquered Wall

Street and Nearly Destroyed It. Crown Business.12 . V. Sampath (2009) ‘The need for greater focus on nontraditional risks: the case of

Northern Rock,’ Journal of Risk Management in Financial Institutions , 2(3): 301–305. 13. H.S. Shin (2009) ‘Reflections on Northern Rock: the bank run that heralded the

global financial crisis,’ Journal of Economic Perspectives, 23(1): 101–119. 14 . P. Goldsmith-Pinkham, T. Yorulmazer (2010) ‘Liquidity, bank runs, and bailouts:

Spillover effects during the Northern Rock episode,’ Journal of Financial Service Research, 37(2/3): 83–98.

15 . R. Shelp, A. Ehrbar (2009) Fallen Giant: The Amazing Story of Hank Greenberg and the History of AIG. Wiley.

16 . Ibid.17 . Ibid.18 . J.F. Egginton, J.I. Hilliard, A.P. Liebenberg, I.A. Liebenberg (2010) ‘What effect did

AIG’s bailout, and the preceding events, have on its competitors?’ Risk Management and Insurance Review, 13(2): 225–249. w

19 . J. Hobbs (2011) ‘Financial derivatives, the mismanagement of risk and the case of AIG,’ CPCU eJournal, 64(7): 1–8.

20 . P.M. Linsley, R.E. Slack (2013) ‘Crisis management and an ethic of care: The case of Northern Rock Bank,’ Journal of Business Ethics, 113(2): 285–295.

5 Financial Risk Forecast Using Machine Learning andSentiment Analysis

1. D. Dong, Q. Dong (2003) ‘HowNet – a hybrid language and knowledge resource,’Proceedings of 2003 International Conference on Natural Language Processing and Knowledge Engineering, 820–824, October 26–29.gg

2 . T. Bollerslev (1986) ‘Generalized autoregressive conditional heteroskedasticity,’ Journal of Econometrics, 31(3): 307–327.

3. B. Freisleben, K. Ripper (1997) ‘Volatility estimation with a neural network,’ Proceedings of the IEEE/IAFE on Computational Intelligence for Financial Engineering,gg177–181, March 24–25; C. Burges (1998) ‘A tutorial on support vector machines forpattern recognition,’ Data Mining and Knowledge Discovery , 2(2): 121–167. yy

4 . B. Freisleben and K. Ripper (1997) ‘Volatility estimation with a neural network,’ Proceedings of the IEEE/IAFE on Computational Intelligence for Financial Engineering, gg177–181, March 24–25.

5 . J.A.K. Suykens, T.V. Gestel, J.D. Brabanter et al. (2002) ‘Least squares support vectormachines,’ Singapore :World Scientific Press; H.F. Wang, D.J. Hu (2005) ‘Comparisonof SVM and LS-SVM for Regression,’ International Conference on Neural Networks and Brain 2005, 1, 279–283, October 13–15, 2005.

6 Online Stock Forum Sentiment Analysis

1 . B. Watkins (2003) ‘Riding the wave of sentiment: an analysis of return consistencyas a predictor of future returns,’ Journal of Behavioral Finance , 4(4): 191–200.

224 Notes

2. W. Antweiler, M. Frank (2004) ‘Is all that talk just noise? The information content of internet stock message boards,’ Journal of Finance, 59(3): 1259–1295.

3. R.F. Engle (1982) ‘Autoregressive conditional heteroscedasticity with estimates of variance of United Kingdom inflation,’ Econometrica, 50: 987–1008.

4. C. Burges (1998) ‘A tutorial on support vector machines for pattern recognition,’Data Mining and Knowledge Discovery, 2(2): 121–167.yy

5 . W. Chan (2003) ‘Stock price reaction to news and no-news: drift and reversal afterheadlines,’ Journal of Financial Economics, 70: 223–260.

6 . M. Baker, J. Wurgler (2006) ‘Investor sentiment and the cross-section of stock returns,’ Journal of Finance, 61(4): 1645–1680.

7 . J. Seigel (2002) Stocks for the Long Run. 3rd ed. McGraw-Hill. 8 . D. Bathia, D. Bredin (2012) ‘An examination of investor sentiment effect on G7 stock

market returns,’ European Journal of Finance , DOI:10.1080/1351847X.2011.636834.

7 DEA Risk Scoring Model of Internet Stocks

1 . S.A. Ross, R.M. Westerfield, B.D. Jordan (2007) Corporate Finance Essentials. McGraw-Hill/Irwin.

2. G.J. Fielding, T.T. Babitsky, M.E. Brenner (1985) ‘Performance evaluation for bustransit,’ Transportation Research, 19A(1): 73–82; L.V. Utikin (2007) ‘Risk analysis under partial prior infermation and nonmonotone utility functions,’ International Journal of Information Technology and Decision Making, 6: 625–647. gg

3. C.-T. Ho and D.S. Zhu (2004) ‘Performance measurement of Taiwan’s commercial banks,’ International Journal of Productivity and Performance Management, 53(5): 425–434; D. Wu (2009) ‘Performance evaluation: an integrated method using data envelopment analysis and fuzzy preference relations,’ European Journal of Operational Research, 194(1): 227–235.

4. K. Cengiz, C. Ufuk and U. Ziya (2003) ‘Multi-criteria supplier selection using fuzzy AHP,’ Logistics Information Management, 16(6): 382–394; T.L. Saaty (2008) ‘Decision making with the analytic hierarchy process,’ International Journal of Services Sciences,1(1): 83–98.

5. T.S. Felix and H.J. Chan (2003) ‘An innovative performance measurement methodfor supply chain management,’ Supply Chain Management: An International Journal,8(3): 209–223.

6 . C.-T. Ho (2006) ‘Measuring bank operations performance: an approach based ongrey relation analysis,’ Journal of the Operational Research Society, 57, 227–349.

7 . R.S. Kaplan and D.P. Norton (2006) Alignment: Using the Balanced Scorecard to CreateCorporate Synergies. Harvard Business School Press Books.

8. P. Espahbodi (1991) ‘Identification of problem banks and binary choice models,’ Journal of Banking and Finance, 15, 53–71.

9. D. Wu (2006) ‘A note on DEA efficiency assessment using ideal point: an improvementof Wang and Luo’s model,’ Applied Mathematics and Computation, 2: 819–830.

10. M.J. Farrell (1957) ‘The measurement of productive efficiency,’ Journal of the Royal Statistical Society, 120, 253–281.

11 . W. Charnes, A. Cooper, E. Rhodes (1978) ‘Measuring the efficiency of decision making units,’ European Journal of Operational Research, 2: 429–444.

12 . R.D. Banker, A. Charnes, W. Cooper (1984) ‘Some models for estimating technicaland scale inefficiencies in data envelopment analysis,’ Management Science, 30,1078–1092.

Notes 225

13 . P.S. Sudarsanam and R.J. Taffler (1995) ‘Financial ratio proportionality and inter-temporal stability: an empirical analysis,’ Journal of Banking & Finance, 19(1): 45–60.

14 . C.-T. Ho, D.S. Zhu (2004) ‘Performance measurement of Taiwan’s commercial banks,’International Journal of Productivity and Performance Management, 53(5): 425–434.

15 . S.N. Huang, T.L. Kao (2006) ‘Measuring managerial efficiency in non-life insurance companies: an application of two-stage data envelopment analysis,’ International Journal of Management, 23(3): 699–720.

16 . L.M. Seiford, J. Zhu (1999) ‘Profitability and marketability of the top 55 U.S. com-mercial banks,’ Management Science, 45(9): 1270–1288; M. Gulser, M. Ilhan (2001) ‘Risk and return in the world’s major stock markets,’ Journal of Investing, (Spring):62–67; X. Luo (2003) ‘Evaluating the profitability and marketability efficiencyof large banks: an application of data envelopment analysis,’ Journal of BusinessResearch, 56: 627–635; A. Barua, P.L. Brockett, W.W. Cooper, H. Deng, B.R. Parker, T.W. Ruefli, A. Whinston (2004) ‘Multi-factor performance measure model with an application to fortune 500 companies,’ Socio-Economic Planning Sciences, 38: 233–253; C.S. Carlos, F.C. Yolanda, M.M. Cecilio (2005) ‘Measuring DEA efficiencyin internet companies,’ Decision Support Systems, 38: 557–573; D. Wu, Z. Yang, L. Liang (2006) ‘Using DEA-neural network approach to evaluate branch effi-ciency of a large Canadian bank,’ Expert Systems with Applications, 31(1): 108–115; S.F. Lo, W.M. Lu (2006) ‘Does size matter? finding the profitability and market-ability benchmark of financial holding companies,’ Journal of Operational Research, 23(2): 229–246; H.C. Tsai, C.M. Chen, G.H. Tzeng (2006) ‘The comparative prod-uctivity efficiency for global telecoms,’ International Journal of Production Economics, 103: 509–526.

17 . D. Wu (2006) ‘A note on DEA efficiency assessment using ideal point: an improvement of Wang and Luo’s model,’ Applied Mathematics and Computation, 2: 819–830; N. Ahmad, D. Berg, G.R. Simons (2006) ‘The integration of analytic hierarchy process and data envelopment analysis in a multi-criteria decision-making problem,’International Journal of Information Technology and Decision Making, 5: 263–276.

18 . J. Yao, Z. Li, K.W. Ng (2006) ‘Model risk in VaR estimation: an empirical study,’ International Journal of Information Technology and Decision Making, 5: 503–512.

19 . Y. Shi, Y. Peng, G. Kou, Z. Chen (2006) ‘Classifying credit card accounts for businessintelligence and decision making: a multiple-criteria quadratic programmingapproach,’ International Journal of Information Technology and Decision Making, 4: 1–19; S. Deng, Z. Xia (2006) ‘A real options approach for pricing electricity tolling agreements,’ International Journal of Information Technology and Decision Making,5, 421–436.

8 Bank Credit Scoring

1 . G. Dickinson (2001) ‘Enterprise risk management: its origins and conceptual foun-dation,’ The Geneva Papers on Risk and Insurance, 26(3): 360–366.

2 . E.G. Baranoff (2004) ‘Risk management: a focus on a more holistic approach threeyears after September 11,’ Journal of Insurance Regulation, 22(4): 71–81.

3 . M. Crouhy, D. Galai, R. Mark (1998) ‘Model risk,’ Journal of Financial Engineering, 7(3/4): 267–288, reprinted in Model Risk: Concepts, Calibration and Pricing (ed. R. gGibson), Risk Book, 2000, 17–31; M. Crouhy, D. Galai, R. Mark (2000) ‘A comparativeanalysis of current credit risk models,’ Journal of Banking & Finance, 24: 59–117.

226 Notes

4. G.J. Alexander, A.M. Baptista (2004) ‘A comparison of VaR and CVaR constraints on portfolio selection with the mean-variance model,’ Management Science, 50(9):1261–1273; V. Chavez-Demoulin, P. Embrechts, J. Nešlehová (2006) ‘Quantitativemodels for operational risk: extremes, dependence and aggregation,’ Journal of Banking & Finance, 30: 2635–2658; R. Garcia, É. Renault, G. Tsafack (2007) ‘Proper conditioning for coherent VaR in portfolio management,’ Management Science, 53(3): 483–494; N. Taylor (2007) ‘A note on the importance of overnight information inrisk management models,’ Journal of Banking & Finance, 31: 161–180.

5. T. Jacobson, J. Lindé, K. Roszbach (2006) ‘Internal ratings systems, implied creditrisk and the consistency of banks’ risk classification policies,’ Journal of Banking & Finance, 30, 1899–1926.

6 . H. Elsinger, A. Lehar, M. Summer (2006) ‘Risk assessment for banking systems,’Management Science, 52(9): 1301–1314.

7 . R.S. Kaplan, D.P. Norton (1992) ‘The balanced scorecard – measures that drive per-formance,’ Harvard Business Review, 70(1): 71–79; and R.S. Kaplan, D.P. Norton (2006) Alignment: Using the Balanced Scorecard to Create Corporate Synergies. Harvard Business School Press Books.

8. D. Bigio, R.L. Edgeman, T. Ferleman (2004) ‘Six sigma availability management of information technology in the office of the chief technology officer of Washington, DC.,’ Total Quality Management, 15(5–6): 679–687; S. Scandizzo (2005) ‘Risk mapping and key risk indicators in operational risk management,’ Economic Notes by Banca Monte dei Paschi di Siena SpA, 34(2): 231–256.

9 . A. Papalexandris, G. Ioannou, G. Prastacos, K.E. Soderquist (2005) ‘An integratedmethodology for putting the balanced scorecard into action,’ European Management Journal, 23(2): 214–227.

10 . J. Calandro, Jr., S. Lane, S. (2006) ‘An introduction to the enterprise risk scorecard,’Measuring Business Excellence, 10(3): 31–40.

11. U. Anders, M. Sandstedt (2003) ‘An operational risk scorecard approach,’ Risk, 16(1):47–50; H. Wagner (2004) ‘The use of credit scoring in the mortgage industry,’ Journal of Financial Services Marketing, 9(2): 179–183.

12. S. Caudle (2005) ‘Homeland security,’ Public Performance & Management Review,28(3): 352–375.

13 . H.S.B. Herath, W.G. Bremser (2005) ‘Real-option valuation of research and devel-opment investments: implications for performance measurement,’ Managerial Auditing Journal, 20(1): 55–72.

14 . F. Lhabitant (2000) ‘Coping with model risk,’ in The Professional Handbook of Financial Risk Management, M. Lore, L. Borodovsky (eds), Butterworth-Heinemann. t

15 . J. Sobehart, S. Keenan (2001) ‘Measuring default accurately,’ Credit Risk Special Report,Risk, 14: 31–33.

9 Credit Scoring using Multiobjective Data Mining

1. C.L. Hwang, K. Yoon (1981) Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag.

2. Y. Peng (2000) Management Decision Analysis . Science Publication; T.-C. Chu (2002) ‘Facility location selection using fuzzy TOPSIS under group decisions,’ International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 10(6): 687–701.

3. D.L. Olson, D. Wu (2005) ‘Decision making with uncertainty and data mining,’ Advanced Data Mining and Applications: First International Conference, ADMA,

Notes 227

X. Li, S. Wang, Z.Y. Dong eds, Lecture Notes in Artificial Intelligence. Keynote paper.Springer, 1–9.

4 . D.L. Olson (2005) ‘Comparison of weights in TOPSIS models,’ Mathematical and Computer Modelling, 40: 721–727.

5 . M. Freimer, P.L. Yu (1976) ‘Some new results on compromise solutions for groupdecision problems,’ Management Science, 22(6): 688–693; T.E. Dielman (2005) ‘Leastabsolute value regression: recent contributions,’ Journal of Statistical Computation & Simulation, 75(4): 263–286.

6 . S.C. Caples, M.E. Hanna (1997) ‘Least squares versus least absolute value in real estate appraisals,’ Appraisal Journal, 65(1): 18–24.

7 . G.W. Bassett, Jr. (1997) ‘Robust sports rating based on least absolute errors,’ American Statistician, 51(2): 99–105.

8 . Olson (2005), ibid.9 . S.M. Lee, D.L. Olson (2004) ‘Goal programming formulations for a comparative ana-

lysis of scalar norms and ordinal vs. ratio data,’ Information Systems and Operational Research, 42(3): 163–174.

10 . A. Barnes (1987) ‘The analysis and use of financial ratios: a review article’, Journal of Business and Finance Accounting, 14: 449–461; H. Deng, C.-H. Yeh, R.J. Willis(2000) ‘Inter-company comparison using modified TOPSIS with objective weight’,Computers & Operations Research, 27: 963–973.

11 . D.L. Olson (2004) ‘Data set balancing’, Lecture Notes in Computer Science: Data Mining and Knowledge Management, Y. Shi, W. Xu, Z. Chen, eds. Springer, 71–80.t

12 . J. Laurikkala (2002) ‘Instance-based data reduction for improved identification of difficult small classes’, Intelligent Data Analysis, 6(4): 311–322.

13 . B. Bull (2005) ‘Exemplar sampling: Nonrandom methods of selecting a sample which characterizes a finite multivariate population,’ American Statistician, 59(2):166–172.

14 . D.L. Olson, D. Wu (2006) ‘Simulation of fuzzy multiattribute models for grey rela-tionships,’ European Journal of Operational Research , 175(1): 111–120.

10 Online Banking Efficiency and Risk Evaluation with PrincipalComponent Analysis

1. K. Furst, W.W. Lang, D. Nolle (2000) ‘Internet banking: developments and pros-pects,’ Economic and Policy Analysis , Working Paper 2000–9.

2 . Dominion (2001) ‘Internet banking struggles for profits,’ available at www.stuff.co.nz/inl/index/0,1008,779016a28,FF.html.

3 . B. Stafford (2001) ‘Risk management and internet banking: what every banker needsto know,’ Community Banker, 10(2): 48–49.rr

4 . C-T.B. Ho, D. Wu (2009) ‘Online banking performance evaluation using data envelopment analysis and principal component analysis,’ Computers & Operations Research, 36(6): 1835–1842.

5 . Jupiter Research.(2004) ‘FIND research, Institute for Information Industry,’ availableat http://www.find.org.tw.

6 . C. Serrano-Cinca, Y. Fuertes-Calle’n, C. Mar-Molinero (2005) ‘Measuring DEA effi-ciency in Internet companies,’ Decision Support Systems , 38: 557–573.

7 . H.D. Sherman, F. Gold (1985) ‘Bank branch operating efficiency: evaluation with data envelopment analysis,’ Journal of Banking and Finance , 9(2): 297–316.

228 Notes

8. A. Soteriou, S.A. Zenios, (1999) ‘Operations, quality, and profitability in the pro-vision of banking services,’ Management Science , 45(9): 1221–1238.

9. H. Tulkens (1993) ‘On FDH efficiency analysis: some methodological issues andapplications to retail banking, courts and urban transit,’ Journal of Productivity Analysis, 4(1–2): 183–210.

10. A.N. Berger, D.B. Humphrey (1992) ‘Measurement and efficiency issues in com-mercial banking,’ in Z. Griliches, ed., Output Measurement in the Service Sectors, NBER Studies in Income and Wealth, 245–300. The University of Chicago Press.

11 . A.N. Berger, D.B. Humphrey (1997) ‘Efficiency of financial institutions: inter-national survey and direction for future research,’ European Journal of Operational Research, 98:175–212.

12 . A.N. Berger, D. Hancock, D.B. Humphrey (1993) ‘Bank efficiency derived from theprofit function,’ Journal of Banking and Finance , 17(2–3): 317–348.

13. D.D. Wu (2009) ‘Performance evaluation: an integrated method using dataenvelopment analysis and fuzzy preference relations,’ European Journal of Operational Research, 194(1): 227–235.

14. K. Eriksson, K. Kerem, D. Nilsson (2008) ‘The adoption of commercial innovationsin the former Central and Eastern European markets: the case of internet banking in Estonia,’ International Journal of Bank Marketing, 26(3): 154–169.gg

15 . Bank of America (2007) Annual Report 2007 , available at www.rbs.com/microsites/77gra2007/downloads/RBS_GRA_2007.pdf.

16 . Citibank (2007) Annual Report 2007 , available at www.citi.com/citi/fin/data/k07c.77pdf.

17 . HSBC (2007) Annual Report 2007, available at www.investis.com/reports/hsbc_77ar_2007_En/report.php?type=1.

18 . Barclays (2007) Annual Report 2007 , available at www.barclaysannualreport.com/77index.html.

19. Chase (2007) Annual Report 2007 , available at: http://investor.shareholder.com/77common/.

20 . Wells Fargo (2007) Annual Report 2007 , available at www.wellsfargo.com/downloads/77pdf/invest_relations/wf2007annualreport.pdf.

21 . Lloyds (2007) Annual Report 2007, available at www.investorrelations.lloydstsb.com/77media/pdf_irmc/ir/2007/2007_LTSB_Group_R&A.pdf.

22. Royal Bank of Scotland (2007) Annual Report 2007 , available at www.rbs.com/micro-77sites/gra2007/downloads/RBS_GRA_2007.pdf.

23 . SunTrust (2007) Annual Report 2007 , available at www.suntrustenespanol.com/77suntrust.

24 . Wachovia (2007) Annual Report 2007 , available at www.wachovia.com/file/2007_77Wachovia_Annual_Report.pdf.

25 . Basel (2005) ‘ Amendment to the Capital Accord to the Incorporate Market Risks,’‘ Basel’Committee on Banking Supervision, Basel.

11 Economic Perspective

1. F.H. Knight (1921) Risk, Uncertainty and Profit. Hart, Schaffner & Marx. 2 . J.G. Courcelle-Seneuil (1852) ‘Profit,’ in Coquelin and Guillaumin, eds, Dictionnaire

de l’ė’ conomie politique, 2nd ed. 3. J.H. Von Thünen (1826) The Isolated State.

Notes 229

4 . F.B. Hawley (1907) Enterprise and the Productive Process . 5. A. Ganegoda, J. Evans (2012) ‘A framework to manage the measurable, immeas-

urable and the unidentifiable financial risk,’ Australian Journal of Management , 39(5):t5–34.

6 . D. Li (2000) ‘On default correlation: a copula approach,’ Journal of Fixed Income , 9(4):43–54.

7 . H.M. Markowitz (1952) ‘Portfolio selection,’ The Journal of Finance, 17(1): 77–91. 8 . E.F. Fama (1965) ‘Random walks in stock market prices,’ Financial Analysts Journal ,

51(1): 404–419. 9 . K. Buehler, A. Freeman, R. Hulme (2008) ‘The new arsenal of risk management,’

Harvard Business Review, 86(9): 93–100. 10 . W.F. Sharpe (1970) Portfolio Theory and Capital Markets . McGraw-Hill Book

Company. 11 . D. Kahneman, A. Tversky (1972) ‘Subjective probability: a judgment of representa-

tiveness,’ Cognitive Psychology, 3: 430–454. yy12 . C. Mackay (1841) Extraordinary Popular Delusions and the Madness of Crowds. 13 . S. Patterson (2010) The Quants: How a New Breed of Math Whizzes Conquered Wall

Street and Nearly Destroyed It. Crown Business.14 . J.N. Stanard, M.G. Wacek (1991) ‘The spiral in the catastrophe retrocessional market,’

Casualty Actuarial Society Discussion Paper, May, Arlington, VA.rr15 . R. Lowenstein (2000) When Genius Failed: The Rise and Fall of Long-Term Capital

Management. Random House. 16 . N.N. Taleb (2007) The Black Swan: The Impact of the Highly Improbable. Penguin

Books. 17 . D. Kahneman, A. Tversky (2000) Choices, Values, and Frames. The University of

Cambridge. 18 . G.A. Akerlof, R.J. Shiller (2009) Animal Spirits: How Human Psychology Drives the

Economy, and Why It Matters for Global Capitalism. Princeton University Press.19 . N. Doherty, J. Lamm-Tennant, L.T. Starks (2009) ‘Lessons from the financial crisis

on risk and capital management: the case of insurance companies,’ Journal of Applied Corporate Finance, 21(4): 52–59.

20 . M.R. Powers, T.Y. Powers, S. Gao (2012) ‘Risk finance for catastrophe losses with Pareto-calibrated Lévy-stable severities,’ Risk Analysis , 32(11): 1967–1977.

12 British Petroleum Deepwater Horizon

1 . R. Zolkos, M. Bradford (2011) ‘Risk management faulted in probe of BP disaster’, Business Insurance, 45(36): 4–25.

2. R.T. Silves, L.K. Comfort (2012) ‘The Exxon Valdez and BP Deepwater Horizon oil spills: Reducing risk in socio-technical systems’, American Behavioral Scientist, 56(1): 76–103.

3 . C. Perrow (1984) Normal Accidents: Living with High-risk Technologies, Basic Books.4 . A. Borison, G. Hamm (2011) ‘Black swan or black sheep?’ Risk Management , 58(3): t

48–53. 5 . P. Eckle, P. Burgherr (2013) ‘Bayesian data analysis of severe fatal accident risk in the

oil chain’, Risk Analysis , 33(1): 146–160.6. H. Abbasinejad, Y. Gudarzi Farahani, E. Ashari Ghara (2012) ‘Energy consumption

in Iran with Bayesian approach’, OPEC Energy Review, 36(4): 444–455. w

230 Notes

13 Bank Efficiency Analysis

1 . H.D. Sherman, F. Gold (1985) ‘Bank branch operating efficiency: evaluation with data envelopment analysis,’ Journal of Banking and Finance, 9(2): 297–316; H. Tulkens(1993) ‘On FDH efficiency analysis: some methodological issues and applica-tions to retail banking, courts and Urban transit,’ Journal of Productivity Analysis,4(1/2): 183–210; A.N. Berger, D.B. Humphrey (1997) ‘Efficiency of financial insti-tutions: international survey and directions for future research,’ European Journal of Operational Research, 98, 175–212; J.A. Clark (1996) ‘Economic cost, scale effi-ciency, and competitive viability in banking,’ Journal of Money, Banking and Credit ,t28(3): 342–364; R. Deyoung (1997) ‘A diagnostic test for the distribution-free effi-ciency estimator: an example using US commercial bank data,’ European Journal of Operational Research, 98(2): 243–249.

2. S.H. Wang (2003) ‘Adaptive non-parametric efficiency frontier analysis: a neural-network-based model,’ Computers & Operations Research, 30: 279–295.

3 . A.D. Athanassopoulos, S.P. Curram (1996) ‘A comparison of data envelopment ana-lysis and artificial neural networks as tool for assessing the efficiency of decisionmaking units,’ Journal of the Operational Research Society, 47(8): 1000–1016. yy

4. A. Costa, R.N. Markellos (1997) ‘Evaluating public transport efficiency with neuralnetwork models,’ Transportation Research C, 5(5): 301–312.

5. A.R. Fleissig, T. Kastens, D. Terrell (2000) ‘Evaluating the semi-nonparametric fourier, aim, and neural networks cost functions,’ Economics Letters, 68(3): 235–244.

6. D. Santin, F.J. Delgado, A. Valino (2004) ‘The measurement of technical efficiency: a neural network approach,’ Applied Economics , 36(6): 627–635.

7 . P.C. Pendharkar, J.A. Rodger (2003) ‘Technical efficiency-based selection of learningcases to improve forecasting accuracy of neural networks under monotonicity assumption,’ Decision Support Systems , 36(1): 117–136.

8. Athanassopoulos and Curram (1996), op cit. 9 . R.D. Banker, A. Charnes, W.W. Cooper (1984) ‘Some models for estimating tech-

nical and scale inefficiencies in data envelopment analysis,’ Management Science , 30: 1078–1092.

10 . A. Charnes, W.W. Cooper, E. Rhodes (1978) ‘Measuring the efficiency of decisionmaking units,’ European Journal of Operational Research , 6(2): 429–444.

11. R. Hecht Nielsen (1990) ‘Neural computing,’ Addison Wesley , 124–133; J.W. Shavlik, yyR.J. Mooney, G. G.Towell (1991) ‘Symbolic and neural learning algorithms: an experimental comparison,’ Machine Learning , 6: 111–143.gg

12. Pendharkar and Rodger (2003), op. cit.13 . M.D. Troutt, A. Rai, A. Zhang (1995) ‘The potential use of DEA for credit applicant

acceptance systems,’ Computers and Operations Research, 4: 405–408.

14 Catastrophe Bond and Risk Modeling

1 . A. Dassios, J. Jang (2003) ‘Pricing of catastrophe reinsurance and derivatives usingthe cox process with shot noise intensity,’ Finance and Stochastics , 7: 73–95.

2. H. Geman, M. Yor (1997) ‘Stochastic time changes in catastrophe option pricing,’ Insurance: Mathematics and Economics, 21: 185–193.

3 . R.T. Silves, L.K. Comfort (2012) ‘The Exxon Valdez and BP Deepwater Horizon oilspills: reducing risk in socio-technical systems,’ American Behavioral Scientist , 56(1):t76–103.

Notes 231

4 . Dassios and Jang (2003), op cit. 5 . S. Jaimungal, T. Wang (2005) ‘Catastrophe options with stochastic interest rates and

compound Poisson losses,’ Insurance: Mathematics and Economics, 38: 469–483. 6 . S. Cox, H. Pedersen (2000) ‘Catastrophe risk bonds,’ North American Actuarial Journal,

48: 56–82. 7 . J. Lee, M. Yu (2007) ‘Variation of catastrophe reinsurance with catastrophe bonds,’

Insurance: Mathematics and Economics, 41: 264–278. 8 . R.C. Merton (1974) ‘On the pricing of corporate debt: the risk structure of interest

rates,’ The Journal of Finance, 29(2): 449–470. 9 . C. Perrow (1984) Normal Accidents: Living with High-risk Technologies, Basic Books.

10 . V.E. Vaugirard (2003) ‘Pricing catastrophe bonds by an arbitrage approach,’ TheQuarterly Review of Economics and Finance, 43: 119–132.

11 . L. Zhu (2008) ‘Double exponential jump diffusion model for catastrophe bonds pricing,’ Journal of Fujian University of Technology, 6: 336–338 (in Chinese). yy

12 . L.F. Chang, M.W. Hung (2009) ‘Analytical valuation of catastrophe equity optionswith negative exponential jumps,’ Insurance: Mathematics and Economics, 44:59–69.

13 . Geman and Yor (1997), op cit.; Dassios and Jung (2003), op cit.14 . Andrieu, N. de Freitas, A. Doucet, M.I. Jordan (2003) ‘An introduction to MCMC for

machine learning,’ Machine Learning , 50: 5–43.gg15 . D. Wu, D.L. Olson (2010) ‘Enterprise risk management: coping with model risk in a

large bank,’ Journal of the Operational Research Society , 61(2): 179–190.yy

15 Bilevel Programming Merger Analysis in Banking

1 . K. Aquino, A. Reed II (1998) ‘A social dilemma perspective on cooperative behavior in organizations: the effects of scarcity, communication, and unequal access on the use of a shared resource,’ Group & Organization Management, 23: 390–413. t

2 . P. Bonacich (1987) ‘Communication networks and collective action,’ Social Networks,9: 389–396; J.A. Sniezek, D.R. May, J.E. Sawyer (1990) ‘Social uncertainty and inter-dependence: a study of resource allocation decision in groups,’ Organizational Behavior and Human Decision Processes, 46: 155–180.

3 . E.A. Mannix (1993) ‘Organizations as resource dilemmas: the effects of power balance on coalition formation in small groups,’ Organizational Behavior and HumanDecision Processes, 55: 1–22.

4 . MEI Computer Technology Group Inc. (2011) ‘2011 Trade Promotion ManagementTrends.’

5 . Jemison, B. David, S.B. Sitkin (1986) ‘Corporate acquisitions: a process perspective,’Academy of Management Review, 11(1): 145–163.w

6 . J. Paradi, S. Vela, H. Zhu (2010) ‘Adjusting for cultural differences, a new DEA modelapplied to a merged bank,’ Journal of Productivity Analysis, 33: 109–123.

7 . S. Kreipl, M. Pinedo (2004) ‘Planning and scheduling in supply Chain: an overview of issues in practice,’ Production and Operations Management , 13(1): 29–77. t

8 . D.D. Wu, J.R. Birge (2012) ‘Serial chain merger evaluation model and application tomortgage banking,’ Decision Sciences , 43(1): 5–36.

9 . Mannix (1993), op cit. 10 . J. Bard (1998) Practical Bilevel Optimization: Algorithms and Applications. Kluwer

Academic Publishers.

232 Notes

11 . P. Hansen, B. Jaumard, G. Savard (1992) ‘New branch and bound rules for linearbilevel programming,’ SIAM Journal on Scientific and Statistical Computing, 13(5): gg1194–1217.

12 . Bard (1998), op cit.13. W.W.Cooper, L.M. Seiford, K. Tone (2000) Data Envelopment Analysis. Kluwer. 14. S.C. Ray (2004) Data Envelopment Analysis: Theory and Techniques for Economics and

Operations Research. Cambridge University Press, 189–208.15 . P. Bogetoft, D. Wang (2005) ‘Estimating the potential gains from mergers,’ Journal of

Productivity Analysis, 23: 145–171.16. Wu and Birge (2012), op cit.17 . R. Maddigan, J. Zaima (1985) ‘The profitability of vertical integration,’ Managerial

and Decision Economics, 6(3): 178–179.18 . E.H. MacDonald (2001) ‘GIS in banking: evaluation of Canadian Bank mergers,’

Canadian Journal of Regional Science, 24(3): 419–442. 19 . S. Finkelstein, H. Jerayr (2002) ‘Understanding acquisition performance: the role of

transfer effects,’ Organization Science, 13(1): 36–47. 20. Cooper et al. (2000), op cit. 21 . C.H. Wang, R. Gopal, S. Zionts (1997) ‘Use of data envelopment analysis in assessing

information technology impact on firm performance,’ Annals of Operations Research,73: 191 –213.

22. Ibid. 23 . J.D. Cummins, X. Xie (2008) ‘Mergers and acquisitions in the US property-liability

insurance industry: productivity and efficiency effects,’ Journal of Banking & Finance,2(1): 30–55.

16 Sustainability and Risk in Globalization

1. E.G. Baranoff (2004) ‘Risk management: a focus on a more holistic approach threeyears after September 11,’ Journal of Insurance Regulation, 22(4): 71–81.

2. D.B. McDonald (2011) ‘When risk management collides with enterprise sustain-ability,’ Journal of Leadership, Accountability and Ethics , 8(3): 56–66.

3 . I.I. Mitroff, M.C. Alpaslan (2003) ‘Preparing for evil,’ Harvard Business Review, 81(4): w109–115.

4. C. Perrow (1999) Normal Accidents: Living with High-Risk Technologies. Princeton University Press.

5. M. Drew (2007) ‘Information risk management and compliance – expect the unex-pected,’ BT Technology Journal, 25(1), 19–29.

6. T. Lambooy (2011) ‘Corporate social responsibility: sustainable water use,’ Journal of Cleaner Production, 19(8): 852–866.

7 . D. Ng, P.D. Goldsmith (2010) ‘Bio energy entry timing from a resource based view and organizational ecology perspective,’ International Food & AgribusinessManagement Review, 13(2): 69–100. w

8. D. Meyler, J.P. Stimpson, M.P. Cutghin (2007) ‘Landscapes of risk,’ Organization & Environment, 20(2): 204–212. t

9. M. Santiago (2011) ‘The Huasteca rain forest,’ Latin American Research Review , 46: w32–54.

10 . T.K. Zhelev (2005) ‘On the integrated management of industrial resources incorpor-ating finances,’ Journal of Cleaner Production, 13(5): 469–474.

Notes 233

11. T.M. Mata, R.L. Smith, D.M. Young, C.A.V. Costa (2005) ‘Environmental analysis of gasoline blending components through their life cycle,’ Journal of Cleaner Production ,13(5): 517–523.

12 . H. Von Blottnitz, M.A. Curran (2007) ‘A review of assessments conducted on bio-ethanol as a transportation fuel from a net energy, greenhouse gas, and environ-mental life cycle perspective,’ Journal of Cleaner Production, 15(7): 607–619.

13. A. Akcil (2006) ‘Managing cyanide: health, safety and risk management prac-tices at Turkey’s Ovacik gold–silver mine,’ Journal of Cleaner Production, 14(8): 727–735.

14 . N. Gȕlpinar, E. Canakoglu, D. Pachamanova (2014) ‘Robust investment decisionsunder supply disruption in petroleum markets,’ Computers & Operations Research, 44: 75–91.

15 . F. Cucchiella, M. Gastaldi (2006) ‘Risk management in supply chains: a real option approach,’ Journal of Manufacturing Technology Management , 17(6): 700–720. t

16 . B. Ritchie, C. Brindley (2007) ‘An emergent framework for supply chain risk man-agement and performance measurement,’ Journal of the Operational Research Society , yy58: 1398–1411.

17 . F.B. Hawley (1907) Enterprise and the Productive Process .18 . F.H. Knight (1921) Risk, Uncertainty, and Profit. Hart, Schaffner & Marx. 19 . D. Kahneman, A. Tversky (2000) Choices, Values, and Frames. The University of

Cambridge.

17 Risk from Natural Disasters

1 . C. McDonald (2009) ‘New PRIMA president sees public RMs as masters of dis-aster,’ National Underwriter/Property & Casualty Risk & Benefits Management, 113(21): t17–31.

2 . W.-J. Tan, P. Enderwick (2006) ‘Managing threats in the global era: the impact andresponse to SARS,’ Thunderbird International Business Review, 48(4): 515–536. w

3 . B. Lee, F. Preston (2012) Preparing for High-impact, Low-probability Events: Lessons from Eyjafjallajökull. Chatham House Report.

4 . N.N. Taleb, D.G. Goldstein, M.W. Spitznagel (2009) ‘The six mistakes executivesmake in risk management,’ Harvard Business Review, 87(10): 78–81. w

5 . K. Hopkins (2003) ‘Value opportunity three: improving the ability to fulfill demand,’Business Week, January 13.

6 . A.S. Mukherjee (2008) The Spider’s Strategy: Creating Networks to Avert Crisis, Create Change, and Really Get Ahead. FT Press.

7 . N. Kapucu, M. Van Wart (2008) ‘Making matters worse: an anatomy of lead-ership failures in managing catastrophic events,’ Administration & Society , 40(7): yy711–740.

8 . D. Alexander (2003) ‘Towards the development of standards in emergencymanagement training and education,’ Disaster Prevention and Management, 12:t113–123.

9 . G. Suder, D.W. Gillingham (2007) ‘Paradigms and paradoxes of agricultural risk gov-ernance,’ International Journal of Risk Assessment and Management, 7(3): 444–457. t

10 . L.A. Reilly, O. Courtenay (2007) ‘Husbandry practices, badger sett density andhabitat composition as risk factors for transient and persistent bovine tuberculosison UK cattle farms,’ Preventive Veterinary Medicine , 80(2–3): 129–142.

234 Notes

11. K.S. Markel, L.A. Barclay (2007) ‘The intersection of risk management and human resources: an illustration using genetic mapping,’ International Journal of Risk Assessment and Management, 7(3): 326–340. t

12. D.H. Smaltz, R. Carpenter, J. Saltz (2007) ‘Effective IT governance in healthcare organizations: a tale of two organizations,’ International Journal of Healthcare Technology and Management, 8(1/2): 20–41.t

13 . D. Dalcher (2007) ‘Why the pilot cannot be blamed: a cautionary note about excessive reliance on technology,’ International Journal of Risk Assessment and Management,t7(3): 350–366.

14. M. Baucells, F.H. Heukamp (2009) ‘Probability and time tradeoff,’ Working Paper,http://ssrn.com/abstract=970570.

15. J. Pan, M. Wang, D. Li, J. Le4 (2009) ‘Automatic generation of seamline network using area Voronoi diagrams with overlap,’ IEEE Transactions on Geoscience and Remote Sensing, 47(6): 1737–1744. gg

16. D. Engel (2009) ‘Hi-tech solutions for crisis management,’ African Business ,352: 50.

17 . J. Wei, D. Zhao, L. Liang (2009) ‘Estimating the growth models of news stories ondisasters,’ Journal of the American Society for Information Science and Technology , 60(9): yy1741–1755.

18. M. Saadatseresht, A. Mansourian, M. Taleai (2009) ‘Evacuation planning usingmultiobjective evolutionary optimization approach,’ European Journal of Operational Research, 198(1): 305–314.

19. R. Morelli, A. Tucker, N. Danner, T.R. de Lanerolle, H.J.C. Ellis, O. Izmirli, D. Krizanc,G. Parker (2009) ‘Revitalizing computing education through free and open sourcesoftware for humanity,’ Communications of the ACM, 52(8): 67–75.M

20. N. Santella, L.J. Steinberg, K. Parks (2009) ‘Decision making for extreme events: Modeling critical infrastructure interdependencies to aid mitigation and responseplanning,’ Review of Policy Research, 26(4): 409–422.

21 . F. Aleskerov, A.L. Say, A. Toker, H.OL. Akin, G. Altay (2005) ‘A cluster-based decisionsupport system for estimating earthquake damage and casualties,’ Disasters , (3): 255–276.

18 Pricing of Carbon Emission Exchange in the EU ETS

1 . M. Kainuma, Y. Matsuoka, T. Morita (1999) ‘Development of AIM (Asian-PacificIntegrated Model) for coping with global warming,’ Proceedings of the IEEEInternational Conference on System Man and Cybernetics, 6: 569–574.

2. W.D. Nordhaus, J.G. Boyer (1999) ‘Requiem for Kyoto: an economic analysis of the Kyoto protocol’, The Energy Journal, 20: 93–130.

3 . W.D. Nordhaus (2001) ‘Climate change: global warming economics’, Science,294(5545): 1283–1284.

4. P. Capros, L. Mantzos (2000) ‘The economic effects of industry-level emissiontrading to reduce greenhouse gases’, Report to DG environment, E3M-Laboratory 21 tat ICCS/NTUA; P. Criqui, A. Kitous (2003) ‘Impacts of linking JI and CDM credits tothe European emission allowance trading scheme,’ KPI technical report; G. Klepper,S. Peterson (2004) ‘The EU emissions trading scheme: allowance prices, trade flows, competitiveness effects’, European Environment, 14(4): 201–218; G. Klepper, S.tPeterson (2006) ‘Emissions trading, CDM, JI and more – the climate strategy of theEU’, Energy Journal, 27(2): 1–26.

Notes 235

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6. M. Uhrig-Homburg, M. Wagner (2006) ‘Success chances and optimal design of derivatives on CO2 emission certificates,’ Working Paper, University of Karlsruhe.

7 . M.S. Paolella, L. Taschini (2006) ‘An econometric analysis of emission tradingallowances,’ Research Paper Series 06–26, FINRISK: National Center of Competence in Research Financial Valuation and Risk Management.

8 . E. Benz, S. Trück, (2006) ‘CO2 emission allowances trading in Europe – specifying anew class of assets’, Problems and Perspectives in Management, 4(3): 30–40. t

9 . S. Borak, W. Härdle, S. Trück, R. Weron (2006) ‘Convenience yields for CO 2 emissionallowance future contracts,’ SFB 649 discussion paper 2006–076, SFB Economic Risk Berlin.

10 . J. Seifert, M. Uhrig-Hombur, M. Wagner (2008) ‘Dynamic behavior of CO 2 spot prices,’ Journal of Environmental Economics and Management, 56(2): 180–194.

11 . D. Burtraw (1996) ‘Cost savings sans allowance trades? Evaluating the SO 2 emissiontrading program to date,’ Discussion Paper 95–30-REV.

12 . J.M. Burniaux, J.O. Martins (2000) ‘Carbon emission leakages: a general equilibriumview,’ OECD Economics Department Working Papers No. 242.

13. T.J. Considine (2000) ‘The impacts of weather variations on energy demand andcarbon emissions,’ Resource and Energy Economics , 22: 295–314.

14 . J.Sijm, S. Bakker, Y. Chen, H. Harmesen, W. Lise (2005) ‘CO 2 price dynamics: the implications of EU emissions trading on the price of electricity,’ Report ECNC-05–81, Energy Research Center of the Netherlands (ECN).

15 . U. Ciorba, A. Lanza, F. Pauli (2001) ‘Kyoto protocol and emission trading: does theUS make a difference?’ FEEM working paper 90.2001, Milan.

16 . U. Springer (2003) ‘The market for tradable GHG permits under the Kyoto Protocol:a survey of model studies’, Energy Economics , 25: 527–551.

17 . U. Springer, M. Varilek (2004) ‘Estimating the price of tradable permits for green-house gas emissions in 2008–2012’, Energy Policy, 32: 611–621.yy

18 . M. Manasanet-Bataller, A. Pardo, E. Valor (2007) ‘CO2 prices, energy and weather’.The Energy Journal, 28(3): 73–92.

19 . D.B. Nelson (1991) ‘Conditional heteroskedasticity in asset returns: a new approach’, Econometrica, 59, 347–370.

19 Volatility Forecasting of the Crude Oil Market

1 . R. Bacon, M. Kojima (2008) ‘Coping with Oil Price Volatility,’ Energy sector man-agement assistance program, Energy Security Special Report 005/08.

2 . J.C. Hung, M.C. Lee, H.C. Liu (2008) ‘Estimation of value-at-risk for energy com- modities via fat-tailed GARCH models,’ Energy Economics , 30(3):1173–1191.

3 . P.K. Narayan, S. Narayan, A. Prasad (2008) ‘Understanding the oil price-exchangerate nexus for the Fiji islands,’ Energy Economics, 30(5): 2686–2696.

4 . F. Malik, B.T. Ewing (2009) ‘Volatility transmission between oil prices and equity sector returns,’ International Review of Financial Analysis, 18(3): 95–100.

5 . A.H. Alizadeh, N.K. Nomikos, P.K. Pouliasis (2008) ‘A Markov regime switching approach for hedging energy commodities,’ Journal of Banking & Finance , 32(9):1970–1983.

236 Notes

6. C. Aloui, R. Jammazi (2009) ‘The effects of crude oil shocks on stock market shiftsbehaviour: a regime switching approach,’ Energy Economics, 31(5): 789–799.

7. F. Klaassen (2002) ‘Improving GARCH volatility forecasts with regime-switching GARCH,’ Empirical Economics, 27: 363–394.

8. A. Cologni, M. Manera (2009) ‘The asymmetric effects of oil shocks on output growth: a Markov–Switching analysis for the G-7 countries,’ Economic Modelling,gg26(1): 1–29.

9 . Y. Fan, Y.J. Zhang, H.T. Tsaic, Y.M. Wei (2008) ‘Estimating ‘Value at Risk’ of crude oilprice and its spillover effect using the GED-GARCH approach,’ Technological Changeand the Environment, 30(6): 3156–3171.t

10. C. Aloui, S. Mabrouk (2009) ‘Value-at-risk estimations of energy commodities vialong-memory, asymmetry and fat-tailed GARCH models,’ Energy Policy, 38(5):2326–2339.

11 . P. Agnolucci (2009) ‘Volatility in crude oil futures: a comparison of the pre-dictive ability of GARCH and implied volatility models,’ Energy Economics, 31(2): 316–321.

12. C. Engel (1994) ‘Can the Markov switching model forecast exchange rates? ‘ Journal of International Economics, 36(1): 151–165.

13. M.T. Vo (2009) ‘Regime-switching stochastic volatility: evidence from the crude oilmarket,’ Energy Economics , 31(5): 779–788.

14. E. Fama (1970) ‘Efficient capital markets: a review of theory and empirical work,’ Journal of Finance, 25: 383–417.

15. R.F. Engle (1982) ‘Autoregressive conditional heteroscedasticity with estimates of variance of United Kingdom inflation,’ Econometrica, 50: 987–1008.

16. T. Bollerslev (1986) ‘Generalized autoregressive conditional heteroskedasticity,’Journal of Econometrics, 31: 307–327.

17 . D.B. Nelson (1991) ‘Conditional heteroskedasticity in asset returns: a new approach,’Econometrica, 59: 347–370.

18. J.E. Raymond, R.W. Rich (1997) ‘Oil and the macroeconomy: a Markov state-switching approach,’ Journal of Money, Credit and Banking, 29(2): 193–213. gg

19. J.D. Hamilton (1989) ‘A new approach to the economic analysis of nonstationary time series and the business cycle,’ Econometrica, 57(2): 357–384.

20. D. Cousineau, S. Brown, A. Heathcote (2004) ‘Fitting distributions using maximumlikelihood: methods and packages,’ Behavior Research Methods, Instruments, & Computers, 36: 742–756.

20 Confucius Three-stage Learning of Risk Management

1. D. Gardner (2007) The Four Books. The Teachings of the Later Confucian Tradition.Hackett Publishing.

2. X. Yao, H. Yao (2000) An Introduction to Confucianism. Cambridge University Press. 3. A. Ben-Tal, A. Nemirovski (2000) ‘Robust solutions of linear programming problems

contaminated with uncertain data’, Mathematical Programming, 88, 411–424. 4. J.C. Smith (2009) Pseudoscience and Extraordinary Claims of the Paranormal: A Critical

Thinker. Wiley-Blackwell. ISBN 978–1405181228. rr 5. D. Wu, D.L. Olson (2009) ‘Introduction to the special section on optimizing risk

management. Methods and tools’, Human and Ecological Risk Assessment, 15(2):220–226.

Notes 237

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7 . H.M. Markowitz (1959) Portfolio Selection: Efficient Diversification of Investments . JohnWiley & Sons (reprinted by Yale University Press, 1970).

8 . W.F. Sharpe (1964) ‘Capital asset prices: a theory of market equilibrium under condi-tions of risk’, Journal of Finance , 19(3): 425–442.

9 . J. Tobin (1958) ‘Liquidity preference as behavior towards risk’, The Review of Economic Studies, 25: 65–86.

10 . G.A. Akerlof (1970) ‘The market for “lemons”: quality uncertainty and the marketmechanism,’ Quarterly Journal of Economics, 84(3): 488–500.

11 . Counterparty Risk Management Policy Group III (2008) Containing Systemic Risk,August 6.

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253

Index

accounting perspective, 3–7Adelphia, 13AIG, 2, 15, 29–30, 73Air Canada, 147allocative efficiency (AE), 61Ameriquest, 26Analytic Hierarchy Process (AHP),

58, 59anchoring, 111Arbitrage pricing theory, 110Arthur Andersen, 13, 164artificial neural networks (ANN), 32, 43,

48, 125, 127–134asset price volatility, 32autoregressive conditional

heteroscedasticity (ARCH) model, 192, 199, 201

autoregressive moving average (ARMA) model, 201

autoregressive process, 121, 139availability, 111

backpropagation neural networks, 127–128

bags-of-words, 33balanced scorecard, 58, 60, 73–74Bank Credit Scoring, 72–86Bank Efficiency Analysis, 99–107,

124–135Bank of America, 2, 100, 104Banker, Charnes & Cooper (BCC) DEA

model, 62, 67–70, 126, 156Barclay’s, 100Basel Accords, 16, 73, 106Bayesian analysis, 121–122Beta (book to market value), 66bilevel programming, 145–162binomial option pricing model, 110Black, Scholes, and Merton, 24, 112British Petroleum, 118–123bubbles, 15, 24, 111–112

California electricity, 11, 12–13Canadian Imperial Bank of Commerce

(CIBS), 147Capital Asset Pricing Model (CAPM), 16,

109, 110Capital market instruments, 73carbon emission pricing, 183–198catastrophe bonds, 73, 136–144catastrophe equity puts (cat-e-puts), 73catastrophe risk instruments, 136–138Charnes, Cooper & Rhodes DEA model,

62, 127, 156Chase, 101Chinese earthquakes, 1, 136, 137, 175Citigroup, 100closeness coefficient, 89coincidence matrix, 94–95collateralized debt obligations (CDOs), 16,

73, 108collateralized mortgage obligations

(CMOs), 21Committee on Sponsoring Organizations

(COSO), 3–4, 6, 7Compound Poisson loss model, 138Conditional-Value-at-Risk (CVaR), 18Confucius three-stage learning, 215–220contingent surplus notes, 73copulas, 15, 20–21COSO ERM Cube, 4COSO framework, 3–4COSO internal control process, 3Countrywide, 26credit default swaps (CDSs), 16, 21,

29, 30credit rating, 74credit scorecard, 75–84credit scoring, 90–97Critical Infrastructure Protection

Decision Support System (CIPDSS), 180–181

crude oil, 199–214

254 Index

daily volatility model, 38–39Data Envelopment Analysis (DEA), 57–71,

100, 124, 126–127, 149–162data mining, 87–90decision making unit (DMU), 61, 66, 70,

126, 131, 149, 150, 151decision support system (DSS), 180–181decision tree, 93–96Deep Water Horizon, 118–123derivatives, 24, 73Distribution Free Approach, 100, 124double marginalization, 151DuPont model, 57

economic perspective, 108–117Efficient Market Hypothesis, 23efficient market theory, 109emergency management, 179–180emergency management support systems

(EMSS), 180–181energy risk, 167Enron, 11–14, 15, 72, 163, 164enterprise resource planning (ERP)

systems, 14Ericsson, 176ERM process, 7–8European Climate Exchange (ECX),

184, 189Expected Utility Theory, 16Exponential Generalized Autoregressive

Conditional Heteroscedasticity (EGARCH) model, 193–194, 197, 202

Eyjafjallajokull, 168, 175

family regulation, 217–218FEMA, 179financial risk forecasting, 32–48financial risk management, 15–22financial statement analysis, 58, 60–61Florida hurricanes, 1food risk, 166Fourier transformations, 17framing, 111free-disposal hull, 100, 124Fuzzy set theory, 58, 59–60

Gaussian copula, 20, 108Generalized Autoregressive Conditional

Heteroscedasticity (GARCH) model,

33, 34–42, 48, 49, 55–56, 192–197, 199–200, 202, 206–210

globalization, 168–169, 171–173Golden West, 26Green Tree, 26grey relation analysis, 58, 60

H1N1 virus, 2hedging, 15, 27herding, 111HSBC, 100

Icelandic volcano, 1ICTCLAS System, 51implementation issues, 7–8incentive incompatibility, 151indifference theory, 110information sentiment, 33–34Innovative Support To Emergencies,

Diseases And Disasters (InSTEDD), 179

International Organization forStandardization (ISO), 14

investment collars, 17

Kolmogorov-Smirnov statistic, 75, 77, 142Kyoto protocol, 183, 184, 187

Lehman Brothers, 15, 30Levy process, 17Lexicon approach, 50Li David X., 20, 108Lloyd’s of London, 1, 72, 101, 104, 174London Market Exchange (LMX), 112Long Term Capital Management (LTCM),

15, 24, 112, 164Lorenz curve, 75, 78

machine learning, 32–48, 50Macondo, 118, 119, 120malicious activities, 164marginal abatement cost curve, 185Markov chain Monte Carlo, 144Markov process, 17Markov regime switching model,

210–214Markowitz, 16, 109mean variance, 110merger evaluation, 150–152

Index 255

Merrill Lynch, 2, 15Minerals Management Service (MMS),

120, 121Monte Carlo simulation, 19, 96, 110, 121,

140, 144, 209Moody’s, 72moral hazard, 115mortgage system, 26multiattribute utility, 181Multivariate Statistical Analysis, 58, 106mutualization, 115

National Disaster Medical System, 181natural disasters, 164, 175–182neural networks, see artificial neural

networksNokia, 176Nord Pool, 184Northern Rock, 25, 26–29

online banking, 99–107options-pricing model, 110overall efficiency (OE), 61overconfidence, 111

Pacific Gas & Electric, 12Pareto distribution, 17part of speech (POS) tagging, 50Peregrine Systems, 13performance validation, 74–75perturbation, 96Philips Electronics, 176polarity tagging, 49PowerNext, 184principal component analysis (PCA),

99–107

real estate crash of 2008, 23–31real estate cycle, 25regime switching models, 203Regional Integrated Model of Climate and

The Economy (RIMCE), 185Reinhart and Rogoff, 23return on assets (ROA), 63return on equity (ROE), 63risk analysis, 7, 165risk appetite, 6risk exchange swaps, 73risk identification, 7

risk management definition, 2risk management framework, 110risk management modeling, 9risk management responsibilities, 8risk management theories, 111risk mitigation, 114risk scoring, 64risk tolerance, 114Royal Bank of Scotland, 101, 104

SAHANA, 179San Diego Gas & Electric Company, 12Sarbanes-Oxley Act, 3, 13–14self cultivation, 216–217semantic techniques, 32sentiment analysis, 39–41, 44–48, 49–56Sharpe, William, 109squared correlation coefficient, 45Stackelberg game, 148Standard & Poor’s (S&P), 72state harmonization, 218–219state preference theory, 110Stochastic frontier analysis, 100, 124, 156Stock Forum, 49–51, 52, 56stock price volatility, 54subprime banking crisis, 2SunTrust, 101supply chains, 147–149, 169, 170–171support vector machines (SVM), 32, 39,

41–42, 43, 48, 49, 55–56sustainability, 163–174sustainable risk, 166–168Swine Flue epidemic, 136systemic failures, 164

Taleb, N.N., 20, 113technical efficiency (TE), 61terrorism, 1thick frontier approach, 100, 124TOPSIS, 87–98trading volume volatility, 48tranches, 15, 21–22, 26Treadway Commission, 3tsunamis, 1, 164, 176Tyco International, 13

underinvestment problem, 110United Nations intergovernmental panel

on climate change (UNIPCC), 183

256 Index

Value-at-risk (VaR), 15, 17–20, 31, 73, 121, 200

variable selection, 64–66volatility forecasting model, 33–39,

200–214volatility trend forecast accuracy, 45volcanoes, 1

Wachovia, 101weather derivatives, 73Wells Fargo, 101Wenchuan earthquake, 136, 137, 175word segmentation, 50, 51WorldCom, 13, 15, 72, 163, 164