Examining the spillover effects of volatile oil prices on Iran’s stock market using wavelet-based multivariate GARCH model

Abstract

Fluctuations in the oil market can significantly influence various sectors of the economy, such as the stock markets of countries that rely heavily on oil revenues. Oil prices are one of the key influential external factors affecting the stock exchange index of oil-dependent Iran. This paper investigates the spillover effects of oil prices on Iran’s stock exchange index weekly from March 2009 to March 2020. Using a time-series wavelet decomposition approach, a series of OPEC oil prices and Iran’s total stock market index were decomposed into various time scales (4 levels) to analyze oil market spillover into the stock market using the multivariate GARCH TBEKK model. The results confirmed that volatility spillover from the oil to the stock market occurred in all the time scales (short, medium, and long term). However, the spillover in the long term is more pronounced than over the short, demonstrating that stock market volatility is strongly influenced by long-term exogenous oil price fluctuations. Hence, oil market shocks are one of the influential factors affecting stock market turbulence in Iran.

An Application of the Novel Hybrid Model in Monthly Prediction of the Electricity Cost in the Iraqi Market System

“…Furthermore, the investigation of various kinds of ARMAX-GARCH models based on their statistical properties and capacity to evaluate and estimate the time series of electricity market prices demonstrates that these models are able to predict price changes. This demonstrates that under the circumstances that currently exist in Iran's electricity market, information asymmetry plays a lesser role, and standard GARCH models are the best simulations currently available [12]. Catalão et al [13] have used the technique of fast learning machine (ELM) and also have made use of wavelet transformation in addition to a combination of neural network (ANN) and fuzzy logic in order to provide a short-term forecast model of electricity prices in a market that is competitive.…”

On the linkage of oil prices and oil uncertainty with US equities: a combination analysis based on the wavelet approach and quantile-on-quantile regression

“…Furthermore, several studies have adopted a combination of two or more models to enhance the analytical framework. Some studies have employed a combination of SVAR and M-GARCH models [58], the SVAR-NARDL model [59], wavelet and Granger causality approaches [34,60], wavelet and M-GARCH models [61,62], and the quantile-ARDL model [63], among others. This methodological diversity reflects the complexity of the relationship under investigation and underscores the importance of considering various aspects, such as volatility, causality, and time-frequency dynamics, in capturing the intricate interplay between equity and oil markets.…”