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We consider the intensity-based approach for the modeling of default times of one or more companies. In this approach the default times are defined as the jump times of a Cox process, which is a Poisson process conditional on the…

Computational Finance · Quantitative Finance 2008-12-02 Vincent Leijdekker , Peter Spreij

The non-gaussianity of processes observed in financial markets and relatively good performance of gaussian models can be reconciled by replacing the Brownian motion with Levy processes whose Levy densities decay as exp(-lambda|x|) or…

Statistical Mechanics · Physics 2008-12-02 Sergei Levendorskii

The utility-based pricing of defaultable bonds in the case of stochastic intensity models of default risk is discussed. The Hamilton-Jacobi- Bellman (HJB) equations for the value functions is derived. A finite difference method is used to…

Computational Finance · Quantitative Finance 2010-03-23 Regis Houssou , Olivier Besson

We build a general model for pricing defaultable claims. In addition to the usual absence of arbitrage assumption, we assume that one defaultable asset (at least) looses value when the default occurs. We prove that under this assumption, in…

Pricing of Securities · Quantitative Finance 2010-05-04 Delia Coculescu

This paper presents comparison results and establishes risk bounds for credit portfolios within classes of Bernoulli mixture models, assuming conditionally independent defaults that are stochastically increasing with a common risk factor.…

Risk Management · Quantitative Finance 2025-12-24 Jonathan Ansari , Eva Lütkebohmert

For the analysis of clustered survival data, two different types of models that take the association into account, are commonly used: frailty models and copula models. Frailty models assume that conditional on a frailty term for each…

Methodology · Statistics 2014-01-10 Leen Prenen , Roel Braekers , Luc Duchateau

This paper introduces a novel stochastic model for credit spreads. The stochastic approach leverages the diffusion of default intensities via a CIR++ model and is formulated within a risk-neutral probability space. Our research primarily…

Risk Management · Quantitative Finance 2026-01-09 Mohamed Ben Alaya , Ahmed Kebaier , Djibril Sarr

Copulas are essential tools in statistics and probability theory, enabling the study of the dependence structure between random variables independently of their marginal distributions. Among the various types of copulas, Ratio-Type Copulas…

Statistics Theory · Mathematics 2025-05-21 Ziad Adwan , Nicola Sottocornola

Time-to-event semi-competing risk endpoints may be correlated when both events are occurring on the same individual. These events and the association between them may also be influenced by individual characteristics. In this paper, we…

Methodology · Statistics 2023-04-07 Yinghui Wei , Malgorzata Wojtys , Lexy Sorrell , Peter Rowe

Implicit copulas are the most common copula choice for modeling dependence in high dimensions. This broad class of copulas is introduced and surveyed, including elliptical copulas, skew $t$ copulas, factor copulas, time series copulas and…

Methodology · Statistics 2021-09-13 Michael Stanley Smith

In conditional copula models, the copula parameter is deterministically linked to a covariate via the calibration function. The latter is of central interest for inference and is usually estimated nonparametrically. However, when a…

Methodology · Statistics 2014-03-19 Elif F. Acar , Radu V. Craiu , Fang Yao

We propose a new class of extreme-value copulas which are extreme-value limits of conditional normal models. Conditional normal models are generalizations of conditional independence models, where the dependence among observed variables is…

Methodology · Statistics 2021-02-16 Pavel Krupskii , Marc G. Genton

In the paper [Hainaut, D. and Colwell, D.B., {\rm A structural model for credit risk with switching processes and synchronous jumps}, The European Journal of Finance 22(11) (2016): 1040-1062], the authors exploit a synchronous-jump…

Numerical Analysis · Mathematics 2021-12-14 Davood Damircheli , Mohsen Razzaghi , Seyed-Mohammad-Mahdi Kazemi , Ali Foroush Bastani

Dependence strucuture estimation is one of the important problems in machine learning domain and has many applications in different scientific areas. In this paper, a theoretical framework for such estimation based on copula and copula…

Machine Learning · Computer Science 2019-09-11 Jian Ma , Zengqi Sun

Weak convergence of the empirical copula process is shown to hold under the assumption that the first-order partial derivatives of the copula exist and are continuous on certain subsets of the unit hypercube. The assumption is…

Statistics Theory · Mathematics 2012-07-06 Johan Segers

We investigate in multidimensional compound Poisson processes (CPP) the relation between the dependence structure of the jump distribution and the dependence structure of the respective components of the CPP itself. For this purpose the…

Statistics Theory · Mathematics 2014-10-01 Christian Palmes

Prior elicitation methods for Bayesian analyses transfigure prior information into quantifiable prior distributions. Recently, methods that leverage copulas have been proposed to accommodate more flexible dependence structures when…

Methodology · Statistics 2024-11-22 Luke Hagar , Nathaniel T. Stevens

Under the International Financial Reporting Standards (IFRS) 9, credit losses ought to be recognised timeously and accurately. This requirement belies a certain degree of dynamicity when estimating the constituent parts of a credit loss…

Risk Management · Quantitative Finance 2025-12-16 Arno Botha , Tanja Verster

Analysing dependent risks is an important task for insurance companies. A dependency is reflected in the fact that information about one random variable provides information about the likely distribution of values of another random…

Applications · Statistics 2021-03-22 Sen Hu , Adrian O'Hagan

Thanks to their ability to capture complex dependence structures, copulas are frequently used to glue random variables into a joint model with arbitrary marginal distributions. More recently, they have been applied to solve statistical…

Methodology · Statistics 2022-08-22 Thomas Nagler , Thibault Vatter