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 763 articles , 1 standard article )

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  1. Bagnato, Luca; Punzo, Antonio; Zoia, Maria Grazia: Leptokurtic moment-parameterized elliptically contoured distributions with application to financial stock returns (2022)
  2. Blier-Wong, Christopher; Cossette, Hélène; Marceau, Etienne: Stochastic representation of FGM copulas using multivariate Bernoulli random variables (2022)
  3. Boudabsa, Lotfi; Filipović, Damir: Machine learning with kernels for portfolio valuation and risk management (2022)
  4. Cheung, Ka Chun; Yam, Sheung Chi Phillip; Zhang, Yiying: Satisficing credibility for heterogeneous risks (2022)
  5. Coblenz, Maximilian; Grothe, Oliver; Herrmann, Klaus; Hofert, Marius: Smooth bootstrapping of copula functionals (2022)
  6. Eini, Esmat Jamshidi; Khaloozadeh, Hamid: Tail variance for Generalized Skew-Elliptical distributions (2022)
  7. Embrechts, Paul; Schied, Alexander; Wang, Ruodu: Robustness in the optimization of risk measures (2022)
  8. Erik Hintz, Marius Hofert, Christiane Lemieux: Multivariate Normal Variance Mixtures in R: The R Package nvmix (2022) not zbMATH
  9. Flores, Yuri Salazar; Díaz-Hernández, Adán: The general tail dependence function in the Marshall-Olkin and other parametric copula models with an application to financial time series (2022)
  10. Fontana, Roberto; Semeraro, Patrizia: Computational and analytical bounds for multivariate Bernoulli distributions (2022)
  11. Hasan, Mirza Nazmul; Braekers, Roel: Modelling the association in bivariate survival data by using a Bernstein copula (2022)
  12. Hediger, Simon; Michel, Loris; Näf, Jeffrey: On the use of random forest for two-sample testing (2022)
  13. Jaunė, Eglė; Šiaulys, Jonas: Asymptotic risk decomposition for regularly varying distributions with tail dependence (2022)
  14. Kaya, Davy Cardorel Nzaba; Kulik, Rafal; Banzouzi, Bernédy Nel Messie Kodia: Comparison of estimation methods for value-at-risk (2022)
  15. Kim, Dowon; Ryu, Heelang; Lee, Jiwoong; Kim, Kyoung-Kuk: Balancing risk: generation expansion planning under climate mitigation scenarios (2022)
  16. Kim, Sojung; Weber, Stefan: Simulation methods for robust risk assessment and the distorted mix approach (2022)
  17. Lodi, Andrea; Malaguti, Enrico; Nannicini, Giacomo; Thomopulos, Dimitri: Nonlinear chance-constrained problems with applications to hydro scheduling (2022)
  18. Meng, Jin; Chan, Kung-Sik: Penalized quasi-likelihood estimation of generalized Pareto regression -- consistent identification of risk factors for extreme losses (2022)
  19. Meraou, Mohammed A.; Al-Kandari, Noriah M.; Raqab, Mohammad Z.; Kundu, Debasis: Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims (2022)
  20. Pitera, Marcin; Schmidt, Thorsten: Estimating and backtesting risk under heavy tails (2022)

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