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 566 articles , 1 standard article )

Showing results 1 to 20 of 566.
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  1. Staino, Alessandro; Russo, Emilio: Nested conditional value-at-risk portfolio selection: a model with temporal dependence driven by market-index volatility (2020)
  2. Wozabal, David; Rameseder, Gunther: Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity (2020)
  3. Zwingmann, Tobias; Holzmann, Hajo: Weak convergence of quantile and expectile processes under general assumptions (2020)
  4. Abadir, Karim M.; Cornea-Madeira, Adriana: Link of moments before and after transformations, with an application to resampling from fat-tailed distributions (2019)
  5. Bahraoui, Zuhair; Bahraoui, M. Amin: Extreme quantiles and tail index of a distribution based on kernel estimator (2019)
  6. Calabrese, Raffaella; Osmetti, Silvia Angela: A new approach to measure systemic risk: a bivariate copula model for dependent censored data (2019)
  7. Cossette, Hélène; Marceau, Etienne; Mtalai, Itre: Collective risk models with dependence (2019)
  8. Côté, Marie-Pier; Genest, Christian: Dependence in a background risk model (2019)
  9. Gallaugher, Michael P. B.; McNicholas, Paul D.: Three skewed matrix variate distributions (2019)
  10. Gerber, Hans U.; Shiu, Elias S. W.; Yang, Hailiang: A constraint-free approach to optimal reinsurance (2019)
  11. Gribkova, Nadezhda; Zitikis, Ričardas: Weighted allocations, their concomitant-based estimators, and asymptotics (2019)
  12. Hofert, Marius; Koike, Takaaki: Compatibility and attainability of matrices of correlation-based measures of concordance (2019)
  13. Ignatieva, Katja; Landsman, Zinoviy: Conditional tail risk measures for the skewed generalised hyperbolic family (2019)
  14. Jamalizadeh, Ahad; Balakrishnan, Narayanaswamy: Conditional distributions of multivariate normal mean-variance mixtures (2019)
  15. Kamnitui, N.; Genest, C.; Jaworski, P.; Trutschnig, W.: On the size of the class of bivariate extreme-value copulas with a fixed value of Spearman’s rho or Kendall’s tau (2019)
  16. Kim, Bara; Kim, Jeongsim: Stochastic ordering of Gini indexes for multivariate elliptical risks (2019)
  17. Kim, Nam-Hwui; Browne, Ryan: Subspace clustering for the finite mixture of generalized hyperbolic distributions (2019)
  18. Klüppelberg, Claudia; Seifert, Miriam Isabel: Financial risk measures for a network of individual agents holding portfolios of light-tailed objects (2019)
  19. Koike, Takaaki; Minami, Mihoko: Estimation of risk contributions with MCMC (2019)
  20. Kojadinovic, Ivan; Stemikovskaya, Kristina: Subsampling (weighted smooth) empirical copula processes (2019)

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