Alim Al Ayub Ahmed, Harish Paruchuri, Siddhartha Vadlamudi, Apoorva Ganapathy
Digital finance is assuming a significant part in the arrangement of financial services all over the world. Fast growth with digitalization, data analysis, and computing capacities allows for a whole new scope of financial services and transactions. This financial development empowered by digital financial technology (Fintech) has pulled in a ton of attention, as it could offer some potential for economic growth and development. As a part of the Fintech environment, cryptography has started to grow quickly and digital assets are acquiring in favorability among financial bankers and investors. Human behavior as they engage with financial activities is personally associated with the noticed market elements. However, with many existing theories and studies on the fundamental motivations of the conduct of people in financial frameworks, there is still restricted experimental derivation of the behavioral conduct of the financial agents from a definite market analysis. Cryptocurrency technology has given a map to this analysis with its voluminous data and its transparency of financial transactions. It has empowered us to perform inference on the personal conduct standards of users in the market, which we analyze in the bitcoin and ethereum cryptocurrency markets. In our study, we initially decide different properties of the cryptography users by complex network analysis. Financial cryptography is a difficult subject that necessitates abilities from a variety of seemingly unrelated fields. There is a serious risk that attempts to establish Financial Cryptography frameworks would simplify or omit key disciplines because they are caught between central banking and cryptography. This paper discusses research that attempts to limit the scope of Financial Cryptography. This model should assist the project, administrative, and requirements personnel by classifying each discipline into a seven-layer model of basic nature, where the link between each adjoining layer is evident. While this model is shown as effective, all models have cutoff points. This one does not present a design system or a protocol agenda. Furthermore, given the model's initial adaptation and the field, it should be viewed as a suggestion of complexity rather than a definitive approach.
Jan A. Fischer, Andres Palechor, Daniele DellâAglio, Abraham Bernstein · 5 authors
Bitcoin is built on a blockchain, an immutable decentralised ledger that allows entities (users) to exchange Bitcoins in a pseudonymous manner. Bitcoins are associated with alpha-numeric addresses and are transferred via transactions. Each transaction is composed of a set of input addresses (associated with unspent outputs received from previous transactions) and a set of output addresses (to which Bitcoins are transferred). Despite Bitcoin was designed with anonymity in mind, different heuristic approaches exist to detect which addresses in a specific transaction belong to the same entity. By applying these heuristics, we build an Address Correspondence Network: in this representation, addresses are nodes are connected with edges if at least one heuristic detects them as belonging to the same entity. %addresses are nodes and edges are drawn between addresses detected as belonging to the same entity by at least one heuristic. %nodes represent addresses and edges model the likelihood that two nodes belong to the same entity %In this network, connected components represent sets of addresses controlled by the same entity. In this paper, we analyse for the first time the Address Correspondence Network and show it is characterised by a complex topology, signalled by a broad, skewed degree distribution and a power-law component size distribution. Using a large-scale dataset of addresses for which the controlling entities are known, we show that a combination of external data coupled with standard community detection algorithms can reliably identify entities. The complex nature of the Address Correspondence Network reveals that usage patterns of individual entities create statistical regularities; and that these regularities can be leveraged to more accurately identify entities and gain a deeper understanding of the Bitcoin economy as a whole.
This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods. Two extensions are offered to compare the performance of the linear specification of the market model (LMM), which allows for the measurement of the cryptocurrency price beta risk. The first is the generalized additive model, which permits flexibility in the rigid shape of the linearity of the LMM. The second is the time-varying linearity specification of the LMM (Tv-LMM), which is based on the state space model form via the Kalman filter, allowing for the measurement of the time-varying beta risk of the cryptocurrency price. The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization, using the Crypto Currency Index 30 (CCI30) as a market proxy and 1-day and 7-day forward predictions. Such a comparison of cryptocurrency prices has yet to be undertaken in the literature. The empirical findings favor the Tv-LMM, which outperforms the others in terms of modeling and forecasting performance. This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear, especially during the COVID-19 period.
In this study, we study the price dynamics of cryptocurrencies using adaptive complementary ensemble empirical mode decomposition (ACE-EMD) and Hilbert spectral analysis. This is a multiscale noise-assisted approach that decomposes any time series into a number of intrinsic mode functions, along with the corresponding instantaneous amplitudes and instantaneous frequencies. The decomposition is adaptive to the time-varying volatility of each cryptocurrency price evolution. Different combinations of modes allow us to reconstruct the time series using components of different timescales. We then apply Hilbert spectral analysis to define and compute the instantaneous energy-frequency spectrum of each cryptocurrency to illustrate the properties of various timescales embedded in the original time series.
AI and data driven solutions have been applied to different fields and achieved outperforming and promising results. In this research work we apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers for detecting the trend problem of three cryptocurrency markets. We use these classifiers to design a strategy to trade in those markets. Our input data in the experiments include price data with and without technical indicators in separate tests to see the effect of using them. Our test results on unseen data are very promising and show a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest profit factor for an unseen 66 day span is 1.60. We also discuss limitations of these approaches and their potential impact on Efficient Market Hypothesis.
This paper sets out to explore the nexus between economic policy uncertainty (EPU) and digital currencies. An integrated survey takes place based on eleven primary studies. Furthermore, an econometric analysis is conducted by the threshold ARCH, simple asymmetric ARCH and non-linear ARCH specifications covering the bull and the bear markets as well as the highly volatile period up to the present. Threshold ARCH is found to provide the best fit for estimations. Outcomes reveal that Bitcoin is strongly connected with EPU while Ethereum and Litecoin are not but are strongly linked with Bitcoin performance. Moreover, weak negative effects of the VIX on both cryptocurrencies are detected while oil exerts weak positive impacts on Ethereum. Overall, Ethereum and Litecoin could serve for diversifiers against Bitcoin or hedgers against traditional assets during highly stressed periods with the advantage of not being affected by economic policy uncertainty news.
In recent years blockchain consensus mechanisms based on Proof of Stake gained increasing attention as an alternative to Proof of Work, which requires high energy consumption. In its original version Proof of Stake hinges on the idea that, for a user, the likelihood to confirm the next block is positively related to the amount of currency units held in the wallet, and possibly also on the time length which the money has been unspent for. In a simple framework with risk neutral users we provide some early insights on the monetary equilibrium of Proof of Stake based platforms. In particular, we find that the aggregate demand and supply of currency may not coincide, which implies that users could hold suboptimal quantities of the currency. Furthermore, we also discuss how symmetric stationary states of the system could be implausible. As a consequence, a long run uniform distribution of money would seem unlikely unless appropriate measures are introduced.
Purpose: The purpose of this research is to analyze the price movements of bitcoin, which has become a new phenomenon in financial markets since 2009, the first year of its release, and can be defined as virtual money or crypto money, to be seen as a financial investment tool. Design/Methodology: In the study, volatility, return behavior and reliability as a financial investment tool are examined with autoregressive Conditional Variable Variance modeling. In this context, symmetrical and asymmetrical ARCH models were used. Findings: As a result of the analysis; it has been found that it has an asymmetric effect in the first period for the bitcoin return series examined with symmetric and asymmetric ARCH models. In addition, it has been determined that shocks occurring in the bitcoin return series according to the half-life criteria are exposed to the volatility effect for more than 30 days in each period. It has been determined that bitcoin, which is examined by periods, has higher volatility in its first years. Limitations: The volatility of bitcoin, which has become a new phenomenon in financial markets today, can be defined as virtual money or crypto money, has been analyzed. Originality/Value: In fact, there are many virtual currencies or cryptocurrencies traded in the market. However, among many virtual currencies, bitcoin is the most known and the most market volume. Analyzing the price movements of bitcoin, which has started to be seen as a financial investment tool, is of great importance in the framework of reliability. The examination made in this respect constitutes the original value of the research.
Abstract Bitcoin has become a commodity traded by millions of traders from all over the world. This is one of the causes of fluctuating price movements. From the data we got on coinmarketcap, Bitcoin is traded on various cryptocurrency trading exchanges. And at every exchange that has a reputation, of course, has an API service to access historical data about the price movements of all the crypto commodities they have traded from the start. By using the PHP programming language and implementation of the CURL function for JSON readings, we can pull Bitcoin movement data in real time. In this paper, Bitcoin is specifically observed because it is the forerunner and the main cryptocurrency commodity traded and is a determinant of Alternative coin price movements in general. In this paper Bitcoin price monitoring is carried out at 30 reputable exchange places through API access provided by each exchange place. Furthermore, conclusions are drawn about the various variants of how to access the API from the 30 bitcoin exchange places. The program code that is displayed directly in this paper can then be used as an initial reference if you want to develop a Cryptocurrency price movement monitoring application for Bitcoin. At the end of the paper, an example of the application of Bitcoin price monitoring will be presented using a web-based application containing charts and supporting indicators as well as a telegram bot to display price depth charts.
Xiao Fan Liu, Huanhuan Ren, Si-Hao Liu, Xian-Jian Jiang
Abstract The cryptocurrency economy provides a comprehensive digital trace of human economic behavior: almost all cryptocurrency usersâ activities are faithfully recorded in transactions on public blockchains. However, the user identifiers in the transaction records, i.e., blockchain addresses, are anonymous. That is, they cannot be associated with any real âoff-chainâ identify of actual users. Nonetheless, identifying the economic roles of the addresses from their past behaviors is still feasible. This paper analyzes Ethereum token transactions, characterizes key economic agentsâ behavior from their transaction patterns, and explores their identifiability through interpretable machine learning models. Specifically, six types of most active economic agents are considered, including centralized cryptocurrency exchanges, decentralized exchanges, cryptocurrency wallets, token issuers, airdrop services, and gaming services. Transaction patterns such as trading volume, transaction tempo, and structural properties of transaction networks are defined for individual blockchain addresses. The results showed that cryptocurrency exchanges and online wallets have signature behavior patterns and hence can be accurately distinguished from other agents. Token issuers, airdrop services, and gaming services can sometimes be confused. Moreover, transaction networksâ features provide the richest information in the economic agentâs identification.
The Bitcoin exchange rate (BER) is influenced by many variables such as human speculation and policies and, thus, is dependent on the financial system. The fluctuation of BER submitted has been extensively investigated. However, the correlation analysis of the short- and long-term effects by indicators of online sentiment is unexplored. Therefore, this study establishes a VAR model for BER which provides a framework to the Google search volume index (SVI), the investor fear gauge (VIX), and the S&P500 Index. The findings of the analysis suggest that BER and Google SVI have a Granger causality feedback relationship in both the short- and long-term co-integration equilibrium, and the VIX is significantly related to BER in the long-term co-integration.
Abstract This study examined the evolving oil market efficiency by applying daily historical data to the three benchmark cryptocurrencies (Bitcoin, Ethereum, and Ripple), gold, and West Texas Intermediate (WTI) crude oil. The data coverage of daily returns was from August 2015 to April 2019. We applied two alternative tests to examine linear and nonlinear dependency, i.e., automatic portmanteau and generalized spectral tests. The analysis of observed results validated the adaptive market hypothesis (AMH) in all markets, but the degree of adaptability between the data was different. In this study, we also analyzed the existence of evolutionary behavior in the market. To achieve this goal, we checked the results by applying the rolling-window method with three different window lengths (50, 100, and 150 days) on the test statistics, which was consistent with the findings of AMH.
Miguel Ăngel Echarte FernĂĄndez, Sergio Luis Nåñez Alonso, Javier Jorge-VĂĄzquez, Ricardo Francisco Reier Forradellas
This article analyzes the monetary policy of major central banks during the economic crisis generated by the COVID-19 pandemic. Rising public debt in many countries is being financed through asset purchases by monetary authorities. Although these stimulus policies predate the pandemic, they have been significantly boosted as many governments face large financing needs. We have been in a low interest rate environment for years and some governments have issued debt securities at negative rates. In addition, the rise of decentralized cryptocurrencies, based on blockchain technology, has created greater competition in the international monetary system and many governments have considered the creation of centralized virtual currencies, known as central bank digital currencies (CBDCs). We will analyze some relevant cases, with an emphasis on the digital euro project. The methodology is based on the analysis of the evolution of monetary variables. Pearsonâs correlation will be used to establish some relationships between them. There is a strong similarity in the expansionary monetary policies of central banks. Although the growth of the money supply has not been passed on to the CPI, it has been passed on to the financial markets and the price of assets such as Bitcoin or gold.
Chetan G. Shinde, Atharav Upare, Vishal Pawar, Ajay Raut
We propose a new blockchain-based framework for a completely decentralized stock market and bitcoin exchange in this paper. By proposing a groundbreaking framework utilizing blockchain to build a decentralized bitcoin and stock exchange network, this paper discusses the shortcomings of conventional centralized stock exchange platforms, High transaction costs, vulnerable centralized governance, and a lack of clarity in consumer behavior and algorithms are just a few of the problems. Blockchain technology consists of a large number of computer nodes that share a shared ledger securely without the need for intermediaries of any sort. The proposed blockchain-based solution addresses the disadvantages of the centralized stock exchange architecture by ensuring the integrity and security of the properties and orders of the owner, by self-enforcing intelligent agreements between parties, and by consensus algorithms, by achieving democratic and effective decisions on the execution and settlement of orders. Intelligent contracts are used in the proposed architecture to enforce the validation of the owner's rights as well as the proper execution and settlement of orders, reducing the need for a central authority to ensure that the stock exchange process is accurate. The proposed system proposes a hybrid platform that incorporates cryptocurrency and stock trading. The solution was tested for a subset of rules for the Stock Exchange by implementing a prototype in Ethereum.
First, a big data analysis of the transactions and smart contracts made on\nthe Ethereum blockchain is performed, revealing interesting trends in motion.\nNext, these trends are compared with the public's interest in Ether and\nBitcoin, measured by the volume of online searches. An analysis of the crypto\nprices and search trends suggests the existence of big players (and not the\nregular users), manipulating the market after a drop in prices. Lastly, a\ncross-correlation study of crypto prices and search trends reveals the pairs\nproviding more accurate and timely predictions of Ether prices.\n
The cryptocurrency market is amongst the fastest-growing of all the financial markets in the world. Unlike traditional markets, such as equities, foreign exchange and commodities, cryptocurrency market is considered to have larger volatility and illiquidity. This paper is inspired by the recent success of using machine learning for stock market prediction. In this work, we analyze and present the characteristics of the cryptocurrency market in a high-frequency setting. In particular, we applied a machine learning approach to predict the direction of the mid-price changes on the upcoming tick. We show that there are universal features amongst cryptocurrencies which lead to models outperforming asset-specific ones. We also show that there is little point in feeding machine learning models with long sequences of data points; predictions do not improve. Furthermore, we solve the technical challenge to design a lean predictor, which performs well on live data downloaded from crypto exchanges. A novel retraining method is defined and adopted towards this end. Finally, the trade-off between model accuracy and frequency of training is analyzed in the context of multi-label prediction. Overall, we demonstrate that promising results are possible for cryptocurrencies on live data, by achieving a consistent 78% accuracy on the prediction of the mid-price movement on live exchange rate of Bitcoins vs. US dollars.
Virtual currency represents a specific technological innovation on financial markets. Bitcoin and other cryptocurrencies are popular alternatives to traditional cash and investment. We indicate a research gap in the literature review. We find out that current research focused rarely on portfolio diversification using bibliographic analysis in VOSviewer. We think that portfolio diversification is extremely important on the crypto market for most investors because virtual currencies are very risky compared to traditional assets. The primary aim is to construct an optimal portfolio consisting of several cryptocurrencies without traditional assets using a modern theory portfolio. The total sample consists of 16 virtual currencies from 1 October 2017 to 13 January 2020. We mainly obtain historical data on the daily close price of cryptocurrencies from Yahoo Finance. The results show that the optimal portfolio using Markowitz approach consists of Cardano, Binance Coin, and Bitcoin. In addition, virtual currencies are moderately Correlated, with the exception of Tether based on correlation analysis. The high correlation is dangerous for cryptocurrency in portfolio diversification. However, Tether is an atypical virtual currency compared to other cryptocurrencies.
The legitimacy of virtual currencies as an alternative form of monetary exchange has been the centre of an ongoing heated debated since the catastrophic global financial meltdown of 2007-2008. Our study tests the informational market efficiency of cryptomarkets by investigating the weak-form efficiency of the top-five cryptocurrencies using random walk testing procedures which are robust to asymmetries and unobserved smooth structural breaks. Moreover, our study employs two frequencies of cryptocurrency returns, one corresponding to daily returns and the other to weekly returns. Our findings validate the random walk hypothesis for daily series hence validating the weak-form efficiency for daily returns. On the other hand, weekly returns are observed to be stationary processes which is evidence against weak-form efficiency for weekly returns. Overall, our study has important implications for market participants within cryptocurrency markets.
In this paper, we present an analysis of the non-Gaussianity of the Bitcoin market, in which we focus on the scale dependence of the variance 2 estimated by fitting the Castaing equation to the detrended price series. Our analysis showed scale invariance across a large range of scales in the years 2012-2019, which indicates that the Bitcoin market is in a critical state.
In this paper I document a positive relation between the volatility of liquidity and expected returns. Specifically, I analyze the relationship between the idiosyncratic volatility of market liquidity and the returns of the five largest cryptocurrencies by market capitalization. I find that the correlation between liquidity volatility and returns is overall significantly positive, but highly time-varying. This implies that investors demand a premium for a high variation in liquidity volatility. I furthermore find that the correlation between returns and the level of liquidity is mostly positive, thus, when liquidity is low, expected returns are high. The results corroborates results from other financial markets.
Unlike conventional cryptocurrencies, Islamic ones are new technologies backed by tangible assets and are characterised by their fundamental values. After the COVID-19 outbreak, cryptocurrency responses have shown different behaviour to stock market reactions. However, there is a lack of studies on the efficiency of Islamic and green cryptocurrencies during the pandemic. This paper attempts to analyse the behaviour of three typical families of cryptocurrencies (conventional, Islamic, and green) extracted according to their availability in daily frequencies during COVID-19. For this purpose, their efficiency levels are studied before and after the outbreak by employing multifractal detrended fluctuation analysis (MFDFA) to make the best predictions and strategies. The inefficiency of the cryptocurrencies is assessed through a magnitude of long-memory (MLM) efficiency index, and the impact of COVID-19 on their efficiency is evaluated. The primary results show that HelloGold was the most efficient market before the COVID-19 outbreak and that subsequently Ethereum has been the most efficient. In addition, the findings reveal that the cryptocurrency reactions are not similar and show more resilience in the Ethereum and Litecoin markets than in other cryptocurrency markets. The main contribution of this study is the evaluation of the impact of COVID-19 on the various classes of crypto money. This work has practical implications, as it provides new insights into trading opportunities and market reactions. Moreover, he work has theoretical implications based on its evaluation of three distinct models from different doctrine viewpoints.
We model the dynamics of the cryptocurrency (CC) asset class via a stochastic volatility with correlated jumps (SVCJ) model with rolling-window parameter estimates. By analyzing the time-series of parameters, stylized patterns are observable which are robust to changes of the window size and supported by cluster analysis. During bullish periods, volatility stabilizes at low levels and the size and volatility of jumps in mean decreases. In bearish periods though, volatility increases and takes longer to return to its long-run trend. Furthermore, jumps in mean and jumps in volatility are independent. With the rise of the CC market in 2017, a level shift of the volatility of volatility occurred. All codes are available on Quantlet.com.
Jiahua Xu, Nazariy Vavryk, Krzysztof Paruch, Simon Cousaert
As an integral part of the Decentralized Finance (DeFi) ecosystem, Automated
Market Maker (AMM) based Decentralized Exchanges (DEXs) have gained massive
traction with the revived interest in blockchain and distributed ledger
technology in general. Most prominently, the top six AMMs -- Uniswap, Balancer,
Curve, Dodo, Bancor and Sushiswap -- hold in aggregate 15 billion USD worth of
crypto-assets as of March 2021. Instead of matching the buy and sell sides,
AMMs employ a peer-to-pool method and determine asset price algorithmically
through a so-called conservation function. Compared to centralized exchanges,
AMMs exhibit the apparent advantage of decentralization, automation and
continuous liquidity. Nonetheless, AMMs typically feature drawbacks such as
high slippage for traders and divergence loss for liquidity providers. In this
work, we establish a general AMM framework describing the economics and
formalizing the system's state-space representation. We employ our framework to
systematically compare the mechanics of the top AMM protocols, deriving their
slippage and divergence loss functions.