BITCOIN: A REGRESSION ANALYSIS OF CRYPTOCURRENCY INFLUENCE ON RUSSIAN ECONOMY
ANNA LOSEVA,
LOMONOSOV MOSCOW STATE UNIVERSITY, ECONOMICS FACULTY
ANNETTE_ELLE@MAIL.RU
Abstract. TThe article examines the electronic payment system «Bitcoin» with the eponymous digital currency. The goal was to analyze the impact of this cryptocurrency on the Russian economy and determine the nature of this influence. Various models of regression were built and the most suitable of them were chosen. During the analysis of actual data the writer has come to the conclusion that at the moment bitcoins do not have any negative impact on the Russian economy and, therefore, should not be banned.
Keywords: bitcoin, analysis, cryptocurrency, Russian economy, money supply, inflation, regression.
JEL classification: E 440.
LOSEVA, ANNA (2016) "BITCOIN: A REGRESSION ANALYSIS OF CRYPTOCURRENCY INFLUENCE ON RUSSIAN ECONOMY". Journal of Russian Review (ISSN 2313-1578), VOL. 1(4), 1-7.
1. Introduction
The Development of the Internet has had a significant impact on the economic performance of most countries of the world. For most of human history money was and still is a tangible thing that passes «hand to hand» or through real mediators, but the recent advent of cryptocurrency provides the opportunity to carry out virtual transactions, through the World Wide Web.
The invention of prepaid cards, mobile and Internet payments has attracted huge number of customers to the electronic cash system. The invention of cryptocurrency has also brought about the system that includes a huge number of users worldwide and their number is constantly growing.
Bitcoin - the first cryptocurrency in the world – is most intensive. Today the market of this virtual currency accounts for 15 404 700 units in circulation, and the exchange rate of bitcoin since the creation of the eponymous peer-to-peer payment system has reached 416,28 dollars per «coin» [4]. It is not surprising that many experts wonder what may be the impact of this system on the global financial system and the finances of individual countries.
There is no common attitude to the cryptocurrency. In some countries, the turnover of bitcoins is prohibited by law, as it may happen in Russia; in others there are outdoor ATMs already installed to exchange digital currency for fiat (cash) money. If seems to be necessary to introduce some clarity into this issue in this particular case and evaluate the impact of the cryptocurrency «bitcoin» on the money supply and inflation in Russia.
2. Literature review
In the selection process of sources for this article, the writer used the materials of the two digital libraries: Elibrary (Russia) and SSRN (one of the largest English-language online resources in the world, based in USA).
In Elibrary the search was conducted for keywords «bitcoin, analysis», and as a result, the system showed only 3 works, one of which analyzed the legal aspect of cryptocurrency (Chimarov N. C, 2015). Another considered the system of «mining» as an innovative business idea (Manahov, V. A., 2014), and the third (Danilin, M. V., 2015) examined digital currencies in terms of their advantages and disadvantagts thus giving valuable information about the system in general. As none of them fit the specific theme of this study, the writer expanded the search, leaving only the key word «bitcoin». The system gave 95 results. 12 of them dealt with legal aspects of cryptocurrencies and 82 articles were solely descriptive. The only article that really caught the attention of the writer, «Bitcoin – a national threat to the economy or the monetary equivalent of a new electronic currency?» (J. S. Fomin, N. Gutorov.And., 2014), was inaccessible to reading.
In the database, «Social Science Research Network» the search was carried out on the same keywords translated into English – «bitcoin, analysis». The system gave 46 results. The only related paper was «Price Fluctuations and the Use of Bitcoin: An Empirical Inquiry» (M. Polasik and others, 2015), which discussed econometric analysis of the main determinants of the price of bitcoin and the reasons why e-businesses are using it as a means of payment. The models built in this study are correct and are extremely useful when making recommendations to managers of e-Commerce. Moreover, the writer found several papers that analyze the dynamics of exchange rate of bitcoin and its nature, but have no use for macroeconomic conclusions. The rest of the articles either contained a description of various options for regulation of the blockchain-system, or examined it from non-economic point of view.
In addition, the reporter used the official website «Bitcoin» [5] to study some specific terms and find information about conferences and other events organized to discuss cryptocurrency.
3. Methodology
For data analysis, the author used the econometric package GRETL and built a regression model for the following data (values are selected for each month from 1 March 2014 to 31 March 2016):
Variable | Description | Source |
M0 | Money supply (monetary aggregate M0), RUR | Bank of Russia |
M1 | Money supply (monetary aggregate M1), RUR | Bank of Russia |
P | Monthly growth of price level (the rate of increase in CPI to the previous month) | Federal State Statistics Service |
REAL_M0 | = Ì0/Ð | |
REAL_M1 | = Ì1/Ð | |
KEY_INTEREST_RATE | The Central Bank key rate, % | «GARANT» |
DOLLAR | Weighted average exchange rate RUB/USD. (with calculations "today"), RUR | Bank of Russia |
EURO | Weighted average RUR/EUR (with calculations "today"), RUR | Bank of Russia |
BTC_ADDRESS | The number of bitcoin wallets in Russia | BLOCKCHAIN info |
BTC_NUMBER | The number of bitcoins in circulation in Russia | BLOCKCHAIN info |
BTC_CAP | The market capitalization of Bitcoin (number of bitcoins in circulation multiplied by the exchange rate), RUR | BLOCKCHAIN info |
BTC_PAY | The number of transactions per day in Russia (on the last day of each month), BTC1 | BLOCKCHAIN info |
When estimating regressions, the following hypotheses were tested:
4. The progress of analysis
To test the first hypothesis, the writer built two sets of regressions: a model for monetary aggregate M0 as a dependent variable and the same model for the M1. In each variant two subspecies of the dependent variable were considered: the logarithm of the real money supply and the logarithm of the nominal money supply (M0 and M1). All models were built using OLS-method, adjusted for heteroscedasticity and, if necessary, tested for redundancy of the variables (the Wald test, estimation of the reduced model). Consistent selection of the most appropriate models built can be seen in the Appendix.
Attempts to construct an equation with the dependent variable ln_Real_M0 with different combinations of coefficients yielded no positive results; the same is about variable ln_Real_M1^{2}.
Test of Hypothesis 1. Money supply
The first acceptable model was ln_M1 variable regression on variables P, Key_Interest_Rate, Euro, ln_BTC_Number and ln_BTC_Cap . The resulting equation is:
The coefficients used vary in significance and some dependencies in this model are questioned. According to this equation, if inflation rises, the money supply should decrease, which contradicts economic common sense. On the other hand, M1 includes not only cash in circulation, but funds on settlement and current bank accounts. Then this dependence can be interpreted as follows: price increases encourage Russians to keep their money in longer-term deposits or to transfer their accounts to foreign banks, cryptocurrency accounts included.
Moreover, note the multidirectional influence of the number of bitcoins in circulation and their market capitalization to money supply. Ñeteris paribus, if the amount of bitcoins in the economy increases by 1%, the money supply will decrease by 0,474% due to the variable ln_BTC_Number and will grow by 0.08% due to the ln_BTC_Cap. Consequently, overall monetary aggregate M1 will be reduced by 0,394%, which means that Hypothesis 1 is proven.
Another model, which is worth paying attention to, is a regression with a dependent variable ln_M0 below:
Immediately we are struck by the opposite character of the dependence of money supply on the dollar and the euro: negative in the first case and positive in the second. According to economic theory: the more expensive a foreign currency is, the more rubles should be in the economy. However, if you increase the value of the dollar by 1 ruble the real money supply will decrease by 0.9%, which contradicts the theory. This can probably be related to the current crisis in Russia: due to the unstable exchange rate of the ruble, the population prefers to keep money in the U.S. currency and therefore buy dollars even at a high rate, reducing the money supply in rubles.
However, the impact of Bitcoin on the money stock was positive and not negative, as the hypothesis suggests: the growth in the number of bitcoins in circulation by 1% increases the money supply by 0,617%. The coefficients R2 and R2adj for this regression are significantly lower than for the previous one, therefore, the first model should be considered of better quality, and Hypothesis 1 is confirmed.
Test of Hypothesis 2. Inflation
To assess the impact of Bitcoin system on monthly growth rate of the price level the writer selected model of regression of variable P on variables Key_Interest_Rate, Dollar, ln_M1, ln_BTC_Number and ln_BTC_Cap. The corresponding equation looks like this:
Here there is a very interesting correlation: the dollar affects the dynamics of price level in direct ratio and an increase in the number of bitcoins in the Russian economy, as expected, reduces the rate of inflation. The more the people abandon rubles in favor of virtual currencies, the more goods they buy for bitcoins and less for rubles. The lower the demand for ruble-denominated goods, the lower the price. Expressed numerically, 1: monthly growth in the number of bitcoins in the economy by 1% leads to a reduction in the inflation rate of about 0,237%. Therefore, we can say that Hypothesis 2 was confirmed.
There are alternative models for P for the same variables with the addition of regressor ln_M0. The regression equation is:
Here the inflation rate is also negatively dependent on the growth of cryptocurrencies, i.e. when the number of bitcoins in the economy increases by 1% monthly, inflation rate decreases by about 0.24%. However, there is a contradiction in this model: monetary aggregates M0 and M1 have opposite effects on the price level. While the first aggregate increases it, the second leads to a decrease. Perhaps the reason for this is the different nature of the influence of the increase in the volume of cash rubles in circulation and money on current accounts. If, for example, the current accounts increase in volume due to a reduction in cash and this money is kept in bank accounts for a while, the amount of money in circulation is reduced, and inflation is slowing. The same dynamics is typical of investments in cryptocurrencies.
As for the quality of this model, the writer considers it more correct, as all the coefficients in it are significant, and the R2 and R2adj are higher than in the first case. Thus, Hypothesis 2 is confirmed.
5. Conclusions
6. References
7. Appendices
Appendix 7.1. Real money supply
Regression ln_Real_M0:
(1) | (2) | (3) | (4) | (5) | |
const | 30,062*** | 31,180*** | 27,936*** | 25,104*** | 29,216*** |
KEY_INTEREST_RATE | 0,012 | - | - | - | - |
DOLLAR | - 0,029** | - 0,022* | - 0,016** | - 0,014 | - 0,022** |
EURO | 0,022* | 0,019* | 0,016** | 0,015* | 0,019** |
BTC_NUMBER | - 0,003 | - 0,009 | 0,005 | 0,004** | - |
BTC_PAY | 0,003* | 0,003* | 0,003 | - | 0,003 |
BTC_ADDRESS | 0,017* | 0,015 | - | - | 0,012* |
ln_BTC_CAP | - 0,033 | - 0,066 | 0,048 | 0,147 | - |
R2 | 0.719 | 0,693 | 0,550 | 0,431 | 0,674 |
R2adj | 0.596 | 0,585 | 0,426 | 0,311 | 0,606 |
Regression ln_Real_M1:
(1) | |
const | 32,274*** |
KEY_INTEREST_RATE | - |
DOLLAR | - 0,013 |
EURO | 0,011 |
BTC_NUMBER | - 0,004 |
BTC_PAY | 0,004* |
BTC_ADDRESS | 0,015 |
ln_BTC_CAP | -0,052 |
R2 | 0,728 |
R2adj | 0,632 |
Appendix 7.2. Nominal money supply
Regression ln_M0:
(1) | (2) | (3) | |
const | 13,216** | 20,874*** | 18,530*** |
Ð | 0,015 | - | - |
KEY_INTEREST_RATE | 0,001 | - | - |
DOLLAR | -0,012*** | -0,009*** | -0,013*** |
EURO | 0,010*** | 0,010*** | 0,012*** |
ln_BTC_NUMBER | 1,151** | 0,585*** | 0,805*** |
ln_BTC_PAY | 0,003 | - | - |
ln_BTC_ADDRESS | -0,012 | - | - |
ln_BTC_CAP | 0,040* | 0,032* | - |
R2 | 0,79 | 0,72 | 0,66 |
R2adj | 0,67 | 0,67 | 0,60 |
Regression ln_M1:
(1) | (2) | |
const | 30,606*** | 30,146*** |
Ð | -0,026** | -0,006 |
KEY_INTEREST_RATE | 0,006** | 0,003 |
DOLLAR | 0,001 | - |
EURO | 0,003* | 0,001*** |
BTC_NUMBER | -0,007 | - |
BTC_PAY | 0,001** | 0,0005 |
BTC_ADDRESS | -0,002 | - |
BTC_CAP | 0,00009*** | 0,00009*** |
R2 | 0,90 | 0,86 |
R2adj | 0,85 | 0,82 |
(3) | (4) | (5) | |
const | 39,702*** | 34,926*** | 23,937*** |
Ð | -0,034*** | -0,034*** | - |
KEY_INTEREST_RATE | 0,008*** | 0,009*** | - |
DOLLAR | 0,002 | - | -0,005** |
EURO | 0,002* | 0,004*** | 0,006*** |
ln_BTC_NUMBER | -0,827** | -0,474** | 0,464* |
ln_BTC_PAY | 0,025 | - | - |
ln_BTC_ADDRESS | 0,0002 | - | - |
ln_BTC_CAP | 0,076*** | 0,080*** | - |
R2 | 0,91 | 0,88 | 0,61 |
R2adj | 0,86 | 0,84 | 0,56 |
Appendix 7.3. Inflation
Regression P:
(1) | (2) | (3) | (4 | |
const | 568,286*** | 551,259*** | 654,080*** | 646,122*** |
KEY_INTEREST_RATE | 0,176*** | 0,179*** | 0,202*** | 0,185*** |
DOLLAR | 0,156** | 0,113*** | 0,066* | 0,114*** |
EURO | -0,042 | - | 0,037 | - |
ln_Ì0 | 8,237** | 5,307** | - | - |
ln_Ì1 | -13,626*** | -12,985*** | -11,437*** | -11,143*** |
ln_BTC_NUMBER | -31,428*** | -25,101*** | -25,144*** | -25,032*** |
ln_BTC_PAY | 0,205 | - | 0,118 | - |
ln_BTC_ADDRESS | 0,427 | - | 0,479 | - |
ln_BTC_CAP | 0,911** | 1,041** | 1,176*** | 1,301*** |
R2 | 0,97 | 0,96 | 0,95 | 0,94 |
R2adj | 0,94 | 0,94 | 0,92 | 0,93 |