首页|Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network
Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network
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NSTL
Elsevier
? 2022 Elsevier LtdThis paper is the first attempt to forecast the time-varying total return and volatility connectedness between the oil prices and the Islamic stock indices of seven oil-exporting countries, namely Iran, Oman, Saudi Arabia, Qatar, Kuwait, Bahrain, and United Arab Emirates. The entire analysis follows two main stages. The first carries out the connectedness analysis using the Bayesian time-varying parameter vector autoregressive (BTVP-VAR) model, while the second stage applies the training process in a cascade-forward backpropagation network (CFBPN) to five groups of input (spillover TO others, spillover FROM others, both spillovers TO & FROM others, raw returns, and raw volatilities, and ALL inputs) to forecast the dynamic total connectedness of returns/volatilities. The results of the connectedness analysis show that the Islamic stock index of Iran is not connected to the oil market and the Islamic stock indices of other Islamic oil-exporting countries. The Islamic stock indices of the United Arab Emirates and Saudi Arabia have a leadership role in the network. Besides, oil demonstrates a net receiving spillover status. In addition, connectedness increases during periods of crisis and significant oil price changes. Finally, the forecast analysis shows that the transmitted return and volatility spillovers from markets (TO spillovers) are the most important factor information in predicting the total connectedness of the network. The findings showcase important implications for investors, policy makers, and future studies.
NSTL
Elsevier
