Oil Price Shocks and Indian Stock Market Volatility: Asymmetric GARCH Spillover Analysis
Oil prices fluctuate often. These changes create shocks that affect economies worldwide. India imports a large share of its crude oil. Therefore, oil price movements influence the Indian stock market significantly. Researchers apply asymmetric GARCH models to study these effects. This approach captures volatility spillovers effectively.
First, experts collect daily data on oil prices and major Indian indices. They focus on periods from 2010 to 2026. This timeframe includes major global events. Next, they test for stationarity and ARCH effects. These steps confirm the suitability of GARCH family models. Moreover, they extend the analysis with EGARCH and TGARCH specifications. These variants handle asymmetry in volatility responses.
Oil price shocks raise volatility in Indian stocks. Positive shocks often produce stronger effects than negative ones. Investors react more to sudden price increases. In addition, spillover effects vary across sectors. Energy and transportation stocks show higher sensitivity. On the other hand, IT and pharmaceutical sectors exhibit lower transmission. Transition words like “however” highlight these differences clearly.
The study reveals important patterns. For instance, bad news in oil markets amplifies stock volatility more than good news. This leverage effect persists over time. Furthermore, multivariate GARCH models demonstrate dynamic conditional correlations. These correlations strengthen during crisis periods. Policymakers monitor such links closely. They adjust monetary and fiscal tools accordingly.
Researchers also evaluate model performance. They compare forecasting accuracy using various diagnostics. EGARCH models often outperform others in this context. Additionally, robustness checks confirm the findings. These include alternative data frequencies and sub-period analyses.
Spillover analysis offers practical insights. Portfolio managers use these results to hedge risks. They diversify across less sensitive sectors. Moreover, regulators strengthen energy security measures. India reduces import dependence through renewable sources. This strategy limits future vulnerabilities.
Nevertheless, limitations exist in the current work. Data constraints and external events influence results. Therefore, future studies should incorporate high-frequency data. They can also integrate machine learning hybrids. These advancements will improve prediction accuracy.
In conclusion, oil price shocks drive significant volatility in the Indian stock market. Asymmetric GARCH models reveal clear spillover patterns. Investors and policymakers benefit from this understanding. They make informed decisions to manage risks. Continued research in this area will support economic stability in India.