Into the Business - ING

 

The market risk model validation team is responsible for validating a wide range of models:

 

I am originally from China. I did my BSC in Statistics in China, and in 2003 I came to Delft to study Applied Mathematics. I finished my PhD in 2009, at the height of the financial crisis, and then I started working at Fortis Bank Nederland, which was just bought by the Dutch state and later merged with the Dutch part of ABN AMRO. In 2011 I moved to Rabobank, where I worked for 2 years. And now I have been working for ING, doing market risk model validation, for the past 6 years. So I basically passed all the big banks in the Netherlands!

 

Well, I mainly focus on the content of my job, so to me, there isn’t a big difference between the banks, I always focus on the models, either on model development or validation. ING is a nice place to work being very international!

 

We have a team with about 20 people, coming from all over the world like Belgium, China, Colombia, Hungary, Italy, Russia, Slovakia, Turkey… 

 

So let me describe what I do, starting at the basics. Banks are exposed to three types of risk: credit risk, market risk, and operational risk. To cover the potential losses, coming from these risks, banks are required to hold capital. The capital ratio is probably one of the most important numbers regulators or investors will look at, to assess how healthy a bank is. To calculate the required capital, a bank will develop models. And in big banks, there are typically two types of teams, teams developing these models, and teams validating these models. 

 

In essence, models are only simplistic representations of reality, and they will never be perfect. You always have risk associated with the models, being a model risk. And model risk can be very important to a bank. 

 

Let me give you two examples: 

So, you see, model risk is very important, and model validation plays an important role to mitigate that risk. Model validation has three key objectives. 1) We need to make sure the model is reliable, compliant with policies and regulations, and that it is valid for the intended use. 2) We need to find and fully understand the limitations and weaknesses of the model. 3) We need to contribute to the ongoing model development by providing recommendations for improvement. 

 

What I studied was mostly probability theory and stochastics, and I still use my knowledge daily, and I also use a lot of econometrics knowledge. The two are very much connected! Time series analysis is very important for my job for example, and there are quite some econometricians in my team, as well as mathematicians and statisticians.

 

You need to have good theoretical knowledge of statistics, probability theory, and you also need very good programming skills. In our team, you have the opportunity to learn and evaluate a wide range of models, on a variety of topics. A (validation) project takes around 3-4 months, and then you move on to another project, so you need to understand the topic and the model inside out in a short time period, and that can be very intensive. But this is also very fun as you learn a lot about the financial world!

 

When you are a student, you study a lot of complex models and theory. However, what I see is that banks typically use rather simple models. If you use a GARCH(1,1) model in a bank, it is already considered as a complex model, whereas for a student it might seem rather basic. But this is because banks deal with other problems. For example, you need to model over 50 currencies. Although it might be very easy to model one currency very well, to model all 50 at the same time is way more difficult due to the dependence among the currencies and the explosion of the number of parameters. The type of problems you solve in real life can be different from what you study at university.

 

Personally, I like simple models, if they work of course. If you introduce more parameters, making the model more complex, you will always improve the fit (R squared) clearly. However, you will need to be able to understand the complex models. It’s important to strike a balance between usability and complexity.

 

There will always be something that can be done better. For me this is also the fun part of model validation, you can really see you contribute something to the model. It makes my work more fulfilling.

 

Let’s say you have an opinion about a model, and how it can be improved. But the model developers might not agree. because it may lead to a significant amount of work for the model developers, and it can have consequences to the business. You need to convince them that implementing our recommendation improves the model and is necessary. So, besides a lot of technical skills, we also need strong persuasive skills. Sometimes it can be a big challenge to convince them! And I’m still working on improving my persuasiveness.

 

We start our validation by asking for relevant model documentation. Model developers send documents for us to study. Many developers are here in the Netherlands so we can meet them to discuss some aspects of the model more efficiently, but for some projects, we must rely on conference calls and emails as there are developers in eg., UK, US, Italy, Spain, Poland, Australia. 

 

We spend typically 3-4 months on a project, analyzing the assumptions underlying the model, checking the correctness of the implementation, assessing the model performance, etc. We work in pairs, so you always have someone to talk to, especially when you are not sure about something. This is also a good way of working for juniors to learn from more senior colleagues. 

 

The outcome of our validation is a report, where we issue some recommendations to improve the model. This report is discussed and agreed with the model developers and then approved at a committee. 

 

Well, most of the time you just sit at your desk and work on your project, eg, writing the validation report, performing data analysis, etc. From time to time you talk to your co-validator on your project. Some days you have meetings with the developers to discuss problems or solutions, other days you have meetings with the team.

 

Each quarter we organize a make-it-happen week, we leave our daily work aside and work on innovation and automation ideas to improve our way of working. For example, one colleague built a python tool that everyone in the team can easily retrieve a large amount of market data with just a few clicks on an internal webpage.

 

It certainly can happen and it did happen a few times in the past. In this case, we need to resolve any dispute with the model developers to agree that the models are not adequate. Subsequently, the model must be redeveloped.  

 

Yes, in reality, we have a shortage of resources, there are too many projects that need to be done! We are constantly looking for people to join our team! Right now we are actively searching for people with knowledge in financial econometrics. People are hired from abroad because we cannot find enough people here in the Netherlands. We have opportunities for full-time positions, work-along internships, and also MSc thesis internship (if you are interested, check out ING’s career website, or connect on LinkedIn).

 

Right now python is what we mostly use, together with Matlab or R. While we do get codes from the developers, when possible we have to write codes ourselves. There are also standard tests we have to perform, and those are done in Python. 

 

Study hard of course, and program more! Programming is so important in this field, and it is becoming important in all fields.