Wavelet time-scale persistence analysis of cryptocurrency market returns and volatility
| Autor: | Nana Kwame Akosah, Maurice Omane-Adjepong, Paul Alagidede |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Cryptocurrency Condensed Matter Physics 01 natural sciences 010305 fluids & plasmas Trend following Efficient-market hypothesis Investment decisions Wavelet 0103 physical sciences Econometrics Economics Regime shift Market return Volatility (finance) 010306 general physics |
| Zdroj: | Physica A: Statistical Mechanics and its Applications. 514:105-120 |
| ISSN: | 0378-4371 |
| DOI: | 10.1016/j.physa.2018.09.013 |
| Popis: | This paper explores persistence of eight largest cryptocurrency markets using daily data from 25 ∕ 08 ∕ 2015 – 13 ∕ 03 ∕ 2018 , across time and trading scale. Employing ARFIMA-FIGARCH class of models under two different distributions and a modified log-periodogram method, we generally uncovered informational (in)efficiency and volatility persistence to be highly sensitive to time-scale, the measure of returns and volatilities, and regime shift. In particular, evidence of persistence was found to be concealed in full-sample conditional returns and a break regime, where three crypto markets showed characteristics contrary to the Efficient Market Hypothesis. These results suggest that empirical examination of persistence in markets should be mindful of volatility measures, trading horizons, and switching regimes. More so, scale-conscious traders or investors could rely on our findings and the implications thereof in making investment decisions in the market. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect