Haozhe Su is a Lecturer in Accounting and Finance at Nottingham Business School. He teaches to both undergraduate and postgraduate students and researches financial technology, numerical methods, quantitative finance, and financial machine learning/deep learning areas.
Haozhe currently contributes to a variety of moduels within the School, including undergraduate Research Project (Acc & Fin); postgraduate Introduction to Programming & Data Analytics for Finance and FinTech; Financial Modelling; Risk Management; Research Methods for Finance & Accounting.
Haozhe is an incoming visiting research fellow at the University of Bath. Haozhe had also taken MSc dissertation supervisor role for the University of Nottingham.
Haozhe has a strong academic background in mathematics and financial engineering. He was awarded a PhD degree in Quantitative Finance by the University of Nottingham in 2018. He also had placement experience working for an investment bank in London. His first two degrees are maths related – an MSc degree in Numerical Techniques for Finance awarded by the University of Nottingham and a BSc degree in Statistics awarded by Sun Yat-sen University in China.
Haozhe’s main research interest is in financial technology, numerical methods, quantitative finance, and financial machine learning/deep learning areas. Haozhe is collaborating with researchers from academia as well as the finance industry.
- Su, H., Tretyakov, M.V. & Newton, D.P. (2021). ‘Option Valuation through Deep Learning of Transition Probability Density’. Working paper. arXiv https://arxiv.org/abs/2105.10467
- Su, H. & Newton, D. P. (2020). ‘Widening the Range of Underlyings for Derivatives Pricing with QUAD by Using Finite Difference to Calculate Transition Densities -- Demonstrated for the No-Arbitrage SABR Model’. The Journal of Derivatives, The Journal of Derivatives Winter 2020, 28 (2) pp. 22-46; DOI: https://doi.org/10.3905/jod.2020.1.105
- Su, H., Chen, D. & Newton, D. P. (2017), ‘Option Pricing via QUAD: From Plain Vanilla to Heston with Jumps’, The Journal of Derivatives, 24(3), pp. 9-27. DOI: https://doi.org/10.3905/jod.2017.24.3.009