Stressed Value-at-Risk (VaR) is a statistic used to measure an entity's exposure to market risk by evaluating possible extreme portfolio losses. Stressed VaR scenarios can be used as a metric to describe the state of the financial market and can be used to detect and counter procyclicality by allowing central clearing counterparities (CCP) to increase margin requirements. This thesis aims to implement and evaluate machine learning methods (e.g., neural networks) for selecting stressed VaR scenarios in price return stock datasets where one liquidity day is assumed. The models are implemented to counter the procyclical effects present in NASDAQ's dual lambda method such that the selection maximises the total margin metric. Three machine learning models are implemented together with a labelling algorithm, a supervised and unsupervised multilayer perceptron and a random forest model. The labelling algorithm employs a deviation metric to differentiate between stressed VaR and standard scenarios. The models are trained and tested using 5000 scenarios of price return values from historical stock datasets. The models are tested using visual results, confusion matrix, Cohen's kappa statistic, the adjusted rand index and the total margin metric. The total margin metric is computed using normalised profit and loss values from artificially generated portfolios. The implemented machine learning models and the labelling algorithm manage to counter the procyclical effects evident in the dual lambda method and selected stressed VaR scenarios such that the selection maximise the total margin metric. The random forest model shows the most promise in classifying stressed VaR scenarios, since it manages to maximise the total margin overall.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-504315 |
| Date | January 2023 |
| Creators | Tennberg, Moa |
| Publisher | Uppsala universitet, Avdelningen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC F, 1401-5757 ; 23034 |
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