Importance Sampling for a Simple Markovian Intensity Model Using Subsolutions

Autor:	Boualem Djehiche, Henrik Hult, Pierre Nyquist
Rok vydání:	2022
Předmět:	Modeling and Simulation Probability (math.PR) FOS: Mathematics Primary 65C05 60J75 secondary 91G40 91G60 Mathematics - Probability Computer Science Applications
Zdroj:	ACM Transactions on Modeling and Computer Simulation. 32:1-25
ISSN:	1558-1195 1049-3301
DOI:	10.1145/3502432
Popis:	This paper considers importance sampling for estimation of rare-event probabilities in a specific collection of Markovian jump processes used for e.g. modelling of credit risk. Previous attempts at designing importance sampling algorithms have resulted in poor performance and the main contribution of the paper is the design of efficient importance sampling algorithms using subsolutions. The dynamics of the jump processes causes the corresponding Hamilton-Jacobi equations to have an intricate state-dependence, which makes the design of efficient algorithms difficult. We provide theoretical results that quantify the performance of importance sampling algorithms in general and construct asymptotically optimal algorithms for some examples. The computational gain compared to standard Monte Carlo is illustrated by numerical examples. Comment: 25 pages; to appear in ACM TOMACS
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::26b3cb801b3c9ed1ce34bad84086560f https://doi.org/10.1145/3502432 Zobrazit plný text záznamu