S&amp;P 500 Index Price Spillovers around the COVID-19 Market Meltdown

Lento, Camillo; Gradojević, Nikola

doi:10.3390/jrfm14070330

Cited by 15 publications

(11 citation statements)

References 48 publications

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“…Stock market worldwide reported the largest one-week decline in the stock market in 2020 due to COVID-19 [5]. This was the largest decline since the 2008 financial crisis and was associated with price spillovers.…”

Section: Introductionmentioning

confidence: 99%

The Dynamic Impact of COVID-19 Pandemic on U.S Stock Market Returns

Geng¹

2023

BCPBM

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The global stock market experienced significant detrimental effect during the COVID-19 pandemic era. In the United States, the pandemic had considerable influence on the stock market. Notably, the pandemic negatively impacted the stock market from March, 2020, before there was a recovery from Dec, 2020. During this period, the pandemic significantly increased risks and uncertainties in the U.S. stock market. Particularly, the S&P 500 index during that period exhibits the dynamic significance of coronavirus on the stock market. Price movements in the financial markets resulted from price changes in the S&P 500, which led to a market meltdown. The COVID-19 index raised volatility, which sparked frenetic trading on the stock market. Notably, the epidermic flare-up had caused the S&P 500 index to lose 34% of its value as of August 2020. Also, the index's value fell by 86% as a result of the lengthy market collapse. However, the timely, obligatory, and successful anti-epidemic measures taken by governments led to a positive performance of stocks. As a result, the COVID-19 contagion’s impact on stock returns in the U.S. transformed from negative to positive performance.

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Section: Introductionmentioning

confidence: 99%

The Dynamic Impact of COVID-19 Pandemic on U.S Stock Market Returns

Geng¹

2023

BCPBM

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“…For example, researchers have investigated different aspects of the S&P500 Index, of the NASDAQ Index, of the DOWJONES Index and of the NIKKEI Index. To this regard, in fact, for example, Lee and Hao [13] have considered the S&P500 Index in order to study the asymmetric effects of error corrections between oil prices in the U.S.A. and the prices of the aforementioned index, while Lento and Gradojevic [14] focused their attention on understanding the price spillovers of the S&P500 Index related to the effects on the market of COVID-19. Moreover, Metghalchi et al [16] have explored the trading strategies related to the moving average with regard to the NASDAQ Index, Giannarakis et al [9] have carried out an analysis of the Dow Jones Sustainability Index and Christiansen and Koldertsova [7] have taken into account several indexes, such as the NASDAQ Index, in order to explore the role of stock exchanges in corporate governance.…”

Section: Introductionmentioning

confidence: 99%

The dynamics of the prices of the companies of the STOXX Europe 600 Index through the logit model and neural network

Mecchia¹,

Gaudenzi²

2022

Preprint

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The aim of the present work is analysing and understanding the dynamics of the prices of companies, depending on whether they are included or excluded from the STOXX Europe 600 Index. For this reason, data regarding the companies of the Index in question was collected and analysed also through the use of logit models and neural networks in order to find the independent variables that affect the changes in prices and thus determine the dynamics over time.

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“…Following Granger, many causality tests are proposed in the literature by authors such as Toda–Yamamoto [ 2 ] and Hatemi-J [ 3 ] in their own causality tests. Furthermore, in the literature, many nonlinear causality analysis methods are presented [ 4 , 5 , 6 , 7 ]; however, all these methods are based on a hypothesis-testing procedure. In these procedures, a test statistic is computed and, according to the value of this statistic, the existence of causality is determined.…”

Section: Introductionmentioning

confidence: 99%

Causality Analysis for COVID-19 among Countries Using Effective Transfer Entropy

Ünal

2022

Entropy

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In this study, causalities of COVID-19 across a group of seventy countries are analyzed with effective transfer entropy. To reveal the causalities, a weighted directed network is constructed. In this network, the weights of the links reveal the strength of the causality which is obtained by calculating effective transfer entropies. Transfer entropy has some advantages over other causality evaluation methods. Firstly, transfer entropy can quantify the strength of the causality and secondly it can detect nonlinear causal relationships. After the construction of the causality network, it is analyzed with well-known network analysis methods such as eigenvector centrality, PageRank, and community detection. Eigenvector centrality and PageRank metrics reveal the importance and the centrality of each node country in the network. In community detection, node countries in the network are divided into groups such that countries in each group are much more densely connected.

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S&P 500 Index Price Spillovers around the COVID-19 Market Meltdown

Cited by 15 publications

References 48 publications

The Dynamic Impact of COVID-19 Pandemic on U.S Stock Market Returns

The Dynamic Impact of COVID-19 Pandemic on U.S Stock Market Returns

The dynamics of the prices of the companies of the STOXX Europe 600 Index through the logit model and neural network

Causality Analysis for COVID-19 among Countries Using Effective Transfer Entropy

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