Implied volatility forecast and option trading strategy

Dehong Liu, Yucong Liang, Lili Zhang, Peter Lung and Rizwan Ullah

International Review of Economics & Finance, 2021, vol. 71, issue C, 943-954

Abstract: We examine the implied volatility derived from an improved Artificial Bee Colony with Back Propagation (BP) neural network model that is Artificial Bee Colony-Back Propagation (ABC-BP) neural network model. We find that the improved model can better predict the implied volatility than basic BP neural network model and Monte Carlo simulation. Nevertheless, the option price derived from the Monte Carlo simulation is more efficient when we apply the simulation to the option straddle trading strategy. Additionally, in a robustness test we find that our proposed neural network model performs better than the traditional GARCH model in building up option trading strategies.

Keywords: ABC-BP neural network model; Implied volatility; Options trading strategy (search for similar items in EconPapers)
JEL-codes: C6 D9 G1 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1059056020302513
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:reveco:v:71:y:2021:i:c:p:943-954

DOI: 10.1016/j.iref.2020.10.023

Access Statistics for this article

International Review of Economics & Finance is currently edited by H. Beladi and C. Chen

More articles in International Review of Economics & Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().