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Titlebook: Copulae in Mathematical and Quantitative Finance; Proceedings of the W Piotr Jaworski,Fabrizio Durante,Wolfgang Karl H?rd Conference procee

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發(fā)表于 2025-3-21 19:58:43 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Copulae in Mathematical and Quantitative Finance
副標(biāo)題Proceedings of the W
編輯Piotr Jaworski,Fabrizio Durante,Wolfgang Karl H?rd
視頻videohttp://file.papertrans.cn/239/238183/238183.mp4
概述A new reference book for copula-based stochastic models in quantitative finance.An up-to-date account about recent developments in copula-based financial models.Includes supplementary material:
叢書(shū)名稱(chēng)Lecture Notes in Statistics
圖書(shū)封面Titlebook: Copulae in Mathematical and Quantitative Finance; Proceedings of the W Piotr Jaworski,Fabrizio Durante,Wolfgang Karl H?rd Conference procee
描述.Copulas are mathematical objects that fully capture the dependence structure among random variables and hence offer great flexibility in building multivariate stochastic models. Since their introduction in the early 1950s, copulas have gained considerable popularity in several fields of applied mathematics, especially finance and insurance. Today, copulas represent a well-recognized tool for market and credit models, aggregation of risks, and portfolio selection. Historically, the Gaussian copula model has been one of the most common models in credit risk. However, the recent financial crisis has underlined its limitations and drawbacks. In fact, despite their simplicity, Gaussian copula models severely underestimate the risk of the occurrence of joint extreme events. Recent theoretical investigations have put new tools for detecting and estimating dependence and risk (like tail dependence, time-varying models, etc) in the spotlight. All such investigations need to be further developed and promoted, a goal this book pursues. The book?includes surveys that provide an up-to-date account of essential aspects of copula models in quantitative finance, as well as the extended versions o
出版日期Conference proceedings 2013
關(guān)鍵詞Gaussian copula model; Random variables; Tail dependence; Time-varying models; quantitative finance
版次1
doihttps://doi.org/10.1007/978-3-642-35407-6
isbn_softcover978-3-642-35406-9
isbn_ebook978-3-642-35407-6Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer-Verlag Berlin Heidelberg 2013
The information of publication is updating

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Strategic Planning of Spectrum and Services,listic graphical models in particular have been ignorant of the framework of copulas. At the same time, the complementing strengths of the two fields suggests the great fruitfulness of a synergy. The purpose of this paper is to survey recent copula-based constructions in the field of machine learnin
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Setting Up Physical Infrastructure,nctions. These copulas are then mixed with respect to a continuous distribution resulting in a nonsingular parametric copula.The Singular Mixture Copulas we construct have a Lebesgue density and in special cases even a closed form representation. Moreover, they have positive lower and upper tail dep
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Strategic Planning of Spectrum and Services, a norm of this random vector, and it is often used in studying heavy-tail phenomena observed in data analysis in various fields, such as finance and insurance. Multivariate regular variation can be analyzed in terms of the intensity measure or spectral measure but can also be studied by using the c
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發(fā)表于 2025-3-23 03:27:33 | 只看該作者
Setting Up Physical Infrastructure,sented. Desirable statistical properties of dependent default times are introduced in an axiomatic manner and related to the unified framework. It is shown how commonly used models, stemming from quite different mathematical and economic motivations, can be translated into a multivariate frailty mod
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發(fā)表于 2025-3-23 05:38:03 | 只看該作者
Setting Up Physical Infrastructure,), which is a multivariate analogue of the univariate generalized linear models (GLMs). A unified framework of such regression models is established with the utility of Gaussian copula, accommodating discrete, continuous and mixed vector outcomes. Both full likelihood and composite likelihood estima
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