Sunčica Stanković, Bojan Đorđević, Nataša Milojević
The increase in the value of cryptocurrencies, market capitalization, and volume of trading on crypto exchanges resulted in a significant increase in the interest of researchers in this decentralized financial system. The two most popular cryptocurrencies today - bitcoin and ethereum - have captured the greatest attention of researchers. Given that cryptocurrency trading is similar to stock trading, the author's assumption is that their returns are determined by the price of gold and the volatility index – VIX, representing this paper's research hypothesis. Testing through vector autoregression (VAR) models, Granger causality tests, and impulse response function (IRF) shows that gold returns do not impact, unlike the VIX volatility index and Ethereum, indicating a significant relationship between cryptocurrencies bitcoin and US stock markets. On the other hand, Bitcoin returns and the volatility index cause ethereum returns, while gold returns do not.
Blockchain is the foundation of cryptocurrency and enables decentralized transactions through its immutable ledger. The technology uses hashing to ensure secure transactions and is becoming increasingly popular due to its wide range of applications. Python is a performant, secure, scalable language well-suited for blockchain applications. It provides developers free tools for faster code writing and simplifies crypto analysis. Python allows developers to code blockchains quickly and efficiently as it is a completely scripted language that does not require compilation. Different models such as SVR, ARIMA, and LSTM can be used to predict cryptocurrency prices, and many Python packages are available for seamlessly pulling cryptocurrency data. Python can also create one's cryptocurrency version, as seen with Facebook's proposed cryptocurrency, Libra. Finally, a versatile and speedy language is needed for blockchain applications that enable chain addition without parallel processing, so Python is a suitable choice.
Marcin Wątorek, Maria Skupień, Jarosław Kwapień, Stanisław Drożdż
This paper investigates the temporal patterns of activity in the cryptocurrency market with a focus on Bitcoin, Ethereum, Dogecoin, and WINkLink from January 2020 to December 2022. Market activity measures - logarithmic returns, volume, and transaction number, sampled every 10 seconds, were divided into intraday and intraweek periods and then further decomposed into recurring and noise components via correlation matrix formalism. The key findings include the distinctive market behavior from traditional stock markets due to the nonexistence of trade opening and closing. This was manifest in three enhanced-activity phases aligning with Asian, European, and U.S. trading sessions. An intriguing pattern of activity surge in 15-minute intervals, particularly at full hours, was also noticed, implying the potential role of algorithmic trading. Most notably, recurring bursts of activity in bitcoin and ether were identified to coincide with the release times of significant U.S. macroeconomic reports such as Nonfarm payrolls, Consumer Price Index data, and Federal Reserve statements. The most correlated daily patterns of activity occurred in 2022, possibly reflecting the documented correlations with U.S. stock indices in the same period. Factors that are external to the inner market dynamics are found to be responsible for the repeatable components of the market dynamics, while the internal factors appear to be substantially random, which manifests itself in a good agreement between the empirical eigenvalue distributions in their bulk and the random matrix theory predictions expressed by the Marchenko-Pastur distribution. The findings reported support the growing integration of cryptocurrencies into the global financial markets.
Bitcoin is one of the cryptocurrencies that has gained popularity in recent years. Previous studies have shown that closing price alone is not enough to forecast its future level, and other price-related features are necessary to improve forecast accuracy. We introduce a new set of time series and demonstrate that a subset is necessary to improve directional accuracy based on a machine learning ensemble. In our experiments, we study which time series and machine learning algorithms deliver the best results. We found that the most relevant time series that contribute to improving directional accuracy are open, high, and low, with the largest contribution of low in combination with an ensemble of a gated recurrent unit network and a baseline forecast. The relevance of other Bitcoin-related features that are not price-related is negligible. The proposed method delivers similar performance to the state of the art when observing directional accuracy.
Pasquale De Rosa, Pascal Felber, Valerio Schiavoni
Cryptocoins (i.e., Bitcoin, Ether, Litecoin) are tradable digital assets. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins among owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. The extreme volatility of such trading prices across all different sets of crypto-assets remains undisputed. However, the relations between the trading prices across different cryptocoins remains largely unexplored. Major coin exchanges indicate trend correlation to advise for sells or buys. However, price correlations remain largely unexplored. We shed some light on the trend correlations across a large variety of cryptocoins, by investigating their coin/price correlation trends over the past two years. We study the causality between the trends, and exploit the derived correlations to understand the accuracy of state-of-the-art forecasting techniques for time series modeling (e.g., GBMs, LSTM and GRU) of correlated cryptocoins. Our evaluation shows (i) strong correlation patterns between the most traded coins (e.g., Bitcoin and Ether) and other types of cryptocurrencies, and (ii) state-of-the-art time series forecasting algorithms can be used to forecast cryptocoins price trends. We released datasets and code to reproduce our analysis to the research community.
This paper examines the market efficiency of the most significant cryptocurrencies, Bitcoin and Ethereum. In the paper, we use several different tests to check the normality of return distribution, long-run correlation and heteroscedasticity of return volatility.We compare the characteristics of cryptocurrency returns with the returns on stocks of the most important companies producing hardware components for cryptocurrency mining. The correlation of returns, trading volume and volatility between cryptocurrencies and selected stocks is tested using a Granger causality test. The research results reject the efficient market hypothesis and show that the cryptocurrency market is a completely new speculative market that is weakly correlated with the stock market.
Considering that both the entropy-based market information and the Hurst exponent are useful tools for determining whether the efficient market hypothesis holds for a given asset, we study the link between the two approaches. We thus provide a theoretical expression for the market information when log-prices follow either a fractional Brownian motion or its stationary extension using the Lamperti transform. In the latter model, we show that a Hurst exponent close to 1/2 can lead to a very high informativeness of the time series, because of the stationarity mechanism. In addition, we introduce a multiscale method to get a deeper interpretation of the entropy and of the market information, depending on the size of the information set. Applications to Bitcoin, CAC 40 index, Nikkei 225 index, and EUR/USD FX rate, using daily or intraday data, illustrate the methodological content.
Cryptocurrencies are digital assets built on blockchain technology, known for their high volatility and lack of underlying assets to justify their intrinsic value. The trading and investing in cryptocurrencies heavily rely on price trends and a few supporting parameters. To facilitate cryptocurrency analysis, our platform provides a price chart that showcases the current short-term, medium-term, and long-term trends of each currency. This analysis is supported by additional parameters such as market capitalization, 24-hour percentage-wise price change, and concise information about each element.Moreover, with the wide array of cryptocurrencies available, individuals struggle to maintain a watchlist that reflects their prioritized selection. To address this challenge, we have developed a priority watchlist that assists users in both short-term trading and long-term investing. Our solution is web-based and utilizes various methodologies including an Application Programming Interface (API) for data retrieval, a library for displaying price charts, and an algorithm for implementing the priority watchlist functionality.
Abstract This paper explores financial networks of cryptocurrency prices in both time and frequency domains. We complement the generalized forecast error variance decomposition method based on a large VAR model with network theory to analyze the dynamic network structure and the shock propagation mechanisms across a set of 40 cryptocurrency prices. Results show that the evolving network topology of spillovers in both time and frequency domains helps towards a more comprehensive understanding of the interactions among cryptocurrencies, and that overall spillovers in the cryptocurrency market have significantly increased in the aftermath of COVID-19. Our findings indicate that a significant portion of these spillovers dissipate in the short-run (1–5 days), highlighting the need to consider the frequency persistence of shocks in the network for effective risk management at different target horizons.
People are starting to see the cryptocurrency market as a viable source of income and investment, similar to the stock market, as the concept of cryptocurrencies continues to gain popularity. Predicting Bitcoin returns is related to financial machine learning, which uses time series to forecast price variance. This study starts with the daily close price of Bitcoin for its initial dataset. The price is transformed into percentages and binary classes, which categorize into “Up” and “Down”, after which a time series is applied to produce two datasets: a categorical dataset for classification and a numerical dataset for regression. For classification that represents a Binary classification in asset-price forecasting, k-fold cross-validation is applied to ensure that the best classifiers are selected for testing and analysis. Most of the regression analysis was based on visualization, which displayed the predicted prices by each regressor in front of the original values and helped analyze the models’ results more accurately. The outcomes of this study were achieved by anticipating bitcoin returns using classification and regression machine learning models, despite the approaches’ low accuracy and significant precision rate to the “Up” class. At this stage, with a significant limitation regarding the dataset and a lack of other indicators, a model capable of predicting future variations is considered a beneficial addition for many trading tools or even for crypto market analysts.
Abstract Volatility of Bitcoin has a long memory, we modeled such a character using FIGARCH processes, afteward we went on for pricing Futures and Options, the price of Futures depends on many factors in the market, we have proposed a model for futures contracts which links their price to spot price and volatility, after calibrating our model the result was consistent with market values, the pandemic of Covid which started earlier in 2020 after hitting the district of Wuhan just before; had not really an effect on derivatives markets until july 2021 when the market started a downward trend. We price Options using a sample of volatilities that we consider determinstic for a matter of calculous. We finally compare our model for Futures to the same model but with a constant volatility. JEL Classification. G13
This study presents an analysis of the impact of asset price bubbles on the markets for cryptocurrencies and con-siders the standard risk management measure Value-at-Risk (“VaR”). We apply the theory of local martingales, present a styled model of asset price bubbles in continuous time and perform a simulation experiment featuring one- and two-dimensional Stochastic Differential Equation (“SDE”) systems for asset value through a Constant Elasticity of Variance (“CEV”) process that can detect bubble behavior. In an empirical analysis across several widely traded cryptocurrencies, we find that the estimated parameters of one-dimensional SDE systems do not show evidence of bubble behavior. However, if we estimate a two-dimensional system jointly with an equity market index, we do detect a bubble, and comparing bubble to non-bubble economies it is shown that asset price bubbles result in materially inflated VaR measures. The implication of this finding for portfolio and risk management is that rather than acting as a diversifying asset class, cryptocurrencies may not only be highly correlated with other assets but have anti-diversification properties that materially inflate the downside risks in portfolios combining these asset types. We also measure the model risk arising from mispecifying the process driving cryptocurrencies by ignoring the relationship to another representative risk asset through applying the principle of relative entropy, where we find that across all cryptocurrencies studied that the distributions of a distance measure between the simulated distributions of VaR are almost all highly skewed to the right and very heavy-tailed. We find that in the majority of cases that the model risk “multipliers” range in about two to five across cryptocurrencies, estimates which could be applied to establish a model risk reserve as part of an economic capital calculation for risk management of cryptocurrencies.
In May 2022, an apparent speculative attack, followed by market panic, led to the precipitous downfall of UST, one of the most popular stablecoins at that time. However, UST is not the only stablecoin to have been depegged in the past. Designing resilient and long-term stable coins, therefore, appears to present a hard challenge. To further scrutinize existing stablecoin designs and ultimately lead to more robust systems, we need to understand where volatility emerges. Our work provides a game-theoretical model aiming to help identify why stablecoins suffer from a depeg. This game-theoretical model reveals that stablecoins have different price equilibria depending on the coin's architecture and mechanism to minimize volatility. Moreover, our theory is supported by extensive empirical data, spanning $1$ year. To that end, we collect daily prices for 22 stablecoins and on-chain data from five blockchains including the Ethereum and the Terra blockchain.
While the majority of earlier studies used autocorrelation-based methodologies to explore the dependency structure for Bitcoin, this paper follows Benoit Mandelbrot in taking a fractal point of view. It shows that both Bitcoin and S&P 500 returns exhibit fractal-like behavior. Further evidence suggests that the infinite-variance-hypothesis cannot be rejected for both assets supporting Mandelbrot’s (1963) early study on cotton price changes. This result holds across non-overlapping subsamples. Following Mandelbrot (2008), Hurst exponents are estimated using rescaled/range analysis. The key findings are that (i) Bitcoin returns exhibit a higher level of persistence than S&P 500 returns across various subsamples, (ii) the level of persistence in Bitcoin returns has not changed across time, (iii) the S&P 500 moved from efficiency in the first subsample to inefficiency in the ex-post June 17, 2018 period, (iv) even if it was assumed that the variance of S&P 500 returns is finite, the kurtosis remains statistically undefined. The study concludes that correlation-based methods used to explore the S&P 500 universe result in misleading answers.
Purpose The aim of this research is to explore multiscale hedging strategies among cryptocurrencies, commodities, and GCC stocks. Particularly, this is done by evaluating the connectedness among these asset classes covering a period with COVID-19 implications. Using the wavelet approach, the present study aims to recommend whether there exist different time horizon-based hedging abilities across the asset classes. Design/methodology/approach The approach used in this study is a multiscale decomposition of time series based on wavelets of daily prices of 13 asset classes. Since the wavelet analysis allows to decompose the time series into its frequency components at different time scales by a filtering process the study covered 1-day, 8-day, and 64-day time horizons to examine the hedging properties across those asset classes. Findings The results of this study show that hedging effectiveness differs among stock markets over time. In some cases, cryptocurrencies may keep their hedging properties across time while in others they switch from safe haven to hedge devices. In almost all cases, the three main cryptocurrencies showed diversifying properties as was observed by the multiscale correlation and hedge ratio estimations. In a competing sense, gold showed safe haven properties across time than cryptocurrencies except at an 8-day time scale where hedge ratios were low, positive and statistically different from zero that could be interpreted as a good hedge device in the medium term. Research limitations/implications Though this research has considered a set of thirteen asset classes, it was limited to a period in which most cryptocurrencies started trading for the first time which reduces the number of observations compared to Bitcoin prices and stable coins such as Ethereum, Ripple, and Bitcoin Cash. Also, the research was focused on the GCC stock markets which may have different results as compared to other regional markets of Asia or Latin America. A comparative analysis in future could be another area of research in future. Practical implications This study has some significant policy implications. The cryptocurrency market is severely affected by demand and risk shocks to crude oil prices during the COVID-19 period. From the investor's point of view, diversification benefits can be obtained by combining cryptocurrencies along with oil-related products during episodes of financial turmoil and COVID-19 pandemic. The GCC region is constantly endeavoring to adopt more scientific tools and mechanisms of investment, and therefore, this study's results will provide some useful directions to the government, policymakers, financial institutions, and investors. Originality/value The current study covers a big bunch of 13 assets spanning across financial and real assets. This is based on literature gap and hence, will be a significant addition to the existing literature. Moreover, the GCC region is emerging as a global investment hub and this study will provide investors dynamic hedging strategies across these asset classes.
Nimish Prabhune, Aman Mahajan, Manu Priyam Mittal, Rishi Kumar
This article focuses on the returns of four major cryptocurrencies. It covers a complete study of the dynamics of cryptocurrencies with traditional financial markets, including stocks, gold and oil prices and the technology proxied by hashrates and social sentiments gauged by Twitter data. The article fits the time series model using the Autoregressive Distributed Lag framework and selects the fitted model using penalized least-square estimates. The study also encompasses a set of control variables to improve the model’s accuracy. The findings confirm the existence of a dynamic relationship between the cryptocurrency market and the traditional financial markets. Our results imply that cryptocurrencies can be used as a tool for portfolio diversification. This study is a unique attempt at identifying the major drivers of the cryptocurrency market at large, by disentangling and modelling the dynamic relationships shared by the leading four cryptocurrencies with the relevant components of the financial markets. This article is a significant contribution to the emerging literature on cryptocurrencies and has implications for practitioners.
Investigating the time–frequency-based phase synchronization between nonstationary time series of the cryptocurrencies’ prices can be a suitable tool to reveal their complicated interactions at different frequencies. In this work, the phase synchronization between 25 cryptocurrencies with the highest capitalization from December 1, 2021 to June 1, 2022 is calculated using the phase-locking value method based on wavelet transform. Then, utilizing the graph theory, the cryptocurrency networks are constructed, and their topological features like path length (PL), clustering coefficient (CC) and node strength are evaluated in various frequencies. This research indicates a strong phase synchronization between the investigated cryptocurrencies, especially in low frequencies. Also, the networks’ high average CC and short PL compared to their equivalent regular and random networks display the small-worldness of the networks. We observe from the obtained results that Bitcoin, Ethereum and Binance currencies, among the most popular cryptocurrencies, have the highest average node strengths at different frequencies. Also, TRON shows the lowest CC and node strength among all currencies, representing its limited phase interaction with other currencies.
Neil Archein I. Gomez, Gernel S. Lumacad, Isabela Loren R. Saludes, Princess Aravela A. Castino · 5 authors
MIR4, is a play to earn game that uses Non-Fungible Tokens (NFT) and cryptocurrency- or in MIR4, Draco Tokens- as a reward. Draco is obtained through mining an in-game resource called Darksteel and is then traded to Wemix Wallet, where real-world money is obtained. Cryptocurrencies are volatile, which gives MIR4 players and traders a decision dilemma of when is the preferable time to buy, sell, or trade Draco Tokens. In this study we present deep learning models, specifically the Long-Short Term Memory (LSTM) neural network, and Neural Prophet (NP) time series machine learning models to forecast future Draco-token exchange value. Historical data of Draco-token value from Yahoo Finance is utilized as a univariate parameter for the analysis, model development, and the forecasting of the future Draco-token exchange values. Performance of formulated models are assessed and compared based on the following regression metrics: RMSE, MSE, MAE and MAPE. Experimental results indicated that the LSTM Neural Network yielded better forecast estimates with lower error than the Neural Prophet. Findings of the study showed that LSTM can be utilized as a tool for forecasting future Draco token exchange values. future research direction suggests improving prediction accuracy by incorporating other parameters such as MIR-4 players sentiments, newly added players, and google search interest over time.
Utilizing wavelet coherence analysis, we investigate the correlation of fluctuations and phase differences between Bitcoin and RMB to identify capital flows between the two currencies. The effects of Digital Currency Electronic Payment (DCEP) on their co-movement are further analyzed. Our findings reveal that the RMB exchange rate leads the price of Bitcoin in all significant co-movement areas. Furthermore, it appears that from February 2017 to September 2018, the Sino-US trade frictions and US dollar interest rate hikes may have resulted in a long-term negative co-movement, which seems to have been driven by RMB and possibly indicated capital flows from RMB to Bitcoin. The short-term positive co-movement between November 2019 and July 2020 could be attributed to the COVID-19 pandemic. Finally, we also demonstrate that the DCEP trial event has the potential to strengthen the positive co-movement between these currencies.
You Liang, A. Thavaneswaran, Alex Paseka, Sulalitha Bowala · 5 authors
A profitable data-driven algorithmic trading algorithm will benefit from a dynamic system that can produce accurate hedge ratio estimates and short-term innovation volatility forecasts. Commonly used pairs and multiple trading strategies are constructed using the Kalman Filter (KF) and exploiting mean reversion in co-integrated nonstationary stock prices. However, KFs are sensitive to model errors. Misspecified modelling produces unstable solutions for dynamic systems. Fading-Memory Filter (FMF) uses a discounting weight to past observations. Compared to a standard KF, FMF addresses more recent observations and is more resilient (less sensitive) to modelling errors. However, the FMF algorithm does not provide slope parameter covariance matrix updates and innovation volatility forecasts. This paper proposes a novel resilient FMF algorithm for pairs trading and multiple trading by defining an appropriate data-driven innovation volatility forecasting model. The FMF-based strategies are implemented through some experiments on the hourly prices (high-frequency data) of Bitcoin, Ethereum and Litecoin. It is shown that the proposed FMF trading strategies outperform the existing KF trading strategies and they are more profitable in the bear market over time, especially for continuous falling of prices and the short-lived and sharp rally recovery where prices are not stationary.