ACM30110

Learning Outcomes:

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).

Indicative Module Content:

- 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 Hours:

Approaches to Teaching and Learning:

Lectures, Tutorial and Labs with use of laptop.

• To stimulate curiosity, independence and significant learning, I introduced a flipped-classroom and enquiry-based student-centred approach. Students gained familiarity with computational thinking practice, data analysis, financial concepts by a combination of lectures with group projects, problem solving activities, real-world applications (O'Connor, 2012). They were engaged in an active learning environment using technology (Python, FinCad and VBA), groups activities and peer-assisted learning. Knowledge was developed through reflections and comparisons (Harland, 2003). To stimulate students’ self-assessment, formative feedbacks were provided after each activity.

• The learning process was supported by teaching assistants. They helped peers in driving brainstorming, discussions, question/answers. They also took field-notes on my teaching practice and students’ feedbacks.

Learning Recommendations:

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)

Incompatibles:
ACM30070 - Computational Finance

Additional Information:
1) FIN20010 or FIN20040 or any intro Finance Module 2) ECON10720 or any Microeconometrics/Macroeconometrics module 3) ACM30220 or ACM30080 or any PDE module, constitute pre-requisites to take ACM30110.


 

Assessment Strategy  

 

Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.