- Email: [email protected]
Cryptocurrency accepting venues, investor attention, and volatility
Journal Pre-proof
Cryptocurrency Accepting Venues, Investor Attention, and Volatility Nasim Sabah PII: DOI: Reference:
S1544-6123(19)30649-X https://doi.org/10.1016/j.frl.2019.101339 FRL 101339
To appear in:
Finance Research Letters
Received date: Revised date: Accepted date:
28 June 2019 19 October 2019 21 October 2019
Please cite this article as: Nasim Sabah , Cryptocurrency Accepting Venues, Investor Attention, and Volatility, Finance Research Letters (2019), doi: https://doi.org/10.1016/j.frl.2019.101339
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Cryptocurrency Accepting Venues, Investor Attention, and Volatility
Nasim Sabah College of Business Framingham State University Framingham, MA 01701 Email: [email protected] Phone: 504-344-9220
Abstract Using a novel dataset of cryptocurrency accepting business venues that accept cryptocurrencies as form of payments, we examine the relationship between new crypto accepting venues and crypto volatility. We argue that the number of new venues is a proxy for investor attention. We find that the number of new venues is a significant driver of crypto volatility. Moreover, venues that do not disclose their type of business as well as venues in Europe, North America and Oceania increase crypto volatility. Granger-causality, VAR estimation and a quasi-natural experiment validate our findings. Keywords: Cryptocurrency, Crypto-accepting venues, Volatility, VAR, Granger causality, Quasi-natural experiment. JEL Classification: G10, G12, G14
1. Introduction On December 11 of 2014, Microsoft started accepting bitcoin as a payment option to buy certain digital products. Cryptocurrencies (crypto hereafter) volatility has increased by 41% on the week following the acceptance compared to the week before the acceptance. On December 26 of 2017, Microsoft halted bitcoin payments. While the company has not released any official statement, several news articles claim that the halt is because of the crypto volatility. 1 Yet, the volatility of crypto has decreased by 49% on the week following the halt compared to the week before the halt. This leads to one particularly interesting question: do crypto accepting merchants increase crypto volatility? Using a novel dataset of cryptocurrency accepting business venues (merchants) that accept cryptocurrencies as a form of payment, we examine the relationship between the number of crypto-accepting new venues and crypto volatility. We find that a one-standard deviation increases in such new venues increases the average volatility by 18%. We categorize new venues based on business types and location and find that undisclosed new venues as well as new venues located in Europe, North America and Oceania significantly increase crypto volatility. Grangercausality and VAR estimation confirm our findings and indicate that the volatility is highest in three weeks following the introduction of the new payment systems and remains high up to 20 weeks. Finally, we design a quasi-natural experiment using seven large firms that started accepting cryptocurrencies and three of these firms that stopped accepting cryptocurrencies during our sample period. We find that the overall crypto volatility increases following the introduction of such crypto payments and decreases when firms withdraw such payment options. A few papers examine the crypto trading and volatility under the investor attention framework. Urquhart (2018) examines the attention of Bitcoin 2 using Google trends data and find that attention is not a significant predictor of realized volatility. Shen et al. (2019) use the number of tweets and find that it is a significant driver of next day realized volatility. We build on their study and investigate the crypto volatility using the number of crypto accepting business 1 2
See www.inverse.com/article/40012-bitcoin-microsoft-cryptocurrency-payments Bitcoin is the original and the largest cryptocurrency by market capitalization.
merchants. We argue that our measure is a proxy for investor attention because businesses that accept crypto as a form of payments require investments in machinery and technology in each of the venues as well as centrally. Moreover, such form of payment provides liquidity in the market, attract a wide range of investors and strengthen their confidence in crypto markets. Factors that influence crypto volatility are also addressed by several papers. Aalborg et al. (2019) examine the Bitcoin volatility using past realized volatility and trading volume and show that these variables can predict daily volatility but not the weekly volatility. Balcilar et al. (2017) find that trading volume does not predict Bitcoin volatility. Walther et al. (2019) find that the Global Real Economic Activity is the most important exogeneous driver of crypto volatility. Additionally, Corbet et al. (2018) find that the introduction of Bitcoin futures has increased the spot volatility of bitcoin. Cheah and Fry (2015) find that Bitcoin price contains a significant speculative component, Baig et al. (2019) show that investor sentiment can explain Bitcoin price clustering, and Blau (2017) argues that the speculative trading is not directly related to the unusual volatility of Bitcoin. Many papers examine the crypto volatility in the context of econometric approach. Among others, Ardia et al. (2019), Conrad et al. (2018), Chu et al. (2017), Dyhrberg (2016), Katsiampa (2017) and Klein et al. (2018) use GARCH models to examine the volatility of bitcoin and other cryptocurrencies. Klein et al. (2018) examine the asymmetric volatility effects of bitcoin. Hafner (2018) examines the bubble-like behavior of cryptocurrencies and find that the bubble is less pronounced under time-varying volatility than constant volatility. Katsiampa et al. (2019) examine the conditional volatility dynamics and volatility co-movements of major cryptocurrencies using the Diagonal BEKK and Asymmetric Diagonal BEKK methodologies. Lahmiri et al. (2018) examine the nonlinear dependence structure of Bitcoin volatility and find that Bitcoin market is high disordered and risky, and Bitcoin is not suitable for hedging. Volatility is one of the key components in investment decisions. Thus, several papers examine the effectiveness of cryptocurrencies as an investment tool. Briere et al. (2015) examine the Bitcoin investment with a diversified portfolio and find that Bitcoin investment offers significant diversification benefits. Using Conditional Value-at Risk framework instead of the traditional mean-variance approach, Eisl et al. (2015) show that Bitcoin returns have very low correlations
with traditional financial instruments and Bitcoin should be included in optimal portfolios. In another study, Trimborn et al. (2019) propose a liquidity constraint investment approach and show that the portfolio risk-return trade off can be improved by including cryptocurrencies. Charfeddine et al. (2019) explore the relevance of cryptocurrencies for investors and find that cryptocurrencies provide diversification benefits but are not appropriate for hedging. This is in contradiction with Guesmi et al. (2019), who find that a short position in Bitcoin market allows hedging the risk. Our findings extend the literature that focuses on the predictability of crypto volatility. Our results suggest that the number of crypto accepting business venues is a significant driver of crypto volatility. Moreover, Granger-causality, VAR estimation and a quasi-natural experiment validate our findings.
2. Data and variable descriptions We collect information about venues that accept cryptocurrencies as a payment method from coinmap.org. This website provides date and time of introducing new venue with latitude and longitude of location, name and business type. We collect the continent of each venue from GeoNames.org using corresponding latitude and longitude. We gather market capitalization and market cap weighted Cryptoz Index Volatility (hereafter, CV) from cryptoz.ai website for top 10, 25, 50 and 100 cryptocurrencies. CV is measured by calculating percentage standard deviation of the current value with respect to moving average of last 30 days value. 3 We gather prices from blockchain.com, and VIX from CBOE. Our sample begins on 9 February 2014 and ends on 31 December 2018. We create returns as
, and size as natural log of
market capitalization. We aggregate all our measures at weekly level. There are 254 weeks in our merged sample. For our regression analysis, we standardize venue including its business and continent categories.
3
For more information on how to construct Cryptoz Index Volatility, see https://cryptoz.ai/#!/cryptoindex/volatility.
During our sample period, 11,648 new venues introduce crypto as a form of payments. Type of business is missing for half of the sample. We create six major business categories from the available data: (1) ATM is the venue type where cryptocurrency can be converted into cash, (2) Entertainment includes venues under nightlife, attraction and sports, (3) Food includes cafe, food, drug store and grocery, (4) Lodging includes lodging and transport services, (5) Shopping includes retailer, shopping and educational business, and (6) Undisclosed venues are all missing venue types. We also categories each venue by the continent they are located: Africa, Asia, Europe, North America, Oceania, and South America. Figure1 plots the location of all cryptoaccepting new venues. Table 1 presents the summary statistics of the variable used in this study. Each week, on average 46 new venues accept cryptocurrencies as their payment method. Among these, on average 21.6 new venues are undisclosed, 8.6 new venues are shopping, 6.5 are food, 3.7 are ATM, 3 are lodging and 2.6 are entertainment venues. Weekly new venues are highest in Europe (19.3), following North America (13.5), South America (7.0), Asia (4.5), Oceania (1.3), and Africa (0.5). CV10, CV25, CV50 and CV100 are the volatility of market cap-weighted top 10, 25, 50 and 100 cryptocurrencies, respectively. The average volatility for these cryptocurrencies are 4.8, 4.9, 5.1 and 5. The average market capitalization of cryptocurrencies is $76.5 billion, average weekly return is 0.65%, and average market wide volatility (VIX) is 14.9 during our sample period. To further motivate our analysis, we report the Pearson correlation coefficients for the variables used in this study in Table 2. This table shows that there is a strong correlation between crypto volatility and number of new venues. The correlation coefficient between CV10 and venue is 0.25. All the venue categories have positive correlations with crypto volatility. Among these, Entertainment, Lodging, Shopping, and Undisclosed venues as well as venues located in all continent except Africa have strong positive correlation with CV10. Size has a positive correlation with CV10 with a coefficient of 0.42. Finally, venue and return have a correlation of 0.16.4
4
On a different note, we examine the relation between the number of new venues and crypto returns using regression analysis, however, we do not find any significant relation.
3. Results Figure 2 shows the time series plot of crypto accepting new venues and crypto volatility (CV10). There is a strong association between new venues and volatility where volatility follows the path of new venues with a lag. In fact, the correlation between volatility and one-week lagged venues is 0.365. Consequently, we attempt to isolate the relationship between crypto volatility and lagged new venues while controlling for other factors that affect volatility. We use the lagged number of venues due to potential endogeneity. 5 We estimate the following equation using time series data using OLS:
Table 3 presents the results using the above equation. We use CV10 as our primary volatility measure. Our control variable includes size, aggregate volatility (VIX), and returns. Venue is the number of weekly new venues. We use different combinations of venue with subcategories. We calculate robust standard errors using Newey-West adjustments for autocorrelation up to 30 lags. Column (1) presents results using the univariate test. It shows that a 1-standard deviation (SD) increase in number of new venues increase the level of volatility by 1.54% in the following week, whereas the average volatility is 4.8%. Thus, the higher the number of new venues, the higher the following week’s volatility. Column (2) shows that a 1-SD increase in new venues increase the volatility by 81 bps, which is 17.7% more than the average volatility, after controlling for factors that influence volatility. This increment is statistically significant and economically meaningful. Next, we examine how different types of new venues and locations affect the volatility. In column (3) - (4), we use six business categories and six continents of venues as our explanatory variables. Column (3) shows that only undisclosed category is strongly positively associated with the crypto volatility. In economic term, a 1-SD increase in undisclosed new venues increase the volatility by 76 bps (15.8% more than average). While all other venue categories such as ATM, Entertainment, Food, Lodging and Shopping serve mostly local customers, the undisclosed
5
We note that our results are consistent if we analyze contemporaneous association between crypto volatility and number of new venues.
venues are half of the new venues and serve all other online and digital purchases. Thus, it is not surprising that a small number of local venues will have no material effect on the global crypto market volatility, and many undisclosed venues serving a wide base of customers will drive our results. Column (4) of Table 3 shows that new venues located in Europe, North America and Oceania increase the crypto volatility. In economic term, a 1-SD increase of new venues in Europe, North America and Oceania increase the crypto volatility by 48 bps (10% more than average), 30 bps (6.3% more than average), and 36 bps (7.5% more than average), respectively. Given that most of the new venues are in Europe and North America, this result is also in line with the previous column that a reasonable number of venues can create a material effect on the crypto volatility. Next, we use three other volatility measures, CV25, CV50 and CV100, as our dependent variable for robustness checks and present results in columns (5) - (7). These results are very similar to that of CV10 in column (2), indicating a strong positive association between crypto volatility and number of crypto-accepting new venues. In economic term, a 1-SD increase in new venues that accept crypto as payment increase CV25 by 81 bps (16.3% more than average), increase CV50 by 88 bps (17.2% more than average), and increase CV100 by 79 bps (15.9% more than average). As for control variables, 1-period lagged volatility, Size, VIX and returns show strong positive association with crypto volatility. Next, we conduct a series of Granger-causality tests to confirm the direction of causation and report our results in table 4. We estimate Wald statistics for the change in venue and each of the four volatility groups. The Wald statistics where the causation flows from venues to volatility (∆Venue → ∆CV) are strong and significant with p-value < 0.0001 for all volatility groups. The coefficients in the reverse direction (∆CV → ∆Venue) are small and insignificant. This table shows that changes in number of crypto accepting new venues lead to changes in crypto volatility. Next, we estimate a VAR process as an attempt to control for endogeneity, and examine the impulse response of crypto volatility to exogenous shocks in number of new venues. Figure 3 plots the simple and orthogonalized impulse response functions (IRFs) for changes in volatility (CV10) to an exogenous, a 1-SD shock to changes in number of new venues. The responses are
obtained from estimating a bivariate vector autoregression with n lags (VAR(n) model), where n = 3, 5, or 7. All of these plots show that (1) change in volatility is positive in the weeks following the shock in venues, (2) change in volatility is highest in three weeks following the shock, and (3) change in volatility remains positive for up to 20 weeks after the shock. This figure confirms that the causation flows from venues to volatility. Ideally, we could use the number of new venues that stop accepting cryptocurrencies and examine whether the difference between crypto accepting and stopping new venues can predict crypto volatility. However, the data is not available for venues that stop accepting crypto. To overcome the data limitation, we hand collect the data of seven large firms. These seven firms are Dell, Dish, ExpressVPN, Fiverr, Lionsgate Films, Microsoft and NewEgg. All these firms started accepting crypto during 2014 except Lionsgate Films which started accepting crypto in 2015. Three of these firms, Fiverr, Dell and Microsoft, stopped accepting crypto during 2017. We design a quasi-natural experiment in a diff-in-diff setting using data for these seven firms from 14 days before to 14 after the start and stop. We create a matching sample for each firm where we pick random days from a subsample of days with the following criteria: (1) the matching sample day has zero new crypto accepting venue and not within 28 days of start or stop, and (2) total new venues during 14 days before to 14 days after of matching day fall below 10th percentile of the distribution. Table 5 presents the results using the quasi-natural experiment. We use CV10 as our volatility measure. Our explanatory variable are as follows: Start (Stop) is an indicator variable for days on and after the start (stop) of crypto accepting venues, Treatment is an indicator variable for firms that start (stop) accepting crypto, and an interaction of these two variables. Column (1) presents the results for start sample and column (2) presents for stop sample. In column (1), the Start*Treatment coefficient is 4.91 and significant, indicating an increase in volatility up to 14 days following the new crypto accepting venues. Column (2) shows that the Stop*Treatment coefficient is -3.84 and significant, indicating a decrease in volatility up to 14 days following the stop of crypto acceptance. These two columns show a robust association between crypto accepting venues and crypto volatility. 4. Conclusion
This paper studies the relationship between the numbers of crypto-accepting new venues and whether they are useful in forecasting crypto market volatility. We argue that the number of new business venues that accept cryptocurrencies as a form of payments is a proxy for investor attention. Businesses that accept crypto as an alternative form of payments provide liquidity in the market and attract a wide range of customers. Such a payment form requires real monetary decisions. Accordingly, we find that the number of crypto-accepting new venues is a significant predictor of crypto volatility. We categorize new venues based on business types and location. We find that crypto-accepting undisclosed venues increase crypto volatility. Moreover, new venues in located in Europe, North America and Oceania also increase crypto volatility. We include three additional volatility measures as robustness check and find similar results. Then we conduct a series of Granger-causality tests and a VAR process to examine the direction of causation and find that the volatility is highest in three weeks following the introduction of new venues and remains high up to 20 weeks. Moreover, the causation flows from venues to volatility such that the changes in number of crypto accepting new venues lead to changes in crypto volatility. Finally, we design a quasi-natural experiment using seven large firms that started accepting cryptocurrencies and three of these firms that stopped accepting cryptocurrencies during our sample period. We find that the overall crypto volatility increases following the introduction of such crypto payments and decreases when firms withdraw such payment options. We conclude that the number of crypto-accepting new venues can significantly predict future crypto volatility. Nevertheless, our results should be interpreted with caution. While a fair number of crypto accepting venues offer digital products or online crypto payments, other venues serve local customers only. We link both local and online customers’ decisions to the global crypto market, and the channels through which these decisions are incorporated into global market could be complex and is left for future research.
References Aalborg, H.A., Molnar, P., de Vries, J.E., 2019. What can explain the price, volatility and trading volume of bitcoin? Finance Research Letters 29, 255 – 265. Ardia, D., Bluteau, K., Ruede, M., 2019. Regime changes in bitcoin garch volatility dynamics. Finance Research Letters 29, 266 – 271. Baig, A., Blau, B.M., Sabah, N., 2019. Price clustering and sentiment in bitcoin. Finance Research Letters 29, 111 – 116. Balcilar, M., Bouri, E., Gupta, R., Roubaud, D., 2017. Can volume predict bitcoin returns and volatility? a quantiles-based approach. Economic Modelling 64, 74–81. Blau, B., 2017. Price dynamics and speculative trading in bitcoin. Research in International Business and Finance 41, 493–499. Bri`ere, M., Oosterlinck, K., Szafarz, A., 2015. Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management 16, 365–373. Charfeddine, L., Benlagha, N., Maouchi, Y., 2019. Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors. Economic Modelling. Cheah, E., Fry, J., 2015. Speculative bubbles in bitcoin markets? an empirical investigation into the fundamental value of bitcoin. Economics Letters 130, 32–36. Chu, J., Chan, S., Nadarajah, S., Osterrieder, J., 2017. Garch modelling of cryptocurrencies. Journal of Risk and Financial Management 10, p.17. Conrad, C., Custovic, A., Ghysels, E., 2018. Long- and short-term cryptocurrency volatility components: A garch-midas analysis. Journal of Risk and Financial Management, 11–23. Corbet, S., Lucey, B., Peat, M., Vigne, S., 2018. Bitcoin futures—what use are they? Economics Letters 172, 23 – 27. Dyhrberg, A.H., 2016. Bitcoin, gold and the dollar – a garch volatility analysis. Finance Research Letters 16, 85 – 92. Eisl, A., Gasser, S., Weinmayer, K., 2015. Caveat emptor: Does bitcoin improve portfolio diversification? Working Paper, Available at https://ssrn.com/abstract=2408997. Guesmi, K., Saadi, S., Abid, I., Ftiti, Z., 2019. Portfolio diversification with virtual currency: Evidence from bitcoin. International Review of Financial Analysis 63, 431 – 437.
Hafner, C.M., 2018. Testing for bubbles in cryptocurrencies with time- varying volatility. Journal of Financial Econometrics. Katsiampa, P., 2017. Volatility estimation for bitcoin: A comparison of garch models. Economics Letters 158, 3–6. Katsiampa, P., Corbet, S., Lucey, B., 2019. High frequency volatility co- movements in cryptocurrency markets. Journal of International Financial Markets, Institutions and Money. Klein, T., Thu, H., Walther, T., 2018. Bitcoin is not the new gold – a comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis 59, 105–116. Lahmiri, S., Bekiros, S., Salvi, A., 2018. Long-range memory, distributional variation and randomness of bitcoin volatility. Chaos, Solitons & Fractals 107, 43 – 48. Shen, D., Urquhart, A., Wang, P., 2019. Does twitter predict bitcoin? Economics Letters 174, 118–122. Trimborn, S., Li, M., Hardle, W.K., 2019. Investing with cryptocurrencies—a liquidity constrained investment approach. Journal of Financial Econometrics. Urquhart, A., 2018. What causes the attention of bitcoin? Economics Letters 166, 40–44. Walther, T., Klein, T., Bouri, E., 2019. Exogenous drivers of bitcoin and cryptocurrency volatility – a mixed data sampling approach to forecasting. Journal of International Financial Markets, Institutions and Money.
Figure 1: This figure shows the location of crypto-accepting new venues during 9 February 2014 to 31 December 2018.
Figure 2: This figure shows the weekly number of crypto-accepting new venues and volatility during 9 February 2014 to 31 December 2018.
Figure 3: This figure shows the simple and orthogonalized impulse response functions (IRFs) for week-to-week changes in crypto volatility (CV10) to a 1-SD shock in week- to-week changes in the number of crypto accepting venues along with lower and higher 1-SD bands (dotted lines). The responses are obtained from estimating a bivariate vector autoregression with n lags (VAR(n) model), where n = 3, 5, or 7.
Table 1: Weekly Summary Statistics Variable Venue ATM Entertainment Food Lodging Shopping Undisclosed Africa Asia Europe North America Oceania South America CV10 CV25 CV50 CV100 Market Cap (billions) Return VIX
Mean 45.7 3.7 2.6 6.5 3.0 8.6 21.6 0.5 4.5 19.3 13.5 1.3 7.0 4.8 4.9 5.1 5.0 76.5 0.65% 14.9
Std. Dev Minimum Maximum 35.5 2 277 6.8 0 63 3.2 0 32 9.9 0 121 3.8 0 33 12.6 0 135 18.2 1 158 1.3 0 16 6.6 0 65 18.6 1 212 16.5 0 145 1.8 0 11 8.1 0 63 4.2 0.3 19.6 4.6 0.2 25.6 4.9 0.2 28.6 4.5 0.2 20.2 118.3 3.3 625.6 9.88% -30.29% 33.02% 4.1 9.3 31.8
Table 2: Correlation Coefficients
[1] Venue [2] ATM [3] Entertainment [4] Food [5] Lodging [6] Shopping [7] Undisclosed [8] Africa [9] Asia [10] Europe [11] North America [12] Oceania [13] South America [14] CV10 [15] CV25 [16] CV50 [17] CV100 [18] Market Cap [19] Return [20] VIX
[1] 1.00
[2] 0.39 1.00
[3] 0.52 0.12 1.00
[4] 0.58 0.04 0.17 1.00
[5] 0.42 0.08 0.32 0.11 1.00
[6] 0.66 0.14 0.35 0.20 0.23 1.00
[7] 0.84 0.22 0.41 0.37 0.30 0.32 1.00
[8] 0.36 0.07 0.08 0.16 0.12 0.55 0.18 1.00
[9] 0.47 -0.02 0.48 0.17 0.20 0.51 0.35 0.20 1.00
[10] 0.82 0.43 0.45 0.30 0.36 0.59 0.71 0.30 0.27 1.00
[11] 0.71 0.22 0.23 0.74 0.23 0.29 0.62 0.19 0.15 0.33 1.00
[12] 0.53 0.00 0.29 0.27 0.20 0.50 0.44 0.27 0.40 0.37 0.33 1.00
[13] 0.46 0.25 0.34 0.11 0.30 0.34 0.39 0.15 0.20 0.28 0.09 0.19 1.00
[14] 0.25 0.04 0.23 0.07 0.21 0.23 0.19 0.01 0.14 0.21 0.13 0.21 0.18 1.00
[15] 0.23 0.04 0.21 0.07 0.19 0.22 0.17 -0.01 0.13 0.20 0.11 0.20 0.17 0.98 1.00
[16] 0.20 0.03 0.19 0.06 0.17 0.20 0.15 -0.01 0.13 0.18 0.09 0.20 0.17 0.90 0.92 1.00
[17] 0.23 0.05 0.21 0.07 0.20 0.22 0.17 0.00 0.13 0.20 0.12 0.20 0.18 0.98 0.99 0.93 1.00
[18] 0.34 0.29 0.39 0.04 0.35 0.33 0.18 0.02 0.26 0.34 -0.01 0.16 0.54 0.42 0.44 0.46 0.45 1.00
[19] -0.16 0.01 -0.16 -0.05 -0.07 -0.18 -0.13 -0.10 -0.21 -0.12 -0.06 -0.20 -0.12 0.00 0.01 0.01 -0.01 -0.15 1.00
[20] -0.04 -0.02 -0.07 -0.11 0.04 0.11 -0.10 0.02 0.08 -0.05 -0.11 -0.03 0.06 0.08 0.10 0.08 0.08 -0.01 -0.12 1.00
Table 3: Regression analysis of cryptocurrency Volatility and crypto-accepting venues VARIABLES
(1) CV10 1.54*** (0.32)
(2) CV10 0.81*** (0.14)
(3) CV10
(4) CV10
(5) CV25 0.81*** (0.15)
(6) CV50 0.88*** (0.18)
(7) CV100 0.79*** (0.14)
0.60*** (0.04) 0.35*** (0.08) 5.15** (2.12) 0.09** (0.03) -0.41 (0.55)
0.46*** (0.11) 0.59*** (0.15) 5.38** (2.36) 0.08* (0.04) -0.29 (0.68)
0.62*** (0.04) 0.33*** (0.09) 4.21*** (1.62) 0.08** (0.04) -0.30 (0.59)
254 0.54 Yes
254 0.43 Yes
254 0.56 Yes
0.07 (0.22) -0.04 (0.24) 0.15 (0.12) -0.07 (0.16) 0.13 (0.15) 0.76*** (0.19)
Observations R-squared Newey-West SE
4.79*** (0.52)
0.62*** (0.05) 0.28*** (0.10) 3.81** (1.48) 0.08** (0.04) -0.24 (0.58)
0.61*** (0.05) 0.35** (0.15) 4.34*** (1.43) 0.08** (0.04) -0.46 (0.72)
-0.15 (0.16) 0.02 (0.19) 0.48** (0.22) 0.30* (0.16) 0.36** (0.15) 0.17 (0.21) 0.61*** (0.05) 0.27*** (0.09) 3.97** (1.67) 0.08** (0.04) -0.18 (0.54)
254 0.13 Yes
254 0.56 Yes
254 0.56 Yes
254 0.56 Yes
Robust standard errors in parentheses are calculated using Newey-West adjustments for autocorrelation up to 30 lags *** p<0.01, ** p<0.05, * p<0.1
Table 4: Granger-causality tests - Wald statistics Direction
*** p<0.01, ** p<0.05, * p<0.1
Coefficient 31.37*** (<0.0001) 1.91 (0.384) 26.91*** (<0.0001) 2.19 (0.333) 25.86*** (<0.0001) 0.83 (0.661) 28.23*** (<0.0001) 2.26 (0.322)
Table 5: Regression analysis using quasi-natural experiment
Variables Start
(1) CV10 -3.52*** (0.45)
Stop Treatment Start*Treatment
-2.79*** (0.51) 4.91*** (0.69)
Stop*Treatment Constant
5.48*** (0.40)
Observations 392 R-squared 0.14 Robust SE Yes Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
(2) CV10
0.22 (0.17) 7.25*** (1.08)
-3.84*** (1.23) 1.15*** (0.14) 168 0.36 Yes
Cryptocurrency Accepting Venues, Investor Attention, and Volatility Nasim Sabah PII: DOI: Reference:
S1544-6123(19)30649-X https://doi.org/10.1016/j.frl.2019.101339 FRL 101339
To appear in:
Finance Research Letters
Received date: Revised date: Accepted date:
28 June 2019 19 October 2019 21 October 2019
Please cite this article as: Nasim Sabah , Cryptocurrency Accepting Venues, Investor Attention, and Volatility, Finance Research Letters (2019), doi: https://doi.org/10.1016/j.frl.2019.101339
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Cryptocurrency Accepting Venues, Investor Attention, and Volatility
Nasim Sabah College of Business Framingham State University Framingham, MA 01701 Email: [email protected] Phone: 504-344-9220
Abstract Using a novel dataset of cryptocurrency accepting business venues that accept cryptocurrencies as form of payments, we examine the relationship between new crypto accepting venues and crypto volatility. We argue that the number of new venues is a proxy for investor attention. We find that the number of new venues is a significant driver of crypto volatility. Moreover, venues that do not disclose their type of business as well as venues in Europe, North America and Oceania increase crypto volatility. Granger-causality, VAR estimation and a quasi-natural experiment validate our findings. Keywords: Cryptocurrency, Crypto-accepting venues, Volatility, VAR, Granger causality, Quasi-natural experiment. JEL Classification: G10, G12, G14
1. Introduction On December 11 of 2014, Microsoft started accepting bitcoin as a payment option to buy certain digital products. Cryptocurrencies (crypto hereafter) volatility has increased by 41% on the week following the acceptance compared to the week before the acceptance. On December 26 of 2017, Microsoft halted bitcoin payments. While the company has not released any official statement, several news articles claim that the halt is because of the crypto volatility. 1 Yet, the volatility of crypto has decreased by 49% on the week following the halt compared to the week before the halt. This leads to one particularly interesting question: do crypto accepting merchants increase crypto volatility? Using a novel dataset of cryptocurrency accepting business venues (merchants) that accept cryptocurrencies as a form of payment, we examine the relationship between the number of crypto-accepting new venues and crypto volatility. We find that a one-standard deviation increases in such new venues increases the average volatility by 18%. We categorize new venues based on business types and location and find that undisclosed new venues as well as new venues located in Europe, North America and Oceania significantly increase crypto volatility. Grangercausality and VAR estimation confirm our findings and indicate that the volatility is highest in three weeks following the introduction of the new payment systems and remains high up to 20 weeks. Finally, we design a quasi-natural experiment using seven large firms that started accepting cryptocurrencies and three of these firms that stopped accepting cryptocurrencies during our sample period. We find that the overall crypto volatility increases following the introduction of such crypto payments and decreases when firms withdraw such payment options. A few papers examine the crypto trading and volatility under the investor attention framework. Urquhart (2018) examines the attention of Bitcoin 2 using Google trends data and find that attention is not a significant predictor of realized volatility. Shen et al. (2019) use the number of tweets and find that it is a significant driver of next day realized volatility. We build on their study and investigate the crypto volatility using the number of crypto accepting business 1 2
See www.inverse.com/article/40012-bitcoin-microsoft-cryptocurrency-payments Bitcoin is the original and the largest cryptocurrency by market capitalization.
merchants. We argue that our measure is a proxy for investor attention because businesses that accept crypto as a form of payments require investments in machinery and technology in each of the venues as well as centrally. Moreover, such form of payment provides liquidity in the market, attract a wide range of investors and strengthen their confidence in crypto markets. Factors that influence crypto volatility are also addressed by several papers. Aalborg et al. (2019) examine the Bitcoin volatility using past realized volatility and trading volume and show that these variables can predict daily volatility but not the weekly volatility. Balcilar et al. (2017) find that trading volume does not predict Bitcoin volatility. Walther et al. (2019) find that the Global Real Economic Activity is the most important exogeneous driver of crypto volatility. Additionally, Corbet et al. (2018) find that the introduction of Bitcoin futures has increased the spot volatility of bitcoin. Cheah and Fry (2015) find that Bitcoin price contains a significant speculative component, Baig et al. (2019) show that investor sentiment can explain Bitcoin price clustering, and Blau (2017) argues that the speculative trading is not directly related to the unusual volatility of Bitcoin. Many papers examine the crypto volatility in the context of econometric approach. Among others, Ardia et al. (2019), Conrad et al. (2018), Chu et al. (2017), Dyhrberg (2016), Katsiampa (2017) and Klein et al. (2018) use GARCH models to examine the volatility of bitcoin and other cryptocurrencies. Klein et al. (2018) examine the asymmetric volatility effects of bitcoin. Hafner (2018) examines the bubble-like behavior of cryptocurrencies and find that the bubble is less pronounced under time-varying volatility than constant volatility. Katsiampa et al. (2019) examine the conditional volatility dynamics and volatility co-movements of major cryptocurrencies using the Diagonal BEKK and Asymmetric Diagonal BEKK methodologies. Lahmiri et al. (2018) examine the nonlinear dependence structure of Bitcoin volatility and find that Bitcoin market is high disordered and risky, and Bitcoin is not suitable for hedging. Volatility is one of the key components in investment decisions. Thus, several papers examine the effectiveness of cryptocurrencies as an investment tool. Briere et al. (2015) examine the Bitcoin investment with a diversified portfolio and find that Bitcoin investment offers significant diversification benefits. Using Conditional Value-at Risk framework instead of the traditional mean-variance approach, Eisl et al. (2015) show that Bitcoin returns have very low correlations
with traditional financial instruments and Bitcoin should be included in optimal portfolios. In another study, Trimborn et al. (2019) propose a liquidity constraint investment approach and show that the portfolio risk-return trade off can be improved by including cryptocurrencies. Charfeddine et al. (2019) explore the relevance of cryptocurrencies for investors and find that cryptocurrencies provide diversification benefits but are not appropriate for hedging. This is in contradiction with Guesmi et al. (2019), who find that a short position in Bitcoin market allows hedging the risk. Our findings extend the literature that focuses on the predictability of crypto volatility. Our results suggest that the number of crypto accepting business venues is a significant driver of crypto volatility. Moreover, Granger-causality, VAR estimation and a quasi-natural experiment validate our findings.
2. Data and variable descriptions We collect information about venues that accept cryptocurrencies as a payment method from coinmap.org. This website provides date and time of introducing new venue with latitude and longitude of location, name and business type. We collect the continent of each venue from GeoNames.org using corresponding latitude and longitude. We gather market capitalization and market cap weighted Cryptoz Index Volatility (hereafter, CV) from cryptoz.ai website for top 10, 25, 50 and 100 cryptocurrencies. CV is measured by calculating percentage standard deviation of the current value with respect to moving average of last 30 days value. 3 We gather prices from blockchain.com, and VIX from CBOE. Our sample begins on 9 February 2014 and ends on 31 December 2018. We create returns as
, and size as natural log of
market capitalization. We aggregate all our measures at weekly level. There are 254 weeks in our merged sample. For our regression analysis, we standardize venue including its business and continent categories.
3
For more information on how to construct Cryptoz Index Volatility, see https://cryptoz.ai/#!/cryptoindex/volatility.
During our sample period, 11,648 new venues introduce crypto as a form of payments. Type of business is missing for half of the sample. We create six major business categories from the available data: (1) ATM is the venue type where cryptocurrency can be converted into cash, (2) Entertainment includes venues under nightlife, attraction and sports, (3) Food includes cafe, food, drug store and grocery, (4) Lodging includes lodging and transport services, (5) Shopping includes retailer, shopping and educational business, and (6) Undisclosed venues are all missing venue types. We also categories each venue by the continent they are located: Africa, Asia, Europe, North America, Oceania, and South America. Figure1 plots the location of all cryptoaccepting new venues. Table 1 presents the summary statistics of the variable used in this study. Each week, on average 46 new venues accept cryptocurrencies as their payment method. Among these, on average 21.6 new venues are undisclosed, 8.6 new venues are shopping, 6.5 are food, 3.7 are ATM, 3 are lodging and 2.6 are entertainment venues. Weekly new venues are highest in Europe (19.3), following North America (13.5), South America (7.0), Asia (4.5), Oceania (1.3), and Africa (0.5). CV10, CV25, CV50 and CV100 are the volatility of market cap-weighted top 10, 25, 50 and 100 cryptocurrencies, respectively. The average volatility for these cryptocurrencies are 4.8, 4.9, 5.1 and 5. The average market capitalization of cryptocurrencies is $76.5 billion, average weekly return is 0.65%, and average market wide volatility (VIX) is 14.9 during our sample period. To further motivate our analysis, we report the Pearson correlation coefficients for the variables used in this study in Table 2. This table shows that there is a strong correlation between crypto volatility and number of new venues. The correlation coefficient between CV10 and venue is 0.25. All the venue categories have positive correlations with crypto volatility. Among these, Entertainment, Lodging, Shopping, and Undisclosed venues as well as venues located in all continent except Africa have strong positive correlation with CV10. Size has a positive correlation with CV10 with a coefficient of 0.42. Finally, venue and return have a correlation of 0.16.4
4
On a different note, we examine the relation between the number of new venues and crypto returns using regression analysis, however, we do not find any significant relation.
3. Results Figure 2 shows the time series plot of crypto accepting new venues and crypto volatility (CV10). There is a strong association between new venues and volatility where volatility follows the path of new venues with a lag. In fact, the correlation between volatility and one-week lagged venues is 0.365. Consequently, we attempt to isolate the relationship between crypto volatility and lagged new venues while controlling for other factors that affect volatility. We use the lagged number of venues due to potential endogeneity. 5 We estimate the following equation using time series data using OLS:
Table 3 presents the results using the above equation. We use CV10 as our primary volatility measure. Our control variable includes size, aggregate volatility (VIX), and returns. Venue is the number of weekly new venues. We use different combinations of venue with subcategories. We calculate robust standard errors using Newey-West adjustments for autocorrelation up to 30 lags. Column (1) presents results using the univariate test. It shows that a 1-standard deviation (SD) increase in number of new venues increase the level of volatility by 1.54% in the following week, whereas the average volatility is 4.8%. Thus, the higher the number of new venues, the higher the following week’s volatility. Column (2) shows that a 1-SD increase in new venues increase the volatility by 81 bps, which is 17.7% more than the average volatility, after controlling for factors that influence volatility. This increment is statistically significant and economically meaningful. Next, we examine how different types of new venues and locations affect the volatility. In column (3) - (4), we use six business categories and six continents of venues as our explanatory variables. Column (3) shows that only undisclosed category is strongly positively associated with the crypto volatility. In economic term, a 1-SD increase in undisclosed new venues increase the volatility by 76 bps (15.8% more than average). While all other venue categories such as ATM, Entertainment, Food, Lodging and Shopping serve mostly local customers, the undisclosed
5
We note that our results are consistent if we analyze contemporaneous association between crypto volatility and number of new venues.
venues are half of the new venues and serve all other online and digital purchases. Thus, it is not surprising that a small number of local venues will have no material effect on the global crypto market volatility, and many undisclosed venues serving a wide base of customers will drive our results. Column (4) of Table 3 shows that new venues located in Europe, North America and Oceania increase the crypto volatility. In economic term, a 1-SD increase of new venues in Europe, North America and Oceania increase the crypto volatility by 48 bps (10% more than average), 30 bps (6.3% more than average), and 36 bps (7.5% more than average), respectively. Given that most of the new venues are in Europe and North America, this result is also in line with the previous column that a reasonable number of venues can create a material effect on the crypto volatility. Next, we use three other volatility measures, CV25, CV50 and CV100, as our dependent variable for robustness checks and present results in columns (5) - (7). These results are very similar to that of CV10 in column (2), indicating a strong positive association between crypto volatility and number of crypto-accepting new venues. In economic term, a 1-SD increase in new venues that accept crypto as payment increase CV25 by 81 bps (16.3% more than average), increase CV50 by 88 bps (17.2% more than average), and increase CV100 by 79 bps (15.9% more than average). As for control variables, 1-period lagged volatility, Size, VIX and returns show strong positive association with crypto volatility. Next, we conduct a series of Granger-causality tests to confirm the direction of causation and report our results in table 4. We estimate Wald statistics for the change in venue and each of the four volatility groups. The Wald statistics where the causation flows from venues to volatility (∆Venue → ∆CV) are strong and significant with p-value < 0.0001 for all volatility groups. The coefficients in the reverse direction (∆CV → ∆Venue) are small and insignificant. This table shows that changes in number of crypto accepting new venues lead to changes in crypto volatility. Next, we estimate a VAR process as an attempt to control for endogeneity, and examine the impulse response of crypto volatility to exogenous shocks in number of new venues. Figure 3 plots the simple and orthogonalized impulse response functions (IRFs) for changes in volatility (CV10) to an exogenous, a 1-SD shock to changes in number of new venues. The responses are
obtained from estimating a bivariate vector autoregression with n lags (VAR(n) model), where n = 3, 5, or 7. All of these plots show that (1) change in volatility is positive in the weeks following the shock in venues, (2) change in volatility is highest in three weeks following the shock, and (3) change in volatility remains positive for up to 20 weeks after the shock. This figure confirms that the causation flows from venues to volatility. Ideally, we could use the number of new venues that stop accepting cryptocurrencies and examine whether the difference between crypto accepting and stopping new venues can predict crypto volatility. However, the data is not available for venues that stop accepting crypto. To overcome the data limitation, we hand collect the data of seven large firms. These seven firms are Dell, Dish, ExpressVPN, Fiverr, Lionsgate Films, Microsoft and NewEgg. All these firms started accepting crypto during 2014 except Lionsgate Films which started accepting crypto in 2015. Three of these firms, Fiverr, Dell and Microsoft, stopped accepting crypto during 2017. We design a quasi-natural experiment in a diff-in-diff setting using data for these seven firms from 14 days before to 14 after the start and stop. We create a matching sample for each firm where we pick random days from a subsample of days with the following criteria: (1) the matching sample day has zero new crypto accepting venue and not within 28 days of start or stop, and (2) total new venues during 14 days before to 14 days after of matching day fall below 10th percentile of the distribution. Table 5 presents the results using the quasi-natural experiment. We use CV10 as our volatility measure. Our explanatory variable are as follows: Start (Stop) is an indicator variable for days on and after the start (stop) of crypto accepting venues, Treatment is an indicator variable for firms that start (stop) accepting crypto, and an interaction of these two variables. Column (1) presents the results for start sample and column (2) presents for stop sample. In column (1), the Start*Treatment coefficient is 4.91 and significant, indicating an increase in volatility up to 14 days following the new crypto accepting venues. Column (2) shows that the Stop*Treatment coefficient is -3.84 and significant, indicating a decrease in volatility up to 14 days following the stop of crypto acceptance. These two columns show a robust association between crypto accepting venues and crypto volatility. 4. Conclusion
This paper studies the relationship between the numbers of crypto-accepting new venues and whether they are useful in forecasting crypto market volatility. We argue that the number of new business venues that accept cryptocurrencies as a form of payments is a proxy for investor attention. Businesses that accept crypto as an alternative form of payments provide liquidity in the market and attract a wide range of customers. Such a payment form requires real monetary decisions. Accordingly, we find that the number of crypto-accepting new venues is a significant predictor of crypto volatility. We categorize new venues based on business types and location. We find that crypto-accepting undisclosed venues increase crypto volatility. Moreover, new venues in located in Europe, North America and Oceania also increase crypto volatility. We include three additional volatility measures as robustness check and find similar results. Then we conduct a series of Granger-causality tests and a VAR process to examine the direction of causation and find that the volatility is highest in three weeks following the introduction of new venues and remains high up to 20 weeks. Moreover, the causation flows from venues to volatility such that the changes in number of crypto accepting new venues lead to changes in crypto volatility. Finally, we design a quasi-natural experiment using seven large firms that started accepting cryptocurrencies and three of these firms that stopped accepting cryptocurrencies during our sample period. We find that the overall crypto volatility increases following the introduction of such crypto payments and decreases when firms withdraw such payment options. We conclude that the number of crypto-accepting new venues can significantly predict future crypto volatility. Nevertheless, our results should be interpreted with caution. While a fair number of crypto accepting venues offer digital products or online crypto payments, other venues serve local customers only. We link both local and online customers’ decisions to the global crypto market, and the channels through which these decisions are incorporated into global market could be complex and is left for future research.
References Aalborg, H.A., Molnar, P., de Vries, J.E., 2019. What can explain the price, volatility and trading volume of bitcoin? Finance Research Letters 29, 255 – 265. Ardia, D., Bluteau, K., Ruede, M., 2019. Regime changes in bitcoin garch volatility dynamics. Finance Research Letters 29, 266 – 271. Baig, A., Blau, B.M., Sabah, N., 2019. Price clustering and sentiment in bitcoin. Finance Research Letters 29, 111 – 116. Balcilar, M., Bouri, E., Gupta, R., Roubaud, D., 2017. Can volume predict bitcoin returns and volatility? a quantiles-based approach. Economic Modelling 64, 74–81. Blau, B., 2017. Price dynamics and speculative trading in bitcoin. Research in International Business and Finance 41, 493–499. Bri`ere, M., Oosterlinck, K., Szafarz, A., 2015. Virtual currency, tangible return: Portfolio diversification with bitcoin. Journal of Asset Management 16, 365–373. Charfeddine, L., Benlagha, N., Maouchi, Y., 2019. Investigating the dynamic relationship between cryptocurrencies and conventional assets: Implications for financial investors. Economic Modelling. Cheah, E., Fry, J., 2015. Speculative bubbles in bitcoin markets? an empirical investigation into the fundamental value of bitcoin. Economics Letters 130, 32–36. Chu, J., Chan, S., Nadarajah, S., Osterrieder, J., 2017. Garch modelling of cryptocurrencies. Journal of Risk and Financial Management 10, p.17. Conrad, C., Custovic, A., Ghysels, E., 2018. Long- and short-term cryptocurrency volatility components: A garch-midas analysis. Journal of Risk and Financial Management, 11–23. Corbet, S., Lucey, B., Peat, M., Vigne, S., 2018. Bitcoin futures—what use are they? Economics Letters 172, 23 – 27. Dyhrberg, A.H., 2016. Bitcoin, gold and the dollar – a garch volatility analysis. Finance Research Letters 16, 85 – 92. Eisl, A., Gasser, S., Weinmayer, K., 2015. Caveat emptor: Does bitcoin improve portfolio diversification? Working Paper, Available at https://ssrn.com/abstract=2408997. Guesmi, K., Saadi, S., Abid, I., Ftiti, Z., 2019. Portfolio diversification with virtual currency: Evidence from bitcoin. International Review of Financial Analysis 63, 431 – 437.
Hafner, C.M., 2018. Testing for bubbles in cryptocurrencies with time- varying volatility. Journal of Financial Econometrics. Katsiampa, P., 2017. Volatility estimation for bitcoin: A comparison of garch models. Economics Letters 158, 3–6. Katsiampa, P., Corbet, S., Lucey, B., 2019. High frequency volatility co- movements in cryptocurrency markets. Journal of International Financial Markets, Institutions and Money. Klein, T., Thu, H., Walther, T., 2018. Bitcoin is not the new gold – a comparison of volatility, correlation, and portfolio performance. International Review of Financial Analysis 59, 105–116. Lahmiri, S., Bekiros, S., Salvi, A., 2018. Long-range memory, distributional variation and randomness of bitcoin volatility. Chaos, Solitons & Fractals 107, 43 – 48. Shen, D., Urquhart, A., Wang, P., 2019. Does twitter predict bitcoin? Economics Letters 174, 118–122. Trimborn, S., Li, M., Hardle, W.K., 2019. Investing with cryptocurrencies—a liquidity constrained investment approach. Journal of Financial Econometrics. Urquhart, A., 2018. What causes the attention of bitcoin? Economics Letters 166, 40–44. Walther, T., Klein, T., Bouri, E., 2019. Exogenous drivers of bitcoin and cryptocurrency volatility – a mixed data sampling approach to forecasting. Journal of International Financial Markets, Institutions and Money.
Figure 1: This figure shows the location of crypto-accepting new venues during 9 February 2014 to 31 December 2018.
Figure 2: This figure shows the weekly number of crypto-accepting new venues and volatility during 9 February 2014 to 31 December 2018.
Figure 3: This figure shows the simple and orthogonalized impulse response functions (IRFs) for week-to-week changes in crypto volatility (CV10) to a 1-SD shock in week- to-week changes in the number of crypto accepting venues along with lower and higher 1-SD bands (dotted lines). The responses are obtained from estimating a bivariate vector autoregression with n lags (VAR(n) model), where n = 3, 5, or 7.
Table 1: Weekly Summary Statistics Variable Venue ATM Entertainment Food Lodging Shopping Undisclosed Africa Asia Europe North America Oceania South America CV10 CV25 CV50 CV100 Market Cap (billions) Return VIX
Mean 45.7 3.7 2.6 6.5 3.0 8.6 21.6 0.5 4.5 19.3 13.5 1.3 7.0 4.8 4.9 5.1 5.0 76.5 0.65% 14.9
Std. Dev Minimum Maximum 35.5 2 277 6.8 0 63 3.2 0 32 9.9 0 121 3.8 0 33 12.6 0 135 18.2 1 158 1.3 0 16 6.6 0 65 18.6 1 212 16.5 0 145 1.8 0 11 8.1 0 63 4.2 0.3 19.6 4.6 0.2 25.6 4.9 0.2 28.6 4.5 0.2 20.2 118.3 3.3 625.6 9.88% -30.29% 33.02% 4.1 9.3 31.8
Table 2: Correlation Coefficients
[1] Venue [2] ATM [3] Entertainment [4] Food [5] Lodging [6] Shopping [7] Undisclosed [8] Africa [9] Asia [10] Europe [11] North America [12] Oceania [13] South America [14] CV10 [15] CV25 [16] CV50 [17] CV100 [18] Market Cap [19] Return [20] VIX
[1] 1.00
[2] 0.39 1.00
[3] 0.52 0.12 1.00
[4] 0.58 0.04 0.17 1.00
[5] 0.42 0.08 0.32 0.11 1.00
[6] 0.66 0.14 0.35 0.20 0.23 1.00
[7] 0.84 0.22 0.41 0.37 0.30 0.32 1.00
[8] 0.36 0.07 0.08 0.16 0.12 0.55 0.18 1.00
[9] 0.47 -0.02 0.48 0.17 0.20 0.51 0.35 0.20 1.00
[10] 0.82 0.43 0.45 0.30 0.36 0.59 0.71 0.30 0.27 1.00
[11] 0.71 0.22 0.23 0.74 0.23 0.29 0.62 0.19 0.15 0.33 1.00
[12] 0.53 0.00 0.29 0.27 0.20 0.50 0.44 0.27 0.40 0.37 0.33 1.00
[13] 0.46 0.25 0.34 0.11 0.30 0.34 0.39 0.15 0.20 0.28 0.09 0.19 1.00
[14] 0.25 0.04 0.23 0.07 0.21 0.23 0.19 0.01 0.14 0.21 0.13 0.21 0.18 1.00
[15] 0.23 0.04 0.21 0.07 0.19 0.22 0.17 -0.01 0.13 0.20 0.11 0.20 0.17 0.98 1.00
[16] 0.20 0.03 0.19 0.06 0.17 0.20 0.15 -0.01 0.13 0.18 0.09 0.20 0.17 0.90 0.92 1.00
[17] 0.23 0.05 0.21 0.07 0.20 0.22 0.17 0.00 0.13 0.20 0.12 0.20 0.18 0.98 0.99 0.93 1.00
[18] 0.34 0.29 0.39 0.04 0.35 0.33 0.18 0.02 0.26 0.34 -0.01 0.16 0.54 0.42 0.44 0.46 0.45 1.00
[19] -0.16 0.01 -0.16 -0.05 -0.07 -0.18 -0.13 -0.10 -0.21 -0.12 -0.06 -0.20 -0.12 0.00 0.01 0.01 -0.01 -0.15 1.00
[20] -0.04 -0.02 -0.07 -0.11 0.04 0.11 -0.10 0.02 0.08 -0.05 -0.11 -0.03 0.06 0.08 0.10 0.08 0.08 -0.01 -0.12 1.00
Table 3: Regression analysis of cryptocurrency Volatility and crypto-accepting venues VARIABLES
(1) CV10 1.54*** (0.32)
(2) CV10 0.81*** (0.14)
(3) CV10
(4) CV10
(5) CV25 0.81*** (0.15)
(6) CV50 0.88*** (0.18)
(7) CV100 0.79*** (0.14)
0.60*** (0.04) 0.35*** (0.08) 5.15** (2.12) 0.09** (0.03) -0.41 (0.55)
0.46*** (0.11) 0.59*** (0.15) 5.38** (2.36) 0.08* (0.04) -0.29 (0.68)
0.62*** (0.04) 0.33*** (0.09) 4.21*** (1.62) 0.08** (0.04) -0.30 (0.59)
254 0.54 Yes
254 0.43 Yes
254 0.56 Yes
0.07 (0.22) -0.04 (0.24) 0.15 (0.12) -0.07 (0.16) 0.13 (0.15) 0.76*** (0.19)
Observations R-squared Newey-West SE
4.79*** (0.52)
0.62*** (0.05) 0.28*** (0.10) 3.81** (1.48) 0.08** (0.04) -0.24 (0.58)
0.61*** (0.05) 0.35** (0.15) 4.34*** (1.43) 0.08** (0.04) -0.46 (0.72)
-0.15 (0.16) 0.02 (0.19) 0.48** (0.22) 0.30* (0.16) 0.36** (0.15) 0.17 (0.21) 0.61*** (0.05) 0.27*** (0.09) 3.97** (1.67) 0.08** (0.04) -0.18 (0.54)
254 0.13 Yes
254 0.56 Yes
254 0.56 Yes
254 0.56 Yes
Robust standard errors in parentheses are calculated using Newey-West adjustments for autocorrelation up to 30 lags *** p<0.01, ** p<0.05, * p<0.1
Table 4: Granger-causality tests - Wald statistics Direction
*** p<0.01, ** p<0.05, * p<0.1
Coefficient 31.37*** (<0.0001) 1.91 (0.384) 26.91*** (<0.0001) 2.19 (0.333) 25.86*** (<0.0001) 0.83 (0.661) 28.23*** (<0.0001) 2.26 (0.322)
Table 5: Regression analysis using quasi-natural experiment
Variables Start
(1) CV10 -3.52*** (0.45)
Stop Treatment Start*Treatment
-2.79*** (0.51) 4.91*** (0.69)
Stop*Treatment Constant
5.48*** (0.40)
Observations 392 R-squared 0.14 Robust SE Yes Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
(2) CV10
0.22 (0.17) 7.25*** (1.08)
-3.84*** (1.23) 1.15*** (0.14) 168 0.36 Yes