QRM

R package QRM: Provides R-language Code to Examine Quantitative Risk Management Concepts. This package is designed to accompany the book Quantitative Risk Management: Concepts, Techniques and Tools by Alexander J. McNeil, Rudiger Frey, and Paul Embrechts: This book is primarily a textbook for courses in quantitative risk management (QRM) aimed at advanced undergraduate or graduate students and professionals from the financial industry. The book has a secondary function as a reference text for risk professionals interested in a clear and concise treatment of concepts and techniques used on practice. Different courses can be devised based on different chapters of the book: market risk, credit risk, operational risk, risk-measurement and aggregation concepts, risk-management techniques for financial econometricians. Material from various chapters could be used as interesting examples for statistics courses on subjects like multivariate analysis, time series analysis and generalized linear modelling.


References in zbMATH (referenced in 260 articles , 1 standard article )

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  1. Belzile, Léo R.; Nešlehová, Johanna G.: Extremal attractors of Liouville copulas (2017)
  2. Cambou, Mathieu; Hofert, Marius; Lemieux, Christiane: Quasi-random numbers for copula models (2017)
  3. Du, Jiangze; Lai, Kin Keung: Copula-based risk management models for multivariable RMB exchange rate in the process of RMB internationalization (2017)
  4. Murray, Paula M.; Browne, Ryan P.; McNicholas, Paul D.: Hidden truncation hyperbolic distributions, finite mixtures thereof, and their application for clustering (2017)
  5. Su, Jianxi; Furman, Edward: Multiple risk factor dependence structures: distributional properties (2017)
  6. Allen, D.E.; Powell, R.J.; Singh, A.K.: Take it to the limit: innovative CVaR applications to extreme credit risk measurement (2016)
  7. Artikis, Panagiotis T.: Deriving advantage over a crisis by incorporating a new class of stochastic models for risk control operations (2016)
  8. Asimit, Alexandru V.; Gerrard, Russell; Hou, Yanxi; Peng, Liang: Tail dependence measure for examining financial extreme co-movements (2016)
  9. Bae, Taehan; Iscoe, Ian: On the limit of conditional Spearman’s rho under the common factor model (2016)
  10. Bignozzi, Valeria; Mao, Tiantian; Wang, Bin; Wang, Ruodu: Diversification limit of quantiles under dependence uncertainty (2016)
  11. Bücher, Axel; Kojadinovic, Ivan: An overview of nonparametric tests of extreme-value dependence and of some related statistical procedures (2016)
  12. Chan, Stephen; Nadarajah, Saralees; Afuecheta, Emmanuel: An R package for value at risk and expected shortfall (2016)
  13. C^oté, Marie-Pier; Genest, Christian; Abdallah, Anas: Rank-based methods for modeling dependence between loss triangles (2016)
  14. Das, Bikramjit; Ghosh, Souvik: Detecting tail behavior: mean excess plots with confidence bounds (2016)
  15. Durante, Fabrizio; Girard, Stéphane; Mazo, Gildas: Marshall-Olkin type copulas generated by a global shock (2016)
  16. Embrechts, Paul; Jakobsons, Edgars: Dependence uncertainty for aggregate risk: examples and simple bounds (2016)
  17. Ignatieva, Katja; Trück, Stefan: Modeling spot price dependence in Australian electricity markets with applications to risk management (2016)
  18. Landsman, Zinoviy; Makov, Udi; Shushi, Tomer: Multivariate tail conditional expectation for elliptical distributions (2016)
  19. Lehmann, Christoph; Tillich, Daniel: Consensus information and consensus rating. A note on methodological problems of rating aggregation (2016)
  20. Liao, Xin; Peng, Liang; Peng, ZuoXiang; Zheng, YanTing: Dynamic bivariate normal copula (2016)

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