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.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant mathematics or statistics related discipline.”

Funding

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 applying for competition and self-funded projects.

For more information, visit our funding page or search our funding database for specific scholarships, studentships and awards you may be eligible for.

Before you apply

We strongly recommend that you contact the supervisor for this project before you apply.

How to apply

Apply online through our website: https://uom.link/pgr-apply-fap

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

After you have applied you will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (please make sure that the contact email you provide is an official university/work email address as we may need to verify the reference)
• English Language certificate (if applicable)

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

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We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).