Journal article

Quantifying the model risk inherent in the calibration and recalibration of option pricing models


Publication Details

Author list: Feng Y, Rudd R, Baker C, Mashalaba Q, Mavuso M, Schlögl

Publisher: MDPI AG

Publication year: 2021

Journal: Risks

Volume number: 9

Issue number: 13

Start page: 1

End page: 20

Total number of pages: 20

eISSN: 2227-9091

URL: https://www.mdpi.com/journal/risks


Abstract

Abstract: 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.

Keywords: model risk; option pricing; relative entropy; model calibration; stochastic volatility
1. Introduction


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Keywords

risk, Risk factors, Risk reduction strategy


Last updated on 2021-29-01 at 05:18