MSc Statistics and Computational Finance

Program Overview

This MSc program in Statistics and Computational Finance equips students with advanced statistical modeling and computational finance skills. It emphasizes data analysis, providing contemporary statistical methodologies and computational tools like Python and R. Graduates are prepared for careers in finance, data analysis, and academia, with a strong foundation in statistical theory and practical applications.

Program Outline

Degree Overview:

Objectives:

• Develop the knowledge and skills necessary to translate problems from the workplace into contemporary statistical ideas and methodologies.
• Solve problems using advanced knowledge in statistical modeling and computational finance.
• Interpret and communicate results effectively.
• Gain a strong grounding in statistical data analysis and modeling techniques.
• Acquire computational skills required by finance-based employers, such as Python and R languages.
• Choose an option module and dissertation topic that aligns with personal interests, resulting in a flexible program.

Description:

This MSc program emphasizes data analysis and provides students with contemporary statistical ideas and methodologies that are attractive to prospective employers. The skills gained are useful for a wide range of financial data analysis and in other sectors where data analysis is required, such as sociology, health science, medical science, or biology. This program also serves as an ideal foundation for further academic study, with many graduates progressing to PhD programs.

Other:

• The program is delivered by the Statistics and Probability Group at the University of York, which has a thriving research culture.
• The group works with mainstream statistics to develop new methodology and apply it to real-world problems.
• The team produces world-class research, publishing in top journals.
• Graduate students have full access to this expertise and are exposed to forefront research through regular seminars and working groups.

Outline:

The program consists of the following components:

• Core Modules:
• Generalised Linear Models
• Statistical Pattern Recognition
• Statistics for Finance and Insurance
• Multivariate Data Analysis
• Computational Finance with Python
• Option Modules:
• Decision Theory and Bayesian Statistics
• Mathematical Methods of Finance
• Mathematical Finance in Discrete Time
• Dissertation:
• Students complete a dissertation on a selected topic in Statistics or Computational Finance.
• It is an independent piece of work completed over the summer with guidance from a project supervisor.
• Recent dissertations have investigated topics such as:
• Feature Selection in Trading Behaviour in Financial Markets - A Big Data Analysis
• Modelling the UK and USA GDP Data: Estimation and Prediction
• The day of the week effect in different countries' stock markets

Assessment:

All taught modules are assessed through a combination of closed book written exams, coursework, projects, and presentations.

• Closed book written exams: Assess subject-specific knowledge through theoretical and practical questions and open-ended problems.
• Coursework and projects: Often require the use of software, giving students an opportunity to develop their technical skills.
• Assess subject knowledge and analytical, theoretical skills, as well as practical aspects of application, implementation, and interpretation.
• Presentations: Enhance communication skills for diverse audiences, from the general public to subject experts.
• Independent study module: Relies on student research, developing critical reasoning and digital literacy skills, including programming.
• Assessed through a dissertation, further developing written communication skills.

Teaching:

• Teaching is informed by the latest research, ensuring students focus on the newest ideas and models.
• A wide range of teaching methods caters to different learning styles, including:
• Lectures
• Problem classes
• Seminars
• Tutorials
• Some modules include practical classes, computer laboratories, or workshops.
• Lectures introduce new concepts, while problem classes focus on practical application.
• Seminars involve small, interactive sessions that cater to individual needs.
• The Virtual Learning Environment supplements lectures and seminars.
• Includes short videos to reinforce knowledge on specific topics, accompanied by dedicated study notes.
• Project and dissertation supervision involves regular meetings with an academic supervisor who offers advice and support.
• Supervisors have specialist knowledge of the investigated area.
• Teaching location: Department of Mathematics in James College on Campus West.
• Small group teaching mainly occurs in the Dusa McDuff room, with larger classes taking place nearby in James College, Derwent College, and elsewhere on Campus West.

Careers:

• The big data analysis skills developed in this program offer attractive employment opportunities in various industries with high demand for such skills.
• The course also serves as a strong foundation for further studies at the PhD level.

Career opportunities:

• Quantitative analyst
• Auditor
• Account manager for a bank
• Trainee chartered accountant
• Management associate
• Software developer

Transferable skills:

• Confidence with high-level financial statistical analysis
• Logical thinking
• Problem analysis and solving
• Flexibility, ability to learn and apply complex ideas quickly and precisely
• Digital literacy
• Time management
• Communication skills
• Research skills

Tuition Fees and Payment Information:

Annual tuition fees for 2024/25 Study modeUK (home)International and EU Full-time (1 year) £15,890£32,260