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On the use of Value-at-Risk based models for the Fixed Income market as a risk measure for Central Counterparty clearing
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Användningen av Value-at-Risk baserade modeller för Fixed Income marknaden som riskmått för Central Counterparty clearing (Swedish)
Abstract [en]

In this thesis the use of VaR based models are investigated for the purpose of setting margin requirements for Fixed Income portfolios. VaR based models has become one of the standard ways for Central Counterparties to determine the margin requirements for different types of portfolios. However there are a lot of different ways to implement a VaR based model in practice, especially for Fixed Income portfolios. The models presented in this thesis are based on Filtered Historical Simulation (FHS). Furthermore a model that combines FHS with a Student’s t copula to model the correlation between instruments in a portfolio is presented. All models are backtested using historical data dating from 1998 to 2016. The FHS models seems to produce reasonably accurate VaR estimates. However there are other market related properties that must be fulfilled for a model to be used to set margin requirements. These properties are investigated and discussed.

Abstract [sv]

I denna uppsats undersöks användningen av VaR baserade modeller för att sätta marginkrav för Fixed Income portföljer. VaR baserade modeller har blivit en standardmetod för Central Counterparties för att räkna ut marginkrav för olika typer av portföljer. Det finns många olika tillvägagångssätt för att räkna ut VaR i praktiken, speciellt för Fixed Income portföljer. Modellerna som presenteras i den här uppsatsen är baserade på Filterad Historisk Simulering (FHS). Dessutom presenteras en modell som kombinerar FHS med en Student’s t copula för att modellera korrelationen mellan olika instrument. Alla modeller backtestas på historisk data från 1998 till 2016. Modellerna ger rimliga VaR skattningar i backtesterna. Däremot finns det andra marknadsrelaterade egenskaper som en modell måste uppfylla för att kunna användas för att sätta margin. Dessa egenskaper undersöks och diskuteras.  

Place, publisher, year, edition, pages
TRITA-MAT-E, 2016:16
National Category
Probability Theory and Statistics
URN: urn:nbn:se:kth:diva-187464OAI: diva2:930360
Subject / course
Mathematical Statistics
Educational program
Master of Science - Mathematics
Available from: 2016-06-01 Created: 2016-05-23 Last updated: 2016-06-01Bibliographically approved

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