| Field |
Value |
Language |
|
dc.contributor.author |
Feng, Y |
|
|
dc.contributor.author |
Rudd, R |
|
|
dc.contributor.author |
Baker, C |
|
|
dc.contributor.author |
Mashalaba, Q |
|
|
dc.contributor.author |
Mavuso, M |
|
|
dc.contributor.author |
Schlögl, E |
|
|
dc.date.accessioned |
2022-02-14T03:21:50Z |
|
|
dc.date.available |
2022-02-14T03:21:50Z |
|
|
dc.date.issued |
2021-01-04 |
|
|
dc.identifier.citation |
Risks, 2021, 9, (1), pp. 13-13 |
|
|
dc.identifier.issn |
2227-9091 |
|
|
dc.identifier.issn |
2227-9091 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/154487
|
|
|
dc.description.abstract |
<jats:p>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.</jats:p> |
|
|
dc.language |
en |
|
|
dc.publisher |
MDPI AG |
|
|
dc.relation.ispartof |
Risks |
|
|
dc.relation.isbasedon |
10.3390/risks9010013 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.subject |
1502 Banking, Finance and Investment, 1503 Business and Management |
|
|
dc.title |
Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
9 |
|
|
utslib.for |
1502 Banking, Finance and Investment |
|
|
utslib.for |
1503 Business and Management |
|
|
utslib.for |
1502 Banking, Finance and Investment |
|
|
utslib.for |
1503 Business and Management |
|
|
pubs.organisational-group |
/University of Technology Sydney |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Business |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Science |
|
|
pubs.organisational-group |
/University of Technology Sydney/Strength - QFRC - Quantitative Finance Research Centre |
|
|
pubs.organisational-group |
/University of Technology Sydney/Students |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences |
|
|
utslib.copyright.status |
open_access |
* |
|
pubs.consider-herdc |
true |
|
|
dc.date.updated |
2022-02-14T03:21:47Z |
|
|
pubs.issue |
1 |
|
|
pubs.publication-status |
Published |
|
|
pubs.volume |
9 |
|
|
utslib.citation.issue |
1 |
|
