Early COVID-19 policy response on healthcare equity prices

Gurrib, Ikhlaas

doi:10.1108/sef-02-2021-0075

Cited by 11 publications

(9 citation statements)

References 49 publications

(45 reference statements)

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“…Therefore, fluctuations in oil price can have a direct impact on the cost of production and company profitability. For example, while Gurrib (2021) and Prabheesh et al (2020) recently found that shocks to the number of crude oil price volatility impacted stocks during early COVID-19, Gurrib (2018), Gurrib (2019); and Lescaroux and Mignon (2008) find crude oil prices to be poor predictors in other markets. This is in line with Kinateder et al (2021) who compared correlations across different asset classes and found deteriorating relationships.…”

Section: Introductionmentioning

confidence: 99%

Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

Mahmoudi

Ghaneei

2022

SEF

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Purpose This study aims to analyze the impact of the crude oil market on the Toronto Stock Exchange Index (TSX). Design/methodology/approach The focus is on detecting nonlinear relationship based on monthly data from 1970 to 2021 using Markov-switching vector auto regression (VAR) model. Findings The results indicate that TSX return contains two regimes: positive return (Regime 1), when growth rate of stock index is positive; and negative return (Regime 2), when growth rate of stock index is negative. Moreover, Regime 1 is more volatile than Regime 2. The findings also show the crude oil market has a negative effect on the stock market in Regime 1, while it has a positive effect on the stock market in Regime 2. In addition, the authors can see this effect in Regime 1 more significantly in comparison to Regime 2. Furthermore, two-period lag of oil price decreases stock return in Regime 1, while it increases stock return in Regime 2. Originality/value This study aims to address the effect of oil market fluctuation on TSX index using Markov-switching approach and capture the nonlinearities between them. To the best of the author’s knowledge, this is the first study to assess the effect of the oil market on TSX in different regimes using Markov-switching VAR model. Because Canada is the sixth-largest producer and exporter of oil in the world as well as the TSX as the Canada’s main stock exchange is the tenth-largest stock exchange in the world by market capitalization, this paper’s framework to analyze a nonlinear relationship between oil market and the stock market of Canada helps stock market players like policymakers, institutional investors and private investors to get a better understanding of the real world.

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Section: Introductionmentioning

confidence: 99%

Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

Mahmoudi

Ghaneei

2022

SEF

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“…Furthermore, in the short run, new COVID‐19 death cases have a positive impact on the Refinitiv Global Healthcare Price Return Index and the Refinitiv Global Pharmaceuticals & Medical Research Price Return Index, as argued by Gurrib ( 2021 ); Harjoto and Rossi ( 2021 ); He et al ( 2020 ); Mazur et al ( 2021 ); Mushafiq ( 2021 ). Likewise, according to Alam et al ( 2021 ), the positive influence is asserted by rising demand for medical devices and pharmaceutics.…”

Section: Empirical Findingsmentioning

confidence: 95%

Modeling the impact of the COVID‐19 outbreak on environment, health sector and energy market

Armeanu

Gherghina

Vasile

et al. 2022

Sustainable Development

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The global outbreak of COVID‐19 disease had a significant impact on the entire globe. Such a notable public health event can be seen as a “black swan” that brings unpredictable and unusual forces into the economic context and that it could typically lead to a chain of adverse reactions and market disruptions. Hence, the purpose of this study is to examine how COVID‐19 affects the environment, health, and the oil and energy markets. To achieve this objective, we used daily data for several measures that refer to the environment, health, and oil and energy, for the first wave of the COVID‐19 pandemic (December 31, 2019–May 22, 2020). The variable integration mix led to the approach of the ARDL model, and the Granger causality test was also employed. These empirical techniques allowed us to examine the cointegration between variables and causal relationships. The econometric results of the ARDL models exhibited that the global new cases and new deaths of COVID‐19 have short and long‐term effects on the environment, the health sector, the oil, and energy measures. However, no significant causal connection was found between the pandemic and the environment, the health sector, or the oil and energy industry, according to the Granger causality test. The uniqueness of current approach consists in the investigation of pandemic impact on the health, environment, oil, and energy sector by applying the ARDL model that permits the analysis of cointegration both in the long run and in the short term. This study provides important insights for investors and policy makers.

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“…This impact is similar in various countries where governments ordered businesses to shut and families to stay at home except for essential activities. Shehzad et al (2020) find the conditional variance of stock markets to be bigger during the COVID-19 compared to the global financial crisis of -2008. Gurrib (2021 similarly find shocks to the number of COVID-19 cases affected healthcare sector companies but lasted only a few days.…”

Section: Introductionmentioning

confidence: 94%

High Frequency Return and Risk Patterns in U.S. Sector ETFs during COVID-19

Gurrib

Kamalov

Alshareif

2022

IJEEP

Self Cite

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This study investigates intraday patterns in the eleven sectors of the United States (U.S.). Key contributions are (i) risk and return patterns at specific trading periods on the New York Stock Exchange (NYSE), (ii) whether a specific day return model can predict the next 15-minute positive return, and (iii) the impact of the first vaccination rollout in the U.S. on intraday Exchange-Traded-Funds (ETF) returns. Time-dependent regressions capture risk and return relationships, decision trees in machine learning compare return models, and impulse responses capture the effect of the 2019 coronavirus (COVID-19) vaccine rollout in U.S. 15-minute Standard & Poor’s Depository Receipts (SPDR) Select Sector ETF data is used over 12th March 2020-23rd February 2021. Findings support sector ETF returns fluctuate the most in the first and last 15 minutes. Average returns in the first 15 minutes are the highest, converging to near zero as the trading session continues. Overnight returns contribute the most to volatility. U-shaped patterns into both return and risk exist, especially on Mondays. Mondays and Fridays have the most significant positive returns 15 minutes after the open. Prediction scores using an all-return model were superior to any specific day return model. The first vaccination rollout has a positive effect only in energy, technology, and financial sector ETFs, however with a short-lasting effect on ETFs returns.

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Early COVID-19 policy response on healthcare equity prices

Cited by 11 publications

References 49 publications

Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

Detection of structural regimes and analyzing the impact of crude oil market on Canadian stock market: Markov regime-switching approach

Modeling the impact of the COVID‐19 outbreak on environment, health sector and energy market

High Frequency Return and Risk Patterns in U.S. Sector ETFs during COVID-19

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