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 602 articles , 1 standard article )

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  1. Bielecki, Tomasz R.; Cialenco, Igor; Pitera, Marcin; Schmidt, Thorsten: Fair estimation of capital risk allocation (2020)
  2. Bonnefont, Michel; Juillet, Nicolas: Couplings in (L^p) distance of two Brownian motions and their Lévy area (2020)
  3. Cheung, K. C.; Yuen, F. L.: On the uncertainty of VaR of individual risk (2020)
  4. Fang, Jun; Jiang, Fan; Liu, Yong; Yang, Jingping: Copula-based Markov process (2020)
  5. Furman, Edward; Hackmann, Daniel; Kuznetsov, Alexey: On log-normal convolutions: an analytical-numerical method with applications to economic capital determination (2020)
  6. Herrmann, Klaus; Hofert, Marius; Mailhot, Mélina: Multivariate geometric tail- and range-value-at-risk (2020)
  7. Hou, Yanxi; Li, Deyuan; Liu, Aiai; Peng, Liang: Jackknife empirical likelihood test for the equality of degrees of freedom in t-copulas (2020)
  8. Liu, Fangda; Cai, Jun; Lemieux, Christiane; Wang, Ruodu: Convex risk functionals: representation and applications (2020)
  9. Liu, Wei; Li, Ying Qiu: Sign-based test for mean vector in high-dimensional and sparse settings (2020)
  10. Naderi, Mehrdad; Hashemi, Farzane; Bekker, Andriette; Jamalizadeh, Ahad: Modeling right-skewed financial data streams: a likelihood inference based on the generalized Birnbaum-Saunders mixture model (2020)
  11. Roozegar, Roohollah; Balakrishnan, Narayanaswamy; Jamalizadeh, Ahad: On moments of doubly truncated multivariate normal mean-variance mixture distributions with application to multivariate tail conditional expectation (2020)
  12. Scholz, Roland W.; Czichos, Reiner; Parycek, Peter; Lampoltshammer, Thomas J.: Organizational vulnerability of digital threats: a first validation of an assessment method (2020)
  13. Staino, Alessandro; Russo, Emilio: Nested conditional value-at-risk portfolio selection: a model with temporal dependence driven by market-index volatility (2020)
  14. Wang, Ruodu; Zitikis, Ričardas: Weak comonotonicity (2020)
  15. Wozabal, David; Rameseder, Gunther: Optimal bidding of a virtual power plant on the Spanish day-ahead and intraday market for electricity (2020)
  16. Zwingmann, Tobias; Holzmann, Hajo: Weak convergence of quantile and expectile processes under general assumptions (2020)
  17. Abadir, Karim M.; Cornea-Madeira, Adriana: Link of moments before and after transformations, with an application to resampling from fat-tailed distributions (2019)
  18. Bahraoui, Zuhair; Bahraoui, M. Amin: Extreme quantiles and tail index of a distribution based on kernel estimator (2019)
  19. Barbeito, Inés; Cao, Ricardo: Smoothed bootstrap bandwidth selection for nonparametric hazard rate estimation (2019)
  20. Botev, Zdravko I.; Salomone, Robert; Mackinlay, Daniel: Fast and accurate computation of the distribution of sums of dependent log-normals (2019)

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