MATHS 769 : Stochastic Differential and Difference Equations


2020 Semester One (1203) (15 POINTS)

Course Prescription

Differential and difference equations are often used as preliminary models for real world phenomena. The practically relevant models that can explain observations are, however, often the stochastic extensions of differential and difference equations. This course considers stochastic differential and difference equations and applications such as estimation and forecasting.

Course Overview

Stochastic difference and differential equations arise in science, technology, engineering, business, ecology and several other fields of research. Most often, these models treat phenomena that evolve with time. The first part of the course focuses on stochastic differential equations (SDEs). These are similar to ordinary differential equations, except that they also model random effects that might influence a model. We will look at a number of  applications and derive the famous Black-Scholes SDE for options pricing. In the second part of the course we look at discrete time processes and consider topics such as prediction (forecasting) and estimation of time series models.

Course Requirements

Prerequisite: B- in both MATHS 340 and 361

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity

Learning Outcomes

By the end of this course, students will be able to:
  1. Identify analytic and numerical methods suitable for the analysis of SDEs; (Capability 1, 2 and 5)
  2. Use and apply analytic and numerical methods, as appropriate, for the analysis of SDEs (Capability 1, 2 and 3)
  3. Write appropriately detailed and structured mathematical arguments in simple contexts; (Capability 1, 2, 3 and 4)
  4. Understand the theory of multivariate random variables and the main ideas about multivariate normal distributions, including conditional mean and covariance in the context of discrete time stochastic processes; (Capability 1, 2, 3 and 4)
  5. Understand and be able to compute mean square estimates. (Capability 1, 2, 3, 4 and 5)
  6. Understand the theory of discrete signals (sequences), the related linear operators and the basics of spectral theory (Capability 1, 2, 3, 4 and 5)


Assessment Type Percentage Classification
Three Assignments (equally weighted) 40% Individual Coursework
Final Exam (2 hours) 60% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6
Three Assignments (equally weighted)
Final Exam (2 hours)

There is no plussage in MATHS 769. This means that all components of the assessment count towards your final grade. Attempt all assessments!

You need to achieve at least 35% on the final exam to pass the course.


Whanaungatanga and manaakitangi are fundamental principles of our Tuakana Mathematics programme which provides support for Maori and Pasifika students who are taking mathematics courses. The Tuakana Maths programme consists of workshops and drop-in times, and provides a space where you are able to work alongside our Tuakana tutors and  other Maori and Pasifika students who are studying mathematics.

 For further information, please visit

Key Topics

Introduction: Probability, random variables:  Introduction to sources and applications, Review of multivariate statistics.
Continuous Time Processes (stochastic differential equations, Random walks and Wiener processes, Itô calculus, Applications of SDE's.
Discrete Time Processes (stochastic difference equations):  Sequences and related linear operators (filters), Mean square estimation, forecasting, Linear recursive models.

Learning Resources

All lecture notes and supporting reading material, as well as some Matlab codes, will be available on Canvas.

Special Requirements

There is no compulsory attendance and no must-pass assessments. The use of calculators in the exam is not permitted. 

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 3 hours of lectures, no tutorial, 4 hours of reading and thinking about the content and 3 hours of work on assignments and/or test preparation.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page:

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 11/01/2020 03:11 p.m.