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The Rolling Window Method: Precisions of Financial Forecasting
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Rullande Fönster Metoden: Precision av Finansiell Prediktering (Swedish)
Abstract [en]

In this thesis we set out to study the prediction accuracy of statistical quantities related to portfolio analysis and risk management implied by a given set of historical data. The considered forecasting procedure rely on rolling-window estimates over varying horizons where the resulting empirical return distributions can be considered the corresponding stationary distributions. By using scenarios generated from a joint interest rate-equity framework the rolling-window method allows to, empirically, study the uncertainty of return statistics as well as risk measures related to market risk. The study shows that, given the chosen models, the method is valid in predicting future statistical quantities related to portfolio return of up to one year. For risk measures, the forecasting uncertainty is found to be too significant and highlights the difficulty in foreseeing extremities of future market movements.

Abstract [sv]

I detta examensarbete ämnar vi oss att studera precisionen av predikterade statistiska storheter relaterade till portföljanalys och riskhantering givet en mängd historisk data. Den använda prediktionsmetoden använder sig av rullande fönster estimeringar över varierande horisonter där de resulterande empiriska avkastningsfördelningarna kan ses som de motsvarande stationära fördelningarna. Genom att använda scenarier generade från ett ramverk för räntor och aktier, möjliggör rullande fönster metoden att, empiriskt, studera osäkerheter i skattade avkastnings statistikor och riskmått relaterade till marknads risk. Studien visar, givet de ingående modellerna, att metoden är giltig att använda för prediktering av statistiska storheter relaterade till portföljavkastningar upp till ett år. För riskmått visar sig skattningsosäkerhet vara för stor och belyser svårigheten att förutse extremiteter i framtida marknadsutfall.

Place, publisher, year, edition, pages
2017.
Series
TRITA-MAT-E, 2017:12
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-205595OAI: oai:DiVA.org:kth-205595DiVA: diva2:1089425
External cooperation
Kidbrooke Advisory
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2017-04-19 Created: 2017-04-19 Last updated: 2017-04-19Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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More styles
Language
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Output format
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