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2013 | OriginalPaper | Chapter

3. Elements of Stochastic Analysis

Author : Prof. Stéphane Crépey

Published in: Financial Modeling

Publisher: Springer Berlin Heidelberg

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Abstract

Our purpose in this chapter is to give an overview of the basics of stochastic calculus, an important mathematical tool that is used in control engineering, in modern finance and insurance, and in modern management science, among other fields. The chain rule of stochastic calculus, the so-called Itô formula, is one of the most used mathematical (or probabilistic) formulas in the world, since it implicitly sits under every trader’s screen. At least the Itô formula gives rise to one of the mostly posed mathematical exercises, as follows. Let dS t =S t σdW t , starting from today’s observed value for S 0, model the returns of a stock price, where W t is a Brownian motion and σ is the so-called volatility parameter (the “temperature” on financial markets). What are the dynamics for X t =ln(S t )? But this is the celebrated Black–Scholes model! Then we will add jumps, to make it more spicy and because in this book the Brownian motion W t and the Poisson process N t are equally treated on a fair basis, as the prototype and the fundamental driver of continuous and jump processes, respectively. Now, as opposed to the above forward SDE, endowed with an initial condition for S 0 at time 0, it’s now time to consider our first backward SDE. That’s because derivative contracts are defined in terms of a payoff ξ at a future maturity T. This payoff ξ is random and defined in terms of an underlying such as S T , but what we are looking for is the price and the hedge of the derivative at the current pricing time t<T. These price Π t and hedge Δ t are obtained as the solution of a backward SDE such as t t dS t , Π T =ξ. The solution of a BSDE has therefore two components, Π and Δ. In case of American options with early exercise clauses, there is a third component A, intended to maintain the value process Π above the payoff. Otherwise isn’t a BSDE too simple and directly solved by the application of a suitable martingale representation theorem? But that’s only because we forgot the issue of funding our position. Funding costs give rise to an additional term g t (Π t t ) dt in the BSDE. Moreover, since the crisis, in nowadays market environments, funding costs involve some nonlinearities (such as two different rates for lending and borrowing, if you are a risky bank). Dealing with such nonlinearities is precisely what BSDEs were invented for.

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Footnotes
1
Kiyoshi Itô (1915–2008) was awarded the Wolf Prize in Mathematics for his contributions to stochastic analysis in 1987. He was also awarded the first Carl Friedrich Gauss Prize in 2006.
 
2
For the convenience of readers, we signal advanced sections with an asterisk (∗) or even with a double asterisk (∗∗) for the more difficult parts.
 
3
In particular, with almost all trajectories bounded and a bit more than that since locally bounded actually means “locally uniformly bounded”.
 
4
See Sect. 2.​3.​8.
 
5
“Squared-field operators” in English; see Sects. XV.20–26 of Dellacherie and Meyer [95].
 
6
See Remark 3.3.9 regarding the concept of a weak solution.
 
7
Weak solutions are closely related to solutions of the “martingale problem with the data b and σ”, see the comment following Proposition 12.2.2.
 
8
Or of X t , in the case of b and σ, which in view of (3.7) makes no difference in (3.24), by the continuity of t and W t .
 
9
In the strong sense.
 
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Metadata
Title
Elements of Stochastic Analysis
Author
Prof. Stéphane Crépey
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-642-37113-4_3

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