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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Stochastic Control and Dynamic Asset Allocation (MATH11150)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThe course presents an introduction to control theory and its applications. This is an active area of research, both in pure and applied mathematics. The applications are in engineering, finance and economics. The course focuses on developing methods for solving control problems and offers an opportunity to see the connections between different fields, (controlled dynamical systems, optimization, nonlinear PDEs), and the underlying ideas unifying them.
Course description - Discrete time case: Controlled Markov chains, backward induction,
optimal stopping in discrete time.
- Continuous time case: Controlled ODEs, Controlled diffusion processes
- Bellman principle, Hamilton-Jacobi-Bellman equations and verification theorems
- Pontryagin optimality criteria and backward stochastic differential equations
- Applications in finance and economics: Merton's investment problem, optimal execution problems, optimal production, linear-quadratic control problems
- Algorithms for computationally solving control problems: policy iteration, value iteration, method of successive approximation.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Probability, Measure & Finance (MATH10024) OR Stochastic Analysis in Finance (MATH11154)
Co-requisites Students MUST also take: Python Programming (MATH11199) OR Object-Oriented Programming with Applications (MATH11152)
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 16, Seminar/Tutorial Hours 4, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 78 )
Assessment (Further Info) Written Exam 80 %, Coursework 20 %, Practical Exam 0 %
Additional Information (Assessment) Examination 80%
Coursework: 20%
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)Stochastic Control and Dynamic Asset Allocation (MATH11150)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Solve problems involving controlled Markov Chains using the dynamic programming and backward induction.
  2. Solve problems involving controlled SDEs using the Bellman PDE and be able to justify the use of the Bellman PDE in such problems by reference to relevant proofs.
  3. Solve problems involving controlled ODEs or SDEs using the Pontryagin optimality principle and be able to justify the use of Pontryagin optimality in such problems by reference to relevant proofs.
  4. Implement policy iteration / value iteration or method of successive approximation algorithms using an appropriate programming language (e.g. Python) to solve control problems numerically.
Reading List
-H. Pham: Continuous-time stochastic control and optimization with financial applications, Series SMAP, Springer 2009.
-D. Bertsekas: Dynamic Programming and Optimal Control, Vols. I and II,┬┐Athena Scientific, 1995, (4th Edition Vol. I, 2017, 4th Edition Vol. II, 2012).┬┐
- A. Cartea, S. Jaimungal, and J. Penalva. Algorithmic and High-Frequency Trading. Cambridge University Press, 2015.
Additional Information
Graduate Attributes and Skills Not entered
Special Arrangements MSc Financial Modelling and Optimization and MSc Computational Mathematical Finance students only.
Course organiserDr David Siska
Tel: (0131 6)51 9091
Course secretaryMiss Gemma Aitchison
Tel: (0131 6)50 9268
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