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 520 articles , 1 standard article )

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  1. Bahraoui, Zuhair; Bahraoui, M. Amin: Extreme quantiles and tail index of a distribution based on kernel estimator (2019)
  2. Gallaugher, Michael P. B.; McNicholas, Paul D.: Three skewed matrix variate distributions (2019)
  3. Gerber, Hans U.; Shiu, Elias S. W.; Yang, Hailiang: A constraint-free approach to optimal reinsurance (2019)
  4. Jamalizadeh, Ahad; Balakrishnan, Narayanaswamy: Conditional distributions of multivariate normal mean-variance mixtures (2019)
  5. Neumann, André; Bodnar, Taras; Pfeifer, Dietmar; Dickhaus, Thorsten: Multivariate multiple test procedures based on nonparametric copula estimation (2019)
  6. Perreault, Samuel; Duchesne, Thierry; Nešlehová, Johanna G.: Detection of block-exchangeable structure in large-scale correlation matrices (2019)
  7. Tang, Qihe; Tang, Zhaofeng; Yang, Yang: Sharp asymptotics for large portfolio losses under extreme risks (2019)
  8. Wei, Yuhong; Tang, Yang; McNicholas, Paul D.: Mixtures of generalized hyperbolic distributions and mixtures of skew-t distributions for model-based clustering with incomplete data (2019)
  9. Alai, Daniel H.; Landsman, Zinoviy: Lifetime dependence models generated by multiply monotone functions (2018)
  10. Andersen, Lars Nørvang; Laub, Patrick J.; Rojas-Nandayapa, Leonardo: Efficient simulation for dependent rare events with applications to extremes (2018)
  11. Arbenz, Philipp; Cambou, Mathieu; Hofert, Marius; Lemieux, Christiane; Taniguchi, Yoshihiro: Importance sampling and stratification for copula models (2018)
  12. Asimit, Alexandru V.; Li, Jinzhu: Measuring the tail risk: an asymptotic approach (2018)
  13. Asimit, Alexandru V.; Li, Jinzhu: Systemic risk: an asymptotic evaluation (2018)
  14. Aviv, Rom: An extreme-value theory approximation scheme in reinsurance and insurance-linked securities (2018)
  15. Barnard, Roger W.; Pearce, Kent; Trindade, A. Alexandre: When is tail mean estimation more efficient than tail median? Answers and implications for quantitative risk management (2018)
  16. Bee, Marco; Dickson, Maria Michela; Santi, Flavio: Likelihood-based risk estimation for variance-gamma models (2018)
  17. Benlagha, Noureddine; Hemrit, Wael: The dynamic and dependence of takaful and conventional stock return behaviours: evidence from the insurance industry in Saudi Arabia (2018)
  18. Berkhouch, Mohammed; Lakhnati, Ghizlane; Righi, Marcelo Brutti: Extended Gini-type measures of risk and variability (2018)
  19. Bernard, Carole; Bondarenko, Oleg; Vanduffel, Steven: Rearrangement algorithm and maximum entropy (2018)
  20. Bhati, Deepesh; Ravi, Sreenivasan: On generalized log-Moyal distribution: a new heavy tailed size distribution (2018)

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