Application of RQMC for CDO Pricing with Stochastic Correlations under Nonhomogeneous Assumptions

Shuanghong Qu, Lingxian Meng, Hua Li and Ning (Chris) Chen

Complexity, 2022, vol. 2022, 1-8

Abstract: In consideration of that the correlation between any two assets of the asset pool is always stochastic in the actual market and that collateralized debt obligation (CDO) pricing models under nonhomogeneous assumptions have no semianalytic solutions, we designed a numerical algorithm based on randomized quasi-Monte Carlo (RQMC) simulation method for CDO pricing with stochastic correlations under nonhomogeneous assumptions and took Gaussian factor copula model as an example to conduct experiments. The simulation results of RQMC and Monte Carlo (MC) method were compared from the perspective of variance changes. The results showed that this numerical algorithm was feasible, efficient, and stable for CDO pricing with stochastic correlation under nonhomogeneous assumptions. This numerical algorithm is expected to be extended to other factor Copula models for CDO pricing with stochastic correlations under nonhomogeneous assumptions.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:3243450

DOI: 10.1155/2022/3243450

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