Transform MCMC schemes for sampling intractable factor copula models - Archive ouverte HAL Access content directly
Journal Articles Methodology and Computing in Applied Probability Year : 2023

Transform MCMC schemes for sampling intractable factor copula models

Abstract

In financial risk management, modelling dependency within a random vector X is crucial, a standard approach is the use of a copula model. Say the copula model can be sampled through realizations of Y having copula function C: had the marginals of Y been known, sampling X^(i) , the i-th component of X, would directly follow by composing Y^(i) with its cumulative distribution function (c.d.f.) and the inverse c.d.f. of X^(i). In this work, the marginals of Y are not explicit, as in a factor copula model. We design an algorithm which samples X through an empirical approximation of the c.d.f. of the Y marginals. To be able to handle complex distributions for Y or rare-event computations, we allow Markov Chain Monte Carlo (MCMC) samplers. We establish convergence results whose rates depend on the tails of X, Y and the Lyapunov function of the MCMC sampler. We present numerical experiments confirming the convergence rates and also revisit a real data analysis from financial risk management.
Fichier principal
Vignette du fichier
ConvergenceMarginal_with_MCMC_InverseTransform_prefinal_v5.pdf (1.97 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03334526 , version 1 (03-09-2021)

Identifiers

Cite

Cyril Bénézet, Emmanuel Gobet, Rodrigo Targino. Transform MCMC schemes for sampling intractable factor copula models. Methodology and Computing in Applied Probability, 2023, 25 (1), pp.13. ⟨10.1007/s11009-023-09983-4⟩. ⟨hal-03334526⟩
270 View
155 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More