arrow
Return

Pricing futures by deterministic methods

delete2012-04-19
delete0
PRE
AI
O
Olivier Pironneau *
DOI:10.1017/S0962492912000074delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
In this article we will focus on only a small part of financial mathematics, namely the use of partial differential equations for pricing futures. Even within this narrow range it is hard to be systematic and complete, or even to do better than existing books such as Wilmott, Howison and Dewynne (1995), Achdou and Pironneau (2005), or software manuals such as Lapeyre, Martini and Sulem (2010). So this article may be valuable only to the extent that it reflects ten years of teaching, conferences and interaction with the protagonists of financial mathematics. Also, because the theory of partial differential equations is not always well known, we have chosen a pragmatic approach and left out the details of the theory or the proofs of some results, and refer the reader to other books. The numerical algorithms, on the other hand, are given in detail.
Keywords:
STOCHASTIC VOLATILITY
REDUCED-BASIS
OPTIONS
CALIBRATION
DIFFUSION
RETURNS
BLACK
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Acta Numerica cover
Acta Numerica
IF:
11.3
Papers:
89
Citations:
3.4K

Organization

No organization information available
Cited Papers

Cited Papers

No cited papers available