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Investigating the benefits of big data in macroeconomic forecasting and financial investment strategies.


Project Description

Our joint DAFM/KBS PhD programme provides candidates with strong computational and research training, preparing for a variety of careers in the finance industry and the City, central banks and academia. The programme includes the writing of a PhD thesis, PhD courses, and knowledge dissemination.

The PhD project fits into the DAFM research agenda of Forecasting and Quantitative Finance themes. The candidate will conduct research in two areas. In the first half of the programme we will focus on research in macroeconomic nowcasting and forecasting using big data and employing high-dimensional econometric techniques. In the second half of the programme, we switch our focus on the use of big data to optimise portfolio strategies and financial.

The PhD project should extend this line of research.

The ideal candidate should be a keen enthusiast about computational statistics and data analysis in economics and finance. The candidate must be motivated and able to engage in theoretical and empirical work and have good oral and written communication skills.

Candidates who have completed a BSc and MSc in related fields, such as Economics and Finance are eligible to apply. Also, candidates with strong quantitative skills from fields such as Applied Mathematics, Statistics, Computer Science, Applied Physics, and Engineering are also encouraged to apply.

Eligibility

Academic requirements:
• Satisfy the entry requirements of the King’s Business School PhD programme - Master's degree with a Merit and a Bachelor's degree with 2:1 in a relevant subject: https://www.kcl.ac.uk/study/postgraduate/research-courses/management-research-mphil-phd
• English Language requirements (ILETS 7.0 overall with a minimum of 6.5 in each skill): https://www.kcl.ac.uk/study/postgraduate/apply/entry-requirements/english-language

How to Apply

Your application should consist of:
• A letter of interest of up to three pages in length outlining your background, interest and research skills related to the topic, and how you satisfy the requirements described above.
• A detailed CV
• Two academic reference letters, recently dated
These should be e-mailed to

Formal appointment will be subject to checks on references, certificates/qualifications/etc., English test scores (if relevant)
To discuss the project, contact Dr Fotis Papailias ()

Funding Notes

UK/EU applicants will be eligible for a full award paying tuition fees and maintenance.
The 4-year studentship covers:
• Home/EU Tuition fees
• An annual stipend (approx. £17,009)
• A generous annual conference and training grant
• Additional paid teaching opportunities

References

• Buono, D., Mazzi, G.L., Kapetanios, G., Marcellino, M., Papailias, F. (2017). “Big Data types for macroeconomic nowcasting”. EURONA – Eurostat Review on National Accounts and Macroeconomic Indicators, 93-145.
• Chudik, A., Kapetanios, G., Pesaran, M.H. (2018). “A One-Covariate as a Time, Multiple Testing Approach to Variable Selection in High-Dimensional Linear Regression Models”. Econometrica, 86(4), 1479-1512.
• Dias, F.G., Scherrer, C.M., Papailias, F. (2017). “Volatility Discovery”. SSRN Working Paper, https://dx.doi.org/10.2139/ssrn.2831358
• Fan, J., Samworth, R., Wu, Y. (2009). “Ultra High Dimensional Variable Selection: Beyond the Linear Model”, Journal of Machine Learning Research, 10, 1829-1853.
• Kapetanios, G., Marcellino, M., Papailias, F. (2017a). “Big data conversion techniques including their main features and characteristics”. Eurostat Statistical Working Papers, doi: 10.2785/461700
• Kapetanios, G., Marcellino, M., Papailias, F. (2017b). “Filtering techniques for big data and big data based uncertainty indexes”. Eurostat Statistical Working Papers, doi: 10.2785/880943.

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