Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets

Garcin, Matthieu; Klein, Jules; Laaribi, Sana

doi:10.48550/arxiv.2007.09043

Cited by 4 publications

(6 citation statements)

References 37 publications

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“…The COVID-19 affected the world in different ways, which were studied, analyzed reported rigorously, with some of the studies showing the impact of COVID-19 on financial markets. For example, Garcin et al (2020) reported that COVID-19 had shown a limited impact on Chinese market, whereas the strongest impact was observed on US markets, at the same time that slowest recovery was observed for European markets when compared to Chinese and Korean ones. Topcu and Gulal (2020) and Ashraf (2020) using pooled panel OLS regression method added that the impact of COVID-19 on emerging markets gradually fell and ended by the end of April 2020 with the impact of the outbreak observed highest in Asian emerging markets and lower in European markets.…”

Section: Introductionmentioning

confidence: 99%

Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

Zaheer

Aslam

Mohmand

et al. 2022

CFRI

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PurposeCOVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network.Design/methodology/approachDaily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs.FindingsThe findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node; however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed.Originality/valueIt is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere.

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Section: Introductionmentioning

confidence: 99%

Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

Zaheer

Aslam

Mohmand

et al. 2022

CFRI

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“…Equation ( 12) reflects temporal structure of the time series as a function of moments 𝑞, i.e., it represents the scaling dependence of small fluctuations for negative values of and large fluctuations for positives values. If (12) represents linear dependence of 𝑞, the time series is said to be monofractal. Otherwise, if (12) has a nonlinear dependence on 𝑞, then the series is multifractal.…”

Section: Multifractal Detrended Fluctuation Analysis (Mf-dfa)mentioning

confidence: 99%

“…If (12) represents linear dependence of 𝑞, the time series is said to be monofractal. Otherwise, if (12) has a nonlinear dependence on 𝑞, then the series is multifractal. (vi) The different scalings are better described by the singularity spectrum 𝑓 (𝛼) which can be defined as:…”

Section: Multifractal Detrended Fluctuation Analysis (Mf-dfa)mentioning

confidence: 99%

“…Rapidly evolving coronavirus pandemic brings a devastating effect on the entire world and its economy as a whole [1,2,3,4,5,6,7]. Further instability related to COVID-19 will negatively affect not only on companies and financial markets, but also on traders and investors that have been interested in saving their investment, minimizing risks, and making decisions such as how to manage their resources, how much to consume and save, when to buy or sell stocks, etc., and these decisions depend on the expectation of when to expect next critical change [8,9,10,11,12,13,14,15,16,17,18,19,20,21]. Despite the complexity of the problem, the results of recent studies indicate that significant success has been achieved within the framework of interdisciplinary approaches, and the theory of self-organizationsynergetics [22,23].…”

Section: Introductionmentioning

confidence: 99%

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Econophysics of cryptocurrency crashes: a systematic review

Bielinskyi¹,

Serdyuk²,

Семеріков³

et al. 2021

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Cryptocurrencies refer to a type of digital asset that uses distributed ledger, or blockchain technology to enable a secure transaction. Like other financial assets, they show signs of complex systems built from a large number of nonlinearly interacting constituents, which exhibits collective behavior and, due to an exchange of energy or information with the environment, can easily modify its internal structure and patterns of activity. We review the econophysics analysis methods and models adopted in or invented for financial time series and their subtle properties, which are applicable to time series in other disciplines. Quantitative measures of complexity have been proposed, classified, and adapted to the cryptocurrency market. Their behavior in the face of critical events and known cryptocurrency market crashes has been analyzed. It has been shown that most of these measures behave characteristically in the periods preceding the critical event. Therefore, it is possible to build indicators-precursors of crisis phenomena in the cryptocurrency market.

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“…During the COVID-19 pandemic, financial markets became risky (Ali et al 2020;Barro et al 2020), with a drastic decline in the stock market indices (Czech et al 2020;McKibbin and Vines 2020) and causing enormous losses (Zhang et al 2020). This pandemic had larger impacts on the US stock market than other regions (Garcin et al 2020). In comparison, Topcu and Gulal (2020) reported a larger impact on Asian stock markets than on European ones.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Impact of COVID-19 Pandemic on the Intraday Efficiency of Agricultural Futures Markets

Aslam

Ferreira

Ali

2022

JRFM

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The investigation of the fractal nature of financial data has been growing in the literature. The purpose of this paper is to investigate the impact of the COVID-19 pandemic on the efficiency of agricultural futures markets by using multifractal detrended fluctuation analysis (MF-DFA). To better understand the relative changes in the efficiency of agriculture commodities due to the pandemic, we split the dataset into two equal periods of seven months, i.e., 1 August 2019 to 10 March 2020 and 11 March 2020 to 25 September 2020. We used the high-frequency data at 15 min intervals of cocoa, cotton, coffee, orange juice, soybean, and sugar. The findings reveal that the COVID-19 pandemic has great but varying impacts on the intraday multifractal properties of the selected agricultural future markets. In particular, the London sugar witnessed the lowest multifractality while orange juice exhibited the highest multifractality before the pandemic declaration. Cocoa became the most efficient while the cotton exhibited the minimum efficient pattern after the pandemic. Our findings show that the highest improvement is found in the market efficiency of orange juice. Furthermore, the behavior of these agriculture commodities shifted from a persistent to an antipersistent behavior after the pandemic. The information given by the detection of multifractality can be used to support investment and policy-making decisions.

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Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets

Cited by 4 publications

References 37 publications

Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

Temporal changes in global stock markets during COVID-19: an analysis of dynamic networks

Econophysics of cryptocurrency crashes: a systematic review

Analysis of the Impact of COVID-19 Pandemic on the Intraday Efficiency of Agricultural Futures Markets

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