ArticleChaos, solitons, and fractals2021
The effect of COVID-19 on long memory in returns and volatility of cryptocurrency and stock markets.
Article in Chaos, solitons, and fractals, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- GARTFIMA process and its empirical spectral density based estimation.Journal of applied statistics · 2024Article
- Short-term effect of COVID-19 pandemic on cryptocurrency markets: A DCC-GARCH model analysis.Heliyon · 2023Article
- Skewed multifractal scaling of stock markets during the COVID-19 pandemic.Chaos, solitons, and fractals · 2023Article
- Investigating Dynamical Complexity and Fractal Characteristics of Bitcoin/US Dollar and Euro/US Dollar Exchange Rates around the COVID-19 Outbreak.Entropy (Basel, Switzerland) · 2023Article
- Review
- The effect of COVID-19 pandemic on return-volume and return-volatility relationships in cryptocurrency markets.Chaos, solitons, and fractals · 2022Article
- Prediction of crude oil prices in COVID-19 outbreak using real data.Chaos, solitons, and fractals · 2022Article
- Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model.Journal of combinatorial optimization · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We examine long memory (self-similarity) in digital currencies and international stock exchanges prior and during COVID-19 pandemic. Specifically, ARFIMA and FIGARCH models are respectively employed to evaluate long memory parameter in returns and volatility. The dataset contains 45 cryptocurrency markets and 16 international equity markets. The
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.