Mathematical Finance
Mathematical Sciences
Taught
PgDip
Mathematical Finance
Taught
PgDip
Overview
Application Timeline
Tuition
- Year 1
| Student categories | Study | |
|---|---|---|
| Full-time | Part-time | |
| Republic of Ireland | N/A | N/A |
| EU | N/A | N/A |
| International | N/A | N/A |
| England | N/A | N/A |
| Northern Ireland | N/A | N/A |
| Scotland | N/A | N/A |
| Wales | N/A | N/A |
| Channel Islands | N/A | N/A |
Requirements
Language requirements information is currently unavailable.
Modules
Modules is currently unavailable
Please check back later for updates.
About
Program Overview
This distance learning program is an extension of the successful campus-based MSc Mathematical Finance at the University of York, launched in September 2009.
Application Process
- Applications are accepted for September and February intakes.
- Submit applications at least two weeks prior to the intake.
- A Pre-sessional Programme is available for candidates needing to strengthen their mathematics background, requiring application at least three months before the MSc intake.
Skills Development
Students will develop skills in Mathematical and Quantitative Finance, enhancing their employability and career progression opportunities....
Subject Area Information
Financial mathematics is a specialized field that combines mathematical theories and methods with financial practice to solve problems in finance. This discipline is essential for understanding and modeling financial markets, managing financial risks, and making informed investment decisions.
1. Introduction to Financial Mathematics
2. Probability and Statistics for Finance
3. Derivatives and Risk Management
4. Fixed Income Securities
5. Portfolio Theory and Asset Pricing
6. Numerical Methods in Finance
7. Financial Econometrics
8. Advanced Topics in Financial Mathematics
- Understanding fundamental financial concepts
- Calculating present and future values
- Analyzing financial data
- Modeling uncertainty
- Valuing derivative securities
- Designing hedging strategies
- Valuing fixed income securities
- Constructing optimal portfolios
- Implementing numerical algorithms
- Analyzing financial time series data
- Understanding complex financial instruments
- Proficiency in programming languages such as Python, R, or MATLAB
Career
Graduates of financial mathematics programs can pursue a variety of careers in the finance industry, leveraging their strong foundation in mathematics, finance, and computational techniques.
Quantitative Analyst (Quant)
Developing and implementing complex financial models to support trading and risk management.
Risk Manager
Identifying and mitigating financial risks for banks, investment firms, and corporations.
Financial Engineer
Designing new financial products and strategies using advanced mathematical techniques.
Investment Analyst
Analyzing financial data to make investment recommendations.
Actuary
Assessing financial risks in the insurance and pension industries.
Data Scientist
Applying statistical and computational methods to analyze financial data and inform business decisions.
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