Huy Chau (Châu Ngọc Huy)

Lecturer, Department of Mathematics, The University of Manchester

2.119, Alan Turing Building, Oxford Rd, Manchester, M13 9PL, UK

Email: huy.chau@manchester.ac.uk

New updates:  

UoM - IISc Bangalore PhD project

PhDs at Manchester

Teaching 

current courses (2021-2022):

MATH37002 Martingales with Applications to Finance 2021-22 2nd Semester

Research Interests: 

Financial mathematics, Machine learning, Data Science. 

My CV

Preprints and Publications

• 1. Local market viability and superhedging under propositional transaction costs and model uncertainty (with Claudio Fontana and Masaaki Fukasawa). Preprint, available upon request (2024).

  2. A general framework for pricing and hedging under local viability. (with Miklós Rásonyi). 2024 [Arxiv] 

  3. Continuous-time optimal investment with portfolio constraints: a reinforcement learning approach (with Duy Nguyen, Thai Nguyen). In revision (2024).

  4. On short-time behavior of implied volatility in a market model with indexes. (with D.  Nguyen, T. Nguyen).  2024 [Arxiv] 

  5. An efficient method to simulate diffusion bridges (with Lars Kirkby, Dang Nguyen, Duy Nguyen, Nhu Nguyen, Thai Nguyen). Statistics and Computing (2024) 34(4).

  6. On the inversion-free Newton's method and its applications, (with J.L. Kirkby,  D.H. Nguyen, D.  Nguyen, N. Nguyen,  T. Nguyen), International Statistical Review 2022. 

  7. Super-replication with transaction costs under model uncertainty for continuous processes, (with  Masaaki Fukasawa, Miklós Rásonyi), 2021. Mathematical Finance. [Arxiv]

  8. Robust fundamental theorems of asset pricing in discrete time, 2020. To appear in SIAM Journal on Financial Mathematics. [Arxiv]

  9. On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case, (with Éric Moulines, Miklós Rásonyi, Sotirios Sabanis, Ying Zhang), 2021.  SIAM Journal on Mathematics of Data Science.  [Arxiv] 

  10. Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning,  (with Miklós Rásonyi),  Stochastic Processes and their Applications 2022. [Arxiv] 

  11. Behavioural investors in conic market models, (with Miklós Rásonyi), 2019. To appear in Theory of Probability and its Applications. [Arxiv] 

  12. On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case (with M. Barkhagen, E. Moulines, M. Rásonyi, S. Sabanis and Y. Zhang)  [Arxiv]. [Bernoulli].

  13. Robust utility maximization in markets with transaction costs (with Miklós Rásonyi) Finance and Stochastics 2019.   

  14. The value of informational arbitrage (with Andrea Cosso and Claudio Fontana), Finance and Stochastics 2020.

  15. On fixed gain recursive estimators with discontinuity in the parameters (with Chaman Kumar, Miklós Rásonyi and Sotirios Sabanis), ESAIM: Probability and Statistics 2019. [Arxiv] 

  16. On optimal investment with processes of long or negative memory (with Miklós Rásonyi), Stochastic Processes and their Applications, 2017. [Arxiv] 

  17. Skorohod's representation theorem and optimal strategies for markets with frictions (with Miklós Rásonyi), SIAM Journal on Control and Optimization 55.6 (2017): 3592-3608. [Arxiv] 

  18. Arbitrage and utility maximization in market models with an insider (with Peter Tankov and Wolfgang Runggaldier), Mathematics and Financial Economics. [Arxiv] 

  19. Optimal investment with intermediate consumption under no unbounded profit with bounded risk (with Andrea Cosso, Claudio Fontana, and Oleksii Mostovyi), Journal of Applied Probability 54.3 (2017): 710-719. [Arxiv]

  20. Market models with optimal arbitrage (with Peter Tankov), SIAM Journal on Financial Mathematics, Vol. 6(1), 66-85 (2015). [Arxiv]