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

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.

Modeling and Forecasting Historical Volatility Using Econometric and Deep Learning Approaches: Evidence From the Moroccan and Bahraini Stock Markets

“…On one hand, a plethora of studies have shown that non-linear models, especially the EGARCH model, outperformed the linear counterparts in capturing and predicting the conditional variance across short-and long-term horizons (Lin 2018 (Ge et al 2023). These investigations have frequently revealed the superior predictive performance of deep learning architectures in capturing intricate long-range dependencies, often surpassing the performance of classical approaches in volatility forecasting in financial markets (Sahiner et al 2023). Moreover, the integration of deep learning algorithms such as LSTM and GRU with sentiment data has yielded remarkable advancements, showcasing substantial improvements over GARCH models (Yu et al 2023).…”

The Impact of Foreign Stock Market Indices on Predictions Volatility of the WIG20 Index Rates of Return Using Neural Networks

“…In finance neural networks are used in the study of financial series (Krollner et al, 2010;Zhang et al, 2023), stock prices (Bodart & Candelon, 2009;Niu et al, 2023), stock market indices (Radomska, 2021;Alkhoshi & Belkasim, 2018;Kumar & Murugan, 2013;Moghaddam et al, 2016;Song & Choi, 2023;Bhandari et al, 2022;Al-Akashi, 2022), forecasting volatility of many financial variables (Donaldson & Kamstra, 1996a, b;Salchenberger et al, 1992;Kristjanpoller et al, 2014;Ramos-Pérez et al, 2019;Liu et al, 2017;Hamid & Iqbal, 2004;Sahiner et al, 2021). For example, volatility analysis of the S&P 500 index using the LTSM40 network was conducted (Xiong et al, 2016).…”