Our PhD opportunities

Network effects and early warning signal model of risk spillover in stock returns

The main aim of this project is to investigate the significance of stock market connectedness via structural and spatial networks, in order to forecast contagion and risk propagation within stock prices and ultimately financial markets. By investigating the relationship between the main stock market industry linkages (automobile, financial, technology, telecommunications and energy), the study endeavours to reveal how endogenous feedback mechanisms via established stock market networks influence the stock returns and transmission of covariance risk. In other words, the project aims to reveal how the structure of the established spatial stock market linkages can explain stock market integration, and which industry captures the higher share of predictable stock market variation. Next, the project will develop an effective early warning signal model (predicting mechanism) in a regime switching setup to explore network interdependences coupled with risk contagion and transmission, which is representative of both certain and uncertain times that characterise financial markets in general. 

Applicants for this project should have a relevant Master’s degree in either Finance, Economics, Statistics or Mathematics.

Supervisory team: Dr Andrea Eross & Prof. Arnab Bhattacharjee

Research Centre:  The Centre for Social and Economic Data Analytics

For contact details, more information and to apply

When submitting your application for this project area, please select the option ‘Accountancy and Finance, PhD’ from the drop-down list on the online application system.

The nature of the oil market and interactions between oil prices, macro fundamentals, and uncertainty.

There is considerable policy and research interest in the nature of the oil market and interactions between oil prices, macro fundamentals, and uncertainty. We would like to recruit a PhD candidate to contribute to oil market stylised facts by extending the literature’s understanding of uncertainty generated within the oil market. This work would build on a burgeoning research project and ongoing collaboration (Byrne & Ersoy, 2020; Byrne et al., 2019). The project has three core research pathways: (i) expand on existing literature (e.g., Baumeister & Hamilton, 2019; Mumtaz, 2018) by extending our understanding of the impact of endogenous uncertainty accounting for time variation in impact, including but not limited to using Bayesian MCMC VAR estimation; (ii) investigate and extend macro theory on oil and uncertainty, for example building upon Nakov and Pescatori’s (2010) multi-country DSGE with an oil sector from the Macroeconomic Model Data Base; and (iii) examine the forecast performance of the resulting model, which should benefit both oil producers and consumers.

Applicants for this project should have an MSc in Economics (or a closely related area).  In addition, experience with econometric modelling and/or DSGE models is desirable.

Supervisory team: Dr Erkal Ersoy & Prof. Joseph Byrne

Research Centre:  The Centre for Social and Economic Data Analytics

For contact details, more information and to apply.

When submitting your application for this project area, please select the option ‘Economics, PhD’ from the drop-down list on the online application system.

How Did the COVID-19 Crisis Change Asset Pricing?

The COVID-19 pandemic dramatically impacted consumer behaviour, albeit heterogeneously, and has implications for financial markets and asset prices (Lettau et al. 2019). Recent theory in finance and economics links this heterogeneous shock to consumption and wealth via capital share or wealth of entrepreneurs. Previous research illustrates that capital share is a risk factor for asset prices. How did changes in consumer spending and savings alter capital share, if at all? Aggregate consumption, however, has not been a substantial determinant of asset prices, but consumer habits too may have changed leading to possibly new behavioural patterns that affect asset pricing through consumer utility functions. The parameters of habit formation utility may have altered in ways that affect asset pricing. This research shall contain elements of mathematical modelling, numerical simulation and econometric estimation. 

Applicants for this project should have experience of using mathematical modelling, numerical simulation and/or econometric estimation.

Supervisory team: Dr Boulis Ibrahim & Prof. Joseph Byrne

Research Centre:  The Centre for Social and Economic Data Analytics

For contact details, more information and to apply.

When submitting your application for this project area, please select the option ‘Accountancy and Finance, PhD’ from the drop-down list on the online application system.

What impact will a ‘hybrid’ way of working have on productivity, wages, innovation, life satisfaction?

When the WHO declared Covid-19 a pandemic on March 11, 2020, the lives of hundreds of millions of people abruptly changed. Many shifted to working from home, and millions lost their jobs. At present, there is uncertainty about the way we will work in the post-pandemic future and what impact a ‘hybrid’ way of working will have on productivity, wages, innovation, life satisfaction, labour force participation, gender inequality, job searches, mobility patterns, workforce composition, etc. The present project seeks to address these issues either theoretically through the calibration/estimation of a suitable theoretical framework (such as a search and matching model), or through the empirical estimation of econometric models using available survey data (e.g. from organizations such as BLS, ONS, OECD, and the UK Government; from independent research firms such as ClearlyRated, AtlasCloud, GapSquare; and from other sources such as the US Current Population Survey and the Real-Time Population Survey for the US).

Supervisory team:  Dr Cristina Tealdi & Dr Rachel Forshaw (Heriot-Watt University), with Prof. Davide Fiaschi (University of Pisa, Italy).

Research Centre:  The Centre for Social and Economic Data Analytics

For contact details, more information and to apply. 

Applicants for this project should have an MSc in Economics (or a closely related area).

When submitting your application for this project area, please select the option ‘Economics, PhD’ from the drop-down list on the online application system.

Macro-Finance-Labour

Ever since the advent of the Great Recession, policymakers and other market participants have endeavoured to understand and analyse the existing theoretical and empirical models which capture and explain the underlying forces and adjustment processes engendered by the ongoing crisis. Our research agenda broadly encompasses research topics such as the origins of productivity shocks over the business cycle and financial crisis, firms’ monopsony power, wage rigidity, the role of financial constraints on labour share developments, credit shocks, movements in commodity and asset prices, monetary policy frameworks, the role of expectations and expectations formation in policy design. These and other topics which overlap the fields of macroeconomics/finance/labour, in both developed and developing countries, remain unresolved and fertile ground for exciting new research.  The Macro-Finance-Labour Group in CSEDA is an active research grouping within the School which provides a great environment in which to do research. Members include Professor Joe Byrne, Professor Mustafa Caglayan, Dr Cristina Tealdi, Dr Atanas Christev, Professor David Cobham, Dr Andrea Eross, Dr Boulis Ibrahim, and Dr Philippe LeMay-Boucher.

Applicants for this project should have a MSc in Economics or a MSc in Finance.

Supervisory team: Dr Andrea Eross & Dr Cristina Tealdi


Research Centre:  The Centre for Social and Economic Data Analytics

For contact details, more information and to apply.

When submitting your application for this project area, please select the option ‘Accountancy and Finance, PhD’ or ‘Economics, PhD’ from the drop-down list on the online application system.

Big data econometrics and economic applications.

The advent of ‘big data’ has had significant implications for economics, econometrics, and policy. This is creating new opportunities for the development and adoption of innovative technologies and methods with applications in many areas. This PhD project is aimed at studying the theory and applications of high-dimensional methods for econometrics using large datasets. These novel techniques could be applied to numerous domains, such as the analysis of financial, spending and employment decisions using individual-level transactions data; the spatial distribution of deprivation and social inequality, and spillover effects between localities, using Scottish microdata; and understanding distributional consequences of economic shocks and policy through microsimulation or agent-based modelling. This proposal builds upon our expertise and research leadership in the following areas: (a) big data and high dimensional econometrics; (b) energy economics; (c) spatial economics and econometrics; (d) labour economics and microeconometrics. The appointed PhD student would become part of our research centre and focus on machine learning and other big data regularisation tools to address a range of research questions of interest.

Applicants for this project should have an MSc in Economics (or a closely related area).

Supervisory team: Dr Erkal Ersoy & Prof. Mark Schaffer

Research Centre:  The Centre for Social and Economic Data Analytics

For contact details, more information and to apply. 

When submitting your application for this project area, please select the option ‘Economics, PhD’ from the drop-down list on the online application system.