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 668 articles , 1 standard article )

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  1. Hou, Yanxi; Li, Deyuan; Liu, Aiai; Peng, Liang: Jackknife empirical likelihood test for the equality of degrees of freedom in t-copulas (2020)
  2. Ji, Min; Aminzadeh, Mostafa; Deng, Min: Predictive modeling of threshold life tables (2020)
  3. Koutras, Vasileios M.; Koutras, Markos V.: Exact distribution of random order statistics and applications in risk management (2020)
  4. Koutsouri, Aikaterini; Petch, Michael; Knottenbelt, William J.: Stress testing diversified portfolios: the case of the CoinShares gold and cryptoassets index (2020)
  5. Lehtomaa, Jaakko; Resnick, Sidney I.: Asymptotic independence and support detection techniques for heavy-tailed multivariate data (2020)
  6. Leitao, Álvaro; Ortiz-Gracia, Luis: Model-free computation of risk contributions in credit portfolios (2020)
  7. Liu, Fangda; Cai, Jun; Lemieux, Christiane; Wang, Ruodu: Convex risk functionals: representation and applications (2020)
  8. Liu, Wei; Li, Ying Qiu: Sign-based test for mean vector in high-dimensional and sparse settings (2020)
  9. Mao, Tiantian; Wang, Ruodu: Risk aversion in regulatory capital principles (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. Neumann, André; Dickhaus, Thorsten: Nonparametric Archimedean generator estimation with implications for multiple testing (2020)
  12. Nitithumbundit, Thanakorn; Chan, Jennifer S. K.: ECM algorithm for auto-regressive multivariate skewed variance gamma model with unbounded density (2020)
  13. Paraschiv, Florentina; Reese, Stine Marie; Skjelstad, Margrethe Ringkjøb: Portfolio stress testing applied to commodity futures (2020)
  14. 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)
  15. Scholz, Roland W.; Czichos, Reiner; Parycek, Peter; Lampoltshammer, Thomas J.: Organizational vulnerability of digital threats: a first validation of an assessment method (2020)
  16. Shushi, Tomer; Yao, Jing: Multivariate risk measures based on conditional expectation and systemic risk for exponential dispersion models (2020)
  17. Sousa, J. Beleza; Esquível, Manuel L.; Gaspar, Raquel M.: Pulled-to-par returns for zero-coupon bonds historical simulation value at risk (2020)
  18. Staino, Alessandro; Russo, Emilio: Nested conditional value-at-risk portfolio selection: a model with temporal dependence driven by market-index volatility (2020)
  19. Sun, Ning; Yang, Chen; Zitikis, Ričardas: A statistical methodology for assessing the maximal strength of tail dependence (2020)
  20. Tadese, Mekonnen; Drapeau, Samuel: Relative bound and asymptotic comparison of expectile with respect to expected shortfall (2020)

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