Pricing quanto options with market liquidity risk.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2023
Historique:
received: 15 02 2023
accepted: 18 09 2023
medline: 2 10 2023
pubmed: 28 9 2023
entrez: 28 9 2023
Statut: epublish

Résumé

This paper investigates the pricing problem of quanto options with market liquidity risk using the Bayesian method. The increasing volatility of global financial markets has made liquidity risk a significant factor that should be taken into consideration while evaluating option prices. To address this issue, we first derive the pricing formula for quanto options with liquidity risk. Next, we construct a likelihood function to conduct posterior inference on model parameters. We then propose a numerical algorithm to conduct statistical inferences on the option prices based on the posterior distribution. This proposed method considers the impact of parameter uncertainty on option prices. Finally, we conduct a comparison between the Bayesian method and traditional estimation methods to examine their validity. Empirical results show that our proposed method is feasible for pricing and predicting quanto options with liquidity risk, particularly for parameter estimations with a small sample size.

Identifiants

pubmed: 37768985
doi: 10.1371/journal.pone.0292324
pii: PONE-D-23-03660
pmc: PMC10538701
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0292324

Informations de copyright

Copyright: © 2023 Gao, Bai. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Rui Gao (R)

School of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan, China.

Yanfei Bai (Y)

School of Insurance, Shandong University of Finance and Economics, Jinan, China.

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