Machine learning versus econometric jump models in predictability and domain adaptability of index options

H. Jang and J. Lee

Physica A: Statistical Mechanics and its Applications, 2019, vol. 513, issue C, 74-86

Abstract: Econometric jump models dealing with key stylized facts in financial option markets have an explicit underlying asset process based on stochastic differential equations. Machine learning models with improved prediction accuracy have elicited considerable attention from researchers in the field of financial application. An intensive empirical study is conducted to compare two methods in terms of model estimation, prediction, and domain adaptation using S&P 100 American/European put options. Results indicated that econometric jump models demonstrate better prediction performance than the best-performing machine learning models, and the estimation results of the former are similar to those of the latter. The former also exhibited significantly better domain adaptation performance than the latter regardless of domain adaptation techniques in machine learning.

Keywords: Financial time series; Lévy process; Bayesian neural network; Neural network; Support vector regression; Gaussian process regression; Domain adaptation (search for similar items in EconPapers)
Date: 2019
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/S0378437118310331
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:phsmap:v:513:y:2019:i:c:p:74-86

DOI: 10.1016/j.physa.2018.08.091

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().