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.

Recent Research

Alexander, C., Choi, J., Massie, H. R., and Sohn, S. Price discovery and microstructure in ether spot and derivative markets. International Review of Financial Analysis, 71 (2020): 101506.

Alexander, Carol, and Daniel F. Heck. Price discovery in Bitcoin: The impact of unregulated markets. Journal of Financial Stability 50 (2020): 100776.

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

Alexander, C., and Imeraj A. The Bitcoin VIX and Its Variance Risk Premium. The Journal of Alternative Investments 23.4 (2021): 84-109.

Alexander, C., and Lazar, E. The continuous limit of weak GARCH. Econometric Reviews 40.2 (2021): 197-216.

Alexander, C., Lazar, E., and Stanescu, S. Analytic moments for GJR-GARCH (1, 1) processes. International Journal of Forecasting 37.1 (2021): 105-124.

Alexander, C., and Chen, X. Model risk in real option valuation. Annals of Operations Research 299.1-2 (2021):1025-1056.

Farkas, W., Fringuellotti, F., and Tunaru, R. A cost-benefit analysis of capital requirements adjusted for model risk. Journal of Corporate Finance, 65 (2020): 101753.

Fabozzi, F. J., Shiller, R. J. and Tunaru, R. A 30-Year Perspective on Property Derivatives: What Can Be Done to Tame Property Price Risk? Journal of Economic Perspectives 34.4 (2020): 121-45.

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

Duygun, M., Tunaru, R., and Vioto, D. Herding by corporates in the US and the Eurozone through different market conditions. Journal of International Money and Finance 110 (2021): 102311.

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

Meng, X. and Taylor, J. W. (2021). Comparing probabilistic forecasts of the daily minimum and maximum temperature. International Journal of Forecasting.

Tzouvanas, P., Kizys, R., Chatziantoniou, I., and Sagitova, R. Environmental and financial performance in the European manufacturing sector: An analysis of extreme tail dependency. The British Accounting Review, 52(6) (2020): 100863.

Papavasileiou, E. F., and Tzouvanas, P. Tourism carbon Kuznets-curve hypothesis: a systematic literature review and a paradigm shift to a corporation-performance perspective. Journal of Travel Research 60.4 (2021): 896-911.

Kizys, R., Tzouvanas, P. and Donadelli, M. From COVID-19 herd immunity to investor herding in international stock markets: The role of government and regulatory restrictions. International Review of Financial Analysis 74 (2021): 101663.

Zaremba, A., Kizys, R., Tzouvanas, P., Aharon, D. Y., and Demir, E. (2021). The quest for multidimensional financial immunity to the COVID-19 pandemic: Evidence from international stock markets. Journal of International Financial Markets, Institutions and Money, 71, 101284.

Filippidis, M., Tzouvanas, P., and Chatziantoniou, I. (2021) Energy poverty through the lens of the energy-environmental Kuznets curve hypothesis. Energy Economics. ISSN 0140-9883 (Accepted)

Christian, J., and Vu, A. N. Taskā€based structures in open source software: revisiting the onion model. R&D Management 51.1 (2021): 87-100.

Alexander, CarolChoi, JaehyukMassie, Hamish R A and Sohn, Sungbin (2020) Price discovery and microstructure in ether spot and derivative markets. International Review of Financial Analysis. ISSN 1057-5219 (Accepted)

Alexander, CarolLazar, Emese and Stanescu, Silvia (2020) Analytic moments for GJR-GARCH (1,1) processes. International Journal of Forecasting. ISSN 0169-2070 (Accepted)

Alexander, CarolChoi, JaehjukPark, Heungji and Sohn, Sungbin (2020) BitMEX Bitcoin derivatives: price discovery, informational efficiency and hedging effectiveness. Journal of Futures Markets, 40 (1). pp. 23-43. ISSN 0270-7314

Alexander C. and M. Dakos (2020) ‘A Critical Investigation of Cryptocurrency Data and Analysis’ Quantitative Finance. 20:2, 173-188, ISSN 1469-7688

Alexander, Carol and Rauch, Johannes (2020) A general property for time aggregation. European Journal of Operational Research. ISSN 0377-2217

Calmet, Xavier and Shaw, Nathaniel Wiesendanger (2020) An analytical perturbative solution to the Merton Garman model using symmetries. Journal of Futures Markets, 40 (1). pp. 3-22. ISSN 0270-7314

Meng, Xiaochun and Taylor, James W (2019) Estimating value-at-risk and expected shortfall using the intraday low and range data. European Journal of Operational Research, 280 (1). pp. 191-202. ISSN 0377-2217

Tzouvanas, PanagiotisKizys, RenatasChatziantoniou, Ioannis and Sagitova, Roza (2020) Environmental disclosure and idiosyncratic risk in the European manufacturing sector. Energy Economics. ISSN 0140-9883 (Accepted)