A new bivariate approach for modeling the interaction between stock volatility and interest rate: An application to S&P500 returns and options
Luca Vincenzo Ballestra,
D’Innocenzo, Enzo and
Andrea Guizzardi
European Journal of Operational Research, 2024, vol. 314, issue 3, 1185-1194
Abstract:
The GARCH models developed so far do not take into account the interaction between the volatility of asset returns and the dynamics of the interest rate. In this paper, we propose a bivariate GARCH model in which interest rate movements and asset price volatility are fully coupled. This approach yields explicit and simple to implement recursion formulas for the moment generating function, which can be exploited to compute option prices by applying the fast Fourier transform or other convolution techniques. We perform a thorough and comprehensive empirical analysis based on real S&P500 return and option data showing the usefulness and robustness of the suggested methodology. Both in-sample and out-of-sample results reveal the superiority of our approach over the GARCH model with constant interest rates.
Keywords: Finance; GARCH; Asset price; Interest rate; Option pricing (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221723009104
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:314:y:2024:i:3:p:1185-1194
DOI: 10.1016/j.ejor.2023.11.049
Access Statistics for this article
European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati
More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().