Developing Models to Capture Tail Events in Energy Markets
Location: Location flexible: at the candidate’s current location, or in Fortitude Valley, QLD
Duration: 4 months
Proposed start date: June 2019
PLEASE NOTE: Due to funding requirements, students must have Australian or New Zealand Citizenship or Permanent Residency to apply. Any applicants not meeting this requirement will automatically be deemed ineligible for this project.
High impact and low probability events characterise the tails of distributions used to derived ‘at risk‘ measures such as Earnings at Risk (EaR) and Tail Value at Risk (TVaR). These distributions and measures are used by CS Energy as part of its commodity trading capability to help manage market risk and define trading strategy. This project will see the intern located within the Trading Room, developing models that capture tail risks incorporating extreme market conditions, unplanned outages and the dependence structure between relevant factors. The project will have strong financial markets focus but will also involve some interaction with engineering teams to refine time-to-failure models for generation assets.
Research to be Conducted
The research project will involve:
1. Project scoping: Compose a scoping document to demonstrate an understanding of project requirements and timing
2. Background research: Researching and evaluating alternative modelling approaches (the company will provide a starting point and can guide this – CS Energy have mud map forward but are always interested to explore ideas)
3. Model design: Developing tail models incorporating dependence structures between market events and unplanned outages
4. Implementation: Writing error free, efficient and well documented code to implement models
5. Brief evaluation report
We are looking for a PhD student with the following:
· From a quantitative discipline such as: Mathematics, Financial Mathematics, Statistics, Physics.
· Strong mathematical aptitude and competency in programming, ideally MATLAB or Python.
· Some knowledge in probability, statistics and numerical methods.
· A desire to develop financial markets and commodity trading knowledge.
· CS Energy & the Risk Analytics Team may also support a publication
Please visit APR Internship website for more information and details on how to apply.