Oxford Certificate Programmes · Worcester College
Computational Finance & FinTech
How do markets turn future cash flows into prices, risk into expected returns, and promises into tradable claims? The course builds those foundations first, then uses them to study derivatives, central-bank news, Bitcoin, digital money and the payment systems beneath modern finance.
Course materials
Course overview
The first four days establish the common language of finance. Students learn to move cash flows across time, interpret interest-rate quotations, value bonds and shares, and separate diversifiable risk from market risk. The emphasis is not on memorising formulas: every formula answers a valuation or decision problem and is tested against a real or carefully constructed financial case.
The second four days move from underlying assets to contingent claims, market expectations and monetary infrastructure. Forwards, futures and options show how no-arbitrage and hedging work. High-frequency event studies show how policy news enters prices. The final day asks a deeper question: what exactly is the claim being transferred when people use Bitcoin, a stablecoin, commercial-bank money or central-bank money?
Learning outcomes
By the end of the course you can:
- discount dated cash flows using consistent interest-rate conventions, compare financing choices and interpret net present value;
- value bonds from yields and spot rates, explain the price–yield relation, and measure interest-rate exposure with duration and convexity;
- value equity using payout and cash-flow approaches, identify the role of growth and terminal value, and use multiples without confusing price with value;
- compute portfolio risk and beta, apply the CAPM to the cost of equity, and explain where the model's assumptions and empirical limits matter;
- explain forward, futures and option payoffs, derive basic no-arbitrage relations, and evaluate hedges including margin and basis risk;
- extract policy expectations and surprises from market prices, construct a high-frequency event study, and interpret a simple auditable measure of central-bank language;
- compare Bitcoin, stablecoins, central-bank digital currency and commercial-bank money by their economic function, issuer, redemption promise and settlement mechanism.
Eight teaching days
| Day | Topic | |
|---|---|---|
| Week One · Valuation and capital markets | ||
| Mon 17 Aug | The time value of money and interest rates | |
| Tue 18 Aug | Valuing bonds | |
| Wed 19 Aug | Valuing stocks | |
| Thu 20 Aug | Capital markets and the pricing of risk | |
| Week Two · Risk transfer, information and digital finance | ||
| Mon 24 Aug | Forwards, futures and risk management | |
| Tue 25 Aug | Understanding options | |
| Wed 26 Aug | Central-bank news and high-frequency markets | |
| Thu 27 Aug | Bitcoin, digital money and payment systems | |
Full session map, scope and reading list →
Computation as evidence
Python supports the finance; it does not replace it. Some lectures are primarily analytical and discussion-led. The practical work uses small, transparent calculations to verify a valuation, trace a sensitivity, reproduce a payoff, or measure a market response. Student notebooks run in Google Colab or Jupyter and use familiar tools such as NumPy, pandas and Matplotlib.
Teaching and assessment
Each day combines a connected lecture sequence with a short quiz and guided solution. Computational exercises then reproduce or extend the day's core calculation. The quizzes test interpretation as well as arithmetic: students must explain what a price, yield, beta, hedge or event-study estimate means, and what it does not establish.
No prior programming is required. Basic algebra is assumed; probability and statistics are introduced where they become necessary. The mathematical level is appropriate for final-year undergraduates and master's students encountering finance from different disciplinary backgrounds.
