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 718 articles , 1 standard article )

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  1. Kim, Dowon; Ryu, Heelang; Lee, Jiwoong; Kim, Kyoung-Kuk: Balancing risk: generation expansion planning under climate mitigation scenarios (2022)
  2. Aas, Kjersti; Jullum, Martin; Løland, Anders: Explaining individual predictions when features are dependent: more accurate approximations to Shapley values (2021)
  3. Antonelli, F.; Ramponi, A.; Scarlatti, S.: CVA and vulnerable options pricing by correlation expansions (2021)
  4. Araiza Iturria, Carlos Andrés; Godin, Frédéric; Mailhot, Mélina: Tweedie double GLM loss triangles with dependence within and across business lines (2021)
  5. Arellano-Valle, Reinaldo B.; Azzalini, Adelchi: A formulation for continuous mixtures of multivariate normal distributions (2021)
  6. Avanzi, Benjamin; Taylor, Greg; Wong, Bernard; Yang, Xinda: On the modelling of multivariate counts with Cox processes and dependent shot noise intensities (2021)
  7. Bahraoui, Tarik; Kolev, Nikolai: New measure of the bivariate asymmetry (2021)
  8. Barbiero, Alessandro: Inducing a desired value of correlation between two point-scale variables: a two-step procedure using copulas (2021)
  9. Bäuerle, Nicole; Glauner, Alexander: Minimizing spectral risk measures applied to Markov decision processes (2021)
  10. Bee, Marco; Hambuckers, Julien; Santi, Flavio; Trapin, Luca: Testing a parameter restriction on the boundary for the g-and-h distribution: a simulated approach (2021)
  11. Bee, M.; Hambuckers, J.; Trapin, L.: Estimating large losses in insurance analytics and operational risk using the g-and-h distribution (2021)
  12. Birge, John R.; Chavez-Bedoya, L.: Portfolio optimization under the generalized hyperbolic distribution: optimal allocation, performance and tail behavior (2021)
  13. Bouzebda, Salim; Elhattab, Issam; Nemouchi, Boutheina: On the uniform-in-bandwidth consistency of the general conditional (U)-statistics based on the copula representation (2021)
  14. Brogi, Marina; Lagasio, Valentina; Riccetti, Luca: Systemic risk measurement: bucketing global systemically important banks (2021)
  15. Burzoni, Matteo; Frittelli, Marco; Zorzi, Federico: Short communication: Robust market-adjusted systemic risk measures (2021)
  16. Cai, Jun; Wang, Ying: Optimal capital allocation principles considering capital shortfall and surplus risks in a hierarchical corporate structure (2021)
  17. Chaiyakan, Songkomkrit; Thipwiwatpotjana, Phantipa: Bounds on mean absolute deviation portfolios under interval-valued expected future asset returns (2021)
  18. Chaoubi, Ihsan; Cossette, Hélène; Marceau, Etienne; Robert, Christian Y.: Hierarchical copulas with Archimedean blocks and asymmetric between-block pairs (2021)
  19. Chen, Yiqing; White, Toby; Yuen, Kam Chuen: Precise large deviations of aggregate claims with arbitrary dependence between claim sizes and waiting times (2021)
  20. Eini, Esmat Jamshidi; Khaloozadeh, Hamid: The tail mean-variance optimal portfolio selection under generalized skew-elliptical distribution (2021)

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