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.
This paper considers the development of attractive strategies featuring cryptocurrency assets, considering their costs and potential risks. The object of analysis in this paper is cryptocurrency as an investment instrument. The main hypothesis of the research is that modern portfolio theory can be applied to cryptocurrency investments to design an investment portfolio with appropriate risk and profitability characteristics. The authors of the paper: (i) place cryptocurrencies in the context of modern financial market and financial technology development;
This paper is motivated by a hypothesis that the long term value of a cryptocurrency is determined by its future use as money. For a cryptocurrency to be used as a medium of payment, it has to fulfill three independent functions: medium of exchange, a unit of account, and store of value. Currently, cryptocurrencies are held for investment purposes rather than being used for transactions and thus as a medium of exchange. For cryptocurrency to become widely adopted as a means of payment, it first needs to go through a very volatile period because speculative traders see long-run future value in the cryptocurrency. In order to soften transition from speculative asset to medium of payment a trading strategy is proposed, which provides liquidity and reduces volatility. Similar to pairs trading strategy, the proposed solution is based on cointegration and performed in three steps. The main difference is that proposed solution does not include shorting, but holding cryptocurrencies, thus increasing the total available cash and adding to the equilibrium price. Results from an ongoing experiment suggest that the proposed trading strategy is appealing for about 40% of cryptocurrency investors, as the struggle against volatility problem is accompanied by significant financial gains.
Moinak Maiti, Darko Vuković, Victor Krakovich, Maneesh Kumar Pandey
The present study focuses on five cryptocurrencies co-movements physiognomies both in time and frequency domain. The present study highlighted several interesting facts related to cryptocurrencies co-movements both in time and frequency domain that have high policy and investment implications. Overall wavelet coherence diagrams clearly indicate about the very short and long contagion effect among the cryptocurrency pairs for the whole study period. The contagion effect is different at different time scales. Finally wavelet clustering diagram indicates that by investing only in XBP and BitCoin cryptocurrencies investors are not going to get any benefit from diversification. This predictable co-movements pattern among the cryptocurrencies could be the basic investment strategies to gain maximum profit by diversifying the risk in cryptocurrency investments.
Realized volatility (RV) is defined as the sum of the squares of logarithmic returns on high-frequency sampling grid and aggregated over a certain time interval, typically a trading day in finance. It is not a priori clear what the aggregation period should be in case of continuously traded cryptocurrencies at online exchanges. In this work, we aggregate RV values using minute-sampled Bitcoin returns over 3-h intervals. Next, using the RV time series, we predict the future values based on the past samples using a plethora of machine learning methods, ANN (MLP, GRU, LSTM), SVM, and Ridge Regression, which are compared to the Heterogeneous Auto-Regressive Realized Volatility (HARRV) model with optimized lag parameters. It is shown that Ridge Regression performs the best, which supports the auto-regressive dynamics postulated by HARRV model. Mean Squared Error values by the neural-network based methods closely follow, whereas the SVM shows the worst performance. The present benchmarks can be used for dynamic risk hedging in algorithmic trading at cryptocurrency markets.
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.
A total of 1.1 million bitcoins were stolen in the 2013–2017 period. Noting that the average price for a Bitcoin in 2018 was $7572 the corresponding monetary equivalent of losses is $8.9 billion highlighting the societal impact of this criminal activity. Investigating the response of the uncertainty of Bitcoin returns when hacking incidents occur, the results of this study point toward two different responses. After experiencing a contemporaneous effect at day t=0, the volatility increases significantly again at day t+5. Hacking incidents that occur in the Bitcoin market also affect the uncertainty in the Ethereum market with a time delay of five days. Notably, neither Bitcoin nor Ethereum appear to exhibit asymmetric responses to negative innovations.
Abstract We study Bitcoin (BTC) trading at the Chicago Mercantile Exchange (CME) and four settlement spot exchanges that transact $146 million per day in the BTC/USD pair. Spot market median trade sizes are under $1,300 but exceed $18,000 on the CME. Bid‐ask spreads average 0.0298%. Trade sizes of over $1 million move markets by less than 1%. 2.5% of trades and 15.5% of cancellations on Coinbase take place within 50 ms. Bid‐ask spreads exceed 0.8% for only 226 s. Most executions trade‐through better quotes, with estimated losses of $36 million. The CME leads price discovery. BTC leads Ethereum price adjustment.
The emergence of Bitcoin (BTC) has triggered intense discussions. Despite the particular interest of the public, the theoretical understanding of the value of this crypto currency is limited. This is why current research is trying to find better leads to evaluate a complex phenomenon: the BTC price. The volatility of its price presents a certain specificity compared to the traditional currencies. In order to understand the reasons for this volatility, we try to identify and to analyze the main determinants of the BTC price and to estimate their influence. We apply time series to daily data for the period from 19/12/2011 to 06/02/2018. We used several approaches, including the Auto Regressive Distributed Lag ARDL model, the cointegration test at Pesaran et al. (2001) and the Granger causality test in the sense of Toda and Yamamoto (1995). Our estimated results suggest that the number of addresses, the attractiveness indicator and the mining difficulty have a significant impact on the BTC price with variations over time. On the other hand, the transaction volume, the stock, the EUR/USD exchange rate and the macroeconomic and financial development do not determine the price of the BTC in the short term as well as in the long term.
The Lightning Network is a so-called second-layer technology built on top of the Bitcoin blockchain to provide "off-chain" fast payment channels between users, which means that not all transactions are settled and stored on the main blockchain. In this paper, we model the emergence of the Lightning Network as a (bond) percolation process and we explore how the distributional properties of the volume and size of transactions per user may impact its feasibility. The agents are all able to reciprocally transfer Bitcoins using the main blockchain and also - if economically convenient - to open a channel on the Lightning Network and transact "off chain". We base our approach on fitness-dependent network models: as in real life, a Lightning channel is opened with a probability that depends on the "fitness" of the concurring nodes, which in turn depends on wealth and volume of transactions. The emergence of a connected component is studied numerically and analytically as a function of the parameters, and the phase transition separating regions in the phase space where the Lightning Network is sustainable or not is elucidated. We characterize the phase diagram determining the minimal volume of transactions that would make the Lightning Network sustainable for a given level of fees or, alternatively, the maximal cost the Lightning ecosystem may impose for a given average volume of transactions. The model includes parameters that could be in principle estimated from publicly available data once the evolution of the Lighting Network will have reached a stationary operable state, and is fairly robust against different choices of the distributions of parameters and fitness kernels.
This paper studies the behaviour of Bitcoin returns at different sample frequencies. We consider high frequency returns starting from tick-by-tick price changes traded at the Bitstamp and Coinbase exchanges. We find evidence of a smooth intra-daily seasonality pattern, and an abnormal trade- and volatility intensity at Thursdays and Fridays. We find no predictability for Bitcoin returns at or above one day, though, we find predictability for sample frequencies up to 6 h. Predictability of Bitcoin returns is also found to be time–varying. We also study the behaviour of the realized volatility of Bitcoin. We document a remarkable high percentage of jumps above 80 % . We also find that realized volatility exhibits: (i) long memory; (ii) leverage effect; and (iii) no impact from lagged jumps. A forecast study shows that: (i) Bitcoin volatility has become more easy to predict after 2017; (ii) including a leverage component helps in volatility prediction; and (iii) prediction accuracy depends on the length of the forecast horizon.
Carlos Trucíos, Aviral Kumar Tiwari, Faisal Alqahtani
Risk management is an important and helpful process for investors, hedge funds, traders and market makers. One of its key points is the appropriate estimation of risk measures which can improve the investment decisions and trading strategies. The high volatility of cryptocurrencies turns them a really risky investment and consequently, appropriate risk measures estimation is extremely necessary. In this article, we deal with the estimation of two widely used risk measures such as Value-at-Risk and Expected Shortfall in a cryptocurrency context. To face the presence of outliers and the correlation between cryptocurrencies, we propose a methodology based on vine copulas and robust volatility models. Our procedure is illustrated in a seven-dimensional equal-weight cryptocurrency portfolio and displays good performance.
Canh Phuc Nguyen, Nguyen Quang Binh, Thanh Dinh Su
The study examines the diversification capability of seven cryptocurrencies with the largest market size against risks from economic factors as oil price, gold price, interest rate, USD strength, and S&P500. Using the weekly data of Bitcoin, Litecoin, Ripple, Stellar, Monero, Dash, and Bytecoin in the period Aug/2014-Jun/2018, the study finds that there are structural breaks and ARCH disturbance in each cryptocurrency, suggesting a systematic risk within the cryptocurrency market. However, the causality between cryptocurrencies and economic factors is undirected. Interestingly, our findings show that cryptocurrencies are insignificant correlations with economic factors. The result implies that cryptocurrencies can not be assumed as financial assets to hedge systematic risks from economic factors.
Vasily Derbentsev, Natalia Datsenko, Olga Stepanenko, Vitaly Bezkorovainyi
This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).
Cryptocurrencies lack clear measures of fundamental values and are often associated with speculative bubbles. This paper introduces a new way of testing for speculative bubbles based on StockTwits sentiment, which is used as the transition variable in a smooth transition autoregression. The model allows for conditional heteroskedasticity and fat tails of the conditional distribution of the error term, and volatility may depend on the constructed sentiment index. We apply the model to the CRIX index, for which several bubble periods are identified. The detected locally explosive price dynamics, given the specified bubble regime controlled by a smooth transition function, are more akin to the notion of speculative bubble that is driven by exuberant sentiment. Furthermore, we find that volatility increases as the sentiment index decreases, which is analogous to the commonly called leverage effect.
Do the rich always get richer by investing in a cryptocurrency for which new coins are issued according to a proof-of-stake (PoS) protocol? We answer this question in the negative: Without trading, the investor shares in the cryptocurrency are martingales that converge to a well-defined limiting distribution and, hence, are stable in the long run. This result is robust to allowing trading when investors are risk neutral. Then, investors have no incentive to accumulate coins and gamble on the PoS protocol but weakly prefer not to trade. This paper was accepted by Kay Giesecke, finance.
ABSTRACT We find that three factors—cryptocurrency market, size, and momentum—capture the cross‐sectional expected cryptocurrency returns. We consider a comprehensive list of price‐ and market‐related return predictors in the stock market and construct their cryptocurrency counterparts. Ten cryptocurrency characteristics form successful long‐short strategies that generate sizable and statistically significant excess returns, and we show that all of these strategies are accounted for by the cryptocurrency three‐factor model. Lastly, we examine potential underlying mechanisms of the cryptocurrency size and momentum effects.