EU project on BigDataFinance: Two PhD scholarships available at UZH (2016-19)
The Department of Banking and Finance of the University of Zurich invites applications for two Early Stage Researchers (ESR) PhD scholarships in the Marie Sklodowska-Curie ITN BigDataFinance programme. The PhD scholarships (36 months each) are available from February 1st, 2016 (or by arrangement) at the Department of Banking and Finance of the University of Zurich.
Application deadline for both positions: December 15, 2015.
Further information: Prof. Stefano Battiston [firstname.lastname@example.org]
Position 1: Complex Networks in Finance
This project seeks to analyse systemic risk from a network perspective, combining theoretical modelling, empirical analysis, and practical (policy) applications. First, the project aims at contributing to the debate on fundamental questions such as: Is there a resilient architecture to the financial system? Should we put restrictions on institutions that are too big or too connected to fail? Is there a tension between individual banks’ incentives and social welfare? Second, the project will deliver a very practical output by incorporating network effects and positive feedback in policy makers’ day-to-day stress-test methods. https://bigdatafinance.eu/systemic_risk
- Experience with large data analysis, relational databases (MySQL), and non-relational databases (e.g. Neo4j).
- Very good command of English (oral and written).
- Candidates must hold Master’s degree in a relevant field (finance, economics, computer science, applied mathematics, statistics or physics) and the recruited candidate is expected to enroll as a PhD in Finance student at UZH. www.phd-finance.uzh.ch/Program/requirements_en.html
Position 2: High Frequency Trading Risk Management Tools Based on Scaling Law
The project will provide robust risk management tools for the high frequency trading (HFT) community and advance a theoretical and empirical explanation for the occurrence of scaling law for both canonical models (Brownian Motion, Levy processes etc.) and high frequency FX data. The risk management tool will be extended to a multi-asset case and tested and validated with empirical data (FX rates).
- The candidate should have a solid quantitative background in finance gained through an appropriate degree and complemented by experience of academic research in the field of high frequency finance. Sound knowledge of statistics and financial mathematics. A good understanding of financial markets, in particular the Foreign Exchange (FX) market. Practical trading experience is welcomed. Working experience of implementing statistical models in Java and R. Ability to join and contribute to a large research consortium. Open and creative.
- Candidates must hold Master’s degree in a relevant field and the recruited candidate is expected to enroll as a PhD in Finance student at UZH. www.phdfinance.uzh.ch/Program/requirements_en.html