[论文解读] 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

Indrajit Banerjee, Atul Kumar+1 more|arXiv (Cornell University)|Jun 23, 2020

COVID-19 Pandemic Impacts被引用 9

一句话总结

本研究利用VAR模型分析了2020年3月11日至6月30日期间112天内新冠疫情对印度外汇汇率和股市的影响，发现尽管确诊病例增长与汇率上升及SENSEX下跌相关，但总体上其因果影响并不具有统计显著性——尽管在解封前、封锁期和解封期三个阶段中影响存在显著差异，凸显了市场动态的时变特性。

ABSTRACT

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.

研究动机与目标

• 评估疫情期间印度新冠疫情病例增长、汇率与SENSEX之间的动态关系。
• 探究这些变量之间的因果关系是否在不同经济阶段（解封前、封锁期和解封期）发生变化。
• 为政策制定者和投资者提供危机条件下市场行为的时变洞察。
• 通过分段时间段的向量自回归建模，分析因果效应的统计显著性。

提出的方法

• 本研究使用2020年3月11日至6月30日期间每日确诊病例数、汇率和SENSEX指数的二次数据。
• 采用向量自回归（VAR）模型，检验确诊病例增长率（GROWTHC）、汇率（GEX）和SENSEX（GSENSEX）之间的双向因果关系。
• 分析分为三个时间段：解封前、四个封锁阶段和首个解封阶段，以检测关系的时间变化。
• 在VAR建模前，进行相关性分析以评估双变量关联。
• VAR模型估计脉冲响应函数和方差分解，以评估动态相互作用的强度和方向。
• 利用补充图表和统计输出验证模型稳定性，并解释时变依赖关系。

实验结果

研究问题

• RQ1 确诊病例增长率是否对印度汇率和SENSEX产生统计显著的因果影响？
• RQ2 确诊病例、汇率与股价之间的因果关系在解封前、封锁期和解封期如何变化？
• RQ3 确诊病例增长率与印度金融市场表现之间的相关性性质是什么？
• RQ4 观察到的关系是否随时间保持稳定，还是在疫情期间表现出结构性变化？

主要发现

• 确诊病例增长率与汇率增长率之间存在正相关关系，表明感染人数上升与本币贬值压力相关。
• 确诊病例增长率与SENSEX增长率之间存在负相关关系，表明感染人数增加与股市下跌相关。
• 尽管存在这些相关性，但VAR模型显示，在整个时间段内，确诊病例增长率对汇率或SENSEX值均无统计显著的因果影响。
• 因果关系在不同时段存在显著差异：解封前、封锁期和解封期表现出不同的互动模式，表明市场对疫情的敏感性具有时变特征。
• 本研究识别出细致的、与阶段相关的依赖关系，表明市场对疫情的反应并非一致，而是取决于政策和经济背景。
• 研究结果表明，政策制定者和投资者应在危机期间采用基于时间的策略，而非依赖静态的市场假设。