This is a unique opportunity for a PhD at a leading UK university whilst also working alongside market practitioners to develop the next generation of early warning risk systems for financial markets. In this applied research project, you will spend ~40% of your time at the industrial partner site working alongside market practitioners to develop a practitioner’s understanding of the financial markets alongside academic approaches and the various approaches / techniques / datasets for understanding their dynamics. Because of this applied aspect, you will receive a significantly enhanced stipend (see details below).
Application of theories and methods developed by physicists to economic and financial problems can be very instructive. Like systems in nature, markets are complex but coherent. Most markets are characterized by the emergence of bubbles and crashes, driven by traders’ and investor’s herd mentality but similarly to earthquakes, market crashes are sudden but not wholly unexpected events. Informed participants can observe the build-up of such bubbles but the timing of any crash and its extent are rarely anticipated correctly .
Despite the ability of some market participants to identify the emergence of bubbles, the challenge of predicting crashes (critical transitions) is non-trivial due to complex non-linear interactions and the change in behaviour of market participants if the existence of a bubble becomes known. A core challenge is to understand the mechanisms that underly these systems and the likelihood of regime changes. Depending on the purpose of the models these relationships can be captured using a wide range of approaches, including probabilistic models, non-linear dynamical systems, and network models. In the broader context, notions of critical transitions and early warning signals are relevant for any system affected by abrupt shifts in state e.g. asthma attacks, shifts in climate, or wildlife populations that threaten ecosystems.
In this project, you will (1) propose a plausible mechanism for known ‘stylised facts’ (well-known observations or patterns in financial markets that have not yet been explained) regarding market transitions; (2) demonstrate that the proposed mechanism generates the stylised facts; (3) study the dynamics of the mechanism to identify potential ‘early warning’ signs of transition; (4) evaluate the effectiveness of the ‘early warning’ signs using data simulated by the proposed mechanism; (5) evaluate whether those early warning signs are effective on data obtained from real financial market data.
This is a multidisciplinary project in collaboration between the physics and economics departments as well as the Bath-based industrial partner, CheckRisk – provider of risk services to clients around the world. This is an excellent opportunity to accelerate a career by becoming a domain expert in an exciting area of the science of risk.
The successful candidate should hold, or expect to receive, a first class or good 2.1 Master’s degree (or equivalent) in Physics (Theoretical Physics preferred), a mixed Physics/Maths degree or related field (Engineering, Statistics etc.). A keen interest in the topic and a strong work ethic are essential. Also required is basic programming experience (knowledge of Matlab/R/Octave will be beneficial but is not necessary). Previous experience in statistical physics/statistics will be beneficial.
How to apply:
Informal enquiries are welcomed and should be directed to Dr Marcin Mucha-Kruczynski, [email protected]
Formal applications should be made via the University of Bath’s online application form for a PhD in Physics: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUPH-FP01&code2=0013
More information about applying for a PhD at Bath may be found here: http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/
Early application is strongly recommended.
Anticipated start date: 30 September 2019.
UK and EU students who have been resident in the UK since September 2016 will be considered for an EPSRC CASE Conversion studentship with CheckRisk.
The studentship award will cover full UK/EU tuition fees and a training support fee of £1,000/annum. The successful applicant will also receive an attractive enhanced stipend (expected to be £20,009 in 2019/20) and an allowance for research expenses, subject to contract agreement with CheckRisk. Funding is available for 3.5 years.
Unfortunately, applicants classed as Overseas for tuition fee purposes are NOT eligible for this funding.