Page 24 - Azerbaijan State University of Economics

THE JOURNAL OF ECONOMIC SCIENCES: THEORY AND PRACTICE, V.83, # 1, 2026, pp. 20-39

The outcome of the oil price shocks in the oil exporting economies can take the reverse
trend. Good price shocks are able to boost stock performance through the
enhancement of fiscal balances and liquidity conditions. Raifu and Oshota (2023)
support this opinion but studied the Nigerian market in terms of the decomposition of
oil shocks by Kilian (2009) into the supply component, aggregate demand component,
and oil-specific demand component. Their two stage Markov-Switching model
indicated that the supply-driven oil shocks are more likely to have a positive effect in
the stable periods of low volatility, whereas demand-specific stocks are likely to have
negative effects in turbulent periods of high volatility. The point of contrast is that oil-
stock linkages are complex and regime-specific and that the reaction of the market is
determined not only by the source of the shock but also by more general economic
and financial factors. This duality mirrors Bouoiyour et al. (2017), who observed that
demand-side shocks dominate in oil-exporting countries and supports the asymmetric-
transmission hypothesis originally suggested by Mork (1989). Related findings by Le
& Chang (2015), Dhaoui et al. (2018), and Mokni (2020) further confirm that stock-
return responses depend on whether economies are net importers or exporters of oil.

2.3. Regime switching and asymmetry in energy–finance linkages
Markov-switching and other nonlinear time-series frameworks provide a natural tool
for capturing structural breaks, stochastic volatility, and shifts in investor behavior
(Hamilton, 1990; Krolzig, 1997). Bildirici & Badur (2019) and Raifu & Oshota (2023)
both exploit this feature to disentangle low- and high-volatility regimes. The former
estimate MSIAH(3)-VARX(2) and MSIAH(3)-VAR(1) models for Turkey and the
U.S., revealing persistent high-volatility regimes (probabilities > 0.90) and changing
sign effects of oil prices across states. The latter combine SVAR-identified structural
shocks with a two-state Markov process to capture nonlinear adjustments of Nigerian
equity returns to oil market disturbances. The approach reconciles the findings of
Hamilton (1996) and Sadorsky (1999) with more recent nonlinear models (Fallahi,
2011; Basher et al., 2016; Shahrestani & Rafei, 2020), confirming that linear
estimations mask important regime heterogeneity.

Raifu & Oshota’s contribution also extends to state-contingent policy interpretation. They
argue that investors and regulators should anticipate different reactions to oil-supply and oil
demand shocks depending on volatility regimes a notion earlier hinted by Effiong (2014)
and Ndubuisi (2017) in Nigerian data. When volatility is low, expansionary effects
dominate through the cash-flow channel; when volatility rises, the uncertainty channel
prevails, depressing valuations. This is the behavior of a regime-dependent type in
accordance with the concept of the confidence channel provided by Bildirici and Badur
(2019). Investor sentiment as a transmission path and the amplification force in their model
connects the developments in the real economy and the movements in the financial markets.

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