This paper discusses the economics of how Bitcoin achieves data immutability, and thus payment finality, via costly computations, i.e., "proof-of-work." Further, it explores what the future might hold for cryptocurrencies modelled on this type of consensus algorithm. The conclusions are, first, that Bitcoin counterfeiting via "double-spending" attacks is inherently profitable, making payment finality based on proof-of-work extremely expensive. Second, the transaction market cannot generate an adequate level of "mining" income via fees as users free-ride on the fees of other transactions in a block and in the subsequent blockchain. Instead, newly minted bitcoins, known as block rewards, have made up the bulk of mining income to date. Looking ahead, these two limitations imply that liquidity is set to fall dramatically as these block rewards are phased out. Simple calculations suggest that once block rewards are zero, it could take months before a Bitcoin payment is final, unless new technologies are deployed to speed up payment finality. Second-layer solutions such as the Lightning Network might help, but the only fundamental remedy would be to depart from proof-of-work, which would probably require some form of social coordination or institutionalisation.
We incorporate skewness and kurtosis into an optimization process for a unique student-managed fund. Unlike the vast majority of such funds, which hold only equity, our fund includes REITs, cryptocurrency, and peer-to-peer loans. Adding these unusual asset classes allows our students to explore portfolio management concepts more generalizable than just picking stocks. While most of our assets cannot be recommended based solely on traditional mean-variance analysis, they nonetheless offer beneficial contributions. Using polynomial goal programming to incorporate higher moments in our optimization, we find that asset classes dominated in mean-variance space can make meaningful contributions to the full risk-return profile of the portfolio. In particular, we find that including cryptocurrency and peer-to-peer loans can increase the skewness and decrease the kurtosis of our portfolio.
This paper studies the blockchain cryptographic tokens by means of mean-field-type game theory. It introduces the variance-aware utility function per decision-maker to capture the risk of cryptographic tokens associated with the uncertainties of technology adoption, network security, regulatory legislation, and market volatility. We establish a relationship between the network characteristics, token price, number of token holders, and token supply. Both in-chain diversification and cross-chain diversification among tokens are examined by using a mean-variance approach. The results suggest that the number of tokens in circulation needs to be adjusted in order to capture risk-awareness and self-regulatory behavior in blockchain token economics. The Sharpe and Modigliani ratios for cryptographic tokens are revisited.
We suggest that flexible majority rules for currency issuance decisions foster the stability of a cryptocurrency. With flexible majority rules, the voteshare needed to approve a particular currency issuance growth is increasing with this growth rate. By choosing suitable parameters for these flexible majority rules, we show that optimal growth rates can be achieved in simple settings. Moreover, with flexible majority rules, changes in the composition of growth-friendly and growth-adverse agents only have a comparatively moderate impact on growth rates, and extreme growth rates are avoided. Finally, we show that optimal money growth rates are realized if agents entering financial contracts anticipate ensuing inflation rates determined by these flexible majority rules.
This dissertation includes three chapters. The first chapter investigates the impact of the Federal Reserve’s balance sheet\nnormalization using a Bayesian vector autoregression (BVAR) framework. I use counterfactual conditional forecasts to find that a reduction in asset holdings down to a level where the federal funds market is active again will reducereal GDP growth by an average of 0.18 percent per year and core inflation by a non-significant average of 0.07 percent per year under Quantitative Tightening, relative to a scenario where the Federal Reserve maintains a constant dollar amount of assets until 2024.\nThe second chapter models monetary policy using Taylor’s rule for the nominal interest-rate target and examines the difference between the actual Federal Funds Rate and the Taylor Rule model of behavior for distinct structural changes. Both a simple factor ANOVA and regime switching methods find that there were “tight” or “loose” regimes in U.S. monetary policy over the period 1965 to 2008. However, after accounting for the change in inflation measurement from CPI to PCE and then core PCE after 2004, Alan Greenspan’s tenure from 2003 to 2006 is consistent with his earlier symmetric deviations from the Taylor Rule.\nThe final chapter examines the volatility of Bitcoin exchange rates which have gained a great deal of attention since the creation of the currency. Standard measures of volatility reflect the dramatic change in the Bitcoin/US dollar exchange rate, from about $0.05 USD in 2010 to the neighborhood of $20,000 USD at the end of 2017, and down to around $5,000 USD in mid-2019. Characterizing the short-term and long-term volatility gives an impression of the volatility of Bitcoin compared to other assets, as well as implying the viability\nof Bitcoin as a medium of exchange and alternative asset.
Ingolf Gunnar Anton Pernice, Georg Gentzen, Hermann Elendner
The velocity of money is central to the quantity theory of money, which relates it to the general price level. While the theory motivated countless empirical studies to include velocity as price determinant, few find a significant relationship in the short or medium run. Since the velocity of money is generally unobservable, these studies were limited to using proxy variables, leaving it unclear whether the lacking relationship refutes the theory or the proxies. Cryptocurrencies on public blockchains, however, visibly record all transactions, and thus allow one to measure-rather than approximate -velocity. This paper evaluates most suggested proxies for velocity and also proposes a novel measurement approach. We introduce velocity measures for UTXO-based cryptocurrencies, focused on the subset of the money supply effectively in use for the processing of transactions. Our approach thus explicitly addresses the hybrid use of cryptocurrencies as media of exchange and as stores of value, a major distinction in recently-proposed theoretical pricing models. We show that each of the velocity estimators is approximated best by the simple ratio of on-chain transaction volume to total coin supply. Moreover, "coin days destroyed," if used as an approximation for velocity, shows considerable discrepancy from the other approaches.
Economic preferences and personality traits are fundamental explanatory factors in understanding individual decision-making. They explain the heterogeneity within human behavior and are the reason why individuals differ in their actions although the preliminaries are the same. Labor market behavior, educational choices, investment decisions as well as fertility and health outcomes are only a few examples in which inherent characteristics play a key role. These findings rely on one joint assumption: preferences and personality traits do not change across the working age. The point in time when preferences are defined and measured is thus irrelevant. However, if this assumption is violated, theoretical models and empirical studies face the threat of endogeneity biases: preferences do not only affect life's outcomes, life's outcomes may also affect preferences. Testing the exogeneity assumption is thus obligatory. Herein, the present thesis makes its contribution and presents three different studies on the stability of economic preferences and personality traits. The first study in this thesis focuses on the stability of time preferences. So far, evidence on their stability is scarce and considerably restricted by very short time frames, very small sample sizes, or both. The Dutch Household Survey enables these obstacles to be circumvented and the long-term stability of time preferences within a representative sample to be analyzed. By relying on the `consideration of future consequences’ scale -- a behaviorally validated survey measure on time preferences -- this thesis finds that time preferences have, compared to other economic attitudes, a relatively low intra-individual stability. However, the analysis reveals that individuals' valuation of future utility neither varies with age nor changes persistently with past life experiences. Similar findings result from a replication of the analysis with the German Socio-Economic Panel and its ultra-short survey items on patience and impulsiveness. The thesis, therefore, comes to the conclusion that time preferences are stable in the long run but subjected to measurement issues. The second study focuses on the determinants of risk-taking. Using German panel data, we find that people become more risk-averse when losing work. The immediate income loss does not mediate this effect. Risk aversion also seems unrelated to the loss of non-monetary benefits of work. However, the study finds that risk aversion responds more strongly to losing work the more future income is at stake, and the effect manifests itself on the eve of job loss even when people have not yet suffered from the consequences of the event. Lower future income expectations and more uncertainty about future incomes may thus explain the effect of job loss on risk attitude. Nevertheless, the effect is not persistent. After some time, individuals turn back to their initial level of risk attitude. The last chapter of this thesis tests the stability of locus of control, a measure that depicts how much people believe in their ability to affect life outcomes. Using the German Socio-Economic Panel, we find that a job loss due to a plant closure has no long-lasting effect on locus of control. The common assumption of its stability is thus not rejected. However, during unemployment, control perception decreases significantly. The effect holds true independent from unemployment duration or socio-demographic characteristics and vanishes as soon as the unemployed find a new job. In conclusion, measurement of locus of control is affected by unemployment but not the trait itself. Using this trait as the explanatory variable can thus lead to biased estimations if this temporary deviation in measurement is not accounted for. In conclusion, the present thesis neither rejects the stability assumption nor claims that preferences or personality are perfectly stable. All measures analyzed change with time. But, interpreting this instability as proof of endogenous preferences or personality traits appears unjustified. Each of the studies proposes alternative, less controversial interpretations of instability.
This project uses software development to investigate the link between software and finance. The focus of the work is developing and implementing a trading algorithm which seeks to make profit by making trades based on arbitrage opportunities between currencies. Specifically, the sets of currencies examined are two fiat currencies and one cryptocurrency. Trades are made by combining a blockchain system, which maintains the cryptocurrency, and the live foreign exchange market, which enables fiat currency exchange. The main methodologies for carrying out the research are test-driven development and the use of a simulation to facilitate trades. By passing all of the unit tests, the software is verified. In addition, data gathered during runs of the simulation show that the algorithm successfully identifies arbitrage opportunities and turns a profit on average over many runs. This project proposes an interesting topic for further research in the field of blockchain technology used for financial trading.
We study cryptocurrency in a monetary economy with imperfect information. The network imperfection provides traders opportunities to engage in double spending fraud, but the trackability of transaction messages allows us to impose proof-of-work (PoW), proof-of-stake (PoS), and currency exclusion to mitigate fraud incentives. However, PoW consumes energy, and PoS requires extra cryptocurrency to be held as deposits, so deterring fraud may not be optimal. We find that forks can serve as signals to detect double spending fraud and to trigger punishments. If the probability is high that forks appear under double spending, imposing PoW and PoS to deter fraud is optimal; otherwise, it is optimal to save the cost but allow for double spending. Finally, by endogenizing the incentives to double spend and the size of PoW and PoS, we show that cryptocurrency economy can achieve efficient allocation as the imperfectness of the internet is sufficiently low.
We utilize optimization methods to determine equilibria of cryptocurrencies. A core group, the wealthy, fears the loss of assets that can be seized by a government. Volatility may be influenced by speculators. The wealthy must divide their assets between the home currency and the cryptocurrency, while the government decides the probability of seizing a fraction the assets of this group. We establish conditions for existence and uniqueness of Nash equilibria. Also examined is the separate timescale problem in which the government policy cannot be reversed, while the wealthy can adjust their allocation in reaction to the government's designation of probability.
We develop a general equilibrium model of cryptocurrency to study a double spending prevention mechanism without payment confirmations. Agents trade cryptocurrency using a digital wallet, and the cryptocurrency system provides a means to verify a wallet's double spending history. A digital wallet may obtain a good reputation for no double spending attempts based on its transaction history. If a buyer makes a payment with a digital wallet that does not have a good reputation, sellers provide goods after payment confirmations in the blockchain to prevent a double spending attack. On the other hand, sellers deliver goods immediately without payment confirmations if the payment is made through a digital wallet with a good reputation as long as the cost of losing a good reputation outweighs the short-run gain from double spending. As the time required for each confirmation increases, the utility loss from delayed delivery of goods increases so double spending incentives decrease.
I present a model of cryptocurrency price formation that endogenizes both the financial market for coins and the fee-based market for blockchain space. A cryptocurrency has two distinctive features: a price determined by the extent of its usage as money, and a blockchain structure that restricts settlement capacity. Limited settlement space creates competition between users of the currency, so speculative activity can crowd out monetary usage. This crowding-out undermines the ability of a cryptocurrency to act as a medium of payment, lowering its value. Higher speculative demand can reduce prices, contrary to standard economic models. Crowding-out also raises the riskiness of investing in cryptocurrency, explaining high observed price volatility.
The objective of this paper is to simulate the trading of the currency pair BTC/USD, investigating through the theory of the genetic algorithms the best sets of trading strategies, simulating through a realistic order book the bitcoin price formation, and reproducing a bitcoin price series that exhibits some stylized facts found in real-time price series. In this artificial market model two kinds of agents, Chartists and Random traders, perform trading. Chartists trade through the application of trading rules. Specifically, a part of Chartists trades applying the best sets of trading rules selected by a genetic algorithm that simulates a trading system, based on four technical analysis indicators, searching for parameters of each indicator that guarantee the highest profits in the training period; the remaining part trades applying trading rules choosing their parameters in a random way. On the contrary random trader's trade without applying any trading strategy, issuing in a random way sell or buy orders. Results show that the best sets of rules found to guarantee the highest profits both in the training and in the testing periods, and perform well also in the artificial market model where the Chartists who adopt the best sets of trading rules are able to achieve higher profits.