Statistical Methods and Data Analytics for Finance (MATH0099)

Key information

Faculty: Faculty of Mathematical and Physical Sciences
Teaching department: Mathematics
Credit value: 15

Restrictions: N/A
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

This is a course about statistical inference and its applications to problems from finance. It consists of theory based lectures, homework problem sheets and a computational project.

The course covers classical topics from statistical inference such as point estimation, confidence sets and hypothesis testing. Subsequently parametric and non-parametric statistical models will be discussed, as well as the Bayesian approach to inference. Time permitting we will also cover some elements of modern machine learning techniques. Examples from finance will be given throughout the course.

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Module deliveries for 2024/25 academic year

Intended teaching term: Term 1 听听听 Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study: In person
Methods of assessment: 100% Exam
Mark scheme: Numeric Marks

Other information

Number of students on module in previous year: 30
Module leader: Dr Daniel Schwarz
Who to contact for more information: maths.mscteaching@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

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Important information

The catalogue has been updated with key information about the modules that will run during the 2024/25 academic session. Current and prospective students can browse through the catalogue to consider possible module choices for the coming year.

Please note,听information in the catalogue is听subject to change as teaching and assessment arrangements for 2024/25 may need to be adjusted in line with the University's Feedback and Assessment principles and operating model.

Centrally managed exam durations will be provided on students' individual exam timetables. Arrangements for locally managed exams and in-class activity will be confirmed by the teaching department for the module.

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