Managing Operational Risk: Methodology and Prospects
作者： Grigory Temnov
DOI: 10.1007/978-3-540-69532-5_23
关键词:
摘要: In the present paper we describe a combined methodology used for modelling and measuring operational risk. Our basic aim is to choose adjust an efficient combination of techniques in order cover range problems associated with OpRisk justify our choice. We analyze each part briefly overview some recent results, as well prospects future research.
springer.com
sci-hub.st 参考文章(27)
Stefan Mittnik, Svetlozar T. Rachev, Stable Paretian Models in Finance ,(2000)
Gordon E. Willmot, Harry H. Panjer, Stuart A. Klugman, Loss Models: From Data to Decisions, 2nd edition ,(2004)
Marcelo G. Cruz, Modeling, Measuring and Hedging Operational Risk ,(2002)
C Embrechts, P., Klüppelberg, T Mikosch, Modelling Extremal Events: for Insurance and Finance ,(1997)
Stuart Coles, An Introduction to Statistical Modeling of Extreme Values ,(2001)
Alexander J. McNeil, Paul Embrechts, Rdiger Frey, Quantitative Risk Management: Concepts, Techniques, and Tools ,(2005)
Paul Glasserman, Monte Carlo Methods in Financial Engineering ,(2003)
Antoine Frachot, Thierry Roncalli, Mixing Internal and External Data for Managing Operational Risk Social Science Research Network. ,(2002) , 10.2139/SSRN.1032525
Nicolas Baud, Antoine Frachot, Thierry Roncalli, Internal Data, External Data and Consortium Data - How to Mix Them for Measuring Operational Risk Social Science Research Network. ,(2002) , 10.2139/SSRN.1032529
P. SCHALLER, G. TEMNOV, Efficient And Precise Computation Of Convolutions: Applying Fft To Heavy Tailed Distributions Computational Methods in Applied Mathematics Comput. ,vol. 8, pp. 187- 200 ,(2008) , 10.2478/CMAM-2008-0013