We recently had the pleasure of catching up with Raymond Pang to discuss his research, background, and next steps.

Raymond recently completed his PhD at LSE after defending his thesis on “Fire sales and policy interventions in financial networks”, and he was supervised by Prof Luitgard Veraart.

Hi Raymond, let’s start with a bit about yourself. Where did you grow up, and where did you go for your degrees?

My parents are from Hong Kong and China, but I grew up in London, and I’ve lived here most of my life. Regarding my educational background, for my Bachelor’s, I studied maths at King’s College London. I liked modules where you can apply the theorems and techniques from the course to real-world problems. So, throughout the degree, I was drawn to theoretical physics and financial maths. I struggled more with financial maths than theoretical physics. However, I was very motivated and engaged in the subject, so I went to Cambridge University for my master’s degree to pursue this further. After this, I came here for my PhD, where I did more applied work in finance.

As you do more applied work, have you found that your research has given you more insight into the major financial news stories that we all hear about?

Yes, absolutely. It is not uncommon for the news to motivate my research and to inspire new directions. During the Truss government, there was a substantial change in fiscal policy that caused many assets to be sold in large quantities and at heavily discounted prices. One of my areas of research is in fire sales, and this is exactly the sort of behaviour that I would seek to model. During this period, I also spent time with my supervisor discussing the news. In addition to this, I also researched areas such as climate stress testing, so I saw a lot of the topics I researched in the news.

Can you tell us more details about your PhD thesis?

I have been working in an area called financial contagion. So, we can imagine a system of banks where these banks borrow from and lend to each other, and you can think of this as a network. There may be a situation where one of these banks fails, and they may not be able to fulfil an obligation in full. Because of this, this bank may default on their payments, and this may cause other banks to fail. So, you can get a system in which a few banks failing can trigger many banks failing. These are the mathematical models that I was looking at for my research. However, you can model any type of contagion in a similar way, such as disease, or social contagion, which is when information spreads.

To go more into the technical details, you start by looking at the data for the things that you are trying to model, which will give you an insight into the borrowing or lending of these banks. You can then model these as a network, where each node is a bank, an outward edge of this node is a loan made by a bank, and an inward edge is some borrowing made by that bank. You then want to analyse network features to understand how contagion spreads. When it comes to the mathematical tools that are used to do this, we are interested in fixed-point ideas. We would have a function that describes the contagion process, and you iteratively apply this function to show that there is a fixed point, which gives rise to an equilibrium. This is when you can then analyse financial outcomes from this process. You also can verify this analysis using simulations and estimations to see empirically how these results play out.

I would imagine that the banks would start to change their behaviour once a couple of banks have failed, which would change the structure of the graph. Are you also interested in questions where the graph also changes throughout this procedure? 

Even before considering temporal changes, the data that is used to form the graph in the first place is often constrained by privacy issues, or how it is reported. For example, smaller banks and institutions are not required to report their data as openly. So, a key question is if you are missing that component, how much of the risk are you capturing, as these smaller banks do play a key part in the system. Then, it can be even harder to see how things change, as you have these problems, but it can also be difficult to compare data year by year and even harder to predict the behaviour of banks once the contagion has started. This often leads to thinking about what can be said about contagion in more general structures, as these results won’t be as reliant on the data.

What are you interested in doing next?

I am very interested in climate finance, which involves looking at contagion networks but more from a planning perspective. For example, I might consider how a natural disaster might affect a network and then consider how the network could be designed or regulated to limit the impact of the natural disaster.

For our final question, do you have any recommendations for new students in London?

For me, the best places are the small independent cafes that you might only find down a side street. This might be a small ramen place, or a family-run pasta restaurant, for example. Everyone has their places that they enjoy, and this sort of becomes your part of London. If you were going to force me to name one place, I would recommend one in Soho called Koya. They do these cold udon noodles, which are very nice and quite different from the hot soup noodle bowls that you find elsewhere.

That sounds like a great suggestion. Thank you very much!