论文标题
研究Covid-19对外汇和股票市场的影响 - 对锁定对印度经济的可变影响的应用
Examining the Effect of COVID-19 on Foreign Exchange Rate and Stock Market -- An Applied Insight into the Variable Effects of Lockdown on Indian Economy
论文作者
论文摘要
自2020年3月25日以来，印度一直在全国范围内宣布宣布，这是对SARS-COV-2和COVID-19的传播的回应，并诉诸于过去几个月来“解锁”锁定的过程。这项工作试图检查新型冠状病毒2019（Covid-19）及其由此导致的疾病，即Covid-19，使用二级数据在2020年3月11日至6月30日之间的112天内使用二级数据在2020年3月11日至6月30日之间使用二级数据。在不同的前后锁骨后阶段相同，试图通过向量自动回归（VAR）模型捕获任何潜在的变化。在确认病例的增长率和汇率的增长率之间发现正相关，以及确认病例的增长率与Sensex值的增长率之间的负相关性。但是，在应用矢量自回归（VAR）模型时，可以观察到确认的COVID-19案例的增加不会导致汇率和Sensex指数值的显着变化。结果如果分析在不同时间段分开 - 锁定之前，锁定的四个阶段以及解锁的第一阶段。对数字结果的细微和明智的解释表明，与感兴趣的变量之间的关系，整个时间之间的差异很大。有关依赖方式不同模式的详细知识可能有可能帮助印度的政策制定者和投资者，以制定他们应对这种情况的策略。
Since March 25, 2020, India had been under a nation-wide lockdown announced as a response to the spread of SARS-CoV-2 and COVID-19 and has resorted to a process of 'unlocking' the lockdown over the past couple of months. This work attempts to examine the effect of novel coronavirus 2019 (COVID-19) and its resulting disease, the COVID-19, on the foreign exchange rates and stock market performances of India using secondary data over a span of 112 days spanning between March 11 and June 30, 2020. The study explores whether the causal relationships and directions among the growth rate of confirmed cases (GROWTHC), exchange rate (GEX) and SENSEX value (GSENSEX) are remaining the same across different pre and post-lockdown phases, attempting to capture any potential changes over time via the vector autoregressive (VAR) models. A positive correlation is found between the growth rate of confirmed cases and the growth rate of exchange rate, and a negative correlation between the growth rate of confirmed cases and the growth rate of SENSEX value. However, on applying a vector autoregressive (VAR) model, it is observed that an increase in the confirmed COVID-19 cases causes no significant change in the values of the exchange rate and SENSEX index. The result varies if the analysis is split across different time periods - before lockdown, the four phases of lockdown, and the first phase of unlock. Nuanced and sensible interpretations of the numeric results indicate significant variability across time in terms of the relation between the variables of interest. The detailed knowledge about the varying patterns of dependence could potentially help the policy makers and investors of India in order to develop their strategies to cope up with the situation.
