Page 34 - Azerbaijan State University of Economics

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

Notes: A fixed-effects quantile panel regression model is used to produce the
estimates, with quantiles (τ) ranging from 0.2 to 0.8., and robust standard errors are
calculated at the national level using clustering techniques to take into account
possible within-group correlations. Significance: *** p < 0.01, ** p < 0.05, * p < 0.10.
The quantile regression results in Table 5 provide, among other things, a more
complete examination of the relationship between energy and financial markets, and
according to these findings, threshold risks are much larger in the lower quantiles (τ
= 0.2-0.4). The negative and statistically significant coefficients for both oil and
electricity returns within these ranges indicate that energy shocks have a larger impact
during market downturns, which is consistent with the downward asymmetry
discussed by Bouoiyour et al. (2017) and Mokni (2020), which implies that investors
typically perceive more risk during market downturns. This position is further
supported by the behavioral explanation of Bildirici and Badur (2019), which claims
that at times when general sentiment falls, markets become more sensitive to energy-
related shocks, also, policies supporting renewable energy and more comprehensive
transition strategies seem to reduce the effects of oil shocks, where the positive and
significant coefficients for the interaction factors (OIL × RENEW), (OIL × FIT) and
(OIL × CPO) also support this. In agreement with Apergis and Payne (2014) and Le
and Chang (2015), who discovered that economies with higher penetration rates of
renewable energy are less vulnerable to global energy volatility, this data lends
credence to the policy buffer hypothesis. The resilience created by green transition
strategies is demonstrated by the fact that, economically, a one percentage point
increase in the share of renewable energy seems to lessen the marginal impact of oil
shocks on stock returns by about 30%. The decreasing significance of oil-related
coefficients at higher quantiles (τ = 0.6-0.8) indicates that bullish markets are better
able to withstand energy shocks; this is a pattern that has also been observed in
developed economies by Hamilton (2009) and Sadorsky (1999). All of the data
presented in Table 5 together highlight how diverse energy-finance transmission
mechanisms are and help to close the conceptual divide between studies on
sustainability and financial stability. Overall, the findings provide empirical support
for the idea that well-crafted transition and renewable energy policies improve an
economy's resilience to outside shocks and maintain financial stability.

5. Conclusion
The study explored the relationships between changes in oil and electricity prices and
stock markets in six emerging and transitional economies: Croatia, Greece, Slovenia,
India, South Africa, and Vietnam between 2010 - 2024. Using a range of nonlinear
econometric methods, including panel quantile regressions, copula dependence
measures, DCC-GARCH, Markov-Switching Granger causality, and the Panel MS-

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