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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 |
Specified Learning Activities | 16 |
Autonomous Student Learning | 48 |
Total | 100 |
1) FIN20010 Principle of Finance or FIN20040 Foundations in Finance or any introductory Finance Module
2) ECON10720 Microeconometrics for Business or any Microeconometrics or Macroeconometrics module
3) ACM30220 Partial Differential Equation or ACM30080 PDE in Financial Mathematics or any PDE module
constitute pre-requisites to take 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 | ||
---|---|---|---|---|---|
Continuous Assessment: Continuous Assessment: - Homework 1 (10%) - Homework 2 (10 %) |
Varies over the Trimester | n/a | Standard conversion grade scale 40% | No | 20 |
Examination: Final Examination | 2 hour End of Trimester Exam | No | Standard conversion grade scale 40% | No | 80 |
Resit In | Terminal Exam |
---|---|
Autumn | Yes - 2 Hour |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Mr Rian Dolphin | Tutor |
Mr James Hannon | Tutor |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Thurs 11:00 - 11:50 |
Lecture | Offering 1 | Week(s) - 8 | Tues 15:00 - 15:50 |
Tutorial | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Tues 17:00 - 17:50 |
Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Wed 17:00 - 17:50 |