Open Access Journal Article

Forecasting Parameters in the SABR Model

by Li Chen a,b orcid ,  Jianing Zhu c orcid  and  Cunyi Yang a,* orcid
a
Lingnan College, Sun Yat-Sen University, Guangzhou, China
b
Questrom School of Business, Boston University, Boston, USA
c
Paul Merage School of Business, University of California, Irvine, USA
*
Author to whom correspondence should be addressed.
JEA  2022 1(1):6; https://doi.org/10.58567/jea01010005
Received: 3 August 2022 / Accepted: 8 September 2022 / Published Online: 13 September 2022
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Abstract

We present two approaches to forecasting parameters in the SABR model. The first approach is the vector autoregressive moving-average model (VARMA) for the time series of the in-sample calibrated parameters, and the second is based on machine learning techniques called epsilon-support vector regression (ε-SVR). Using daily data of S&P 500 ETF option prices from January 1, 2014, to December 31, 2018, we first calibrate the daily values of the model parameters from the training sample, then conduct out-of-sample forecasting of parameters and pricing of options. Both approaches produce good fits between the forecasted and calibrated parameters for out-of-sample dates. A comparison study shows that using forecasted parameters as inputs, the SABR model generates better pricing results than assuming constant parameters or using lag parameters. We also discuss the market conditions under which one approach outperforms the other.

Keywords: SABR model; VARMA; ε-SVR; Implied volatility; Machine learning; Time-series econometrics;
Copyright: © 2022 by Chen, Zhu and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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ACS Style
Chen, L.; Zhu, J.; Yang, C. Forecasting Parameters in the SABR Model. Journal of Economic Analysis, 2022, 1, 6. https://doi.org/10.58567/jea01010005
AMA Style
Chen L, Zhu J, Yang C. Forecasting Parameters in the SABR Model. Journal of Economic Analysis; 2022, 1(1):6. https://doi.org/10.58567/jea01010005
Chicago/Turabian Style
Chen, Li; Zhu, Jianing; Yang, Cunyi 2022. "Forecasting Parameters in the SABR Model" Journal of Economic Analysis 1, no.1:6. https://doi.org/10.58567/jea01010005
APA style
Chen, L., Zhu, J., & Yang, C. (2022). Forecasting Parameters in the SABR Model. Journal of Economic Analysis, 1(1), 6. https://doi.org/10.58567/jea01010005

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