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ST908 Probability and Stochastic Processes

Please note that all lectures for Statistics modules taught in the 2022-23 academic year will be delivered on campus, and that the information below relates only to the hybrid teaching methods utilised in 2021-22 as a response to Coronavirus. We will update the Additional Information (linked on the right side of this page) prior to the start of the 2022/23 academic year.

Throughout the 2021-22 academic year, we will be adapting the way we teach and assess your modules in line with government guidance on social distancing and other protective measures in response to Coronavirus. Teaching will vary between online and on-campus delivery through the year, and you should read the additional information linked on the right hand side of this page for details of how this will work for this module. The contact hours shown in the module information below are superseded by the additional information.You can find out more about the University’s overall response to Coronavirus at:

All dates for assessments for Statistics modules, including coursework and examinations, can be found in the Statistics Assessment Handbook at

ST908-15 Stochastic Calculus for Finance

Academic year
Taught Postgraduate Level
Module leader
Martin Herdegen
Credit value
Module duration
10 weeks
20% coursework, 80% exam
Study location
University of Warwick main campus, Coventry
Introductory description

This module runs in Term 1 and is core for students on the MSc in Mathematical Finance.
PhD students interested in taking the module should consult the module leader.
This module is not available to undergraduate students.

Module web page

Module aims

This module provides a thorough introduction into discrete-time martingale theory, Brownian motion, and stochastic calculus, illustrated by examples from Mathematical Finance.

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

1 Conditional expectations
(a) Elementary conditional expectations
(b) Measure-theoretic conditional expectations
(c) Properties of conditional expectations
2 Martingale Theory
(a) Stochastic processes and filtrations
(b) Martingales, submartingales, and supermartingales
(c) Discrete stochastic integral
(d) Stopping times and stopping theorem
(e) Martingale convergence theorems
(1) Applications to Finance (option pricing in complete markets)
3 Markov Processes
(a) Markov processes and Markov property
(b) Strong Markov property
4) Brownian motion and continuous local martingales
(a) Definition and fundamental properties of Brownian
(b) Quadratic variation
(c) Continuous local martingales and semimartingales
5) Stochastic calculus
(a) Integration with respect to local martingales
(b) Finite variation processes and Lebesgue-Stieljes integration
(c) Integration with respect to semimartingales
(d) Ito's formula
(e) Levy's characterisation of Brownian motion
(f) Stochastic exponentials and Novikov's condition

(g) Girsanov's theorem
(h) Ito representation theorem
(i) Feynman-Kac formula
(j) Applications to Finance (Black Scholes model)
6) Stochastic differential equations
(a) Strong solutions and Lipschitz-theory
(b) Examples (0U-processes, CIR processes, etc.)

Learning outcomes

By the end of the module, students should be able to:

  • Explain and apply the concept of measure-theoretic conditional expectations
  • Demonstrate an understanding of discrete time martingale theory and apply the theory to option pricing
  • Understand the basic properties of Brownian motions
  • Explain the main steps in the construction of the stochastic integral
  • Be proficient in applying Ito's formula and Girsanov's theorem in problems arising in Mathematical Finance
  • Solve standard SDEs appearing in Mathematical Finance
Indicative reading list

View reading list on Talis Aspire

Subject specific skills

-Explain and apply the concept of measure theoretic conditional expectations
Show an understanding of martingales and the connection with gains from trade
-Understand the Markov property and the strong Markov property and apply it to examples
-Demonstrate the ability to perform calculations involving martingales and stochastic integrals
-Be proficient in applying Ito's formula and Girsanov's theorem to problems in Mathematical finance
-Demonstrate the ability to translate problems from mathematics to finance and vice-versa

Transferable skills

-Demonstrate problem solving skills involving concepts from the module

Study time

Type Required
Lectures 30 sessions of 1 hour (20%)
Tutorials 10 sessions of 1 hour (7%)
Private study 110 hours (73%)
Total 150 hours
Private study description

Weekly revising of lecture notes and materials, solving of problem sheets, and preparing for class tests and the final exam.


No further costs have been identified for this module.

You do not need to pass all assessment components to pass the module.

Students can register for this module without taking any assessment.

Assessment group D2
Weighting Study time
Class Test 1 (20-minute synchronous online assessment) 10%

This class test takes place in the middle of the term during a lecture.

Class Test 2 (20-minute synchronous online assessment) 10%

This class test takes place in the middle of the term during a lecture.

Examination 80%

The examination paper will contain four questions, of which the best marks of THREE questions will be used to calculate your grade.

  • Answerbook Pink (12 page)
Feedback on assessment

Solutions and written cohort level feedback will be provided for the final exam. Oral cohort level feedback will be provided for the class tests.

Scripts are retained for external examiners and will not be returned to you.

Past exam papers for ST908


This module is Core for:

  • Year 1 of TIBS-N3G1 Postgraduate Taught Financial Mathematics