Page 31 - Azerbaijan State University of Economics

THE Luan Vardari, Kiran Sood: Sate-Dependent Transmission of Oil and Electricity Shocks to
Equity Markets: Evidence from Emerging and Transitional Economies

volatility spillovers and short-term, regime-dependent interactions rather than long-
term equilibrium dynamics. The analytical design of the current study is primarily
motivated by this consideration.

State-dependent asymmetries in the transmission of electricity and oil shocks to stock
returns are amply demonstrated by the Markov-Switching VARX(2) estimations
presented in Table 2. According to the estimated coefficients, changes in the price of
oil have a major detrimental impact on stock performance when volatility is high
(Regime 2), but their impact is neutral or even slightly positive when volatility is low.
This asymmetric pattern supports the findings of Wang et al. (2013) for both oil-
importing and oil-exporting economies and Raifu and Oshota (2023) for Nigeria,
demonstrating that the direction and strength of energy-finance linkages are
influenced by larger macro-financial environments. The findings indicate significant
cross-country variations in electricity prices. The impact is usually positive in
economies that rely more on renewable energy, such as Slovenia and Greece, but tends
to be negative in countries that rely more heavily on fossil fuels, such as South Africa
and India, which supports the claims made by Le and Chang (2015) and Dhauqi et al.
(2018) that diversifying the energy mix can reduce the negative effects of oil-related
shocks. Exchange rate fluctuations seem to play a substantial role during turbulent
markets. The positive and significantly statistically coefficient indicates that currency
depreciation exacerbated stock market losses when fuel energy prices rose (Basher et
al., 2016). The persistence of the regimes (p₂₂ = 0.94) also suggests that when the
market moves into a turbulent regime, it is likely to remain in that regime for a longer
period, as found by Krolzig (1997) and Hamilton (1990) when analyzing MS-VAR
models. These results are consistent with the behavioral finance perspective of
Bildirici and Badur (2019), which suggests that investor confidence and market
sentiment are prominent channels that energy shocks cause outcomes in financial
performance across volatility regimes. This idea is further confirmed by the results in
Table 2, which indicate cyclical and nonlinear relationships between energy and
financial dynamics (Niftiyev, 2020), (Babayev, 2020), (Bayramov, 2016).

Table 3: Markov-Switching Model Parameter Estimates and Smoothed Regime
Probabilities
Duration
Regime Mean(μ) σ Probability Description
(months)
1 – Low Stable equity growth; modest energy
Volatility 0.0078 0.011 21 0.61 price movement
2 – High Crisis episodes (2011, 2014, 2020,
Volatility −0.023 0.029 13 0.39 2022)

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