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Influences on the Price of Bitcoin

Master's Thesis 2017 104 Pages

Economics - Finance

Excerpt

Table of Contents

Acknowledgements

1 Introduction
1.1 Bitcoin

2 Research Aim

3 Literature Review

4 Methodology
4.1 Microeconomic
4.1.1 Supply
4.1.2 Demand
4.2 Macroeconomic
4.2.1 Political
4.2.2 Economic
4.2.3 Social
4.2.4 Technological
4.3 Correlation
4.4 Timeline of Major Events and Price of Bitcoin
4.5 Problems with Bitcoin

5 Results
5.1 Microeconomic Analysis
5.2 Macroeconomic Analysis
5.3 Correlation Analysis
5.4 Volatility Analysis
5.5 Future Outlook

6 Conclusion

Bibliography

List of Figures

List of Abbreviation

A Appendices
A.1 Correlation
A.2 Micro Scenario
A.2.1 Descriptive Diagnostic
A.2.2 Regression Diagnostic
A.3 Macro Scenario
A.3.1 Descriptive Diagnostic
A.3.2 Regression Diagnostic

Acknowledgements

This research project would not have been possible without the support and guid- ance of numerous individuals. The author wish to express their gratitude to their supervisors, Prof. Dr. Andreas Dietrich for his continuous support, guidance, and the opportunity to become a part of the master’s program. It has been an immense learning experience full of continuous moments of expanding my comfort zone.

Also, the author would like to express her gratitude to her family and friends for their unconditional support and understanding during the many hours spent away from them working on this thesis.

1. Introduction

Recently, the surge of digitalization has played an important and significant role in the outlook for the financial industry. Where banks have dominated the deposits, savings, currencies, and payments systems, the emergence of Bitcoin and other such cryptocurrencies are slowly but surely changing the level playing field of the traditional financial industry. The role of digital currencies in the age of internet have inadvertently lowered costs for consumers, made products and processes more convenient and faster, and lastly, resulted in a more efficient system as a residual of increased competition. While the upside is endless, such new digital currencies have brought along with them new and complicated social, legal, and economic challenges. It is importance to identify and understand the variables that charac- terize the decentralized, unregulated currencies to further optimize their benefits while minimizing any adverse effects.

While early research has been conducted to identify the impact of internet on money, the extent to which the research progressed over the years has declined. Research for topics such as development of digital currencies was widespread in the 1990s, during the infancy stage of the internet, and many theorized that the progress of internet would lead to new kinds of currencies that is away from traditional-backed currencies, however, as the newness of the internet began to wear off, so did the research and interest in the potential to generate new forms of currency. Furthermore, the media has concentrated greatly on such currencies while attention from lawyers and economists has been deflected [1, Dwyer (2014)]. As of the past fifteen years however, more research and attention has been focused on the formulation, emergence, valuation, and effects of digital currency on the economic and financial landscape.

Electronic money can be categorized into two types: currency and deposits. Cur- rency is mainly defined as an asset which can be passed from one owner to the next and can be validated by the balance that the owner of the currency maintains. Deposits, on the other hand, are defined as money that is validated by an account at a bank, thus proving as assets to its owners and a liability to the institution where it is housed. A distinct characteristic of digital or electronic currency is the store of value in the form of an electronic medium such as one a hard disk. Digital currencies are not drastically different from electronic currency in storage of value except for concerns about theft [1, Dwyer (2014)]. However, digital currency’s transfer without intermediation of a financial institution is the main contrary fac- tor, thus characterizing it as decentralized.

Decentralization factor has lead to ambiguity in regulation of such digital cur- rency. Double-spending problem is yet another issue where it is imperative that users are not able to create multiple copies and spend same digital currencies twice. The double-spending problem can be equated to counterfeiting of paper currency [1, Dwyer (2014)]. In this respect, there is not institution checking to ensure the transfer of purchasing power reflects available funds. For currency to have true value, it is important to ensure that it is not being duplicated or being used twice while double spending problem is exactly where the value of currency is negligible since the cost of reproducing is negligible.

In this paper, I aim to identify and understand the variables influencing the price of bitcoins, explore competitors of bitcoins and understand the relationship between government-backed traditional currencies and new-age digital currencies, if any.

1.1. Bitcoin

Bitcoin is one form of digital currency which is based on a peer-to-peer network using open source software. Unlike government-backed traditional currency which is created by a single issuer, certified by the issuer, and used by many, bitcoin operates on a peer-to-peer network. A peer-to-peer network is organized as a set of nodes into a self organizing connected network. In addition to the peer-to-peer network, bitcoin is also characterized by open source software. The main feature of an open source software is its source code distribution with little or no copyright restrictions and the ability to modify the program as per user needs.

Created by user named Satoshi Nakamoto, the oversight of Bitcoin Foundation was passed on to Gavin Anderson who maintains the open source nature of the digital currency. Bitcoins are created by solution of a computational algorithm by miners. Solving the algorithm provides “proof” that the miner has completed the work himself/herself. The level of difficulty of the algorithm is subject to increasing cost over time along with an eventual limit on the number of bitcoins that can be created, thus limiting the overall supply of the currency, an important aspect which will be addressed later. New bitcoins are created as a reward for transaction pro- cessing work in which users can offer their processing powers to verify and record payments into a public ledger. Also known as mining, individuals or firms employ such activities in exchange for a chance to earn newly created bitcoins. Mining is an important part of cryptocurrency networks which is open source and uncensored. The intention of the resource-intensive activity is to deter anonymous participants in mining who may undermine the system. The assumption that Bitcoin Founda- tion undertakes is that all user nodes of the open source, peer-to-peer network are controlled by fraudsters and users with mal-intentions therefore pre-emptive efforts by increasing costs associated with mining and exploiting have been put in place [7, Hayes (2016)]. The increasing marginal “mining” costs of Bitcoin gives it a superficial resemblance to a precious metal standard [14, Selgin (2013)]. However, whilst the use of more efficient ways to mine precious metals results in an increase in the overall rate of metal output, in case of bitcoins, such innovations alter output shares only, and not total coin output, which is exogenously determined. In other words, production of bitcoins is not vulnerable to supply shocks [14, Selgin (2013)].

The announced limit of bitcoins is 21 million. Any increase is determined by a rule issued by Nakamoto which halves the increase every four years and generates a declining increase over time. Economists view this inelasticity as an advantage and a disadvantage at the same time. Bitcoin value has displayed volatile trends over its lifetime. The price of bitcoin has increased by over 5000% since its in- ception in 2009 [15, Urquhart (2016)]. The mean standard deviation of daily log returns of bitcoins on three exchanges: Mt. Gox, btce, and Bitstamp have been 7.2 percent, 5.1 percent, and 5.5 percent respectively. This is much higher compara- tively to the well-known one percent per day typical standard deviation of broad stock return indices in the United States [1, Dwyer (2014)]. When compared to gold, the monthly standard deviation of daily log returns of gold over the period of 2010-2014 has been 1.1 percent which indicates that holding gold has much less idiosyncratic risks than holding bitcoins [1, Dwyer (2014)]. A comparative advantage of bitcoin is the availability of cheaper venues for trading currency than many alternatives available to consumers today. Due to its unregulated nature, bitcoin holders can also avoid currency controls set in place by national govern- ments. While the currency controls can prevent the use of bitcoin exchanges such as Mt. Gox, they do not prevent owners of bitcoin to carry the currency into a country to exchange for local or preferred currency.

2. Research Aim

Upon reviewing current literature, it was found that many researchers in the past have attempted to classify Bitcoin as a commodity or currency. This paper aims to ask the question whether bitcoin can be classified in either category. To begin the research, the price of bitcoin is set as target variable. With the assumption that prices of commodities are largely influenced by supply and demand, the first part of the analysis aims to analyse a model with clearly defined supply and demand variables. On the other hand, second part of the analysis focuses on bitcoin price as an endogenous variable of macroeconomic factors; this part of the research aims to categorize bitcoin as a currency whose prices are influenced largely by macro- economic factors. Lastly, I analyse the current marketplace of digital currencies by analyzing the top five, based on traded volume as of February 2017, for correlations and volatility. For the sake of clarity, I will refer to the supply and demand analy- sis as microeconomic factors, while all else will be referred to as macro-economic factors. Based on the above, the null hypothesis is:

H0: Macroeconomic factors do not have an impact greater than microeconomic factors on the price of bitcoin.

Subsequently, the alternative hypothesis is claimed as:

H1: Economic factors as sub-category of general macro-economic factors have the largest influence on the price of bitcoin.

Sub H1: The same macro-economic factors which influence the price of bitcoin, also influence the price of alternate digital currencies.

In order to complete the research and answer the above questions, the research is divided into three parts: Part one contains the microeconomic analysis, part two contains macroeconomic analysis, and part three contains the analysis of correlations and volatility among alternative digital currencies.

Part two is further divided into sub categories as per political, economic, social, and technological ; thus known as political, economic, social, and technological (PEST). It is important to note that due to the challenge of data collection at a global scale, and limited data availability, the macroeconomic factors focus on the region of United States.

Should the analysis find that microeconomic factors influence the price of bitcoin more than the macroeconomic factors, it can be concluded that bitcoin is categorized as a commodity more than a currency.

Section three of the paper contains literature review, section four explains the methodology used, section five shows the results of the analysis, section six provides a brief future outlook per the author’s perspective, and lastly, section seven concludes the final thoughts of the research.

3. Literature Review

Much of the research performed in the field of e-commerce and cryptocurrencies has been recent. The most explored aspects of research have been legal, society, and safety. Due to low regulatory environment and high degree of anonymity in transactions, several models have been proposed to determine the components of pricing for bitcoin in particular.

Before analyzing determinants of pricing, I researched for existing literature on relationship between the market characteristics and non-traditional currencies. Ar- ticle published Urquhart titled The Inefficiency of Bitcoin applies the free available information component of efficient markets to the efficiency of bitcoins. Efficient Market Hypothesis being a critical component of finance [5, Fama (1970)] has to be examined for its validity towards digital currencies. Urquhart collects data from www.bitcoinaverage.com which is the first aggregated bitcoin price index which compiles rates from all available bitcoin exchanges around the world and provides a volume weighted average bitcoin price. Using such a platform enables the re- searcher to incorporate the worldwide perspective on price thus satisfying the freely available information aspect. Sample data from 1st August 2010 to 31st July 2016 has been further divided into two subcategories with periods of 1st August 2010 to 31st July 2013 and 1st August 2013 to 31st July 2016. The descriptive analysis from Urquhart show that mean returns of bitcoin are positive with excess kurtosis and negative skewness over the full sample period. The results also found that the mean returns and standard deviation are smaller in the second subsample. In an efficient market, prices follow random walk [15, Urquhart (2016)] and in order to analyze whether bitcoin is efficient, Urquhart utilizes a series of tests to check for randomness in order to avoid spurious results and to capture all dynamics of bit- coin. The research finds that weak-form informational efficiency of bitcoin can be rejected. Results of R/S Hurst exponent shows strong evidence of anti-persistence, indicating the non-randomness of returns. Overall, the sample indicates significant inefficiency in bitcoin due to its infancy and similarities to emerging markets.

Research focused towards the potential adoption of bitcoin is another critical component which may help determine any existing relationships between use of currency and its price. Research completed by [8, Hileman (2016)] in article titled The Bitcoin Market Potential Index (BMPI), quantifies and ranks potential utility of bitcoin across 178 countries. Contrary to the research issued by [15, Urquhart (2016)] about free available information, Hileman suggests to exclude regulation as an index variable due to insufficient data. BMPI, according to the researcher, is the first attempt at providing a rigorous answer to the question of where a cryptocurrency like bitcoin has both, the most and least, potential for adoption. The research quantifies the adoption or utilization of bitcoin through the met- rics such as, number of wallets, number of bitcoin accepting businesses, number of transactions, and exchange trading volume. The paper focuses primarily on market potential, referring to the countries where bitcoin may have the greatest relative potential utility. An interesting perspective proposed by the researcher is the interdisciplinary framework for understanding utility as seen in figure 3.1.

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Figure 3.1.: Interdisciplinary Framework for understanding bitcoin utility [8, Hile- man (2016)]

Additionally, the author raises critical questions that provides a direction for re- search. Questions such as bitcoin’s potential value in degree of store of value, or as medium of exchange, or alternatively, potential non-monetary functionalities of blockchain. Bitcoin Market Potential Index combines and quantifies all three above-mentioned aspects, condensed into 40 variables which are further grouped into seven equally-weighted sub-indices in order to calculate BMPI rankings. Stan- dardized Results of this research show that the 10 countries with the highest relative potential for bitcoin adoption are Argentina and Venezuela on the top of the list, and Iceland and Islamic Republic of Iran on the bottom of the list. United States of America ranks at five, the median range while China is not included in the list. High inflation rates in Argentina in combination with an informal economy, and regular bouts of financial crisis, the country places high in the list as these factors constitute a major part of BMPI. China ranked 27th in the BMPI ranking due to its relatively small informal economy (black market) and fewer recent financial crisis. An issue which this research and other research associated with bitcoins is the limited data availability. Several assumptions need to be undertaken in order to formulate a ranking on a global scale since information availability varies from country to country. Moreover, regulation is not included in BMPI for reasons such as: lack thereof of regulations, impact of existing regulations, and lastly the efficiency of regulations in this environment is still uncertain.

In his research entitled The Economics of Bitcoin and Similar private digital cur- rencies, [1, Dwyer (2016)] explains how the use of technologies and limitations of the quantity produced creates an equilibrium in which a digital currency has pos- itive value. Also, the paper summarizes the rise of 24/7 trading on computerized markets in bitcoin where there are no brokers or other agents. Dwyer explains the demands for digital currency as a result of low cost of transfer from person to per- son. According to Dwyer, bitcoins are not redeemable in anything else from agents or set of agents and due to this, bitcoins are not an immediate store of value. The value of bitcoins is determined by the demand for bitcoins in conjunction with the rules governing supply. Dwyer compiles data sets from Mt. Gox for bitcoins traded and U.S. dollar trades. Upon merging the two datasets, there are 8,364,956 trades included in the data. The second set compiled from a secondary source called www.bitcoincharts.com, shows 3,118,971 trades from September 2011 to March 2014. The data for btce exchange shows 10,834,761 trades from August 2011 to March 2014. Overall, the years 2012 and 2013 stand out as having substantially more trades than the two years earlier. Furthermore, change in the number of trades suggests that trading activity of bitcoins for dollars reflects the clock in the United States in 2011 and 2012. The daily seasonal component is much less promi- nent in 2013 as the researcher finds. The prices of bitcoins for U.S. dollars for the three exchanges, Mt. Gox, btce, www.bitcoincharts.com, have increased substan- tially. Discussions related to volatility of prices of bitcoins have been prominent. Classifying a price of, for example $586, high or low is a critical question which requires benchmarking. The author approaches this issue using two methods; first, examining the aggregate purchasing power in dollars represented by the quantity of bitcoins, and second, examining the aggregate value of bitcoins by comparing their value to the value of reserves in the banking system. The first approach finds that there were approximately 12.5 million bitcoins as of March 2014. At a price of $586 per bitcoin, the total value is $7.32 billion. While not trivial, this is a small number as compared to the value of U.S. M2 of $11.0 trillion as of January 2014. Due to the unpredictability in pricing of bitcoin, the author implies the price of $586 as low. The second approach concludes that prior to the financial crisis of 2007-2008, reserves in the U.S. banking system alone were $8.75 billion. The value of all bitcoins in March 2014 of $7.32 billion is about 84% of this value of reserves in the Federal Reserve. In conclusion, the researcher states that bitcoin and similar digital currencies are likely to undermine government’s ability to generate revenue from substantial inflation. Bitcoin’s ability to evade capital controls may well be a very important factor in the overall adoption, and overall demand of use.

Selgin [14, Selgin (2013)] argues in favor of bitcoin’s superficial resemblance to precious metals. [4, Dyhrberg (2015)] explores this question further by examining the financial asset capabilities of bitcoin using a GARCH model. It is important to classify the asset class that bitcoin belongs to in order to further understand its role in the financial markets for its risk management and portfolio analysis capabilities. As [14, Selgin (2013)] compares bitcoin to precious metals, other economists have measured the financial characteristics and capabilities of bitcoins to gold. Gold has some intrinsic value, however, variables surrounding the current market price (at the time of research carried by Selgin) of $1241.43 US Dollar (USD) per troy ounce [Bloomberg (2016)]. In a perfect competition environment, where users act ratio- nally and perfect information flow exists, bitcoin must have some intrinsic value as well for the current price of $1043.92 USD (2016) to be justified. To compare the similarities, both bitcoin and gold derive their values from the limited supply and cost of mining. Neither of the two are affiliated with a specific nationality or controlled by a government. Both are ‘mined’ by independent users and operators. In the case of use as medium of exchange, gold was abandoned due to its lack of liquidity and same might occur for bitcoins. On the other hand, the differences between gold and bitcoin are only limited to the hedging capabilities of gold as it has a negative correlated with the US dollar while the same is uncertain in the case of bitcoins. Dyhrberg [4, Dyhrberg (2015)] says that the supply, governance, and control of the two assets are different but are comparable when its comes to the observable monetary abilities. The methodology used by Dyhrberg makes use of variables such as federal fund rates, USD-EUR exchange rates, USD-GBP exchange rates, Financial Times Stock Exchange (FTSE) index, Gold cash rates, Gold futures rates. Results of the research indicate that return on bitcoin is more affected by the demand for bitcoin as a medium of exchange and less by temporary shocks to the price which indicate similarities to the currency. Overall, the findings suggest that bitcoin and gold have similarities when it comes to volatility of re- turns. The coefficient of federal funds rate suggests that as the rate increases, so does the return on bitcoin investments. Region specific effects are evident through the increased sensitivity to the value of USD-GBP more than to the value of USD- EUR. Lastly, the coefficient on Financial Times Stock Exchange Index suggests that a positive shock to the stock market may make investors more risk seeking and invest in an alternative asset like bitcoin. To conclude the research, Dyhrberg finds that the results indicate that the returns for bitcoins act similarly to an ex- change rate due to its sensitivity to federal funds rate and medium of exchange characteristics. The return of bitcoin is similar to that of gold, thus, bitcoin can be classifying as a middle ground between currency and commodity. On the contrary, economists such as Paul Krugman [11, Krugman (2013)] are pessimistic about the viability of bitcoin as a medium of exchange due to the liquidity issues associated with the currency caused by the fixed supply.

Joseph Wang explores the bitcoin valuation from a macro-economic perspective. Based on macroeconomic events, the behaviour of bitcoin can be determined claims the researcher. Most importantly, the assumption that the value of bitcoin is de- termined largely by the willingness of bitcoin holders to save bitcoin and not by its transactional use thus counteracting the liquidity trap. The model used by Wang also incorporates two major financial crises in the past and the behaviour change in the price of bitcoin as a result. For example, Cyprus banking crisis in 2013 and rise of mainland China bitcoin exchanges in October 2013 is also a part of the model. Observations are also made regarding the sudden changes in price due to news events. The model predicts an increased usage of bitcoin should manifest itself in larger volumes rather than increased prices as one might predict. All of the above is based on the assumption that bitcoins are saved by users rather than used for transaction purposes. Wang’s research is not supported by empirical ev- idence, therefore cannot be used as support to search for variables for this research.

Another research conducted by Iwamura and Kitamura [12, Mitsuru Iwamura (2014)] discusses the potential and limitation of Bitcoin as a digital currency from an economic viewpoint. The authors argue that despite the recent enthusiasm for bitcoin, is it unlikely that currencies provided by central banks are at any risk of being replaced, primarily due to the instability associated with the price of bitcoins. The authors argue that the instability stems from the lack of flexibility in the bit- coin supply schedule-a pre-set algorithm in which the proof of work is the driving force. Iwamura and Kitamura suggest a new monetary policy rule which excludes the presence of central banks to stabilize the values of bitcoins. An important question raised by the researchers is whether proof of work can contribute to the stability of bitcoin value. In a simple word, the answer is given as: no. As creator [13, Nakamoto (2008)] states that once a predetermined number of coins have entered circulation (in this case 21 million), the incentive can transition entirely to transaction fees and be completely inflation free. This may again imply and indirectly validate the macroeconomic model proposed by Wang where the value of bitcoin is derived through saving of the coin rather than transacting with it. The findings of Iwamura and Kitamura find that values of bitcoin as measured in the U.S. dollar fluctuate wildly compared with those of other foreign currencies. The reason points that demand for bitcoin, regardless of motivation of holding, increases as its price decreases and vice-versa. Therefore, the demand curve for bitcoin is downward sloping while the supply curve of bitcoin at any point of time would be vertical (thus confirming the Dyhrberg finding[4, Dyhrberg (2015)] that bitcoin can be classified somewhere between currency and commodity). This re- search critiques Nakamoto’s creation as naïve since the price volatility cannot be adjusted with a fixed money supply rule. Furthermore, the paper makes it clear that bitcoin system intrinsically manifests dual instability. The first instability stemming from inflexible supply curve of bitcoin, thus amplifying price volatility and lack of price stabilization mechanism. And second instability stemming from risks to the sustainability of mining. for example, during a bitcoin price boom, miners engage in mining activity which guarantees the supply of bitcoin, however, during a bitcoin price depression, there is a lack of a smooth way to assist miners exiting. Therefore, the researchers Iwamura, and Kitamura interpret the bitcoin system as a freezing equilibrium with dual instability. Additionally, bitcoin is for now, compared to the gold rush of California, where it acted as a foundation for later economic prosperity. Same can be said of bitcoin where if fostered appropri- ately, an Improved Bitcoin (IBC) can be designed which can compete with central bank money. An IBC that would be able to accommodate a positive demand shock, however, the model suggested by the researchers cannot react properly to a negative demand shock upon testing. To conclude, the major drivers of success of bitcoin are: the naïve understanding of the currency, the peer-to-peer system, and the employment of easy to understand proof of work system. The downside is the lack of price stabilization mechanisms with uncertain transaction capabilities.

Moving towards existing and tested models that explore the pricing model for bitcoin valuation, researcher Adam Hayes [7, Hayes (2016)] identifies likely deter- minants for cryptocurrency value formation. Using cross-sectional empirical data examining 66 of the most widely used cryptocurrencies, a regression model was estimated that points three main drivers: the level of competition in the network of producers, the rate of unit production, and difficulty of algorithm used to ‘mine’ for the cryptocurrency. Prior to testing for results, the paper removes the external dollar, euro, yuan etc. exchange rates thus removing noise within the data and having a strictly production analysis approach. Variables such as computational power in GigaHashes per second, number of coins found per minute, number of coins found as a percentage of the total available, number of calendar days. An r-square of approximately 84% is derived upon running a regression analysis. The paper excludes macroeconomic factors to a large extent which may skew the results.

Bitcoin has been frequently compared to digital gold. Smith [2, Smith (2016)] explores the volatility of bitcoin prices through the nominal exchange rate implied by the relative price of bitcoin denominated in different currencies. He finds that the implied nominal exchange rate is highly cointegrated with nominal exchange rate determined in conventional foreign currency exchange markets. Furthermore, his research shows that direction of causality flows from conventional markets to the bitcoin market and not vice-versa which can ultimately explain the highly volatile nature of the bitcoin prices. The author makes use of three rates based on economic importance and availability of data (dollar-euro rate, dollar-pound rate, dollar-Australian Dollar rate). His findings show that while bitcoin prices themselves are highly volatile and only weakly correlated with conventional nomi- nal exchange rates, the exchange rates implied by relative bitcoin prices are much less volatile and are highly cointegrated with conventional nominal exchange rates. Similarly, the author finds that gold-implied exchange rate highly cointegrated with market exchange rates and shocks to the conventional market have large persistent effects on gold market but not vice-versa. This similarity between gold and bitcoin speak to the idea that bitcoin is not a currency such as dollar or pound, but are much more akin to physical commodity like gold, coffee, or oil.

Given the acceptance of above conclusion, the understanding of price dynamics of physical commodities being influenced fundamentally by supply and demand, it is critical to analyze the price changes based on supply and demand of bitcoin which this paper will explore further.

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Figure 3.2.: Bitcoin planned issuance [10, Ametrano (2014)]

Ametrano [10, Ametrano (2014)] further confirms the findings of Smith [2, Smith (2016)] that bitcoin can be compared to gold from a price dynamic perspective. He claims that bitcoin has elected to have a fixed deterministic inelastic monetary policy, establishing itself more as digital gold than as a currency. While a com- parison to gold is encouraged, the author negates any comparison to money by a merely comparing the uses of bitcoin and uses of money. The three uses of money being medium of exchange, unit of account, and store of value, cannot be applied to bitcoin. In addition, exchange volumes are comparatively low therefore, bitcoin prices are prone to severe shocks due to fragile ecosystems. Consumers and firms investing in bitcoins often have to resort to fiat currencies to cover their liabilities. Further, as seen in Figure (3.2), bitcoins are released and its size and schedule specify the fully automatic, non-discretionary bitcoin monetary policy which is the inelastic fixed supply, increasing at a decreasing rate as it approaches zero. Given the above, bitcoin is more akin to digital gold than to currency, therefore, further encouraging a supply and demand analysis.

Gandal, Halaburda [3, Gandal (2016)] examines the relationship between differ- ent cryptocurrencies. The paper examines Bitcoin, Litecoin, Peercoin, Novacoin to begin with. The researchers have analyzed data on competing crytptocurrencies to ask whether early dynamics can help predict the outcome in the form of pricing. The findings suggest that neither reinforcement effect (the notion that popular currencies attract more users, eventually leading to dominating the whole market), nor substitution effect (the notion that availability of other, better or simply dif- ferentiated cryptocurrencies), dominate the markets. For the period of 2013, the prices of the currencies analysed increase or fall together with the price of bitcoin, therefore implying correlation. Such dynamics are consistent with cryptocurrencies being purchased as financial assets rather than for usage as currency. For the pe- riod of Q2 2014, there is clear reinforcement effect favouring bitcoin. The price of bitcoin increased in USD while the prices of all other currencies declined in USD, thus suggesting strong network effects and a winner-takes-all dynamic.

It is important to note whether bitcoin should be classified as asset or currency in order to streamline the macroeconomic factors to be analyzed. Glaser [20, Glaser (2014)], Zimmerman, Haferkorn, Weber, Siering analyze the question whether users interest in digital currencies is driven by its appeal as currency or as an asset for the period of 2013. Three hypothesis are formulated:

«1. An increase in the number of bitcoin participants is associated with an increase in the bitcoin network volume »

«2. An increase in bitcoin participants is positively associated with an increase in the bitcoin exchange volume »

«3. Prices are not influenced by negative news regarding bitcoin pro- tocol itself but are influenced by positive news regarding the same context »

Main variables used are bitcoin transaction and network volume with negative and positive news playing as dummy variables. Finally, the researchers conclude that exchange users buying bitcoin for the first time are likely to keep these bitcoins in their exchange wallet for speculation purposes and do not have an intention to use as payment method. Furthermore, the conclusion that bitcoin is more of an asset than currency is supported by the fact that bitcoin returns reach to news events related to the currency. The above conclusion provides support for the analysis of supply and demand to treat bitcoin as an asset rather than currency which may or may not be significantly influenced directly by changing macro-economic factors.

4. Methodology

Having explored through existing literature the application of existing ideologies such as Efficient Market Hypothesis, on new-age digital currencies, along with the technological perspective that is offered by Hayes [7, Hayes (2016)] this paper would approach the topic from a perspective of classifying bitcoin between cur- rency and commodity. This assumption is key to the approach that is planned for research.

Considering the first purpose of the research; to find what influences the price of bitcoin, I combine variables from a macro-economic perspective, such as the equity markets, crude oil prices, gold spot prices to name a few, and the impact of changes in Federal Fund rates; and from a micro-economic perspective which examines the demand and supply of bitcoin and its impact on price.

4.1. Microeconomic

First part of the analysis pertains to the microeconomic factors associated with the price of bitcoin. Specifically, two categories of microeconomic factors have been created: Supply category and demand category. Cost per transaction, hash rate, total supply of bitcoins are classified as supply variables, while bitcoin market capitalisation, number of transactions, number of blockchain wallets, and exchange traded volume are classified as demand variables. Time-period of analysis is limited to January 2011 - March 2017.

4.1. MICROECONOMIC

4.1.1. Supply

Cost per transaction: Miner revenue divided by number of transactions.

P ∗R/T

P is the bitcoin price (US Dollar (USD)/BTC), R is the mining rate (BTC/day), and T is the transaction rate (Tx/day)1. It is the total value of the block reward of a block divided by the number of transactions confirmed on the network in that block. It is an essential part of the analysis since the cost per transaction does not directly depend on the market activity, rather, it depends on the value of bitcoin at a given time since the incentive to mine increases with increasing value. As can be seen below, the highest cost per transaction has been recorded on December 30, 2013 at 90 USD, while the lowest has been 0.21 USD on November 16, 2010. At the time of writing this paper, the cost per transaction has been approximately

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Figure 4.1.: Cost per Transaction (source: www.blockchain.info)

4.1. MICROECONOMIC

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Figure 4.2.: Transaction cost vs. bitcoin price (source: Data from

www.blockchain.info, Graph created using excel and normalized data)

It is evident that the price of bitcoin experienced volatility since its creation. While the new concept caused extreme volatility in the early years (2011) for cost per transaction, the year 2013 shows a stochastic trend in the price due to increasing popularity and acceptance and due to negative press related to Mt. Gox scandal, China Central bank banning the currency, peaking in 2014. End of 2013 and the beginning of the year 2014 was tumultuous due to the allegations of money laun- dering and resignation of the Vice Chairman of Bitcoin Foundation, and mostly due to the governmental actions against the Silk Road in 2013. The price of bit- coin dropped 21% within three hours of the closing down of Silk Road. Overall, the variable is an essential part of the analysis of microeconomic factors since the correlation until 2016 and inverse relationship since 2016. This may be due to security issues, volatile value of bitcoin, and the blocks becoming more challenging for miners.

Hash Rate: The estimated number of tera hashes per second (trillions of hashes per second) the Bitcoin network is performing. Hash rates have developed overtime with a positive trend with low volatility associated with value of bitcoin. Overall, hash rates have an increasing positive trend since January 2014 which could be a result of increasing businesses beginning to accept the digital currency. Businesses

4.1. MICROECONOMIC

such as Overstock, Golden Gate Casino and Hotel in Las Vegas, Subway restau- rants, TigerDirect, Dell, etc. were examples of large business showing interest in the digital currency. The first of over-the-counter swap product based on the price of bitcoin was also approved by U.S. Commodity Futures Trading Commis- sion resulting in increased hashes per second and ultimately increased perceived value.

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Figure 4.3.: Hash Rate (source: www.blockchain.info)

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Figure 4.4.: Hashrate vs. bitcoin price (source: Data from www.blockchain.info, Graph created using excel and normalized data)

Total Supply of bitcoin: The total number of bitcoins that have already been mined; in other words, the current supply of bitcoins on the network.

4.1. MICROECONOMIC

Figure 4.5.: Total Supply of bitcoin vs. price of bitcoin (source: Data from

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www.blockchain.info, Graph created using excel and normalized data)

Figure 4.6.: Total Supply of bitcoin (source: www.blockchain.info)

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The graphs above illustrate a steady increase in the total number of bitcoins with a plateau in the most recent times as the algorithms become harder to solve requiring more computing power. Bitcoins are created each time a user discovers a new block. The rate of block creation is adjusted every 2016 blocks to aim for a constant two-week adjustment period (equivalent to 6 per hour.) The number of bitcoins generated per block is set to decrease geometrically, with a 50% reduction every 210,000 blocks, or approximately four years. The result is that the number of bitcoins in existence is not expected to exceed 21 million. This decreasing-supply algorithm was chosen because it approximates the rate at which commodities like

4.1. MICROECONOMIC

gold are mined2.

4.1.2. Demand

Number of Transactions: Total number of transactions carried out during the period analyzed.

Figure 4.7.: Total transactions vs. price of bitcoin (source: Data from

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www.blockchain.info, Graph created using excel and normalized data)

Figure 4.8.: Total number of transactions (source: www.blockchain.info)

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The total number of transactions have shown a positive trend as well. However, while the total supply is increasing at a decreasing marginal rate, the total number

4.1. MICROECONOMIC

of transactions is increasing at an increasing marginal rate thus indicating the pos- itive acceptance of the currency for investment purposes or its purpose as currency.

Number of Wallets: number of blockchain wallet users. Bitcoin wallet: bitcoin wallet address is the public key of a private/public cryptographic key pair. The private/public cryptography is an algorithm in which two mathematically linked separate keys perform complementary functions. The private key, then, is used by the sender to sign the transaction’s details such as currency amount and the receiver’s wallet address (receiver’s public key). The sender’s public key serves the purpose of verification and confirmation when required. Securing a bitcoin wallet consists of storing the private key safely. Bitcoins are effectively owned by whoever can spend them, since the transactions cannot be technically reversed.

Figure 4.9.: Number of wallets (source: www.blockchain.info)

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[...]

Details

Pages
104
Year
2017
ISBN (eBook)
9783668755505
ISBN (Book)
9783668755512
File size
2.3 MB
Language
English
Catalog Number
v432078
Institution / College
Lucerne University of Applied Sciences and Arts – Institute of Finance
Grade
4.5
Tags
Bitcoin Cryptocurrency Ethereum Ripple Banking Finance MSc. Thesis Analysis Macroeconomic factors Microeconomic factors Regression Analysis Correlation Analysis Volatility Analysis Pricing Analysis of Bitcoin

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Title: Influences on the Price of Bitcoin