NEWS: The QRSLab is becomming a joint venture between members in UNSW and members in University College London effective August 1st 2012.


THIS URL LAST UPDATED Aug. 1st 2012 - Please see new address at UCL Department of Statistics.



QRSLab Agenda


The QRSLab was founded by Dr. Gareth W. Peters (UNSW - UCL), Mark Young (formerly Deloitte) and Pavel Shevchenko (CSIRO) as a research laboratory in the Department of Mathematics and Statistics at the University of NSW in 2010. Since this time the laboratory has continued to grow, it will soon embark on a new international phase with a group in University College London, effective August 2012.

The agenda of this laboratory is to produce innovative and cutting edge statistical solutions to modelling in applied financial risk; hedge fund models and risk; time series models; traditional - market, credit and operational risk; and insurance statistical modelling. This laboratory is specifically interested in modelling applied numerical and methodological financial risk problems of signficance in the financial industry - as such it aims to develop strategic partnership of a research nature with industry.

QRSLab Research Interests and Expertise

  1. Model risk and insurance in Basel II / Basel III.
  2. Computations with copula models in finance and model risk (eg. Operational Risk, Insurance settings).
  3. Computations with meta distributions for risk modelling and associated model risk analysis.
  4. Risk sensitivity and stress testing frameworks under Basel II and Solvency II – properties, algorithms, methodology, impact on capital calculation and allocation.
  5. Dynamic latent factor models and copula for Operational Risk.
  6. Cointegration models, estimation, prediction and model risk.
  7. Risk metrics and analysis for the hedge fund and private equity sectors.
  8. Alpha stable heavy tailed models for financial risk.
  9. Claims reserving models for insurance - models and methodology.
  10. Project risk and pricing for mining/agriculture via multifactor sde commodity models online calibration and estimation via Sequential Monte Carlo and Adaptive MCMC.
  11. Tracking VaR and other risk measures dynamically through non-linear filtering techniques.
  12. Rare-Event modelling and simulation methodology.

IF YOU ARE INTERESTED IN COLLABORATION OPPORTUNITIES WITH THE QRSLab ON RESEARCH PROJECTS or honours, MSc., Ph.D. or Post doctoral opportunities - see contacts