Wentao Li

- Lecturer, University of Manchester, UK; 2021 -- present
- Assistant professor, University of Hong Kong, HK; 2019 -- 2021
- Lecturer, Newcastle University, UK; 2017 -- 2018
- Senior Research Associate, Lancaster University, UK; 2013 -- 2017 
- PhD in Statistics, Rutgers, the State University of New Jersey, USA; 2013
Supervisor: Rong Chen, Zhiqiang Tan
- M.S. in Statistics, Colorado State University, USA; 2008
Supervisor: Haonan Wang
- B.S. in Statistics, University of Science and Technology of China, China; 2006

MATH38032 Time Series Analysis

- Bayesian asymptotic theory 

- Bayesian computation methods

- Monte Carlo methods

- Simulation-based inference

- State-space model

- Financial time series data

PhD Studentship in Bayesian computation

Supervisor: Dr. Wentao Li

Start date: September 2025

At Manchester we offer a range of scholarships, studentships and awards at university, faculty and department level, to support both UK and overseas postgraduate researchers. For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.

Closing date for applications: 15th December, 2024

Simulation-based inference for financial econometrics models

In modern statistical applications,  many complicated models have two common features. First the likelihood functions are often difficult to evaluate;  second the model is generative. In particular,  financial time series data pose the following challenges. First, when latent stochastic dynamics are considered, e.g. volatilities and regime switching, the likelihood is intractable. Second, in the big data era, the more sophisticated model is required for high-frequency data and their microstructure. The class of simulation-based methods is often used for statistical inference of intractable likelihood models by using model simulations. The inference is usually conducted under the Bayesian framework, providing uncertainty quantifications for both parameter estimation and prediction. It has seen successful applications and become increasingly popular in a wide range of areas, including population genetics, ecology, astronomy, etc. This project aims to develop new simulation-based statistical computing algorithms with emphasis on financial econometrics models. The underpinning convergence theory will be developed. Building blocks of the new algorithms include approximate Bayesian computation, sequential Monte Carlo, Markov chain Monte Carlo, Bayesian synthetic likelihood, their synergies, etc. 

For informal enquiries, please contact Wentao Li ([email protected]).

To apply, follow these instructions, being sure to specifically mention the title/supervisor of this project. A guideline for PhD applicants is available here.