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Curricular information is subject to change
On completion of this module the student should be able to:
- implement financial models and techniques in both VBA and Python;
- compute future and present values of a security;
- apply the Binomial Tree Method to price a given option under certain conditions and discuss issues related to the convergence;
- implement the Black-Scholes model;
- price American and Asian options;
- use Monte Carlo methods in option pricing and Greeks estimate;
- use Finite Difference Methods in option pricing;
- price barrier options (time permitting).
- Derivatives: Forward, Future, Options. Main features and differences.
- A concise introduction to asset pricing: definitions of fair price, trading strategies, self-financing and admissible portfolio. No arbitrage principle, martingales and information. Martingale Measure. Fundamental Theorem of Asset Pricing.
- The Black-Scholes-Merton Model: assumptions, derivation of the BS PDE, uniqueness of solution and equivalence with the heat equation. BS equations for European Call and Put. Greeks. Implied Volatility.
- The binomial model for option pricing: one step tree, replication and risk neutral argument. Multi-Step trees for European and American Options. Exotic Options. The CRR model. Delta-Hedging. Control Variate Technique. Convergence to the BS model.
- Monte Carlo Method: Geometric Brownian Motion generation and risk-neutral valuation. Option Pricing. Greeks Estimation. Antithetic Variate and Control Variate Techniques. Asian Options.
- Finite Difference Method for option pricing: Derivatives approximations and Boundary Conditions. Explicit Method, Implicit Method, Crank-Nicolson Method. Barrier Options.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Tutorial | 12 |
Laboratories | 24 |
Specified Learning Activities | 20 |
Autonomous Student Learning | 120 |
Total | 200 |
1) FIN20010 Principle of Finance or FIN20040 Foundations of Finance,
2) ECON10720 Microeconometrics for Business,
3) ACM30080 PDE in Financial Mathematics [or ACM30220 Partial Differential Equation],
must be taken before this module.
A prior knowledge of Python coding language is also required (STAT40800 Data Programming with Python (OL) is highly recommended as optional in Autumn Trimester)
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Not yet recorded. |
Resit In | Terminal Exam |
---|---|
Autumn | Yes - 2 Hour |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Ms Giulia Boetti | Tutor |
Mr Rian Dolphin | Tutor |
Mr James Hannon | Tutor |