True versus Spurious Long Memory in Cryptocurrencies
Abstract
1. Introduction
2. Background Literature
3. Long Memory Explained
4. Tests of Long Memory
5. Results
5.1. Data
5.2. Findings
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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1 | Geometric decay would be easily discerned in the bottom right plot in Figure 1 if the number of lags was set at most at 30 (or at any low amount). |
GPH (M = T0.5) | GPH (M = T0.8) | Fourier Estimations | Local Whittle | |
---|---|---|---|---|
Model 1 | 0.141 | 0.557 | 0.467 | 0.140 |
(0.109) | (0.026) | (0.125) | (0.089) | |
Model 2 | 0.284 | 0.741 | 0.447 | 0.282 |
(0.147) | (0.051) | (0.168) | (0.135) |
Bitcoin | Bitcoin Cash | XRP | Litecoin | Ethereum | |
---|---|---|---|---|---|
Mean | 0.00102 | −0.0007 | 0.00149 | 0.00098 | 0.00332 |
Std dev | 0.03897 | 0.07881 | 0.07271 | 0.06444 | 0.06104 |
Skewness | −0.340 | 0.612 | 2.076 | 1.720 | 0.271 |
Kurtosis | 8.459 | 10.651 | 32.91 | 28.618 | 7.052 |
Jarque–Bera | 2761 | 2206 | 88925 | 67783 | 1082 |
N | 2190 | 882 | 2340 | 2435 | 1553 |
Bitcoin | Ethereum | Litecoin | Bitcoin Cash | XRP | ||||||
---|---|---|---|---|---|---|---|---|---|---|
T = 2190 | T = 1553 | T = 2435 | T = 883 | T = 2340 | ||||||
Estimate | p-Value | Estimate | p-Value | Estimate | p-Value | Estimate | p-Value | Estimate | p-Value | |
Panel A: Return | ||||||||||
GPH () | 0.153 | 0.165 | 0.259 ** | 0.036 | 0.170 | 0.111 | 0.121 | 0.405 | 0.057 | 0.589 |
Bias Test | −0.454 | 0.675 | −1.407 | 0.920 | −0.122 | 0.549 | 0.806 | 0.210 | −0.886 | 0.812 |
Skip-Sampling (h = 4) | −0.522 | 0.699 | −0.816 | 0.793 | 0.946 | 0.172 | −1.084 | 0.861 | 0.624 | 0.266 |
Skip-Sampling (h = 8) | 0.378 | 0.353 | 0.168 | 0.433 | 0.044 | 0.482 | −0.486 | 0.686 | 0.544 | 0.293 |
Panel B: Volatility | ||||||||||
Volatility (LHR) | ||||||||||
GPH () | 0.536 *** | 0.000 | 0.505 *** | 0.000 | 0.584 *** | 0.000 | 0.541 *** | 0.001 | 0.486 *** | 0.000 |
Bias Test | −0.590 | 0.722 | 0.044 | 0.483 * | 0.084 | 0.467 | 0.322 | 0.374 | 2.356 *** | 0.009 |
Skip-Sampling (h =4) | −0.073 | 0.529 | 0.431 | 0.333 | −0.768 | 0.779 | −0.451 | 0.674 | −0.406 | 0.658 |
Skip-Sampling (h = 8) | 0.193 | 0.424 | 0.560 | 0.288 | −0.423 | 0.664 | −0.307 | 0.645 | −0.560 | 0.712 |
Volatility (SR) | ||||||||||
GPH () | 0.326 *** | 0.004 | 0.202* | 0.099 | 0.359 *** | 0.000 | 0.174 | 0.235 | 0.191 * | 0.077 |
Bias Test | 1.524 * | 0.064 | −1.865 | 0.969 | 0.365 | 0.357 | 3.119 *** | 0.001 | 0.868 | 0.193 |
Skip-Sampling (h = 4) | −0.860 | 0.805 | 0.637 | 0.262 | 0.142 | 0.443 | 0.585 | 0.279 | −0.737 | 0.770 |
Skip-Sampling (h = 8) | −1.055 | 0.854 | 0.370 | 0.356 | 0.539 | 0.295 | 0.289 | 0.386 | −0.752 | 0.774 |
Volatility (AR) | ||||||||||
GPH () | 0.375 *** | 0.001 | 0.378 *** | 0.003 | 0.466 *** | 0.000 | 0.336 ** | 0.026 | 0.381 *** | 0.001 |
Bias Test | −2.229 | 0.987 | −1.535 | 0.938 | 2.086 ** | 0.018 | 1.957 ** | 0.025 | −1.185 | 0.882 |
Skip-Sampling (h = 4) | −1.027 | 0.848 | 0.423 | 0.336 | −1.229 | 0.890 | −0.013 | 0.505 | 0.070 | 0.472 |
Skip-Sampling (h = 8) | −1.081 | 0.860 | 0.848 | 0.198 | −0.482 | 0.685 | −0.257 | 0.601 | −0.421 | 0.663 |
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Rambaccussing, D.; Mazibas, M. True versus Spurious Long Memory in Cryptocurrencies. J. Risk Financial Manag. 2020, 13, 186. https://doi.org/10.3390/jrfm13090186
Rambaccussing D, Mazibas M. True versus Spurious Long Memory in Cryptocurrencies. Journal of Risk and Financial Management. 2020; 13(9):186. https://doi.org/10.3390/jrfm13090186
Chicago/Turabian StyleRambaccussing, Dooruj, and Murat Mazibas. 2020. "True versus Spurious Long Memory in Cryptocurrencies" Journal of Risk and Financial Management 13, no. 9: 186. https://doi.org/10.3390/jrfm13090186
APA StyleRambaccussing, D., & Mazibas, M. (2020). True versus Spurious Long Memory in Cryptocurrencies. Journal of Risk and Financial Management, 13(9), 186. https://doi.org/10.3390/jrfm13090186