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Artificial Neural Networks and Hybrid Volatility Modeling

In: Non-Linearity in Econometric Modeling, Vol. 1

Author

Listed:
  • Sarit Maitra

    (Alliance University—Central Campus, Chikkahadage Cross Chandapura-Anekal)

Abstract

In Chap. 2 we introduced a hybrid volatility modeling framework based on the ARIMA–GARCH approach, which integrates traditional time-series and econometric techniques. While GARCH models have been the cornerstone of volatility modeling due to their ability to account for volatility clustering and persistence, they rely on parametric assumptions that might limit their flexibility. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI), particularly Artificial Neural Networks (ANNs), offer powerful, data-driven methods that can capture complex, nonlinear patterns and dependencies often present in financial time series but difficult to model with traditional methods. This chapter provides a brief introduction to ANNs and their applications, focusing on volatility modeling, to explore how these models can complement or enhance classical econometric frameworks like ARIMA–GARCH by potentially improving predictive accuracy and capturing nonlinearities without strict distributional assumptions.

Suggested Citation

  • Sarit Maitra, 2025. "Artificial Neural Networks and Hybrid Volatility Modeling," Dynamic Modeling and Econometrics in Economics and Finance, in: Non-Linearity in Econometric Modeling, Vol. 1, chapter 3, pages 93-120, Springer.
    • Handle: RePEc:spr:dymchp:978-3-032-06462-2_3
    DOI: 10.1007/978-3-032-06462-2_3
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