ABSTRACT

We investigate the dependency, risk spillovers, and systemic risk between the sectoral indices returns of the Bombay stock exchange (BSE) and oil prices using recently developed empirical techniques. The dependence is modelled using the time varying Stochastic Autoregressive Copulas (SCAR). Conditional value-at-risk (CoVaR), ΔCoVaR and marginal expected shortfall (MES) measures are used to examine the systemic risk. We find rotated Gumbel and normal copulas to be the best fitting in our analysis. Sectors such as energy, power, and industrial exhibit higher persistence in dependence structure compared to other sectors. Our results reveal that the underlying forces of the dependence between oil prices with other industries vary across time, albeit not so much during stable periods, but increase remarkably during turbulent times. All sectors are affected significantly by extreme oil price movements. The average short-run MES is highest for the metals, materials, and industrials sectors. The lowest average short-run MES values are observed for the fast-moving consumer goods, auto, and carbon sectors. Our risk analysis results reveal that Indian stock sectors are not resistant to oil shocks and there exists significant systemic risk between these markets and the crude oil market.

摘要

我们使用最近开发的实证技术研究了孟买证券交易所 (BSE) 的部门指数回报与石油价格之间的依赖性、风险溢出和系统性风险。使用随时间变化的随机自回归 Copulas (SCAR) 对相关性进行建模。条件风险价值 (CoVaR)、ΔCoVaR 和边际预期短缺 (MES) 措施用于检查系统性风险。我们发现旋转的 Gumbel 和正常的 copula 是我们分析中的最佳拟合。能源、电力、工业等行业与其他行业相比，依存度结构的持久性更高。我们的研究结果表明，油价与其他行业之间依赖的潜在力量随着时间的推移而变化，尽管在稳定时期不会那么大，但在动荡时期会显着增加。所有行业都受到油价极端波动的严重影响。金属、材料和工业部门的平均短期 MES 最高。在快速消费品、汽车和碳行业中观察到的平均短期 MES 值最低。我们的风险分析结果表明，印度股票板块对石油冲击没有抵抗力，这些市场与原油市场之间存在显着的系统性风险。