Abstract
This paper considers a general reduced form pricing model for
credit derivatives where default intensities are driven by some factor process
X. The process X is not directly observable for investors in secondary markets;
rather, their information set consists of the default history and of noisy price
observation for traded credit products. In this context the pricing of credit
derivatives leads to a challenging nonlinear filtering problem. We provide recursive
updating rules for the filter, derive a finite dimensional filter for the
case where X follows a finite state Markov chain and propose a novel particle filtering algorithm. A numerical case study illustrates the properties of the
proposed algorithms.
credit derivatives where default intensities are driven by some factor process
X. The process X is not directly observable for investors in secondary markets;
rather, their information set consists of the default history and of noisy price
observation for traded credit products. In this context the pricing of credit
derivatives leads to a challenging nonlinear filtering problem. We provide recursive
updating rules for the filter, derive a finite dimensional filter for the
case where X follows a finite state Markov chain and propose a novel particle filtering algorithm. A numerical case study illustrates the properties of the
proposed algorithms.
Originalsprache | Englisch |
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Seiten (von - bis) | 495 - 526 |
Fachzeitschrift | Finance and Stochastics |
Jahrgang | 14 |
Ausgabenummer | 4 |
Publikationsstatus | Veröffentlicht - 1 Mai 2010 |