Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models

dc.contributor.authorFeng, Yu
dc.contributor.authorRudd, Ralph
dc.contributor.authorBaker, Christopher
dc.contributor.authorMashalaba, Qaphela
dc.contributor.authorMavuso, Melusi
dc.contributor.authorSchlögl, Erik
dc.date.accessioned2021-10-18T08:54:24Z
dc.date.available2021-10-18T08:54:24Z
dc.date.issued2021-01-04
dc.date.updated2021-01-22T15:47:44Z
dc.description.abstractWe focus on two particular aspects of model risk: the inability of a chosen model to fit observed market prices at a given point in time (calibration error) and the model risk due to the recalibration of model parameters (in contradiction to the model assumptions). In this context, we use relative entropy as a pre-metric in order to quantify these two sources of model risk in a common framework, and consider the trade-offs between them when choosing a model and the frequency with which to recalibrate to the market. We illustrate this approach by applying it to the seminal Black/Scholes model and its extension to stochastic volatility, while using option data for Apple (AAPL) and Google (GOOG). We find that recalibrating a model more frequently simply shifts model risk from one type to another, without any substantial reduction of aggregate model risk. Furthermore, moving to a more complicated stochastic model is seen to be counterproductive if one requires a high degree of robustness, for example, as quantified by a 99% quantile of aggregate model risk.en_US
dc.identifierdoi: 10.3390/risks9010013
dc.identifier.apacitationFeng, Y., Rudd, R., Baker, C., Mashalaba, Q., Mavuso, M., & Schlögl, E. (2021). Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models. <i>Risks</i>, 9(1), http://hdl.handle.net/11427/35262en_ZA
dc.identifier.chicagocitationFeng, Yu, Ralph Rudd, Christopher Baker, Qaphela Mashalaba, Melusi Mavuso, and Erik Schlögl "Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models." <i>Risks</i> 9, 1. (2021) http://hdl.handle.net/11427/35262en_ZA
dc.identifier.citationFeng, Y., Rudd, R., Baker, C., Mashalaba, Q., Mavuso, M. & Schlögl, E. 2021. Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models. <i>Risks.</i> 9(1) http://hdl.handle.net/11427/35262en_ZA
dc.identifier.ris TY - Journal Article AU - Feng, Yu AU - Rudd, Ralph AU - Baker, Christopher AU - Mashalaba, Qaphela AU - Mavuso, Melusi AU - Schlögl, Erik AB - We focus on two particular aspects of model risk: the inability of a chosen model to fit observed market prices at a given point in time (calibration error) and the model risk due to the recalibration of model parameters (in contradiction to the model assumptions). In this context, we use relative entropy as a pre-metric in order to quantify these two sources of model risk in a common framework, and consider the trade-offs between them when choosing a model and the frequency with which to recalibrate to the market. We illustrate this approach by applying it to the seminal Black/Scholes model and its extension to stochastic volatility, while using option data for Apple (AAPL) and Google (GOOG). We find that recalibrating a model more frequently simply shifts model risk from one type to another, without any substantial reduction of aggregate model risk. Furthermore, moving to a more complicated stochastic model is seen to be counterproductive if one requires a high degree of robustness, for example, as quantified by a 99% quantile of aggregate model risk. DA - 2021-01-04 DB - OpenUCT DP - University of Cape Town IS - 1 J1 - Risks KW - model risk KW - option pricing KW - relative entropy KW - model calibration KW - stochastic volatility LK - https://open.uct.ac.za PY - 2021 T1 - Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models TI - Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models UR - http://hdl.handle.net/11427/35262 ER - en_ZA
dc.identifier.urihttps://doi.org/10.3390/risks9010013
dc.identifier.urihttp://hdl.handle.net/11427/35262
dc.identifier.vancouvercitationFeng Y, Rudd R, Baker C, Mashalaba Q, Mavuso M, Schlögl E. Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models. Risks. 2021;9(1) http://hdl.handle.net/11427/35262.en_ZA
dc.language.isoenen_US
dc.publisher.departmentAfrican Inst. of Fin. Markets and Risk Mngnten_US
dc.publisher.facultyFaculty of Commerceen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceRisksen_US
dc.source.journalissue1en_US
dc.source.journalvolume9en_US
dc.source.urihttps://www.mdpi.com/journal/risks
dc.subjectmodel risken_US
dc.subjectoption pricingen_US
dc.subjectrelative entropyen_US
dc.subjectmodel calibrationen_US
dc.subjectstochastic volatilityen_US
dc.titleQuantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Modelsen_US
dc.typeJournal Articleen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
risks-09-00013.pdf
Size:
615.34 KB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
0 B
Format:
Item-specific license agreed upon to submission
Description:
Collections