Quantitative FinTech (QFIN)

The Quantitative FinTech (QFIN) research group at Sussex provides independent, customized and responsible solutions to promote innovation in financial markets. Our flexible approach resolves problems ranging from small boutique model development to advice on implementing complex financial systems.

QFIN aims to provide excellent research on issues currently faced by financial markets and promote stronger links between academic institutions, business and industries.

QFIN is one of two research groups in the Department of Accounting and Finance in the University of Sussex Business School. We aim to provide excellent research on issues currently faced by financial markets (including digital assets and their derivatives such as bitcoin swaps, futures and options, climate change finance and risk management). The problems that our researchers study require a data-driven quantitative approach including the analysis of big data sets derived from trade or order book data at ultra-high frequency.

QFIN has experts in quantitative finance, climate change, crypto asset market microstructure, big data analysis, machine learning and computer science. The network’s core and key associated members all have strong research backgrounds with publications in the top academic journals and some also have significant industry experience having held various roles in top-tier investment banks, hedge funds and relevant industries.

QFIN aims to promote stronger links between academic institutions and business and industries. We work and collaborate with business and industry on research initiatives and projects. We provide bespoke consultancy services, design and deliver tailor-made training courses as well as teach students to meet the challenges for a highly-skilled labour force that the industry requires.

FAST Seminars

QFIN hosts regular FAST (Finance and Stochastics) Seminars. See recent FAST seminars.

Highlights of Recent Research

Alexander, C., Meng, X. and Wei, W. Targetting Kollo skewness with random orthogonal matrix simulation. European Journal of Operational Research, 299 (2022): 362-376.

Alexander, C. and Rauch, J. A general property for time aggregation. European Journal of Operational Research, 291.2 (2021): 536-548.

Alexander, C., Chen, X. and Ward, C. Risk-adjusted valuation for real option decisions. Journal of Economic Behaviour and Organisation, 191 (2021): 1046-1064.

Baamonde-Seoane, M. A., del Carmen Calvo-Garrido, M., Coulon, M., and Vázquez, C. Numerical solution of a nonlinear PDE model for pricing renewable energy Certificates (RECs). Applied Mathematics and Computation, 404 (2021): 126199.

Bevilacqua, M. and Tunaru, R. The SKEW index: Extracting what has been left. Journal of Financial Stability, 53 (2021): 100816.

Filippidis, M., Tzouvanas, P. and Chatziantoniou, I. Energy poverty through the lens of the energy-environmental Kuznets curve hypothesis. Energy Economics, 100 (2021): 105328.

Kaeck A., van Kervel V. and Seeger N.J. Price impact versus bid–ask spreads in the index option market. Journal of Financial Markets, (2021) https://doi.org/10.1016/j.finmar.2021.100675

Meng, X. and Taylor, J. W. Comparing probabilistic forecasts of the daily minimum and maximum temperature. International Journal of Forecasting, 38 (2022): 267-281.