Open AccessBook
Quantitative Risk Management
TLDR
The book’s methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics, to satisfactorily address extreme outcomes and the dependence of key risk drivers.Abstract:
Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book’s methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives.read more
Citations
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Journal ArticleDOI
Goodness-of-fit tests for copulas: A review and a power study
TL;DR: In this paper, the authors present a critical review of the blanket test procedures and suggest new ones for goodness-of-fit testing of copula models, and describe and interpret the results of a large Monte Carlo experiment designed to assess the effect of the sample size and the strength of dependence on the level and power of blanket tests for various combinations of Copula models under the null hypothesis and the alternative.
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Globally networked risks and how to respond
TL;DR: A ‘Global Systems Science’ might create the required knowledge and paradigm shift in thinking to make man-made systems manageable.
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Generalized autoregressive score models with applications
TL;DR: A unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models, referred to as Generalized Autoregressive Score (GAS) models, which encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity.
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Modeling Multivariate Distributions with Continuous Margins Using the copula R Package
Ivan Kojadinovic,Jun Yan +1 more
TL;DR: The copula-based modeling of multivariate distributions with continuous margins is presented as a succession of rank-based tests: a multivariate test of randomness followed by a test of mutual independence and a series of goodness-of-fit tests.
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Dynamic Conditional Correlation: On Properties and Estimation
TL;DR: It is proved that the DCC large system estimator (DCC estimator) can be inconsistent, and that the traditional interpretation of the D CC correlation parameters can lead to misleading conclusions, and a more tractable dynamic conditional correlation model (cDCC model) is suggested.
References
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Journal ArticleDOI
Limiting forms of the frequency distribution of the largest or smallest member of a sample
R. A. Fisher,L. H. C. Tippett +1 more
TL;DR: In this article, the problem of finding the appropriate limiting distribution in any case may be found from the manner in which the probability of exceeding any value x tends to zero as x is increased.
Journal ArticleDOI
Modelling Extremal Events for Insurance and Finance
TL;DR: In this article, Modelling Extremal Events for Insurance and Finance is discussed. But the authors focus on the modeling of extreme events for insurance and finance, and do not consider the effects of cyber-attacks.
Book
Quantitative Risk Management: Concepts, Techniques, and Tools
TL;DR: The most comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management can be found in this paper, where the authors describe the latest advances in the field, including market, credit and operational risk modelling.
Book
Value At Risk: The New Benchmark for Managing Financial Risk
TL;DR: The Value at Risk approach has become the industry standard in risk management as mentioned in this paper, and it has been widely used in the finance community for many years, including in the financial domain.