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Sahiner, M. 
ORCID: 0000-0002-7455-8694, McMillan, D.G. and Kambouroudis, D.,
2023.
Do artificial neural networks provide improved volatility forecasts: evidence from Asian markets.
Journal of Economics and Finance.
ISSN 1055-0925
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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.
| Item Type: | Journal article | ||||||
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| Publication Title: | Journal of Economics and Finance | ||||||
| Creators: | Sahiner, M., McMillan, D.G. and Kambouroudis, D. | ||||||
| Publisher: | Springer | ||||||
| Date: | 16 May 2023 | ||||||
| ISSN: | 1055-0925 | ||||||
| Identifiers: |
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| Rights: | © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | ||||||
| Divisions: | Schools > Nottingham Business School | ||||||
| Record created by: | Laura Ward | ||||||
| Date Added: | 19 May 2023 12:51 | ||||||
| Last Modified: | 19 May 2023 12:51 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/49023 |
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