Association between COVID-19 cases and international equity indices

Abstract

This paper analyzes the impact of COVID-19 on the populations and equity markets of 92 countries. We compare country-by-country equity market dynamics to cumulative COVID-19 case and death counts and new case trajectories. First, we examine the multivariate time series of cumulative cases and deaths, particularly regarding their changing structure over time. We reveal similarities between the case and death time series, and key dates that the structure of the time series changed. Next, we classify new case time series, demonstrate five characteristic classes of trajectories, and quantify discrepancy between them with respect to the behavior of waves of the disease. Finally, we show there is no relationship between countries’ equity market performance and their success in managing COVID-19. Each country’s equity index has been unresponsive to the domestic or global state of the pandemic. Instead, these indices have been highly uniform, with most movement in March.

Cryptocurrency Market Consolidation in 2020–2021

“…Over the past few years, two processes have had a particularly strong impact on financial markets: the emergence of the cryptocurrency market [ 1 , 2 , 3 , 4 , 5 ] and the COVID-19 pandemic [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Each of these processes alone has already been a topic in numerous pieces of the scientific literature, but they also were studied together [ 5 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].…”

Collective correlations, dynamics, and behavioural inconsistencies of the cryptocurrency market over time

“…In the nonlinear dynamics community, such market dynamics have been studied with a range of methodologies, including chaotic systems [ 12 – 14 ], clustering [ 15 , 16 ], sample entropy [ 17 , 18 ], and principal components analysis [ 19 – 21 ]. Various asset classes have attracted interest, including equities [ 22 ], fixed income [ 23 ], and foreign exchange [ 24 ].…”

Estimating a continuously varying offset between multivariate time series with application to COVID-19 in the United States

“…Next, nonlinear dynamics researchers have proposed several sophisticated extensions to the classical predictive SIR model, including finding analytical solutions [ 19 , 20 ], modifications with additional variables [ 21 – 26 ], incorporation of Hamiltonian dynamics [ 27 ] or network models [ 28 ], and a closer analysis of uncertainty in the SIR equations [ 29 ]. Other mathematical approaches to prediction and analysis include power-law models [ 30 – 32 ], forecasting models [ 33 ], fractal curves [ 34 ], Bayesian methods [ 35 ], regression models and feature selection [ 36 , 37 ], Markov chain Monte Carlo models [ 38 ], distance analysis [ 39 , 40 ], network models [ 41 – 43 ], analyses of the dynamics of transmission and contact [ 44 , 45 ], clustering [ 46 , 47 ] and many others [ 48 – 53 ]. Finally, numerous articles have been devoted specifically to the dynamics of COVID-19 in the United States [ 54 ], including incorporating spatial components of the virus’ spread [ 55 – 57 ].…”