Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/76570
Title: | On empirical likelihood option pricing | Authors: | Zhong, X Cao, J Jin, Y Zheng, W |
Issue Date: | Jun-2017 | Source: | The journal of risk, June 2017, v. 19, no. 5, p. 41-53 | Abstract: | The Black-Scholes model is the golden standard for pricing derivatives and options in the modern financial industry. However, this method imposes some parametric assumptions on the stochastic process, and its performance becomes doubtful when these assumptions are violated. This paper investigates the application of a nonparametric method, namely the empirical likelihood (EL) method, in the study of option pricing. A blockwise EL procedure is proposed to deal with dependence in the data. Simulation and real data studies show that this new method performs reasonably well and, more importantly, outperforms classical models developed to account for jumps and stochastic volatility, thanks to the fact that nonparametric methods capture information about higher-order moments. | Keywords: | Nonparametric Option pricing Empirical likelihood Robust Blocking time series |
Publisher: | Incisive Media Ltd. | Journal: | The journal of risk | ISSN: | 1465-1211 | EISSN: | 1755-2842 | DOI: | 10.21314/JOR.2017.357 | Rights: | Copyright © 2017 Incisive Risk Information (IP) Limited The following publication Zhong, X., Cao, J., Jin, Y., & Zheng, W. (2017). On empirical likelihood option pricing. Journal of Risk, 19(5), 41-53 is available at https://doi.org/10.21314/JOR.2017.357 |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Jin_Empirical_Likelihood_Option.pdf | Pre-Published version | 305.04 kB | Adobe PDF | View/Open |
Page views
96
Last Week
0
0
Last month
Citations as of Sep 24, 2023
Downloads
5
Citations as of Sep 24, 2023
SCOPUSTM
Citations
2
Citations as of Sep 28, 2023
WEB OF SCIENCETM
Citations
2
Last Week
0
0
Last month
Citations as of Sep 28, 2023

Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.