Advanced Topics in Stochastic Analysis

6 EC

Semester 1, period 1, 2

5334ATIS6Y

Owner Master Mathematics
Coordinator dr. A. Khedher
Part of Master Mathematics, year 1Master Stochastics and Financial Mathematics, year 1

Course manual 2018/2019

Course content

The central theme of this course is backwards stochastic differential equations (BSDEs). These are stochastic differential equations for which not the initial, but the final value is given. Although this seems like a small difference, the implications are severe as the solution must satisfy adaptivity conditions.The motivation for studying BSDEs comes from, e.g., stochastic control and hedging problems in finance. Another application lies in the link between BSDEs and non-linear PDEs, given by the extension of a Feynman-Kac formula.
Topics that are covered in this course are:- well-posedness of BSDEs with Lipschitz coefficients- the relation between BSDEs and (deterministic) PDEs- examples of BSDEs arising from applications- Malliavin calculus: this involves the question of taking derivatives with respect to a Brownian motion and allows us to analyse the regularity of a (B)SDE.- approximation of BSDEs by numerical methods-well-posedness of BSDEs with quadratic coefficients.

Study materials

Literature

  • lecture notes for the first part of the lecture and slides for the second part (made available at the first lecture)

    David Nualart, ' The Malliavin Calculus and Related Topics', Springer, ISBN 978-3-540-28329-4 (not necessary to buy this).

    Huyên Pham, 'Continuous-time stochastic control and optimization with financial applications', Springer, ISBN 978-3-540-89500-8 (not necessary to buy this)

    https://www.sciencedirect.com/science/article/pii/S0304414904000031

Other

Objectives

  1. The student is able to understand the theory of BSDEs
  2. The student is able to apply the theory of BSDEs in hedging problems and control theory in finance
  3. The student is able to explain the relation between BSDEs and non-linear PDEs
  4. The student is able to understand the theory of Malliavin calculus 
  5. The student is able to apply the theory of Malliavin calculus on forward stochastic differential equations and BSDEs
  6. The student is able to introduce discretization techniques for BSDEs and use the Malliavin calculus to compute convergence rates for the approximation  
  7. The student is able to solve BSDEs numerically and use this to compute hedging positions in financial contracts

Teaching methods

  • Lecture

Learning activities

Activity

Hours

Hoorcollege

28

Oral exam

1

Self study

139

Total

168

(6 EC x 28 uur)

Attendance

The programme does not have requirements concerning attendance (OER-B).

Assessment

Item and weight Details

Final grade

60%

Oral exam

40%

Assignments

There will be an oral exam for each part of the course. Sonja Cox will give an oral exam for the first part of the course. Asma Khedher will give an oral exam for the second part of the course. To have the exam, you make an appointment with the lecturers. 

What do you have to know? The theory and homework, i.e. all important definitions and results (lemma's, theorems, etc.). The details about which theory will be discussed two weeks prior to the exam. 

The final grade is a combination of the results of the take home assignments (which counts 40 %) and the first and second oral exam (together 60%). To pass the course the average grade of both oral exams should be higher than 5.0.

Assignments

During the course, the students will have to hand in a total of six sets of homework. The assignments will contain theoretical and numerical exercises. The average homework grade will count for 40% in the final grade

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Weeknummer Onderwerpen Studiestof
1 Stochastic integration recapitulation Chapter 1 (first take home assignment)
2 Introduction and well posedness of BSDEs  Chapter 2, Sections 2.1-2.2
3 Relation between BSDEs and non-linear PDEs Chapter 2, Sections 2.3-2.5 (second take home assignment and deadline of the first one)
4 Wiener-chaos decomposition Chapter 3, Section 3.1
5 Malliavin derivative Chapter 3, Section 3.2 (third take home assignment and deadline of the second one)
6 Divergence operator Chapter 3, Section 3.3
7 Malliavin calculus for SDEs  Chapter 3, Section 3.4 (deadline for the third take home assignment) 
8    
9    
10    
11    
12    
13    
14    
15    
16    

Timetable

The schedule for this course is published on DataNose.

Contact information

Coordinator

  • dr. A. Khedher