  We analyze the price return distributions of currency exchange rates,
cryptocurrencies, and contracts for differences (CFDs) representing stock
indices, stock shares, and commodities. Based on recent data from the years
2017--2020, we model tails of the return distributions at different time scales
by using power-law, stretched exponential, and $q$-Gaussian functions. We focus
on the fitted function parameters and how they change over the years by
comparing our results with those from earlier studies and find that, on the
time horizons of up to a few minutes, the so-called "inverse-cubic power-law"
still constitutes an appropriate global reference. However, we no longer
observe the hypothesized universal constant acceleration of the market time
flow that was manifested before in an ever faster convergence of empirical
return distributions towards the normal distribution. Our results do not
exclude such a scenario but, rather, suggest that some other short-term
processes related to a current market situation alter market dynamics and may
mask this scenario. Real market dynamics is associated with a continuous
alternation of different regimes with different statistical properties. An
example is the COVID-19 pandemic outburst, which had an enormous yet short-time
impact on financial markets. We also point out that two factors -- speed of the
market time flow and the asset cross-correlation magnitude -- while related
(the larger the speed, the larger the cross-correlations on a given time
scale), act in opposite directions with regard to the return distribution
tails, which can affect the expected distribution convergence to the normal
distribution.
