Times Series Analysis of Calibrated Parameters of Two-factor Stochastic Volatility Model
Stochastic volatility models have become essential for financial modelling and forecasting.The present thesis works with a two-factor stochastic volatility model that is reduced to four parameters. We start by making the case for the model that best fits data, use that modelto produce said parameter...
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Mälardalens högskola, Akademin för utbildning, kultur och kommunikation
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ndltd-UPSALLA1-oai-DiVA.org-mdh-446442021-01-12T05:28:50ZTimes Series Analysis of Calibrated Parameters of Two-factor Stochastic Volatility ModelengRios Benavides, RenatoBourelos, ChrysafisMälardalens högskola, Akademin för utbildning, kultur och kommunikationMälardalens högskola, Akademin för utbildning, kultur och kommunikation2019MathematicsMatematikStochastic volatility models have become essential for financial modelling and forecasting.The present thesis works with a two-factor stochastic volatility model that is reduced to four parameters. We start by making the case for the model that best fits data, use that modelto produce said parameters and then analyse the time series of these parameters. Suitable ARIMA models were then used to forecast the parameters and in turn, the implied volatilities.It was established that fitting the model for different groups of maturities produced better results. Moreover, we managed to reduce the forecasting errors by forecasting according to the different maturity groups. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44644application/pdfinfo:eu-repo/semantics/openAccess |
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English |
format |
Others
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Mathematics Matematik |
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Mathematics Matematik Rios Benavides, Renato Bourelos, Chrysafis Times Series Analysis of Calibrated Parameters of Two-factor Stochastic Volatility Model |
description |
Stochastic volatility models have become essential for financial modelling and forecasting.The present thesis works with a two-factor stochastic volatility model that is reduced to four parameters. We start by making the case for the model that best fits data, use that modelto produce said parameters and then analyse the time series of these parameters. Suitable ARIMA models were then used to forecast the parameters and in turn, the implied volatilities.It was established that fitting the model for different groups of maturities produced better results. Moreover, we managed to reduce the forecasting errors by forecasting according to the different maturity groups. |
author |
Rios Benavides, Renato Bourelos, Chrysafis |
author_facet |
Rios Benavides, Renato Bourelos, Chrysafis |
author_sort |
Rios Benavides, Renato |
title |
Times Series Analysis of Calibrated Parameters of Two-factor Stochastic Volatility Model |
title_short |
Times Series Analysis of Calibrated Parameters of Two-factor Stochastic Volatility Model |
title_full |
Times Series Analysis of Calibrated Parameters of Two-factor Stochastic Volatility Model |
title_fullStr |
Times Series Analysis of Calibrated Parameters of Two-factor Stochastic Volatility Model |
title_full_unstemmed |
Times Series Analysis of Calibrated Parameters of Two-factor Stochastic Volatility Model |
title_sort |
times series analysis of calibrated parameters of two-factor stochastic volatility model |
publisher |
Mälardalens högskola, Akademin för utbildning, kultur och kommunikation |
publishDate |
2019 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44644 |
work_keys_str_mv |
AT riosbenavidesrenato timesseriesanalysisofcalibratedparametersoftwofactorstochasticvolatilitymodel AT boureloschrysafis timesseriesanalysisofcalibratedparametersoftwofactorstochasticvolatilitymodel |
_version_ |
1719372593210851328 |