Exchange Traded Options: Evaluating risk management through strategy analysis

Location: Location flexible: at the candidates existing location. or in Adelaide

Duration: 3-5 months, part time possible

Proposed start date: As soon as possible

Project Background

Practical limitations to the Black Scholes model for pricing call and put options have been widely discussed in both the academic and financial communities. Other pricing models have been proposed including binomial and trinomial trees.

Models require that certain assumptions are made so that the model will fit real-world data in most situations. Later these models are expended or adapted to overcome assumptions, become more general in nature or to account for additional unforeseen real world situations.

A key assumption used in pricing call and put options is that asset price returns are either normal or lognormal. The accuracy or otherwise of this assumption is key in determining the price of a call or put option. The exact nature of the normal-ness or lognormal-ness also changes over time.

If an asset price return is not normal or lognormal, then what is the implication for call or put option prices, and is there a different significance for options prices whose strike prices are in- at- or out-of-the-money? Do financial markets misprice options?

Research to be Conducted

  1. Review and summarise academic literature.
  2. Utilising this research, develop and test a tool that models asset price returns for any given asset using historical asset prices and/or other data as source information.
  3. Using this model, price call and put options using a variety of strike prices and expiration dates.
  4. Compare these calculated call and put option prices with prices available in financial markets to determine under what circumstances mispricing occurs.
  5. Analyse the net profits or losses that would have been generated from buying underpriced and selling overpriced assets would have produced.
  6. Document the work completed.

Skills Required

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

Additional Details

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.

Applications Close

 3 April 2019


APR – 0893