Cryptocurrency Impact on India’s Monetary Policy

doi:10.21203/rs.3.rs-3865890/v1

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Cryptocurrency Impact on India’s Monetary Policy

https://doi.org/10.21203/rs.3.rs-3865890/v1

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This study uses the Structural Factor Augmented VAR in exogenous variables (SFAVARx) approach to analyse the impact of cryptocurrency transactions on India’s major macroeconomic variables. Monthly data from May 2013 to October 2021 are sourced from the Reserve Bank of India and statista.com. The current form of cryptocurrency did not have a significant impact on inflation, production, the money supply, or major interest rates. However, given the increasing marginal participation in the crypto market, these important macroeconomic variables can be adversely affected in the future. The Central Bank Digital Currency (CBDC) with features related to India is being proposed as a proactive measure.

Econometrics

Structural Factor Augmented VARx

Cryptocurrency

Monetary Policy

Digital Currency

Dynamic Factors

Crypto is an unregulated digital currency that uses an online ledger with strong encryption for purchasing goods and services. Worldwide speculators have a new found obsession with earing profits from crypto trade. Coase (1972) formulated the theoretical foundation of crypto currencies in synthetic commodity money (SCM). Selgin (2014) beautifully explains synthetic commodity money with the help of Table 5.1 whereby he states that supply of Bitcoin is a semi-elastic synthetic commodity currency – albeit a private digital or “cyber” currency, rather than a government-authorized paper currency – the stock of which automatically grows, though at a rate that eventually declines to zero, transforming it into an inelastic synthetic commodity. However, Sankar (2021) defines virtual currency issued by the Central Banks as a variant of the fiat currency probably because its scarcity is contingent on the decisions of the Central Bank.

Table 5.1

Base Money Types
		Nonmonetary Use
		Yes	No
Scarcity	Absolute	Commodity²	Synthetic Commodity
	Contingent	Coase Durable³	Fiat⁴
Source: Selgin (2014)

In general, if a crypto currency is created, or issued by a single issuer, prior to issuance it is considered centralized. When implemented in distributed control, each cryptocurrency works through distributed ledger technology (usually blockchain⁵). It acts as a public database of financial transactions (D’ Agnolo, 2015). First released as open-source software in 2009, Bitcoin is the first decentralized cryptocurrency (Sagona- Stophel, 2016). Following suit many other crypto currencies have been released in the market⁶.

Figure 5.1 shows observed market capitalisation of Bitcoin, Dogecoin, Litecoin, Ethereum and Ripple (XRP). We see that from July 2017 onwards bitcoin market capitalisation has steadily increased and sky rocketed in 2020 (with the advent of the covid-19). This sudden spike in crypto prices is due to the activities of the speculators that increased crypto demand and thus prices. Since both wholesale and retail investors lost alternative options for investment they retorted to the crypto and stock market investments. Also, several mobile apps have made it easy for public to trade and make quick profits.

This abrupt spike in crypto trade rekindled debate over the feasibility of accepting private crypto currency as legal tender. However, private crypto currencies based on such theoretical foundations has become a threat for the Central Banks worldwide as they are extremely volatile and owned by private players. As clearly stated by Patra, et al. (2021), Central Banks stand for stability – price stability; exchange rate stability; financial stability; regime stability. These nuances of stability fill the lexicon of their vision and mission statements. We see in Fig. 5.2 that standard deviation of Crypto currency market capitalisation is very high, hence accepting crypto currency as legal tender is very risky. The search for a stable unit of account was pursued by stalwarts like Jevons (1875), Marshall (1890), Wicksell (1898), Buchanan & Tullock (1962) and Hayek (1978) and medium of exchange by Friedman (1960).

Cyber security breach is yet another red flag for such privately owned crypto currencies. Further, since no credible authority regulates the crypto currency, it lacks trust. Central Banks worldwide have spent many years to only build credibility and gain trust of public. Fiat currency though not backed by actual commodity works only on this trust gained by the Central Banks worldwide. For the time being, crypto assets are too volatile and too risky to pose much of a threat to fiat currencies (He, 2018).

However, the popularity of crypto over all other money base variants is due to factors such as first, Supply of crypto is through a one-time artificially arranged generation process (called mining in crypto currency) and the management of money need not be left to the Central Bank or free forces of market demand and supply. Secondly, the absolute scarcity implies that the availability of crypto is not dependent on raw-material discoveries or from technological innovations. Thirdly, unlike gold or silver, crypto has no alternative non-monetary uses hence issue of non-monetary demand of the commodity is non-existent. Finally, demand for crypto cannot be influenced by political pressures.

According to Tomić, Todorović and Čakajac (2020) crypto currencies were far more resistant than early electronic money solutions. Due to the great usage of social networks and virality of modern communication, their usage increased exponentially increased. There was not only an increase in the number of users, but also in the functional diversification of the system.

However, considering the sudden crypto obsession of people worldwide it can cause trouble in the future. The Central Banks recognize this and hence have proposed Central Bank Digital Currencies (CBDCs) which will incorporate best of both the synthetic commodity money and fiat money advantages. Bill Gates once said that “Banking is necessary banks are not”, this is subject to debate today more than never before. We further discuss the relevance of banks along with empirical results. Before proceeding we firstly discuss how the proposed CBDC’s are similar (or different) from private crypto currencies and the stocks traded in the stock markets.

We briefly study the nature of current CBDCs, Private Crypto currencies and the Stocks in the market. CBDCs, unlike private crypto currencies such as Bitcoin and Ethereum, are centralized digital currencies issued and managed by a Central Bank (a financial institution responsible for regulating the commercial banks of the country and control the country's money supply among other things). The Shrimpy Team (2021) summarized the core differences between private crypto currencies and CBDCs.

Blockchain Type. While CBDCs use permissioned (private) blockchains, crypto currencies use permission-less (public) blockchains. The former is a centralized structure, the latter is not.
Anonymity. Private Crypto currency users enjoy anonymity. CBDC users will have their identity tied up to an existing bank account and an equal amount of personal information.
Decentralization. On CBDC networks, a Central Bank decides the rules. On crypto networks, the authority is delegated to the user base, which makes decisions by reaching a consensus.
Use case. CBDCs can only be used for payments and other monetary transactions. Crypto currencies can be used for speculative purposes and for payments as well.

The permissioned blockchain type give a benefit of scalability in theory and revealed identity helps maintain stability in the market. Further, stocks have speculative nature but it is backed with equity in the company and the business performances can be evaluated before investing, providing the stocks an element of stability.

In this study we find that for the Indian case crypto currencies do not have the capacity to endanger the traditional monetary system at the current level. For this purpose, we use Structural Factor Augmented VAR in exogenous variables (SFAVARx) model. In this model, crypto currency is considered exogenous factor and other macroeconomic variables⁷ are endogenous. We see that by adding crypto currency as a constraint in the equation does not alter the performance of key macroeconomic variables.

Subsequently, we include crypto currency as endogenous variable in the model and check its impact on key macroeconomic variables. Again, we find that crypto currency in its current form has no impacts on monetary policy in India. Our results reinforce the findings of Tomić, Todorović and Čakajac (2020); McCann (2018); and Paul (2017). However, these studies are primarily qualitative in nature and we make an attempt to quantify the results.

The paper is divided into five sections as follows. Section two, which follows, presents the data and methodology. Section three presents empirical findings. Section four checks for the robustness of the model and section five concludes along with policy implications.

The study examines 54 variables⁸ divided into 5 factors namely: interest rate factor, the money supply factor, the inflation factor, the output factor and the crypto factor. These factors are studied in a Structural Factor Augmented VARx framework. The factors were extracted using Principal component analysis (PCA) and selected on the basis of the variables that maximum explained by it. The variables in each factor are close alternatives of each other. To mention, say for example, the money supply factor includes all narrow, broad and broader measures of money supply. The rationale for finding linkages among all these variables dates back to the 16th and 17th century whereby French philosopher Jean Bodin attributed the increase in rising prices in Western Europe to the monetary metals imported. This further got famous known as classical quantity theory of money in the 19th century and so on Humphrey (1974).

Several studies (Friedman & Schwartz, 1963a; Friedman & Schwartz, 1982; King & Plosser, 1983; Chow & Shen, 2004; Favara & Gordani, 2006; Ćorić, Perović, & Šimić, 2012; and Sharma & Nurudeen, 2020) have been conducted that analyse the relationship between these variables. However, ours is one of the first few FAVAR approaches that have been conducted in the Indian context which is structural and offers economic interpretation to the results.

The 54 variables are monthly frequency spanning from M05 2013 to M10 2021. The data sources include the Reserve Bank of India and crypto data from statista.com. For details of the variables see Appendix 5.2.

The objective of the study is to analyse the linkages among the macroeconomic variables considering crypto currency as an exogenous variable in a ‘data-rich environment’. Several studies, conducted with smaller SVARs, show the existence of a price puzzle which is generally attributed to the lack of information in the model.

Further, the individual series are subjected to Augmented Dickey Fuller unit root test. The level of integration of each series is mentioned in the Appendix on data information. Mostly, the series are first difference stationary or integrated of order 1, that is, I (1). There are three methods in which the factors can be extracted: (i) the principal component analysis (PCA), (ii) the Bayesian method, and (iii) spectral analysis. We have used PCA to generate our factors, pioneered by Pearson (1901) and suggested by Ng & Perron (2001). The factors are derived from the standardised⁹ and stationary series and based on the maximum absolute loadings value. Each variable is assigned to a broader macro factor. Four factors are derived with help of PCA and these factors are taken as given for estimation purpose of the model parameters

In this modelling approach (Bernanke, Boivin, & Eliasz, 2005) observables and non-observables series are introduced. The observables are those series which can directly be measured; similarly, non- observables are those series which cannot directly be measured, for example, output gap. Since output gap cannot be observed, factors extracted can be used to represent the series. In either case, both observables and non-observables are allowed to follow a VAR process.

After the application of Factor Augmentation to VAR framework by Bernanke, Boivin, & Eliasz (2005) many researchers followed suit and applied the methodology in various research works. For example, Stock & Watson (2005a); Stock & Watson (2005b); Eickmeier & Breitung (2005); Eickmeier (2007); Stock & Watson (2002); Negro & Otrok (2008); Bagliano & Morana (2009); Ahmadi & Ritschl (2009); Stock & Watson (2010); and Huh, Kim, Kim & Park (2014). Fransesco & Fabio (2006) also mention the PCA factor extraction method and further a Structural FAVAR analysis.

To understand the application of a FAVAR methodology, first, we demonstrate the factor extraction process. Thus, we consider the equation below in which Zt is a vector with large macroeconomic variables. Now, to disentangle Zt from observed and unobserved elements reported by Y, X and F respectively, we have the following relation.

$${Z}_{t} = {Г}_{1}{F}_{t} + {Г}_{j}{Y}_{t} +{Г}_{k}{X}_{t}+ {e}_{t}$$

where, Z_t is a N*1 vector of macroeconomic variables, Г₁ and (Г_j & Г_k) are Vectors of N*M factor loadings and structural coefficients (Y_t is endogenous and X_t is exogenous) respectively. e_t is the random disturbance term with zero mean and constant volatility. Eq. (1) could be augmented in VAR framework and could be re-written as follows:

$$\left[\begin{array}{c}{F}_{t}\\ \begin{array}{c}{Y}_{t}\\ {X}_{t}\end{array}\end{array}\right]=Q1+Q2\left(L\right)\left[\begin{array}{c}{F}_{t}\\ \begin{array}{c}{Y}_{t}\\ {X}_{t}\end{array}\end{array}\right]+ vt$$

where, Q1 is the vector of intercepts, Q2 is the Matrix of factor loadings and is the Kx1 vector of reduced from errors, which are also assumed to have zero mean and constant variance.

In order take care of the criticism that the estimates from Eq. (2) lack economic meaning, the FAVAR of Eq. (2), could be extended to include structural identification of the factors, thus the model becomes FA-SVAR, and could be specified as below:

AY_t = Ѳ₁ + Ѳ₂ (L) Y_t + Ѳ₃ (L) X_t+ Be_t (3)

here, A is an N*N contemporaneous impact matrix which measures the simultaneous response of the variables. B is an N*N matrix, and it represents the instantaneous impact of the structural shocks. Y_t is N*1 vector of endogenous variables. X_t is n*1 vector of exogenous factor, however, It only acts as a constraint and impulse response is calculated for the endogenous variables considering the exogenous factor only as a constraint.

The term, Ѳ₂(L)Y_t represent the dynamics component of the explanatory variables and e_t is N*1 vector of structural shocks. When we divide both sides by A, we obtain a reduced form equation as follows:

$${A}^{-1}B{e}_{t }$$

where, A is M*N matrix of contemporaneous response, B is the M*K variance co-variance matrix, e_t is the vector of structural shocks.

There are different ways in which restriction can be imposed to identify Eq. (4) based on A model, B model, and or AB model. Thus, the restrictions could be recursive as suggested by Sims (1980). Wold restriction as suggested by Wold (1969), restriction based on heteroskedasticity and signs as in Kilian (2011) and restriction based on economic theory. For the purpose of our study, a recursive restriction is used as suggested by Sims (1980). Thus, the model needs k *(k-1/n) restrictions to identify the model as exactly or over identified. The restrictions on the model are as follows:

A = $\left[\begin{array}{cccc}1& 0& 0& 0\\ 0& 1& 0& 0\\ 0& 0& 1& 0\\ 0& 0& 0& 1\end{array}\right]\left[\begin{array}{c}Ui\\ Uii\\ Uiii\\ Uiѵ\end{array}\right]$ B= $\left[\begin{array}{cccc}b11& 0& 0& 0\\ b21& b22& 0& 0\\ b31& b32& b33& 0\\ b41& b42& b43& b44\end{array}\right]\left[\begin{array}{c}ei\\ eii\\ eiii\\ eiѵ\end{array}\right]$ (5)

where, A is a diagonal matrix, and measures the contemporaneous response of the macroeconomic variables in the system. The B matrix is a lower triangular matrix and it measures the impact of contemporaneous shock. The exogenous factor only acts as a constraint in the model and the impulse responses are generated of the endogenous variables only, however considering crypto as a constraint in the entire process. Comparison of the SFAVAR and SFAVARx is made to check the impact of crypto currency on the behaviour of key macroeconomic variables.

Structural Factors

We partition the vector of economic variables Z_t so that each variable is explained by one of the following structural factors:

Output factor: The output factor includes both quarterly real GDP data and Index of Industrial Production (IIP) data both use -based and sectoral.
Inflation factor: The inflation factor includes all the data of Consumer Price Index (CPI) and Wholesale Price Index (WPI). Since WPI was the focus of RBI prior to the FIT Framework period and CPI subsequently.
Money supply factor: All the broad and broader measures of money supply are included.
Interest Factor: This factor includes the measures of interest rates used by both RBI and market determined rates.
Crypto factor: It includes five most traded crypto currencies in India.

This section discusses the empirical findings. First, we briefly discuss how banks play an important role in the monetary transmission mechanism and the possibilities of private crypto currencies affecting that framework.

Central Banks have reserved a special pipeline for the banks called the monetary transmission mechanism. Pipeline because it goes through several channels and aims to reach the same goal of growth and price stability. As Woodford (2000) rightly mentions, macroeconomic stabilization depends only upon the ability of Central Banks to control a short-term nominal interest rate, and this would continue to be possible, in particular through the use of a "channel" system for the implementation of policy.

As per Schich (2009), Central Banks rely on the capacity of the banking system to create money and provide the economy with adequate liquidity and, despite occasional financial crises, have concluded that the efficiency of the current system outweighs the associated costs. Emerging private Fintech companies have emerged as a good initiative to provide banking services in a disaggregated format. But they still cannot replace the banks (and thus the monetary transmission mechanism) in its current form. Banks still enjoy the “special” status provided to them by the regulators and policy makers.

Central Banks worldwide have adopted the Flexible Inflation Targeting Framework (FIT) and Reserve Bank of India also formally adopted FIT in 2016. Controlled inflation automatically leads to sustainable growth (Sharma & Nurudeen, 2020). In this framework, repo rate is considered the policy rate and call money rate is for the overnight market. The Central Bank manages liquidity to keep the call money rate between the repo and the reverse repo rate. The movements of short to medium and long-term interest rates happen through this interest rate channel of monetary transmission mechanism.

The efficiency of the transmission mechanism is dependent on a well-established credit culture of an economic system whereby the Non-Performing Assets (NPA) are very low, production levels are high (leading to asset creation and employment generation), and adequate liquidity in the system. This needs to improve in India; however, fintech cannot replace the banks to fulfil this weakness of the Indian Economy. Banks are advised to focus on pushing the Micro, Small and Medium Enterprises (MSMEs), uplifting the rural economy and safeguarding interests of weaker sections of the society to grow in a balanced approach. Mere ease of transactions cannot help fintech replace the banks.

Figure 5.3 displays the innovation accounting results of the Structural Factor Augmented VAR in Exogenous variables (SFAVARx) results. The results of SFAVAR and SFAVARx are similar. Hence, inclusion of crypto as an additional constraint to the existing system makes no impact on key macroeconomic variables. We see that production decreases as policy rate increases. Inflation decreases for two months moderately but later increases with increases as a response to increased money supply. Money supply has a positive relationship with output and an inverse relationship with policy rates. Policy rates increase as inflation increases.

Further, a scenario is experimented whereby crypto currency was added as an endogenous variable to check the results in Fig. 5.4. Here, we see that even if crypto becomes a part of the system legally, output does not increase, money supply is marginally affected, inflation however decreases and policy rate also decreases. Benigno (2019) state that currency competition can succeed in calming inflation and preventing the sort of manipulation of interest rates and prices to which governments have historically been prone. But currency competition may also lead to government money losing the function of medium of exchange, which could be risky and lead government currency into further troubles.

The SFAVARx model was checked for residual diagnostics using the LM test for Autocorrelation and Whites Heteroskedasticity test. The model passes all the residual diagnostic tests. Hence, results are valid for meaningful interpretation. Also, to check the stability of the model we used the CUSUM at the 5 percent significance level. Cumulative sum (CUSUM) is used for checking the structure (or parameter) stability in the model over the sample period Pearson & Pearson (1997). The cumulative sum test identifies systematic changes in the regression coefficients. Figure 5.5 show that results fall within the 5 percent confidence interval of parameter stability, hence proving a stable analysis.

We observe that crypto currencies do not affect the monetary system in its current form with the given dataset⁹. Also, if the number of participants in crypto trading increases at the current pace, the monetary system might get affected. Hence, proactive decisions need to be taken instead of reactive. Therefore, there are both challenges and opportunities for Central Banks to evolve into the digital age leading to real time and cost-effective transmission.

Since CBDC’s and crypto currencies differ in their core features, CBDCs in their current form do not seem to be replacing private crypto currencies. Also, both monetary and fiscal policies are not imposing a strict ban on them due to their speculative natures. Efforts to regulate crypto exchanges will determine whether crypto currencies can be classed as securities.

Ahmadi, P. A., & Ritschl, A. (2009). Depression Econometrics: A FAVAR Model of Monetary Policy During the Great Depression. Department of Economic History, London School of Economics, working paper number 130/09.
Bagliano, F., & Morana, C. (2009). International macroeconomic dynamics: A factor vector autoregressive approach. Economic Modelling, vol. 26, issue 2, 432-444.
Benigno, P. (2019). Monetary policy in a world of cryptocurrencies. Centre for Economic Policy and Research (CEPR).
Bernanke, B., Boivin, J., & Eliasz, P. (2005). Measuring the effects of monetary policy: A factor-augmented vector autoregressive (FAVAR) approach. Quarterly Journal of Economics, 120, 387-422.
Bordo, M. D., & Levin, A. T. (2017). Central BAnk Digital Currency and the Future of Monetary Policy. National Bureau of Economic Research (NBER) Working paper series, W.P. NO. 23711.
Buchanan, J., & Tullock, G. (1962). The Calculus of Consent: Logical Foundations of Constitutional Democracy. Michigan.: The University of Michigan Press.
Chow, G. C., & Shen, Y. (2004). Money, price level and output in the Chinese macro economy. Asia-Pacific Journal of Accounting & Economics, 12(2), 91–111.
Coase, R. H. (1972). Durability and Monopoly. The Journal of Law and Economics, Volume 15, Number 1.
Ćorić, B., Perović, L. M., & Šimić, V. (2012). Monetary policy effects on output and prices: International evidence. The young economists’ seminar to 18th Dubrovnik Economic Conference organised by Croatian National Bank, University of Split, Facul.
D’Agnolo, M. (2015, October 26). All You Need To Know About BitCoin. Retrieved from ECONOMICTIMES, : https://web.archive.org/web/20151026140555/http://economictimes.indiatimes.com/news/international/business/all-you-need-to-know-about-bitcoin/articleshow/48910867.cms
Dwork, C., & Naor, M. (. (1993). "Pricing via Processing, Or, Combatting Junk Mail, Advances in Cryptology". CRYPTO'92: Lecture Notes in Computer Science No. 740., Springer: 139–147.
Eickmeier, S. ( 2007). Business cycle transmission from the US to Germany--A structural factor approach. European Economic Review, vol. 51, issue 3, 521-551.
Eickmeier, S., & Breitung, J. (2005). Dynamic Factor Models. Economic Studies, 38.
Favara, G., & Gordani, P. (2006). Reconsidering the role of money for output, prices and interest rates. Journal of Monetary Economics, 56(3), 419–430.
Francesco, B., & Fabio, M. (2006). Structural Factor-Augmented VARs (SFAVARs) and the Effects of Monetary Policy. The BE Journal of Macroeconomics, De Gruyter, vol. 6(3), pages 1-46.
Friedman, M. (1960). A Program for Monetary Stability. New York: Fordham University Press.
Friedman, M. (1962). Should there be an independent monetary authority? Cambridge, Massachusetts: Harvard University Press.
Friedman, M., & Schwartz, A. J. (1963a). A Monetary History of the United States. Princeton University Press.
Friedman, M., & Schwartz, A. J. (1982). Monetary Trends in the United States and the United Kingdom: Their Relation to Income, Prices, and Interest Rates. University of Chicago Press.
Hayek, F. A. (1978). Rules and Order. Chicago: University of Chicago Press.
He, D. (2018). Monetary Policy in the Digital Age . Finance and Development, vol. 55, no. 2.
Huh, H.-s., Kim, D., Kim, W. J., & Park, C.-Y. (2014). A Factor-Augmented Vector Autoregression Analysis of Business Cycle Synchronization in East Asia and Implications for a Regional Currency Union. ADB Economics Working Paper Series, No. 3.
Humphrey, T. M. (1974). The quantity theory of money: its historical evolution and role in policy debates. Economic REview, vol 60, 2-19.
Jakobsson, M., & Juels, A. (1999). Proofs of Work and Bread Pudding Protocols. Secure Information Networks: Communications and Multimedia Security. (pp. 258–272). Kluwer Academic Publishers.
Jevons, W. S. (1875). Money and the Mechanism of Exchange. New York, D. Appleton, volume 17.
Kilian, L. (2011). STRUCTURAL VECTOR AUTOREGRESSIONS. . Centre for Economic Policy Research. University of Michigan., Discussion Paper No. 8515.
King, R., & Plosser, C. (1983). Money, credit and prices in a real business cycle. American Economic Review, 74(3), 363–380.
Marshall, A. (1890). Principles of Economics. London: Macmillan.
Negro, M. D., & Otrok, C. (2008). Dynamic Factor Models with Time-Varying Parameters: Measuring Changes in International Business Cycles. Federal Reserve Bank of New York, Staff Reports, Staff Report no. 326.
Ng, S., & Perron, P. (2001). LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power. Econometrica, 69, 1519-1554.
Patra, M. D., Bhoi, B. B., John, J., & Priyadarshi, K. (2021). Flexible Inflation Targeting (FIT) in India. Mumbai, India: Reserve Bank of India bulletin.
Pearson, K. (1901). The scope of Biometrika. Biometrika, 1, 1–2.
Pesaran, M., & Pesaran, B. (1997). Microflt 4.0. England: Oxford University Press.
Sabade, S. (2014). Is money supply the cause of inflation in India? An alternative postulate to understand inflation. Procedia - Social and Behavioral Sciences, 379 – 382.
Sagona-Stophel, K. (2016, August 13). Bitcoin 101 (WHITE PAPER). Thomson Reuters.
Sankar, T. R. (2021). Central Bank Digital Currency – Is This the Future of Money. speech delivered at Vidhi Centre for Legal Policy, New Delhi.
Schich, S. (2019). Do Fintech and Cryptocurrency Initiatives Make Banks less Special? Business and Economic Research, Vol. 9, No. 4.
Selgin, G. (2014). Synthetic commodity money. Journal of Financial Stability.
Sharma, A., & Nurudeen, I. (2020). Monetary Policy Disturbance in India: The Relationships Among Money, Output and Prices. The Indian Economic Journal, Volume 67, Issue 3-4.
Sims. (1980). Macroeconomics and reality. Econometrica, 48(1), 1–48.
Sims, C. A., & Zha, T. (1999). Error Bands for Impulse Responses. Econometrica, vol 67, Issue 5, Pages 1113-1155.
Stock, & Watson. (2002). Forecasting Using Principal Components from a Large Number of Predictors. Journal of the American Statistical Association., Vol. 97, No. 460.
Stock, J. H., & Watson, M. W. (2005a). An empirical comparison of methods for forecasting using many predictors. Working Paper, Princeton University.
Stock, J. H., & Watson, M. W. (2005b). Implications of dynamic factor models for var analysis. National Bureau of Economic Research., Working Paper 11467.
Stock, J. H., & Watson, M. W. (2010). Dynamic Factor Models. Oxford Handbook of Economic Forecasting.
Tomić, N., Todorović, V., & Čakajac, B. (2020). THE POTENTIAL EFFECTS OF CRYPTOCURRENCIES ON MONETARY POLICY. The European Journal of Applied Economics, 17(1):37-48.
Wicksell, K. (1898). Interest and prices. R F Kahn (trans.) Kelley: New York, 1965.
Woodford, M. (2000). MONETARY POLICY IN A WORLD WITHOUT MONEY. NATIONAL BUREAU OF ECONOMIC RESEARCH (NBER) Working paper , working paper no. 7853.

Commodity money is money requires real resources to add to the stock of money. For e.g., gold standard, etc. See (Friedman, A Program for Monetary Stability., 1960) (Friedman, 1962)
The Coase conjecture, developed first by Ronald Coase, is an argument in monopoly theory. The conjecture sets up a situation in which a monopolist sells a durable good to a market where resale is impossible and faces consumers who have different valuations. See (Coase, 1972)
Fiat money is money that is issued by the Central Bank in form of paper currency and deposits that can be converted into paper currencies, usually based on trust of people.
Software called crypto currency wallets helps send crypto currency transactions. Wallets are used by traders to transfer funds from one public address to another. All transactions and balances are recorded in a digital public ledger called the blockchain.
See Appendix 5.1 for details related to Bitcoin, Litecoin, Ethereum and Ripple (XRP).
See Appendix 5.2
See Appendix 5.2
Corrected for mean and standard deviation to avoid weakening of our factor extraction exercise.
Said to be pseudonym for a person or group of persons. Real name is still unknown.

The authors declare no competing interests.

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Cryptocurrency Impact on India’s Monetary Policy

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Abstract

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5.1 INTRODUCTION

5.2. DATA AND METHODOLOGY

5.3 PRESENTATION OF RESULTS AND DISCUSSION

5.4 ROBUSTNESS OF RESULTS

5.5 SUMMARY

References

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