Is the Effect of Coal and Electricity Price Shocks Asymmetric on the Stock Market at the Firm Level A Panel SVAR Approach

To fulfill the objectives of this research work, we consider companies on Indian stock exchanges. Although, Bombay Stock Exchange (BSE) has more listed companies than the National Stock Exchange of India (NSE), based on turnover we select NSE, covering the period from January 1995 to December 2016. The large unbalanced panel of 1786 firms and 143 diverse sectors of monthly frequency are extracted from the Centre for Monitoring Indian Economy (CMIE) Prowess database. For analysis, we consider only energy intensive firms, making a final data set of 1168 firms. We exclude sectors such as finance, trade, Community welfare services from the study. Wholesale Price Index (WPI) number for coal and electricity are taken as a proxy for coal and electricity prices, respectively. We take electricity for industrial use for the analysis. Core inflation is considered by excluding energy prices. Inflation is used as dependent variables in this study to channel the impact of oil price shock on stock returns. These variables are extracted on a monthly frequency from Handbook of statistics of Reserve Bank of India. Before proceeding for any pre-tests, we bring data series about energy price under one constant price of 2004-05 prices using splicing method. Most of the electricity price shock specifications show an asymmetric impact on stock returns. All coal price shock specifications are asymmetric towards stock returns. •On the other hand, electricity price and its specifications have symmetric impact on inflation. Coal price and its various shock specifications have an expected (symmetric) influence on inflation.

为实现本研究的既定目标，我们选取印度证券交易所的上市企业作为研究样本。尽管孟买证券交易所（Bombay Stock Exchange, BSE）的上市企业数量多于印度国家证券交易所（National Stock Exchange of India, NSE），但基于成交量维度的考量，我们最终选定印度国家证券交易所作为研究场景，研究时段覆盖1995年1月至2016年12月。我们从印度经济监测中心（Centre for Monitoring Indian Economy, CMIE）的Prowess数据库中，提取得到涵盖1786家企业、143个异质性行业的月度频率非平衡大面板数据集。本次分析仅纳入能源密集型企业，最终得到包含1168家企业的有效数据集。我们将金融、贸易、社区福利服务等行业排除在本研究范畴之外。我们分别选取煤炭与电力的批发物价指数（Wholesale Price Index, WPI）作为两类能源价格的代理变量，分析中采用工业用电相关数据。我们通过剔除能源价格项计算核心通胀率。本研究以通胀率作为因变量，以考察油价冲击对股票收益率的传导路径。上述变量均取自《印度储备银行统计手册》，均为月度频率数据。在开展任何预检验工作之前，我们采用拼接法将所有能源价格相关数据序列调整至2004-05年的不变价格水平。多数电力价格冲击的模型设定均对股票收益率呈现非对称影响，所有煤炭价格冲击的模型设定均对股票收益率表现出非对称效应。与之相对，电力价格及其各类冲击模型仅对通胀率产生对称影响；而煤炭价格及其各类冲击模型则如预期般对通胀率产生对称影响。