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• W4210995917 abstract "Free Access Further Reading Book Editor(s):Aldo Soprano, Aldo SopranoSearch for more papers by this authorBert Crielaard, Bert CrielaardSearch for more papers by this authorFabio Piacenza, Fabio PiacenzaSearch for more papers by this authorDaniele Ruspantini, Daniele RuspantiniSearch for more papers by this author First published: 02 January 2012 https://doi.org/10.1002/9781119208778.furread AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Further reading Artzner, P., Delbaen, F., Eber, J.-M. and Heath, D. (1999) Coherent measures of risk. Mathematical Finance, 9, 203– 228. Azzalini, A. and Vedaldi, R. (1987) Introduzione all'Inferenza Statistica Parametrica. Cleup, Padova. Balkema, A. A. and de Haan, L. (1974) Residual lifetime at great age. Annals of Probability, 2, 792– 804. Barndorff-Nielsen, O. and Lindner, A. (2004) Some aspects of L´evy copulas. Preprint. Munich University of Technology. Available at http://www.ma.tum.de/stat/ Basel Committee on Banking Supervision (2003) The 2002 Loss Data Collection Exercise for Operational Risk: Summary of the Data Collected . Basel Committee on Banking Supervision. (2004) Principles for the Home-Host Recognition of AMA Operational Risk Capital. Basel Committee on Banking Supervision. (2005) The Treatment of Expected Losses by Banks Using the AMA Under the Basel II Framework . Basel Committee on Banking Supervision. (2006) Home-host information sharing for effective Basel II implementation. Basel Committee on Banking Supervision. (2007) Principles for Home-Host Supervisory Cooperation and Allocation Mechanisms in the Context of Advanced Measurement Approaches . Basu, A. (2002) Outlier resistant minimum divergence methods in discrete parametric models. Sankhya: The Indian Journal of Statistics (B), 64, 128– 140. Baud, N., Frachot, A. and Roncalli, T. (2002) How to Avoid Over-Estimating Capital Charge for Operational Risk? Operational Risk, February. Baud, N., Frachot, A. and Roncalli, T. (2002) Internal data, external data and consortium data for operational risk measurement: How to pool data properly. Groupe de Recherche Operationnelle, Credit Lyonnais, France, working paper. Bee, M. (2005) Copula-based Multivariate Models with Applications to Risk Management and Insurance. Bee, M. (2005) On Maximum Likelihood Estimation of Operational Loss Distributions. Bingham, N. H., Goldie, C. M., and Teugels, J. L. (1987) Regular Variation. Cambridge University Press, Cambridge. Bocker, K. and Kluppelberg, C. (2006) Multivariate Models for Operational Risk. Bocker, K. and Kluppelberg, C. (2007) Multivariate Operational Risk: Dependence Modelling with Levy Copulas. Brandts, S. (2004) Operational Risk and Insurance: Quantitative and Qualitative Aspects. EFMA 2004 Basel Meetings Paper. Brown, D. and Wang, J. (2005) Discussion on “Quantitative models for operational risk: extremes, dependence and aggregation”. Presentation. Implementing an AMA to Operational Risk, Federal Reserve Bank of Boston, May 18– 20. B¨︂uhlmann, H., Shevchenko, P. and Wuethrich, M. V. (2007) A “Toy” Model for Operational Risk Quantification Using Credibility Theory. Chanseok, P. and Ayanendranath, B. (2003) The generalized Kullback–Leibler divergence and robust inference. Journal of Statistical Computation and Simulation, 73(5), 311– 332. Chavez-Demoulin, V. and Embrechts, P. (2004) Advanced extremal models for operational risk. Chavez-Demoulin, V., Embrechts, P. and Neslehova, J. (2006) Quantitative models for operational risk: extremes, dependence and aggregation. Journal of Banking and Finance, 30(10), 2635– 2658. Chernobai, A. and Rachev, S. (2004) Toward Effective Financial Risk Management: Stable Modeling of Operational Risk . Chernobai A. and Rachev S. T. (2004) Stable modelling of operational risk operational risk modelling and analysis, In M. G. Cruz (ed.) Theory and Practice, RISK Books, London, pp. 139– 169. Chernobai A. and Rachev S. T. (2006) Applying robust methods to operational risk modelling. Journal of Operational Risk, 1(1), 27– 41. Chernobai A., Burnec¸ki K., Rachev S. T., Tr¨︂uck S. and Weron R. (2006) Modelling catastrophe claims with left-truncated severity distributions. Computational Statistics, 21(3), 537– 555. Chernobai, A., Jorion, P. and Yu, F. (2008) The Determinants of Operational Losses. Technical Report, Syracuse University. Chernobai, A., Menn, C., Trueck, S. and Rachev, S. (2004) A Note on the Estimation of the Frequency and Severity Distribution of Operational Losses . Chernobai, A., Menn, C., Rachev, S. and Trueck, S. (2005) Estimation of Operational Value-at-Risk in the Presence of Minimum Collection Thresholds. Chernobai A., Menn C., Rachev S. T. and Tr¨︂uck S. (2006) A note on the estimation of the frequency and severity of operational losses. Mathematical Scientist, 30(2), 87– 97. Chernobai, A.,Menn, C., Rachev, S., Trueck, S. and Moscadelli, M. (2006) Treatment of incomplete data in the field of operational risk: The effects on parameter estimates, EL and UL figures. In E. Davis (ed.) The Advanced Measurement Approach to Operational Risk, RISK Books, London, pp. 145– 168. Chernobai, A., Rachev, S. T. and Fabozzi, F. J. (2007). Operational Risk: A Guide to Basel II Capital Requirements, Models and Analysis. John Wiley & Sons, Inc, Hoboken, NJ. Chernobai, A., Svetlozar, R., and Fabozzi, F. (2005) Composite goodness-of-fit tests for left-truncated loss samples, working paper. Cherubini, U., Luciano, E. and Vecchiato, W. (2004) Copula Methods in Finance. John Wiley & Sons, Ltd, Chichester. Coles, S. (2001) An Introduction to Statistical Modeling of Extreme Value, Springer. Cont, R. and Tankov, P. (2004) Financial Modelling With Jump Processes, Chapman & Hall/CRC, Boca Raton. Cruz, M. G. (2002) Modeling, Measuring and Hedging Operational Risk, John Wiley & Sons, Ltd, Chichester. Cummins,D., Lewis, C. andWei,R. (2004) TheMarketValue Impact ofOperational Risk Events For U.S. Banks and Insurers. Available at: SSRN: http://ssrn.com/abstract=64001. Da Costa Lewis, N. (2004) Operational Risk with Excel and VBA: Applied Statistical Methods for Risk Management. John Wiley & Sons, Ltd, Chichester. Dalla Valle, L., Fantazzini, D. and Giudici, P. (2006) Copulae and Operational Risks. Daul, S., De Giorgi, E., Lindskog, F. and McNeil, A. J. (2003) Using the grouped t copula. Risk, 73–76. de Fondnouvelle, P. and Jordan, J. (2004) Implications of Alternative Operational Risk Modeling Techniques. de Fondnouvelle, P., DeJesus-Rueff, V., Jordan, J. and Rosengren, E. (2003) Capital and Risk: New Evidence on Implications of Large Operational Losses. Degen, M. and Embrechts, P. (2008) EVT-based estimation of risk capital and convergence of high quantiles. Advances in Applied Probability 40(3), 696– 715. Degen, M., Embrechts, P. and Lambrigger, D. D. (2006) The quantitative modeling of operational risk: between g-and-h and EVT. ASTIN Bulletin, 37(2), 265– 291. Dell'Aquila, R. and Embrechts, P. (2006) Extremes and robustness: a contradiction? Financial Markets and Portfolio Management, 20, 103– 118. Demarta, S. and McNeil, A. J. (2005) The t copula and related copulas. International Statistical Review, 73(1), 111– 129. Dutta, K. and Babbel, F. (2002) On measuring skewness and kurtosis in short rate distributions: the case of the US dollars London inter bank offer rates. Wharton – Financial institutions center. Ebnoether, S., Vanini, P., McNeil, A. J. and Antolinez-Fehr, P. (2003) Operational Risk: A practitioner's view. Journal of Risk, 5(3), 1– 15. El-Gamal, M., Inanoglu, H. and Stengel, M. (2006) Multivariate Estimation for Operational Risk with Judicious Use of Extreme Value Theory. Embrechts, P. (2000) Extreme value theory: Potential and limitations as an integrated risk management tool. Derivatives Use, Trading & Regulation, 6, 449– 456. Embrechts, P. (2000) Extremes and integrated risk management. Risk books. Embrechts, P. (2008) Copulas: A personal view. Journal of Risk and Insurance. Embrechts, P. and Frei, M. (2007) Panjer recursion versus FFT for compound distributions. Mathematical Methods of Operations Research. Embrechts, P. and Höing, A. (2006) Extreme VaR scenarios in higher dimensions. Extremes, 9, 177– 192. Embrechts, P. and Mikosch, T. (2000) Mathematical Models in Finance. Embrechts, P. and Puccetti, G. (2006) Aggregating risk capital, with an application to operational risk. The Geneva Risk and Insurance Review 31(2), 71– 90. Embrechts, P. and Puccetti, G. (2006) Bounds for functions of dependent risks. Finance and Stochastics, 10, 341– 352. Embrechts, P. and Puccetti, G. (2006) Bounds for functions of multivariate risks. Journal of Multivariate Analysis 97(2), 526– 547. Embrechts, P. and Puccetti, G. (2008) Aggregating risk across matrix structured loss data: the case of operational risk. Journal of Operational Risk. Embrechts, P., et al. (2001) An Academic Response to Basel II, Financial MarketsGroup, London School of Economics. Embrechts, P., Furrer, H. and Kaufmann, R. (2003) Quantifying regulatory capital for operational risk. Derivatives Use, Trading & Regulation, 9(3), 217– 233. Embrechts, P., Hoeing, A. and Juri, A. (2003) Using Copulae to bound the Value-at-Risk for functions of dependent risks. Finance & Stochastics, 7(2), 145– 167. Embrechts, P., Kaufmann, R. and Samorodnitsky, G. (2004) Ruin theory revisited: stochastic models for operational risk. In C. Bernadell, et al. (eds.) Risk Management for Central Bank Foreign Reserves, European Central Bank, Frankfurt A.M., pp. 243– 261. Embrechts, P., Kl¨︂uppelberg, C. and Mikosch, T. (1997) Modelling Extremal Events for Insurance and Finance. Springer. Embrechts, P., Lambrigger, D. D. W¨︂uthrich, M. V. (2008) Multivariate extremes and the aggregation of dependent risks: examples and counter-examples. Extremes. Embrechts, P., Lindskog, F. and McNeil, A. (2003) Modelling dependence with copulas and applications to risk management. In S. Rachev (ed.) Handbook of Heavy Tailed Distributions in Finance, Elsevier, Chapter 8, pp. 329– 384. Embrechts, P., McNeil, A. and Straumann, D. (2002) Correlation and dependence in risk management: Properties and pitfalls. In M. Dempster (ed.) Risk Management: Value at Risk and Beyond, Cambridge Univesity Press, pp. 176– 223. Embrechts, P., McNeil, A. and Frey, R. (2005) Quantitative riskmanagement. Princeton. Embrechts, P., Resnick, S. and Samorodnitsky, G. (1999) Extreme value theory as a risk management tool. North American Actuarial Journal 3, 30– 41. Federal Reserve System (2005) Results of the 2004 Loss Data Collection Exercise for Operational Risk. Fombrun, C. J. and Van Riel, C. B. M. (2004) Fame and fortune: how successful companies build winning reputaions. Financial Times/Prentice Hall. Frachot, A., Georges, P. and Roncalli, T. (2001) Loss Distribution Approach for operational risk. Groupe de Recherche Operationnelle, Credit Lyonnais, France, working paper. Frachot, A., Moudoulaud, O. and Roncalli, T. (2003) Loss Distribution Approach in Practice. Groupe de Recherche Operationnelle, Credit Lyonnais, France, working paper. Frachot,A., Roncalli, T. and Salomon, E. (2004) TheCorrelation Problem in Operational Risk. Frees, E.W. and Valdez, E. A. (1998) Understanding relationships using copulas. North American Actuarial Journal, 2, 1– 25. Genest, C. and Neslehova J. (2008) Analytical proofs of classical inequalities between Kendall's tau and Spearman's rho. Proceedings of the 8th Tartu Conference on Multivariate Statistics&the 6th Conference on Multivariate Distributions with Fixed Marginals, to appear. Genest, C. and Rivest, L. (1993) Statistical inference procedures for bivariate Archimedean copulas. Journal of the American Statistical Association, 88, 1034– 1043. Giacometti, R., Rachev, S. T., Chernobai, A., Bertocchi, M. and Consigli, G. (2007) Heavy-tailed distributional model for operational losses. Journal of Operational Risk, 2(1), 55– 90. Giacometti, R., Rachev, S. T., Chernobai, A. and Bertocchi, M. (2008) Aggregation Issues in Operational Risk. Journal of Operational Risk, 3(3). Hosking, J. R. M., Wallis, J. R. and Wood, E. F. (1985) Estimation of the generalized extreme-value distribution by the method of probability-weighted moments, Technometrics, 27, 251– 261. Hosking, J. R. M. and Wallis, J. R. (1987) Parameter and quantile estimation for the generalized pareto distribution. Technometrics, 29(3). Jobst, A. (2007) Operational Risk - The Sting is Still in the Tail But the Poison Depends on the Dose. Jorion, P. (2000) Value at Risk. McGraw Hill. Kallsen, J. and Tankov, P. (2006) Characterization of dependence of multivariate Lévy processes using Lévy copulas. Journal of Multivariate Analysis, 97, 1551– 1572. Kim, J. and Lee, S. (1999) An iterative algorithm for the Cramer–von Mises distance estimator . Klüppelberg, C. and Mikosch, T. (1997) Large deviations of heavy-tailed random sums with applications in insurance and finance. Journal of Applied Probability, 34, 293– 308. Kullback, S. (1959) Information Theory and Statistics. Dover Publications, New York. Kullback, S. and Leibler, R. A. (1951) On information and sufficiency. Annals of Mathematics and Statistics, 22, 79– 86. Lane, M. (2002) Alternative Risk Strategies. Risk Books. Larkin, J. (2002) Strategic Reputation Risk Management, Palgrave Macmillan. Lindskog, F, McNeil, A. J. and Schmock, U. (2003) Kendall's tau for elliptical distributions. In Bol Nakhaeizadeh, Ridder Rachev and Vollmer., (eds.) Credit Risk -Measurement, Evaluation and Management, Physica-Verlag Heidelberg. McConnell, P. J. (2006) A Perfect Storm - Why Are Some Operational Losses Larger than Others? McNeil, A. J. (1997) Estimating the tails of loss severity distributions using extreme value theory. ASTIN Bulletin, 27, 117– 137. McNeil, A. J. (1999) Extreme Value Theory for Risk Managers. McNeil, A. J. (2008) Sampling nested Archimedean copulas. Journal of Statistical Computation and Simulation, 78(6), 567– 581. McNeil, A. J. and Frey, R (2000) Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach. Journal of Empirical Finance, 7, 271– 300. McNeil, A. J. and Saladin, T. (1997) The peaks over thresholds method for estimating high quantiles of loss distributions. Proceedings of 28th International ASTIN Colloquium. McNeil, A. J. and Saladin, T. (2000) Developing scenarios for future extreme losses using the POT method. In P. M. E. Embrechts, (ed.) Extremes and Integrated Risk Management, RISK books, London. Medova, E. (2001) Operational Risk Capital Allocation and Integration of Risks. Mignola, G. and Ugoccioni, R. (2005) Tests of Extreme Value Theory Applied to Operational Risk Data . Moscadelli, M., Chernobai, A. and Rachev S. T. (2005) Treatment of missing data in the field of operational risk: The impacts on parameter estimates, EL, VaR, and CvaR figures. Operational Risk, 6(6), pp. 28– 34. Nam, D. (2001) Value at risk: a quantile-based distribution approach for incorporating skewness and fat-tailedness . INHA University, PhD thesis. Nelsen, R. B. (1999) An Introduction to Copulas. Lecture Notes in Statistics 139, Springer, N.Y. Nguyen, M. and Ottmann, M. (2005) Das dicke Ende. RiskNews, July. Pappadá, A. (2003) I rischi operativi nelle banche. Misurazione e gestione. edibank. Perry, J. and de Fontnouvelle, P. (2005). Measuring Reputational Risk: The market Reaction to Operational Loss Announcements. Technical Report, Federal Reserve Bank of Boston. Peters, G., Johansen, A. and Doucet, A. (2007) Simulation of the Annual Loss Distribution in Operational Risk via Panjer Recursions and Volterra Integral Equations for Value at Risk and Expected Shortfall Estimation. Pfeifer, D. and Neslehova, J. (2003) Modeling Dependence in Finance and Insurance: the Copula Approach. Blätter der deutschen Gesellschaft für Versicherungs- und Finanzmathematik, Bd. XXVI/2. Pickands, J. III. (1975) Statistical inference using extreme order statistics. Annals of Statistics, 3, 119– 131. Powosjowski, M. R., Reynolds, D. and Tuenter, J. H. (2002) Dependent events and operational risk. Algo Research Quarterly, 5(2), 65– 73. Rachev, S. T., Chernobai, A. and Menn, C. (2006) Empirical examination of operational loss distributions. In M. Morlock, et al. (eds.) Perspectives on Operational Research, Deutscher Universitaet-Verlag/GWV Fachverlage GmbH, Wiesbaden, pp. 379– 401. Rayner, G. D. and MacGillivray, H. L. (2002) Numerical maximum likelihood estimation for the g-and-k generalized g-and-h distributions. Statistics and Computing, 12(1), 57– 75. Reshetar, A. (2004) Operational Risk and the Effect of Diversification on Capital. Working Paper. Resnick, S. I. (1987) Extreme Values, Regular Variation, and Point Processes, Springer, New York. Romano, C. and Di Clemente, A. (2003) A Copula Extreme Value Theory Approach for Modeling Operational Risk. Working Paper. Rosenberg, J. V. and Schuermann, T., (2006) A general approach to integrated risk management with skewed, fat-tailed risks. The Journal of Financial Economics, 79(3), 569– 614. Shevchenko, P. and Wuethrich, M. V. (2006) The Structural Modelling of Operational Risk via Bayesian Inference: Combining Loss Data with Expert Opinions . Sklar, A. (1996) Random variables, distribution functions, and copulas – a personal look backward and forward. In Distributions with Fixed Marginals and Related Topics, L Rüschendorff, B. Schweizer and M. Taylor (Eds), Institute of Mathematical Statistics, Hayward, CA, pp. 1– 14. Steinhoff, C. and Baule, R. (2006) How to Validate Op Risk Distributions. Tang, A. and Valdez, E. A. (2006) Economic Capital and the Aggregation of Risks Using Copulas . Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Springer. Wegman, E. J. (1981) Density estimation. In Encyclopedia of Statistical Sciences, S. Kotz and N. L. Johnston (Eds), John Wiley & Sons, Inc. New York, 2, 209– 315. Yasuda, Y. (2003) Application of Bayesian Inference to Operational Risk Management. Measuring Operational and Reputational Risk: A Practitioner's Approach ReferencesRelatedInformation" @default.
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• W4210995917 cites W1987037458 @default.
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