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Master Thesis Students Applied Mathematics KU Leuven, working on specific projects in Brussel


ING Financial Markets:

Within the global ING Group and its large client base, ING Financial Markets offers a unique access to all financial products. As a student, you will be able to work with a highly specialised team, “FO Equity & Commodity Derivatives Quants”, which operates from ING Brussels but serves the business globally.

Context Front-Office Equity & Commodity Derivatives Quants

Quantitative modelling involves the specification, calibration and integration of (stochastic) models designed to price and hedge (exotic) derivative positions taken by trading desks in Financial Markets. A range of quantitative methods and models are deployed to address specific pricing and hedging issues in these markets.

The Equity & Commodity Derivatives Front-Office Quants manage, maintain and develop an extensive numerical library that is used in the intra-day and end-of-day valuation and risk management of a broad spectrum of equity and commodity derivative payoffs.

Subject: Application of machine learning techniques to fit future exposure profiles

During previous years, functionality has been developed that allows the price of any position to be calculated under a “future” scenario. This means that at a (range of) specified time-point(s) in the future, the position is priced taking into account the remaining maturity of the instrument (“ageing”) under a given set of market,  payoff and instrument scenarios. The aim is to be able to provide reliable future exposure profiles which can serve as a basis for risk management purposes.

Specifically these profiles will be used for counterparty risk valuation adjustments. The calculations required are extensive, so in order to “simplify” these calculations, we would like to investigate whether the future profiles could be fitted with machine learning techniques, such that the algorithm could be used as an “interpolator” inside the Monte Carlo engine for calculating the counterparty risk valuation adjustments.

We are looking for a bright student:

-          With a motivated, energetic and constructive attitude

-          Willing to investigate and explore thoroughly possible solutions

-          Show perseverance and commitment

We offer the possibility to spend some time as a “stagiair” (July-August-September), which we would recommended in order to gain a better understanding of the topic and context. Also during the Academic Year, we are open to host the student (according to a flexible schedule).

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