Content and teaching | Assessment | Availability

Module content and teaching

Principal aims

To provide both a theoretical and a practical understanding of partial differential equations, including numerical methods, and to link this with problems from Finance. To give an introduction into optimal control and Markov Chain Monte Carlo methods.

Principal learning outcomes

Derive finite-difference formulae for a variety of differential operators on a variety of meshes and obtain the order of accuracy of the approximations; Obtain stability limits for numerical schemes; Know which method is appropriate for a given type of a problem; Understand and be able to program a variety of boundary conditions; Test a program for reliability; Demonstrate analytical skills and critical thinking; Understand the strengths and weaknesses of particular methods used to numerically price derivatives; Ability to choose the appropriate numerical method for a given problem (option pricing).

Departmental link

http://www2.warwick.ac.uk/fac/cross_fac/financial_maths/warwickmsc/outline

Other essential notes

Available for MSc Financial Mathematics students

Module assessment

Assessment group	Assessment name	Percentage
12 CATS (Module code: MA908-12)
C (Assessed/examined work)	Formal Project	50%
	2 hour examination (May)	50%
15 CATS (Module code: MA908-15)
D (Assessed/examined work)	Coursework	20%
	2 hour examination (May)	80%

Module availability

This module is available on the following courses:

Core

• Postgraduate Taught Financial Mathematics (G1P5) - Year 1

Optional Core

N/A

Optional

• Postgraduate Taught Financial Mathematics (N3G1) - Year 1