FINANCIAL DERIVATIVES - 2024/5
Module code: PHY3048
Module Overview
This module covers various the application of statistical physics to model share prices. This mathematics is then applied to calculating prices for some examples of financial derivatives.
Module provider
Mathematics & Physics
Module Leader
NOEL Noelia (Maths & Phys)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 6
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 107
Lecture Hours: 22
Tutorial Hours: 11
Guided Learning: 10
Module Availability
Semester 2
Prerequisites / Co-requisites
None.
Module content
Indicative content includes:
Financial products and markets:
Cash, interest rates, Stocks, dividends, Bonds, Swaps, Commodities, Derivatives, Markets, participants, arbitrage.
Stochastic Processes:
Random Variables, Probabilities, Variance, Normal and Lognormal distributions, Brownian motion, Wiener process, Ito Calculus.
Option Pricing:
Binomial trees, Share price models, drift and volatility, Forward contracts, European and American options, Calls and Puts, Binomial pricing model, Black-Scholes model and hedging.
Portfolio optimisation theory and types of trade.
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Coursework | COURSEWORK | 100 |
Alternative Assessment
Alternative assessment: None
Assessment Strategy
The assessment strategy is designed to provide students with the opportunity to demonstrate:
- Understanding of the concepts of derivative pricing, and underlying mathematics.
The summative assessment for this module consists of:
- Coursework assignment involving data analysis and modeling of financial instruments.
Formative assessment
Problem sheets are issued during the course, and feedback will be given during tutorial sessions.
Module aims
- The aims are to expose the students to the fundamentals of financial derivatives, to explore their underlying science by analogy to physical systems, and to show, by various methods, how the fair price of financial options may be determined.
Learning outcomes
| Attributes Developed | ||
| 001 | Understand the mathematics and models that underpin the analysis of financial data, including the properties of random variables, probability distributions and share price models and be able to assess their validity and remit. | KCT |
| 003 | Know about a range of common financial derivatives, be able to explain financial terminology and produce pay-off and profit diagrams for forward contracts, put and call options. | KCP |
| 004 | Understand and be able to derive and use Binomial Tree models and the Black-Scholes-Merton model. | KCP |
| 005 | Examine and explain the role of quantities such as the “greeks” in financial analysis. | KC |
| 002 | Understand Brownian motion process and Ito’s lemma. | KC |
| 006 | Understand and be able to derive the price of call and put options. | KCPT |
| 007 | Understand basic portfolio optimisation theory and types of trading and traders. | KCP |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
Help students develop an understanding of how the ideas of stochastic processes can be applied to financial derivatives.
The learning and teaching methods include:
33 hours of lecture classes/tutorials and computer-based problem-solving
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: PHY3048
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2024/5 academic year.