# C8.1 Stochastic Differential Equations - Material for the year 2020-2021

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2020-2021
Lecturer(s):
Dr Benjamin Fehrman
General Prerequisites:

Integration theory: Riemann-Stieljes and Lebesgue integral and their basic properties
Probability and measure theory: $\sigma$-algebras, Fatou lemma, Borel-Cantelli, Radon-Nikodym, $L^p$-spaces, basic properties of random variables and conditional expectation,
Martingales in discrete and continuous time: construction and basic properties of Brownian motion, uniform integrability of stochastic processes, stopping times, filtrations, Doob's theorems (maximal and $L^p$-inequalities, optimal stopping, upcrossing, martingale decomposition), martingale (backward) convergence theorem, $L^2$-bounded martingales, quadratic variation;
Stochastic Integration: Ito’s construction of stochastic integral, Ito’s formula.

Course Term:
Michaelmas
Course Lecture Information:

16 lectures

Course Weight:
1.00 unit(s)
Course Level:
M

### Assessment type:

Course Overview:

Stochastic differential equations (SDEs) model evolution of systems affected by randomness. They offer a beautiful and powerful mathematical language in analogy to what ordinary differential equations (ODEs) do for deterministic systems. From the modelling point of view, the randomness could be an intrinsic feature of the system or just a way to capture small complex perturbations which are not modelled explicitly. As such, SDEs have found many applications in diverse disciplines such as biology, physics, chemistry and the management of risk.
Classic well-posedness theory for ODEs does not apply to SDEs. However, when we replace the classical Newton-Leibnitz calculus with the (Ito) stochastic calculus, we are able to build a new and complete theory of existence and uniqueness of solutions to SDEs. Ito formula proves to be a powerful tool to solve SDEs. This leads to many new and often surprising insights about quantities that evolve under randomness. This course is an introduction to SDEs. It covers the basic theory but also offers glimpses into many of the advanced and nuanced topics.

Learning Outcomes:

By the end of this course, students will be able to analyse if a given SDEs admits a solution, characterise the nature of solution and explain if it is unique or not. The students will also be able to solve basic SDEs and state basic properties of the diffusive systems described by these equations.

Course Synopsis:

Recap on martingale theory in continuous time, quadratic variation, stochastic integration and Ito's calculus.

Levy's characterisation of Brownian motion, stochastic exponential, Girsanov theorem and change of measure, Burkholder-Davis-Gundy, Martingale represenation, Dambis-Dubins-Schwarz.

Strong and weak solutions of stochastic differential equations, existence and uniqueness.

Examples of stochastic differential equations. Bessel processes.

Local times, Tanaka formula, Tanaka-Ito-Meyer formula.