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

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  • Jang, H.
  • Lee, J.

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.

Suggested Citation

  • Jang, H. & Lee, J., 2019. "Machine learning versus econometric jump models in predictability and domain adaptability of index options," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 74-86.
    • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:74-86
    DOI: 10.1016/j.physa.2018.08.091
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    Cited by:

    1. Sevcan Uzun & Ahmet Sensoy & Duc Khuong Nguyen, 2023. "Jump forecasting in foreign exchange markets: A high‐frequency analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 578-624, April.
    2. Yossi Shvimer & Avi Herbon, 2020. "Tradability, closeness to market prices, and expected profit: their measurement for a binomial model of options pricing in a heterogeneous market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(3), pages 737-762, July.

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