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New kernel methods for asset pricing: application to natural gas price prediction

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  • Yinan Hu
  • Theodore B. Trafalis

Abstract

Natural gas prices show a non-linear, non-stationary, and time variant behaviour. In this study, we build a regression function for daily natural gas prices using ε-SVR and v-SVR and experiment with different kernels. We compare the proposed methods with artificial neural networks, RBF networks and asymmetric GARCH models. The comparison results demonstrate that the v-SVR with sigmoid kernel is the best of the compared techniques with respect to the mean square error and squared correlation coefficient criteria. The paper is also extended to discuss the price tendency prediction and the performance of SVR without data imputation. The purpose of the paper is to provide an effective way to predict the short term gas price, which can be used as a tool to reduce uncertainty and financial risk in the energy market.

Suggested Citation

  • Yinan Hu & Theodore B. Trafalis, 2011. "New kernel methods for asset pricing: application to natural gas price prediction," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 2(1/2), pages 106-120.
    • Handle: RePEc:ids:ijfmkd:v:2:y:2011:i:1/2:p:106-120
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    Cited by:

    1. Marcin Fałdziński & Piotr Fiszeder & Witold Orzeszko, 2020. "Forecasting Volatility of Energy Commodities: Comparison of GARCH Models with Support Vector Regression," Energies, MDPI, vol. 14(1), pages 1-18, December.

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