Research Area Digital Finance and Blockchain Cryptocurrencies and Asset Pricing Cryptocurrencies and Financial Risk Blockchain Consensus Protocols and Energy Efficiency Crowdfunding (ICO, STO, IEO) Sentiments, Scams, and Frauds 24 January 2020 1 Vaasan yliopisto | Niranjan Sapkota Volatility Spillovers (G10 Currencies and Bitcoin) Predicting Cryptocurrency Defaults (Seminar( Aalto, Hanken, Vaasa, Jyväskylä)) Media Coverage (Forbes, Vaasa Insider) Conference (Finance, Property, Technology and the Economy- UniSA) Cryptocurrencies and Momentum (EL) Assets Market Equilibria (Privacy Vs. Non-privacy Coins) Cryptocurrencies and Liquidity Cryptocurrencies and Technical Trading Strategies (FRL)
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Research Area
24 January 2020 1
Digital Finance and
Blockchain
Cryptocurrencies and
Asset Pricing
Cryptocurrencies and
Financial Risk
Blockchain Consensus
Protocols and Energy Efficiency
Crowdfunding (ICO, STO, IEO)
Sentiments, Scams, and Frauds
24 January 2020 1Vaasan yliopisto | Niranjan Sapkota
Volatility Spillovers (G10 Currencies and Bitcoin)
Conference (Finance, Property, Technology and the Economy- UniSA)
Cryptocurrencies and Momentum (EL)
Assets Market Equilibria(Privacy Vs. Non-privacy Coins)
Cryptocurrenciesand Liquidity
Cryptocurrencies and Technical Trading Strategies
(FRL)
Asset Market Equilibria in Cryptocurrency Markets:
Evidence from a Study of Privacy and Non-Privacy Coins
-Niranjan Sapkota and Klaus Grobys
FinTech Conference UniSA, Dec 2-3, 2019
Outline
Purpose of the study: to find whether asset market equilibria incryptocurrency market exist.
Via: Johansen’s (1991, 1992, 1994, 1995) multivariate cointegrationmethodology to explore whether or not asset market equilibria in linewith Engle and Granger’s (1987) cointegration theory exist.
Research Question: ” Do privacy coins form a distinct submarketwithin the cryptocurrency market?”
Result: Privacy coins and non-privacy coins exhibit two distinctmarket equilibria
24 January 2020 3Vaasan yliopisto | Niranjan Sapkota
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Bitcoin and Privacy
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Bitcoin and Privacy…..contd
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Bitcoin and Privacy…..contd
• How private are the cryptocurrencies like Bitcoin?
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(Source: Goldfeder et al., 2017, MIT Technology Review)
Vaasan yliopisto | Niranjan Sapkota
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The Fungibility Problem (Clean Vs. Dirty Coins)
=?
Traditional Currency(US Dollar)
Cryptocurrency(non-privacy coin)
Vaasan yliopisto | Niranjan Sapkota
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=?Cryptocurrency(Privacy coin)
=?
The Fungibility Problem (Clean Vs. Dirty Coins)
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Non-privacy Coin+
Dark Web
Privacy Coin+
World Wide Web
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Prior Research on Cryptocurrencies and Privacy
• Androulaki, E., Karame, G.O., Roeschlin, M., Scherer, T. and Capkun, S.,2013, April. Evaluating user privacy in bitcoin. In InternationalConference on Financial Cryptography and Data Security (pp. 34-51).Springer, Berlin, Heidelberg.
• Goldfeder, S., Kalodner, H., Reisman, D. and Narayanan, A., 2018. Whenthe cookie meets the blockchain: Privacy risks of web payments viacryptocurrencies. Proceedings on Privacy EnhancingTechnologies, 2018(4), pp.179-199.
• Khalilov, M.C.K. and Levi, A., 2018. A survey on anonymity and privacy inbitcoin-like digital cash systems. IEEE Communications Surveys &Tutorials, 20(3), pp.2543-2585.
• Kumar, A., Fischer, C., Tople, S. and Saxena, P., 2017, September. Atraceability analysis of monero’s blockchain. In European Symposium onResearch in Computer Security (pp. 153-173). Springer, Cham.
• Foley, S., Karlsen, J.R. and Putniņš, T.J., 2019. Sex, drugs, andbitcoin: How much illegal activity is financed throughcryptocurrencies?. The Review of Financial Studies, 32(5),pp.1798-1853.
• Brenig, C., Accorsi, R. and Müller, G., 2015, May. EconomicAnalysis of Cryptocurrency Backed Money Laundering. In ECIS.
• Kethineni, S., and Y. Cao, 2019, The Rise in Popularity ofCryptocurrency and Associate Criminal Activity. InternationalCriminal Justice Review, forthcoming.
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Prior Research on Cryptocurrencies and Illegal Activities
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Table 1 Top 10 Privacy and Non-privacy Coins.
Panel A : Top 10 Non-Privacy Coins
S.No Non-Privacy Coin Symbol January 3, 2016 Coin Rank /572 Coins Capitalization ($) December 30, 2018
10 Prime-XI PXI 322 8889 1701 4236 -52.35 Average 16834.07
Note: This table reports the top 11 non-privacy coins (including Bitcoin) and top ten privacy coins based on their market capitalization as of January 3, 2016. There were 572 cryptocurrencies available (including both privacy and non-privacy coins) as of January 3, 2016, and 2073 coins as of December 30, 2018. Coin Rank shows the position based on a coin’s market capitalization. Three Years’ Market Capitalization Growth shows the percentage growth in market capitalization from January 3, 2016 until December 30, 2018. Panel A shows the top 11 non-privacy coins and Panel B shows the top ten privacy coins in terms of market capitalization (Source: coinmarketcap.com/historical/).
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To test the order of cointegration, we employ the trace test for each submarket of
Table 2 ADF tests for privacy and non-privacy coins.
Privacy coins Non-privacy coins
Model 1 Model 2 Model 1 Model 2 Coin Interceptª Lagsᵈ Intercept
and trendᵇ
Lagsᵈ Coin Interceptª Lagsᵈ Intercept and
trendᵇ
Lagsᵈ
DASH -1.79 0 0.25 0 XRP -0.99 2 -1.19 2 BCN -1.34 1 -0.90 1 LTC -1.13 0 -0.23 0 XDN -1.39 0 -0.91 0 ETH -2.93** 0 -0.63 0 XMR -2.16 0 -0.15 0 DOGE -1.30 0 -1.41 0 CLOAK -1.51 2 -0.72 2 PPC -1.43 0 -1.18 0 AEON -1.46 1 -0.57 1 BTS -1.24 0 -0.41 0 XST -1.49 1 -1.45 1 XLM -0.93 1 -1.45 1 PXI -1.44 2 -1.16 2 NXT -1.37 0 -0.62 0 NAV -1.86 4 -0.60 4 MAID -2.86** 0 -1.69 1 XVG -1.23 4 -1.27 4 NMC -1.65 1 -1.59 1 BTC -1.31 0 -0.06 0 Note: This table reports the results for Augmented Dickey Fuller tests of the daily price series in logs for privacy and non-privacy coins. Model 1 accounts for an intercept in the test regression, whereas model 2 accounts for both an intercept and trend term. The sample period is from January 1, 2016 until December 31, 2018 corresponding to 1096 observations. **Statistically significant on a 5% level. ª Critical values for 10%, 5% and 1% significance levels are -2.57, -2.86 and -3.44. ᵇ Critical values for 10%, 5% and 1% significance levels are -3.13, -3.41 and -3.97. ᵈ Lag-order is chosen by using the Schwarz info criterion. The maximum lag length is chosen by default is 21.
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Table 3 Trace test for cointegration employing privacy coins.
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.0627 283.1797 273.1889 0.0174
At most 1 0.0502 212.5937 228.2979 0.2095 At most 2 0.0381 156.4145 187.4701 0.5996 At most 3 0.0249 114.0723 150.5585 0.8223 At most 4 0.0238 86.63982 117.7082 0.7982 At most 5 0.0211 60.38928 88.80380 0.8456 At most 6 0.0153 37.17442 63.87610 0.9241 At most 7 0.0098 20.37189 42.91525 0.9535 At most 8 0.0050 9.687908 25.87211 0.9376 At most 9 0.0039 4.214442 12.51798 0.7109
Note: This table reports the results for the trace test for cointegration applied to a set of ten privacy coins exhibiting the highest market capitalization as of Jan 3, 2016. The test statistic allows for linear deterministic trend in data, that is, an intercept and trend in cointegration equation but no intercept in the Vector-Autoregression. The sample period is from January 1, 2016 until December 31, 2018 corresponding to 1096 observations. Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
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Table 4 Trace test for cointegration employing non-privacy coins excluding Bitcoin.
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.0636 289.7541 273.1889 0.0079
At most 1 0.0493 218.0979 228.2979 0.1342 At most 2 0.0334 163.0434 187.4701 0.4297 At most 3 0.0328 125.9784 150.5585 0.5074 At most 4 0.0231 89.62467 117.7082 0.7146 At most 5 0.0166 64.13613 88.80380 0.7299 At most 6 0.0163 45.94574 63.87610 0.6029 At most 7 0.0122 28.07154 42.91525 0.6178 At most 8 0.0092 14.69995 25.87211 0.5995 At most 9 0.0042 4.641440 12.51798 0.6484
Note: This table reports the results for the trace test for cointegration applied to a set of ten non-privacy coins (excluding Bitcoin) exhibiting the highest market capitalization as of Jan 3, 2016. The test statistic allows for a linear deterministic trend in data, that is, an intercept and trend in the cointegration equation but no intercept in the Vector-Autoregression. The sample period is from January 1, 2016 until December 31, 2018 corresponding to 1096 observations. Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
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Table 5 Trace test for cointegration employing non-privacy coins including Bitcoin.
Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.0724 345.4557 322.0692 0.0042
At most 1 0.0540 263.4921 273.1889 0.1196 At most 2 0.0371 202.9953 228.2979 0.3928 At most 3 0.0330 161.7328 187.4701 0.4628 At most 4 0.0288 125.1233 150.5585 0.5323 At most 5 0.0257 93.25557 117.7082 0.5990 At most 6 0.0165 64.90058 88.80380 0.7028 At most 7 0.0158 46.75482 63.87610 0.5645 At most 8 0.0127 29.41653 42.91525 0.5370 At most 9 0.0099 15.51272 25.87211 0.5321 At most 10 0.0043 4.671033 12.51798 0.6441
Note: This table reports the results for the trace test for cointegration applied to our set of non-privacy coins
including Bitcoin. The test statistic allows for linear deterministic trend in data, that is, an intercept and trend in the cointegration equation but no intercept in the Vector-Autoregression. The sample period is from January 1, 2016 until December 31, 2018 corresponding to 1096 observations.
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values
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Results
Table 6 Vector-Error Correction model estimates using privacy coins.
i Coin 𝜷𝜷�𝒊𝒊 𝜶𝜶�𝒊𝒊 1 DASH 1
( ̶ ) 2.8E-03 (1.42)
2 BCN 0.82** (2.28)
-1.5E-03 (-0.40)
3 XDN -0.46 (-1.01)
4.9E-03 (1.42)
4 XMR 3.75*** (5.74)
2.7E-03 (1.18)
5 CLOAK 0.04 (0.15)
2.0E-03 (0.45)
6 AEON -0.63 (-1.50)
5.2E-03 (1.39)
7 XST 1.36*** (4.03)
-1.0E-03 (-0.24)
8 PXI -0.49* (-1.78)
5.1E-03 (0.85)
9 NAV -3.24*** (7.52)
3.0E-02*** (7.42)
10 XVG -0.69** (-2.59)
1.7E-02*** (3.52)
𝑡𝑡 -3E-03*** (-3.88)
𝜇𝜇 -8.88 ( ̶ )
Note: This table reports the estimates for a fully specified Vector-Error-Correction Model using our set of privacy coins. The model accounts for an intercept 𝜇𝜇 and a time trend 𝑡𝑡 in the cointegration equilibrium relationship. Our model uses daily data of log prices. The model has a lag-order of 𝑝𝑝 = 5. We report the estimates for the cointegration vector 𝜷𝜷 and the estimates for the adjustment parameter vector 𝜶𝜶. The sample period is from January 1, 2016 until December 31, 2018 corresponding to 1096 observations. ** Statistically significant on a 5% level. *** Statistically significant on a 1% level.
Results
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Table 7 Vector-Error Correction model estimates using non-privacy coins.
i Coin 𝜷𝜷�𝒊𝒊 𝜶𝜶�𝒊𝒊 1 XRP 1
( ̶ ) 3.2E-03 (0.86)
2 LTC 0.35 (1.02)
-6.4E-04 (-0.22)
3 ETH -1.53*** (-5.29)
1.5E-02*** (4.84)
4 DOGE -1.59*** (4.16)
1.4E-02*** (4.13)
5 PPC -0.57 (-1.00)
-2.6E-03 (-0.76)
6 BTS -0.91*** (-2.85)
8.7E-03** (2.24)
7 XLM 0.37* (1.67)
9.1E-03** (2.23)
8 NXT 0.49** (2.10)
1.8E-03 (0.47)
9 MAID 0.68** (2.16)
-1.4E-03 (-0.42)
10 NMC 2.18 (0.44)
-5.4E-03 (-1.16)
𝑡𝑡 9E-04** (1.98)
𝜇𝜇 -1.11 ( ̶ )
Note: This table reports the estimates for a fully specified Vector-Error-Correction Model using our set of non-privacy coins. The model accounts for an intercept 𝜇𝜇 and a time trend 𝑡𝑡 in the cointegration equilibrium relationship. Our model uses daily data of log prices. The model has a lag-order of 𝑝𝑝 = 5. We report the estimates for the cointegration vector 𝜷𝜷 and the estimates for the adjustment parameter vector 𝜶𝜶. The sample period is from January 1, 2016 until December 31, 2018 corresponding to 1096 observations. * Statistically significant on a 10% level. ** Statistically significant on a 5% level. *** Statistically significant on a 1% level.
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Results
Fig. 1. Cointegration relationship of privacy and non-privacy coins. This figure plots the cointegration relationships for privacy and non-privacy coins over time. The sample period is from January 1, 2016 until December 31, 2018 corresponding to 1096 observations. The correlation is estimated at -0.10 implying that those two market equilibria are two distinct phenomena.
-4
-3
-2
-1
0
1
2
3
Jan 07, 2016 Jul 25, 2016 Feb 10, 2017 Aug 29, 2017 Mar 17, 2018 Oct 03, 2018
Privacy coins Non-privacy coins
Conclusion
• Majority of cryptocurrencies are a part of that market equilibrium for both the sub markets.
• Our findings provide evidence for market inefficiency in both submarkets of privacy and non-privacy coins.
• Underlying forces that cause privacy coins equilibrium are unrelated to those at work in the non-privacy coins market.
• It could be that the market actors in the privacy coin market are different from those that trade in the non-privacy coin market.
• Moreover, potential factors that might have caused the cointegration relationships should be the subject of future research.
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Thank You!
24 January 2020 23Vaasan yliopisto | Niranjan Sapkota