Abstract
This study attempts to analyze the time-varying pattern between the exchange rates, stock market return, temperature, and number of confirmed COVID-19 cases in G7 countries caused by the COVID-19 pandemic. We have implemented our analysis using wavelet coherence and partial wavelet coherence (PWC) on independent variables from January 4, 2021 to July 31, 2021. This paper contributes to the earlier work on the same subject by employing wavelet coherence to analyze the effect of the sudden upsurge of the COVID-19 pandemic on exchange rates, stock market returns, and temperature to sustain and improve previous results regarding correlation analysis between the above-mentioned variables. We arrived at the following results: 1) temperature levels and confirmed COVID-19 cases are cyclical indicating daily temperatures have a material bearing on propagating the novel coronavirus in G7 nations; 2) noteworthy correlations at truncated frequencies show that a material long-term impact has been observed on exchange rates and stock market returns of G7 and confirmed COVID-19 cases; 3) accounting for impact of temperature and equity market returns, a more robust co-movement is observed between the exchange rate returns of the respective nations and the surge in COVID-19 cases; and 4) accounting for the influence of temperature and exchange rate returns and the increase in the confirmed number of coronavirus-infected cases and equity returns, co-movements are more pronounced. Besides academic contributions, this paper offers insight for policymakers and investment managers alike in their attempt to navigate the impediments created by the coronavirus in their already arduous task of shaping the economy and predicting stock market patterns.
Highlights
COVID-19 has been one of the most unexpected global events that has challenged the sustainability and resilience of almost every social and economic system
The biwavelet and partial wavelet coherence analyses were applied between the reference variables on daily observations for the sample period of January 4, 2021 to July 31, 2021
Wavelet coherence observed within the dataset of daily temperature and the number of confirmed COVID-19 cases in the G7 nations indicated significant co-movements in both the near-term and longer-term between the two variables
Summary
COVID-19 has been one of the most unexpected global events that has challenged the sustainability and resilience of almost every social and economic system. The continuity of almost every man-made system has been challenged, and the uncertainty of the control and spread of the COVID-19 virus has put to the test every system of estimation and valuation. This event has triggered various studies analyzing correlations between the number of confirmed COVID-19 cases and various other social and economic parameters (Martínez and Cervantes, 2021) (Shahzad et al, 2021). The number of fatalities peaked at 17,700 on February 3, 2021, which reduced to 7,600 on March 8, 2021 This surged back to 14,900 on april 29, 2021 and fell to 6,300 casualties on May 6, 2021. Numerous studies have been conducted using daily average temperature (Huang et al, 2020; Park et al, 2020; Wang et al, 2020) to understand the impact of temperature and weather conditions on the spread and intensity of the COVID-19 virus
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