Analysing options strategies
Location: Location flexible
Duration: 5 months
Proposed start date: 4 March 2019
Exchange traded options (ETOs) enable all types of firms worldwide to manage financial risks in their businesses or transfer those risks to another party.
ETOs, as derivative financial products, are also traded by firms with profit objectives. By buying and selling call options and put options, these firms attempt to generate above-average risk-adjusted returns.
Using mathematical and statistical techniques it is possible to predict with some accuracy the level of risk firms assume by entering into ETO transactions. As ETOs are time sensitive or wasting assets some traders seek to sell these derivative products in the expectation that the risks they take on will not be realised, generating a consequent profit.
Since financial markets exhibit a high degree of randomness it is, however, usually not possible to know beforehand which risks will be realised and which will not. Where the risks are realised, the firms will either suffer a financial loss or must enter into further ETO transactions to parlay these risks.
This project will research the management of scenarios where one or more risks assumed by a firm is likely to be, or has been, realised. Can the outcomes be categorised? If so can a framework be developed to support a decision to either suffer a financial loss or further parlay the risk, and when to make that decision?
Research to be Conducted
The research to be undertaken includes:
- Review academic literature.
- Using a small number of scenarios which involve buying and/or selling ETOs, create a dynamic (real-time) risk measurement tool. There is no pre-determined format to this tool – it may produce a single risk number/value/ranking or a range of risk measurement metrics.
- Develop a decision-making framework using the risk measurement tool as an input.
- Document the work completed
Real time data sources and advanced library functions relating specifically to ETO calculations (such as ETO probability functions, calculation of greeks) will be provided.
This project would suit a PhD student with skills in Quantitative Finance, Mathematics, Statistics, Physics, Computer Engineering, Quantitative Economics or Business.
- Skills in Mathematics and Statistics
- Existing knowledge of ETOs is desirable but not essential
- Programming skills in an appropriate language (VBA is acceptable) is desirable but not essential
The intern will receive $3,000 per month of the internship, usually in the form of stipend payments.
It is expected that the intern will primarily undertake this research project during regular business hours, spending at least 80% of their time on-site with the industry partner. The intern will be expected to maintain contact with their academic mentor throughout the internship either through face-to-face or phone meetings as appropriate.
The intern and their academic mentor will have the opportunity to negotiate the project’s scope, milestones and timeline during the project planning stage.
20 February 2019
INT – 0572