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Anomaly Detection for Portfolio Risk Management: An evaluation of econometric and machine learning based approaches to detecting anomalous behaviour in portfolio risk measures
KTH, School of Industrial Engineering and Management (ITM), Industrial Economics and Management (Dept.), Economics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Avvikelsedetektering för Riskhantering av Portföljer : En utvärdering utav ekonometriska och maskininlärningsbaserade tillvägagångssätt för att detektera avvikande beteende hos portföljriskmått (Swedish)
Abstract [en]

Financial institutions manage numerous portfolios whose risk must be managed continuously, and the large amounts of data that has to be processed renders this a considerable effort. As such, a system that autonomously detects anomalies in the risk measures of financial portfolios, would be of great value. To this end, the two econometric models ARMA-GARCH and EWMA, and the two machine learning based algorithms LSTM and HTM, were evaluated for the task of performing unsupervised anomaly detection on the streaming time series of portfolio risk measures. Three datasets of returns and Value-at-Risk series were synthesized and one dataset of real-world Value-at-Risk series had labels handcrafted for the experiments in this thesis. The results revealed that the LSTM has great potential in this domain, due to an ability to adapt to different types of time series and for being effective at finding a wide range of anomalies. However, the EWMA had the benefit of being faster and more interpretable, but lacked the ability to capture anomalous trends. The ARMA-GARCH was found to have difficulties in finding a good fit to the time series of risk measures, resulting in poor performance, and the HTM was outperformed by the other algorithms in every regard, due to an inability to learn the autoregressive behaviour of the time series.

Abstract [sv]

Finansiella institutioner hanterar otaliga portföljer vars risk måste hanteras kontinuerligt, och den stora mängden data som måste processeras gör detta till ett omfattande uppgift. Därför skulle ett system som autonomt kan upptäcka avvikelser i de finansiella portföljernas riskmått, vara av stort värde. I detta syftet undersöks två ekonometriska modeller, ARMA-GARCH och EWMA, samt två maskininlärningsmodeller, LSTM och HTM, för ändamålet att kunna utföra så kallad oövervakad avvikelsedetektering på den strömande tidsseriedata av portföljriskmått. Tre dataset syntetiserades med avkastningar och Value-at-Risk serier, och ett dataset med verkliga Value-at-Risk serier fick handgjorda etiketter till experimenten i denna avhandling. Resultaten visade att LSTM har stor potential i denna domänen, tack vare sin förmåga att anpassa sig till olika typer av tidsserier och för att effektivt lyckas finna varierade sorters anomalier. Däremot så hade EWMA fördelen av att vara den snabbaste och enklaste att tolka, men den saknade förmågan att finna avvikande trender. ARMA-GARCH hade svårigheter med att modellera tidsserier utav riskmått, vilket resulterade i att den preseterade dåligt. HTM blev utpresterad utav de andra algoritmerna i samtliga hänseenden, på grund utav dess oförmåga att lära sig tidsserierna autoregressiva beteende.

Place, publisher, year, edition, pages
2018. , p. 42
Keywords [en]
Anomaly detection, Outlier Detection, Portfolio management, Risk management, Value-at-Risk, HTM, EWMA, ARIMA, LSTM, GARCH
Keywords [sv]
Anomalidetektering, Avvikelsedetektering, Portföljhantering, Riskhantering, Valueat-Risk, HTM, EWMA, ARIMA, LSTM, GARCH
National Category
Economics
Identifiers
URN: urn:nbn:se:kth:diva-232131OAI: oai:DiVA.org:kth-232131DiVA, id: diva2:1232503
External cooperation
VPD
Subject / course
Computer Engineering with Industrial Economy
Educational program
Master of Science in Engineering - Computer Science and Technology
Presentation
2018-05-30, Sing Sing, KTH, Lindstedtsvägen 30, Stockholm, 10:30 (English)
Supervisors
Examiners
Available from: 2018-07-26 Created: 2018-07-11 Last updated: 2018-07-26Bibliographically approved

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