The 2 PhD positions are co-financed by Enel Global Trading S.p.A. (DM 352/2022). The main research topics are:

• Rough paths and Signature Transforms
  The aim is to study the modern theory of rough paths and signature transform and to analyze the connections with neural networks and, more in general, modern deep learning frameworks. Possible applications are: 1) construction of probabilistic scenarios to backtest trading strategies; 2) predictive and simulation models for commodities prices and financial indices; 3) deep calibration and application to option pricing. Eventually, the Enel Pricing Team will be involved.
• Integrated Global Portfolios
  The business line involved in this project is the Global Energy and Commodity Management, which has as a mission to maximize the Group’s energy profit margins. This is achieved by mitigating commodity-related risk through the management of an integrated global portfolio. The aim of the project is study a framework for hedging a portfolio of derivatives in the presence of market frictions such as transaction costs, liquidity constraints or risk limits using modern deep reinforcement machine learning methods.

Candidates will choose a topic when submitting the online application (possibly both topics as well). Useful links:

• General instructions: Click Here
• Information and PhD research topics: Click Here
• Call (in Italian): Click Here
• Application form: Click Here

Deadline: September 7, 2022, 2:00pm (local time)

• Evaluation Scale: Click Here to dowolad PDF file
• Candidates admitted to the oral exam: Click Here