BACKGROUND: Like Bitcoin or any other cryptocurrencies, non-fungible tokens (NFTs) count on blockchain technology, and NFTs are the latest and the most popular in a series of blockchain solutions. Traders in this ecosystem need to pay a dynamic fee, called a gas fee, for making any transactions on the Ethereum blockchain. The gas fee is measured by gwei, and traders must consider this as an additional cost. So, the current price of this fee may affect the decision of NFT creators or traders. OBJECTIVE: This study investigates the interrelationships between NTFs, cryptocurrencies (Ethereum and BTC), and gas fees using daily market data from January 2019 to November 2021. METHOD: Fourier Shin’s (2016) cointegration test, Fully Modified Ordinary Least Squares, and Group Dynamic Least Squares tests were employed to analyze the data. Then, the variance Decomposition method was applied to determine what other variables explain the percentage of the total variance on NFTs— it also used Impulse-response functions for measuring the response of the NFTs variable for one standard deviation shock. RESULTS: Results show that an increase in gas fees, the daily volume of Bitcoin, and the daily volume of Ethereum decrease NFTs sales. There is a unidirectional relationship between lnSales and lnGasFee variables. Also, there is a determined unidirectional relationship between lnBTC and lnSales variables. Lastly, there is a one-way causality relationship between lnSales and lnETH variables. CONCLUSIONS: The primary causation of the relationship between NFTs, gas fees and Ethereum fees is most likely related to the use of Ethereum as the primary means of payment in the NFTs market and gas fees being a significant cost element in NFTs trading. Another point of view is that the dominance of Bitcoin in the market is very effective in pricing of other cryptocurrencies and in the sales and pricing of NFTs indirectly. It is supported by empirical findings that the main elements in the blockchain ecosystem are interrelated.
<p>The popularity of cryptocurrencies has grown significantly in recent years, and they have become an important asset for internet trading. One of the main drawbacks of cryptocurrencies is the high volatility and fluctuation in value. The value of cryptocurrencies can change rapidly and dramatically, making them a risky investment. Cryptocurrencies are largely unregulated, which can exacerbate their volatility. The high volatility of cryptocurrencies has also led to a speculative bubble, with many investors buying and selling cryptocurrencies based on short-term price fluctuations rather than their underlying values. Therefore, how to reduce the fluctuation risk introduced by exchanges, transform uncertain prices to deterministic value, and promote the benefits of decentralized finance are critical for the future development of cryptos and Web 3.0. </p> <p>To address the issues, this paper proposes a novel theory as Automatic Increase Market Systems (AIMS) for cryptos, which could potentially be designed to automatically adjust the value of a cryptocurrency helping to stabilize the price and increase its value over time in a deterministic manner. We build a crypto, WISH (https://wishbank.wtf), based on AIMS in order to demonstrate how the automatic increase market system would work in practice, and how it would influence the supply of the cryptocurrency in response to market demand and finally make itself to be a stable medium of exchange, ensuring that the AIMS is fair and transparent.</p>
This study provides a volatility estimation based on cross-market spreads by analyzing the behavior of Bitcoin cross-market arbitrageurs. This study crawls real-time price data from different exchanges for empirical analysis and verifies the accuracy and validity of the method employed by comparing it with the existing mainstream methods. The following conclusions are drawn: 1) The more exchanges that can be utilized, the smaller the Bitcoin price volatility, and the larger the cross-market spread, the better the estimation effect of the proposed method; and 2) Volume had no significant effect on the estimation using our method.
The following article explores the correlation between bitcoin and both stocks and gold.A Markov regimeswitching approach was used to identify and date two regimes in each of these financial assets.Stock returns are characterized by short-lived episodes of elevated volatility and negative returns whereas bitcoin returns are characterized by a persistent high volatility state with positive returns.Gold stayed in the low volatility period most of the time and only a few short-lived episodes of high volatility were identified during the first year of the pandemic.A concordance measure was computed to assess the synchronicity and correlation between the regimes.The regimes of bitcoin and gold are uncorrelated suggesting that bitcoin is not yet perceived as a safe haven like gold.The regimes of bitcoin and stocks were also uncorrelated suggesting that bitcoin may be used as a hedge against stocks.
Adrian Moroșan, Oana Oprişan, Eduard Alexandru Stoıca, Cosmin Tileagă
Through our study, we studied the perception of the students of an economic faculty speciality which are at the end of their studies and who will soon become economists, and their attitude towards the cryptocurrencies. Their contacts inside or outside the university led to their professional development because they brought to their attention the widening of the sphere of finance through the prism of a new concept that appeared fifteen years ago, that of cryptocurrency. The main scope of the paper is to understand how students currently relate to cryptocurrencies, after going through all the subjects in the curriculum of their economic specialization. The methodology will involve the use of a structured interview. Important results of our study will be related to the fact that the female students interviewed, who, unlike almost all of the female students, are or say that they will be involved in trading cryptocurrencies in the near future and to the fact that an important part of their information regarding the cryptocurrencies is obtained from outside the faculty. We will recommend, knowing the current situation of the interviewed students, to the teachers who teach various disciplines in the specialization of which the interviewed students are part of that they could try, in the situation where the taught subjects allow it, to offer to the students who will come in the following years additional information about the cryptocurrencies.
In this study, the RiskMetrics method is used to estimate Value at Risk for two exchange rates: BitCoin/dollar and the South African Rand/dollar. Value at Risk is used to compare the riskiness of the two currencies. This is to help South Africans and investors understand the risk they are taking by converting their savings/investments to BitCoin instead of the South African currency, the Rand. The Maximum Likelihood Estimation method is used to estimate the parameters of the models. Seven statistical error distributions, namely Normal Distribution, skewed Normal Distribution, Student’s T-Distribution, skewed Student’s T-Distribution, Generalized Error Distribution, skewed Generalized Error Distribution, and the Generalized Hyperbolic Distributions, were considered when modelling and estimating model parameters. Value at Risk estimates suggest that the BitCoin/dollar return averaging 0.035 and 0.055 per dollar invested at 95% and 99%, respectively, is riskier than the Rand/dollar return averaging 0.012 and 0.019 per dollar invested at 95% and 99%, respectively. Using the Kupiec test, RiskMetrics with Generalized Error Distribution (p &amp;gt; 0.07) and skewed Generalized Error Distribution (p &amp;gt; 0.62) gave the best fitting model in the estimation of Value at Risk for BitCoin/dollar and Rand/dollar, respectively. The RiskMetrics approach seems to perform better at higher than lower confidence levels, as evidenced by higher p-values from backtesting using the Kupiec test at 99% than at 95% levels of significance. These findings are also helpful for risk managers in estimating adequate risk-based capital requirements for the two currencies.
Jason Milionis, Ciamac C. Moallemi, Tim Roughgarden
In decentralized finance ("DeFi"), automated market makers (AMMs) enable traders to programmatically exchange one asset for another. Such trades are enabled by the assets deposited by liquidity providers (LPs). The goal of this paper is to characterize and interpret the optimal (i.e., profit-maximizing) strategy of a monopolist liquidity provider, as a function of that LP's beliefs about asset prices and trader behavior. We introduce a general framework for reasoning about AMMs based on a Bayesian-like belief inference framework, where LPs maintain an asset price estimate. In this model, the market maker (i.e., LP) chooses a demand curve that specifies the quantity of a risky asset to be held at each dollar price. Traders arrive sequentially and submit a price bid that can be interpreted as their estimate of the risky asset price; the AMM responds to this submitted bid with an allocation of the risky asset to the trader, a payment that the trader must pay, and a revised internal estimate for the true asset price. We define an incentive-compatible (IC) AMM as one in which a trader's optimal strategy is to submit its true estimate of the asset price, and characterize the IC AMMs as those with downward-sloping demand curves and payments defined by a formula familiar from Myerson's optimal auction theory. We generalize Myerson's virtual values, and characterize the profit-maximizing IC AMM. The optimal demand curve generally has a jump that can be interpreted as a "bid-ask spread," which we show is caused by a combination of adverse selection risk (dominant when the degree of information asymmetry is large) and monopoly pricing (dominant when asymmetry is small). This work opens up new research directions into the study of automated exchange mechanisms from the lens of optimal auction theory and iterative belief inference, using tools of theoretical computer science in a novel way.
Cryptocurrencies are risky currencies due to their extreme price volatilities and requires an estimation of coherent risk measures for an effective portfolio optimization and risk management. We focus on seven cryptocurrencies (Bitcoin, Ethereum, Litecoin, Ripple, Das, Monero, and Steller) and provide empirical application of Fissler and Ziegel joint loss dynamic models (FZL) for joint Value-at-Risk (VaR) and Expected Shortfall (ES) in a cryptocurrency context at α= 0.01 and α= 0.025 risk levels. Results show Ethereum and Steller as less risky currencies followed by Monero, Das, Litecoin, Bitcoin, and largest for Ripple suggesting that Ethereum and Steller requires the least capital to absorb losses. Following this result, we argue that market participants interested in cryptocurrencies can follow the rankings in this study to hedge, calculate margins, and capital requirement to maximize utility whiles minimizing risk to ensure financial stability in the global economy.
Cryptocurrency is a digital currency which works as a medium of exchange through a computer network. This study aims at modelling the volatility of selected cryptocurrencies by adopting different models of the GARCH family and providing empirical evidence on the fit of conditional volatility. The research is based upon daily U.S. dollar price indexes of Bitcoin (BTC/USD). The data were collected for a time period from October 2021 to April 2022. The every-day price data was further drilled down to arrive at OHLC (open-high-low-close) price for every quarter of the day. The dataset for the analyses were compiled from the website https://cryptowat.ch/ which is an open source and offers free downloadable dataset using Power BI. The first part of the article will focus on the introduction of topic matter concerned. The second part covers around a few literature reviews in the light of volatility in cryptos. The third part will focus on the methodology adopted followed by the results and discussion in the fourth part. Finally, the fifth and last part will provide concluding remarks.
This chapter scrutinizes the dynamic linkages between major cryptocurrencies and fiat currencies of developed and emerging countries. To do this, the authors estimate the Scalar-BEKK GARCH models from September 2017 to January 2022. To shed light on the effects of specific events, the authors also estimate the models for the sub-periods: the great crypto crash, the Covid-19 pandemic, and the vaccination. Empirical results suggest that the time-varying relationships between the crypto- and fiat currencies highly depend on the country- and crypto-specific dynamics. By the decentralized nature of cryptocurrencies, it is not an easy venture to define the stylized facts on those dynamic relationships. The most striking result shows a sharp and massive decline in the conditional covariances between the cryptos and the fiat currencies of developed countries except the Japanese Yen at the onset of the Covid-19 pandemic.
The intensity of volatility persistence is sensitive to time scales, market returns and data regimes. Investors who acquire intangible digital assets in the form of "cryptocurrencies" should consider that they may or may not receive a fiat currency. Sometimes there is a possibility of a loss of the entire investment due to volatility of prices in digital currency/cryptocurrency. Several empirical studies are conducted to measure the volatility behavior of cryptocurrencies using different mathematical models like: i) Autoregressive Distributed Lag (ARDL) Model, ii) Heterogeneous Autoregressive (HAR) Model, iii) Autoregressive Conditional Heteroskedasticity (ARCH) Model, and iv) Generalized Autoregressive Conditional Heteroscedastic (GARCH) Models. This paper focuses on the review of various GARCH Models studied during 1980-2020.
Arthur A. B. Pessa, Matjaž Perc, Haroldo V. Ribeiro
Cryptocurrencies are considered the latest innovation in finance with considerable impact across social, technological, and economic dimensions. This new class of financial assets has also motivated a myriad of scientific investigations focused on understanding their statistical properties, such as the distribution of price returns. However, research so far has only considered Bitcoin or at most a few cryptocurrencies, whilst ignoring that price returns might depend on cryptocurrency age or be influenced by market capitalization. Here, we therefore present a comprehensive investigation of large price variations for more than seven thousand digital currencies and explore whether price returns change with the coming-of-age and growth of the cryptocurrency market. We find that tail distributions of price returns follow power-law functions over the entire history of the considered cryptocurrency portfolio, with typical exponents implying the absence of characteristic scales for price variations in about half of them. Moreover, these tail distributions are asymmetric as positive returns more often display smaller exponents, indicating that large positive price variations are more likely than negative ones. Our results further reveal that changes in the tail exponents are very often simultaneously related to cryptocurrency age and market capitalization or only to age, with only a minority of cryptoassets being affected just by market capitalization or neither of the two quantities. Lastly, we find that the trends in power-law exponents usually point to mixed directions, and that large price variations are likely to become less frequent only in about 28\% of the cryptocurrencies as they age and grow in market capitalization.
In traditional finance, the Black & Scholes model has guided almost 50 years of derivatives pricing, defining a standard to model any volatility-based product. With the rise of Decentralized Finance (DeFi) and constant product Automated Market Makers (AMMs), Liquidity Providers (LPs) are playing an increasingly important role in markets functioning, but, as the recent bear market highlighted, they are exposed to important risks such as Impermanent Loss (IL). In this paper, we tailor the formulas introduced by Black & Scholes to DeFi, proposing a method to calculate the greeks of an LP. We also introduce Impermanent Gain, a product that LPs can use to hedge their position and traders can use to bet on a rise in volatility and benefit from large market moves.
This paper investigates the causes and the consequences of the FTX digital currency exchange’s failure in November 2022. Analysing on-chain data, we report that FTX heavily relied on leveraging and misusing its native token, FTT, and we show how this behaviour exacerbated the company’s fragile financial situation. To gain further insights into the downfall, we employ state-of-the-art network science instruments to model the evolutionary dependency structures of 199 cryptocurrencies on an hourly basis, and we investigate tick-by-tick public trades at the time of the events. We identify the collapse of the Terra-Luna ecosystem as the pivotal event that triggered a significant decrease in the exchange’s liquidity. Results suggest that the crash was actively accelerated by Binance tweets causing a systemic reaction in the cryptocurrency market. Finally, identifying the actors who mostly benefited from the FTX’s collapse and highlighting a generalised trend toward centralisation in the crypto space, we emphasise the importance of genuinely decentralised finance for a transparent, future digital economy.
According to past research utilizing Bitcoin and other cryptocurrencies, Bitcoin has been shown to lead most other cryptocurrencies in terms of price movements. However, existing studies tend to focus on the direction of the lead-lag relationship instead of the duration of the lead-lag time. Furthermore, they are handicapped by the reliance on low-frequency data such as daily prices. This paper showcases the measurement of the lead-lag duration between cryptocurrencies using ultra-high-frequency tick-by-tick data, via the pair of Bitcoin and Cardano. Tick-by-tick data bring unique challenges in terms of methodology. The vast majority of time series econometrics methods are designed for use with data collected at regularly spaced time intervals, such as every hour, every day, etc. Tick-by-tick data, on the other hand, are not synchronized in any way and do not arrive at consistently spaced time intervals. Consequently, an asynchronous data integration methodology is utilized to estimate the Bitcoin price lead over Cardano price for each month beginning in January 2019 and continuing through May 2021. The length of the lead time ranges from 16 seconds to 118 seconds, with an average of around 57 seconds. Throughout the study period, the lengths of the lead time manifest a general trend of decline, which is shown to be statistically significant via non-parametric tests. Testing of seasonal patterns turns out to be not significant. The methodology and the findings of this paper have implications for both academics and practitioners, for example, when studying and implementing statistical arbitrage with cryptocurrencies.
Inferring the heterogeneous connection pattern of a networked system of multivariate time series observations is a key issue. In finance, the topological structure of financial connectedness in a network of assets can be a central tool for risk measurement. Against this, we propose a topological framework for variance decomposition analysis of multivariate time series in time and frequency domains. We build on the network representation of time–frequency generalized forecast error variance decomposition (GFEVD), and design a method to partition its maximal spanning tree into two components: (a) superhighways, i.e. the infinite incipient percolation cluster, for which nodes with high centrality dominate; (b) roads, for which low centrality nodes dominate. We apply our method to study the topology of shock transmission networks across cryptocurrency, carbon emission and energy prices. Results show that the topologies of short and long run shock transmission networks are starkly different, and that superhighways and roads considerably vary over time. We further document increased spillovers across the markets in the aftermath of the COVID-19 outbreak, as well as the absence of strong direct linkages between cryptocurrency and carbon markets.
This paper studies the monthly expiration effect in the bitcoin markets. The emergence of trading in bitcoin futures in regulated markets is an ideal occasion to test this effect on an asset with singular characteristics. Our results with intraday data show that around the time of maturity there are significant changes in the trading volume, volatility and return of bitcoin, an asset that is traded in many exchanges simultaneously. Therefore, there is a clear expiration effect related to bitcoin futures. The closer to the expiration time (shortly beforehand or afterwards), the more intense these effects are. However, in spite of these general results, the expiration effect is not homogeneous across exchanges and depends on the characteristics of the futures contract in question. Robustness tests are also applied to confirm the results. The increasing participation of institutional investors is consistent with our findings, particularly in relation to the expiration effects of cash-settled futures, as these contracts are more appealing for sophisticated investors who could be interested in arbitrage or speculative processes.
L. Thanga Mariappan, J. Arun Pandian, V. Dhilip Kumar, Oana Geman · 6 authors
Cryptocurrency has emerged as a well-known significant component with both economic and financial potential in recent years. Unfortunately, Bitcoin acquisition is not simple, due to uneven business and significant rate fluctuations. Traditional approaches to price forecasting have proven incapable of proving adequate data and solutions because prices can now be forecast in real time. We recommended a machine learning-based alternative for a mortgage lender based on highlighted problems in forecasting the price of Bitcoin. The proposed system included a reinforcement learning algorithm for price estimation and forecasting, as well as a blockchain framework for an efficient and secure environment. The proposed prediction, compared to other state-of-the-art strategies in this sector, demonstrated better performance. In this system, the proposed prediction reached improved consistency, in comparison to other systems, with respect to Monero (XMR), Litecoin (LTC), Oryen (ORY), and Bitcoin (BTC).
Marcin Wątorek, Jarosław Kwapień, Stanisław Drożdż
In this study the cross-correlations between the cryptocurrency market represented by the two most liquid and highest-capitalized cryptocurrencies: bitcoin and ethereum, on the one side, and the instruments representing the traditional financial markets: stock indices, Forex, commodities, on the other side, are measured in the period: January 2020--October 2022. Our purpose is to address the question whether the cryptocurrency market still preserves its autonomy with respect to the traditional financial markets or it has already aligned with them in expense of its independence. We are motivated by the fact that some previous related studies gave mixed results. By calculating the $q$-dependent detrended cross-correlation coefficient based on the high frequency 10 s data in the rolling window, the dependence on various time scales, different fluctuation magnitudes, and different market periods are examined. There is a strong indication that the dynamics of the bitcoin and ethereum price changes since the March 2020 Covid-19 panic is no longer independent. Instead, it is related to the dynamics of the traditional financial markets, which is especially evident now in 2022, when the bitcoin and ethereum coupling to the US tech stocks is observed during the market bear phase. It is also worth emphasizing that the cryptocurrencies have begun to react to the economic data such as the Consumer Price Index readings in a similar way as traditional instruments. Such a spontaneous coupling of the so far independent degrees of freedom can be interpreted as a kind of phase transition that resembles the collective phenomena typical for the complex systems. Our results indicate that the cryptocurrencies cannot be considered as a safe haven for the financial investments.
In the post-pandemic era, two issues including the currency competition between BTC and the US dollar and the competition between the commodity and monetary medium functions of BTC are critical. By applying the Markov switching model, the cyclical nature of the numbers of additional confirmed COVID-19 cases and deaths are verified on daily basis. So, these two factors are assumed to follow the Ornstein–Uhlenbeck process. Then, we estimate parameters to establish the structural characteristics of post-pandemic era and the start of post-pandemic era. In order to clarify these two issues, we use vector autoregression for testing the related macrocosmic and financial variables and BTC. Systematic evidences are provided regarding the relationships among BTC, related macrocosmic, related financial variables, related COVID-19 variables. Our findings provide a useful insight into currency competition and commodity competition on the basis of the impulse response of BTC to US dollar fluctuation and the impulse response of BTC to expected inflation and volatility in the post-pandemic era. These findings indicate increased currency competition between Bitcoin and the US dollar in the post-pandemic era. Therefore, currency competition should be more valued than Commodity Competition in the post-pandemic era. This provides a useful guideline for Bitcoin’s management.
In Bitcoin blockchain, miner nodes are likely to choose transactions with high fee to be included in a block. This makes transactions with high fee being processed fast, affecting the amount of transaction fee that users want to pay. The reward for a winning miner consists of transaction fee and newly issued coins, and hence the amount of newly issued coins also affects the miner decision to participate in the mining competition. In addition, mining reward also affects the total hash computing power, which plays an important role of Bitcoin security for reducing the success probability of security attack by a malicious miner. In this paper, we develop a mathematical model for analyzing the interaction between miner decision making and user actions in terms of transaction fees, transaction-confirmation time, and security. We analyze the transaction-inclusion process with queueing theory, while decision making processes of miners and users are analyzed in the context of Nash equilibrium. The numerical examples show how the mining costs and newly issued coins affect miner decision making.
António Portugal Duarte, Fátima Sol Murta, Nuno Baetas da Silva, Beatriz Rodrigues Vieira
This paper analysis and compares the volatility of seven cryptocurrencies – Bitcoin, Dogecoin, Ethereum, BitcoinCash, Ripple, Stellar and Litecoin – to the volatility of seven centralized currencies – Yuan, Yen, Canadian Dollar, Brazilian Real, Swiss Franc, Euro and British Pound. We estimate GARCH models to analyze their volatility. The results point to a considerably high volatility of cryptocurrencies when compared to that of centralized currencies. Therefore, we conclude that cryptocurrencies still fall far short of fulfilling all the requirements to be considered as a currency, specifically regarding the functions of store of value and unit of account.