Fractal dynamics and wavelet analysis : deep volatility and return properties of Bitcoin, Ethereum and Ripple
Year of publication: |
2020
|
---|---|
Authors: | Celeste, Valerio ; Corbet, Shaen ; Gurdgiev, Constantin |
Published in: |
The quarterly review of economics and finance : journal of the Midwest Economics Association ; journal of the Midwest Finance Association. - Amsterdam [u.a.] : Elsevier, ISSN 1062-9769, ZDB-ID 1114217-0. - Vol. 76.2020, p. 310-324
|
Subject: | Efficient market hypothesis | Fractal market hypothesis | Cryptocurrencies | Wavelet coherence | Continuous wavelet transform | Hurst exponent | Volatilität | Volatility | Effizienzmarkthypothese | Zustandsraummodell | State space model | Virtuelle Währung | Virtual currency | Kapitaleinkommen | Capital income | Theorie | Theory | Stochastischer Prozess | Stochastic process | Zeitreihenanalyse | Time series analysis | Finanzmarkt | Financial market |
-
Long memory and efficiency of Bitcoin under heavy tails
Wu, Liang, (2020)
-
Can Bitcoin be a stable investment?
Çelik, Ismail, (2020)
-
Are the top six cryptocurrencies efficient? : evidence from time-varying long memory
Jena, Sangram Keshari, (2022)
- More ...
-
Fractal Dynamics and Wavelet Analysis : Deep Volatility Properties of Bitcoin, Ethereum and Ripple
Celeste, Valerio, (2018)
-
Corbet, Shaen, (2017)
-
Financial Digital Disruptors and Cyber-Security Risks : Paired and Systemic
Corbet, Shaen, (2017)
- More ...