Abstract
The goal of the paper is the numerical analysis of the performance of Monte Carlo simulation based methods for the computation of credit-portfolio loss-distributions in the context of Markovian intensity models of credit risk. We concentrate on two of the most frequently touted methods of variance reduction in the case of stochastic processes: importance sampling (IS) and interacting particle systems (IPS) based algorithms. Because the subtle differences between these methods are often misunderstood, as IPS is often regarded as a mere particular case of IP, we describe in detail the two kinds of algorithms, and we highlight their fundamental differences. We then proceed to a detailed comparative case study based on benchmark numerical experiments chosen for their popularity in the quantitative finance circles.
Original language | English (US) |
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Pages (from-to) | 577-602 |
Number of pages | 26 |
Journal | International Journal of Theoretical and Applied Finance |
Volume | 13 |
Issue number | 4 |
DOIs | |
State | Published - Jun 2010 |
All Science Journal Classification (ASJC) codes
- General Economics, Econometrics and Finance
- Finance
Keywords
- Importance sampling
- credit portfolios, loss distribution estimation
- interacting particle systems
- rare events