Do artificial neural networks provide improved volatility forecasts: Evidence from Asian markets

Mehmet Sahiner, David G. McMillan () and Dimos Kambouroudis
Additional contact information
Mehmet Sahiner: University of Stirling
David G. McMillan: University of Stirling
Dimos Kambouroudis: University of Stirling

Journal of Economics and Finance, 2023, vol. 47, issue 3, No 10, 723-762

Abstract: Abstract This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and specifically Artificial Neural Network (ANN) models. The ANN models are compared against traditional econometric models for ten Asian markets using daily data for the time period from 12 September 1994 to 05 March 2018. The empirical results indicate that ML algorithms, across the range of countries, can better approximate dependencies compared to traditional benchmark models. Notably, the predictive performance of such deep learning models is superior perhaps due to its ability in capturing long-range dependencies. For example, the Neuro Fuzzy models of ANFIS and CANFIS, which outperform the EGARCH model, are more flexible in modelling both asymmetry and long memory properties. This offers new insights for Asian markets. In addition to standard statistics forecast metrics, we also consider risk management measures including the value-at-risk (VaR) average failure rate, the Kupiec LR test, the Christoffersen independence test, the expected shortfall (ES) and the dynamic quantile test. The study concludes that ML algorithms provide improving volatility forecasts in the stock markets of Asia and suggest that this may be a fruitful approach for risk management.

Keywords: Volatility; Forecasting; Neural Networks; Machine Learning; VaR; ES (search for similar items in EconPapers)
JEL-codes: C22 C58 C63 G12 G17 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s12197-023-09629-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:jecfin:v:47:y:2023:i:3:d:10.1007_s12197-023-09629-8

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/12197/PS2

DOI: 10.1007/s12197-023-09629-8

Access Statistics for this article

Journal of Economics and Finance is currently edited by James Payne

More articles in Journal of Economics and Finance from Springer, Academy of Economics and Finance Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().