Cookies?
Library Header Image
LSE Research Online LSE Library Services

Diffusion transformations, Black-Scholes equation and optimal stopping

Cetin, Umut ORCID: 0000-0001-8905-853X (2018) Diffusion transformations, Black-Scholes equation and optimal stopping. Annals of Applied Probability, 28 (5). pp. 3102-3151. ISSN 1050-5164

[img] Text - Published Version
Download (468kB)
[img]
Preview
Text - Accepted Version
Download (678kB) | Preview

Identification Number: 10.1214/18-AAP1385

Abstract

We develop a new class of path transformations for one-dimensional diffusions that are tailored to alter their long-run behaviour from transient to recurrent or vice versa. This immediately leads to a formula for the distribution of the first exit times of diffusions, which is recently characterised by Karatzas and Ruf [26] as the minimal solution of an appropriate Cauchy problem under more stringent conditions. A particular limit of these transformations also turn out to be instrumental in characterising the stochastic solutions of Cauchy problems defined by the generators of strict local martingales, which are well-known for not having unique solutions even when one restricts solutions to have linear growth. Using an appropriate diffusion transformation we show that the aforementioned stochastic solution can be written in terms of the unique classical solution of an alternative Cauchy problem with suitable boundary conditions. This in particular resolves the long-standing issue of non-uniqueness with the Black-Scholes equations in derivative pricing in the presence of bubbles. Finally, we use these path transformations to propose a unified framework for solving explicitly the optimal stopping problem for one-dimensional diffusions with discounting, which in particular is relevant for the pricing and the computation of optimal exercise boundaries of perpetual American options.

Item Type: Article
Official URL: http://www.imstat.org/aap/
Additional Information: © 2018 Institute of Mathematical Statistics
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 16 Mar 2018 15:27
Last Modified: 13 Sep 2024 13:12
URI: http://eprints.lse.ac.uk/id/eprint/87261

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics