Machine Learning for Market Risk
Door Svetlana Borovkova, Head of Quantitative Modeling
In two recent columns, I discussed the current state of Machine Learning (ML) applications in finance (February 15, 2022) and the use of ML in credit (November 23, 2021). Recently, a new class of machine learning algorithms – the so-called autoencoders – was proposed for market risk applications by Prof. John Hull, well-known for his famous book about derivatives, and his co-author Alexander Sokol.
I heard their talks on this at the latest Risk Minds conference in December and immediately got so excited by this ingenuous application of machine learning, that we are already implementing and extending it in within our quant team at Probability & Partners.
One of the main problems in assessing the market risk of (large) portfolios is the multidimensionality of the task. Portfolios typically comprise tens if not hundreds of assets – stocks, commodities, indices, ETFs and other instruments. Treating each of them as an individual risk factor is not feasible. A similar problem arises when modelling interest rate curves or implied volatility surfaces (volatilities for different option strikes and maturities) – these problems are also multivariate in nature.
To ease the modelling task, one would like to find a smaller set of (often unobservable) latent risk factors, which, when taken together, explain most of the risk of a large portfolio. In interest rate curves, these latent factors are the level, the slope and the curvature of the curve. These three factors can explain 99% of all interest rate moves. But how can we find such factors for more complicated situations, such as large diversified investment portfolios or implied volatility surfaces? This is where autoencoders can help.