Search Results (31)

Cryptocurrency markets are characterized by high volatility, nonlinear dependencies, and limited transparency, making short-term forecasting particularly challenging for both researchers and practitioners. To address these complexities, this study introduces a Temporal Fusion Transformer (TFT)-based forecasting framework that integrates on-chain and technical indicators to improve predictive performance and inform tactical trading decisions. By combining multi-source features—such as Spent Output Profit Ratio (SOPR), Total Value Locked (TVL), active addresses (AA), exchange net flow (ENF), Realized Cap HODL Waves, and the Crypto Fear and Greed Index—with classical signals like Relative Strength Index (RSI) and moving average convergence divergence (MACD), the model captures behavioral patterns, investor sentiment, and price dynamics in a unified structure. Five major cryptocurrencies—BTC, ETH, USDT, XRP, and BNB—serve as the empirical basis for evaluation. The proposed TFT model is benchmarked against LSTM, GRU, SVR, and XGBoost using standard regression metrics to assess forecasting accuracy. Beyond prediction, a signal-based trading strategy is developed by translating model outputs into daily buy, hold, or sell signals, with performance assessed through a comprehensive set of financial metrics. The results suggest that integrating attention-based deep learning with domain-informed indicators provides an effective and interpretable approach for multi-asset cryptocurrency forecasting and real-time portfolio strategy optimization. Full article

This paper explores the current use of crypto-assets for payments, focusing mostly on unbacked crypto-assets, while selectively referring to stablecoins. Although some specific characteristics of crypto-assets, such as their price volatility and unclear legal settlement, render them unsuitable for payments, the rapid technological and regulatory developments in the area of crypto-assets-based payments justify monitoring developments in this area. We therefore try to answer the research questions of which/why/how/where/by whom crypto-assets are used for (retail) payments. We analyse and describe a variety of ways in which crypto-assets are used for making payments, focusing on the period from 2019 to 2023 in Europe and worldwide, based on the publicly available statistical data and literature. We identify and exemplify the main use cases, payment methods, DeFi protocols, and payment gateways, and analyse payments with crypto-assets based on location and market participants. In addition, we describe and analyse the integration of crypto-assets into existing commercial payment services. Our work contributes to understanding the shifting domain of crypto-assets-based payments and provides insights into the monitoring of relevant developments via various dimensions that need to keep being explored, with the objective of contributing to the maintenance of the integrity and stability of the financial ecosystem. Full article

As crypto assets become more widely adopted, crypto asset markets and traditional financial markets may become increasingly interconnected. The close linkages between these markets have potentially important implications for price formation, contagion, risk management and regulatory frameworks. In this study, we assess the correlation between traditional financial markets and selected crypto assets, study factors that may impact the price of crypto assets and identify potentially significant events that may have an impact on Bitcoin and Ethereum price dynamics. For the latter analyses, we adopt a Bayesian model averaging approach to identify change points in the Bitcoin and Ethereum daily price time series. We then use the dates and probabilities of these change points to link them to specific events, finding that nearly all of the change points can be associated with known historical crypto asset-related events. The events can be classified into broader geopolitical developments, regulatory announcements and idiosyncratic events specific to either Bitcoin or Ethereum. Full article

This study is the first to scientifically investigate stock indices and currency exchanges that affect crypto price volatility pre and post the FTX (Future Exchanges) collapse event. Weekly series from 1 January 2020 to 31 December 2024 were utilized for the analysis. The ARDL model suggests positive symmetric short- and long-term effects of USA stock indices on Bitcoin and Ethereum prices (p < 0.10), while Japanese stock indices and currency exchanges have negative symmetric short- and long-term effects on Bitcoin and Ethereum price volatility (p < 0.10). The global index MSCI has no symmetric effect. The asymmetric approach NARDL suggests positive and negative asymmetric short- and long-term effects of USA and Japanese stock indices and currency exchanges on Bitcoin and Ethereum price volatility (p < 0.05). This research helps exchange brokers and crypto traders diversify their holdings, reduce stock index and currency exchange risk, and accurately predict Bitcoin and Ethereum price variations. Full article

According to the latest data from CryptoSlam, as of November 2024, NFT sales have approached USD 7.43 billion, with trading profits exceeding USD 33.303 million. In the buyer–seller market, the potential demand for NFT transactions continues to grow, leading to rapid development in the NFT market and giving rise to various issues, such as price manipulation, counterfeit products, hacking of investment platforms, identity verification errors, data leaks, and wallet security failures, all of which have caused significant financial losses for investors. Currently, the NFT investment market faces challenges such as legal uncertainty, information security, and high price volatility due to speculation. This study conducted expert interviews and adopted a two-stage research methodology to analyze the most common risk factors when selecting NFT investments. It employed the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and the Analytic Network Process (ANP) to explore risk factors such as legal issues, security concerns, speculation, and price volatility, aiming to understand how these factors influence investors in choosing the most suitable NFT investment platform. The survey was conducted between February and June 2023, targeting professionals and scholars with over 10 years of experience in the financial market or financial research, with a total of 13 participants. The empirical results revealed that speculation had the greatest impact compared to legal issues, security concerns, and NFT price volatility. Speculation and price volatility directly influenced other risk factors, potentially increasing the risks faced by NFT investment platforms. In contrast, legal and security issues had less influence on other factors and were more affected by them, indicating a relatively lower likelihood of occurrence. Thus, investors must be cautious of short-term speculation, particularly when dealing with rare NFTs. The best approach is to set an exit price to minimize potential losses if the investment does not proceed as planned. Full article

Cryptocurrency is a cryptography-based digital asset with extremely volatile prices. Around USD 70 billion worth of cryptocurrency is traded daily on exchanges. Trading cryptocurrency is difficult due to the inherent volatility of the crypto market. This study investigates whether Reinforcement Learning (RL) can enhance decision-making in cryptocurrency algorithmic trading compared to traditional methods. In order to address this question, we combined reinforcement learning with a statistical arbitrage trading technique, pair trading, which exploits the price difference between statistically correlated assets. We constructed RL environments and trained RL agents to determine when and how to trade pairs of cryptocurrencies. We developed new reward shaping and observation/action spaces for reinforcement learning. We performed experiments with the developed reinforcement learner on pairs of BTC-GBP and BTC-EUR data separated by 1 min intervals (n = 263,520). The traditional non-RL pair trading technique achieved an annualized profit of 8.33%, while the proposed RL-based pair trading technique achieved annualized profits from 9.94% to 31.53%, depending upon the RL learner. Our results show that RL can significantly outperform manual and traditional pair trading techniques when applied to volatile markets such as cryptocurrencies. Full article

The cryptocurrency market is characterized by extreme volatility, with events such as the Terra-LUNA crash of 2022 raising significant questions about the resilience of algorithmic stablecoins. This paper investigates the collapse of LUNA Classic during the USTC depeg, focusing on the role of trading volumes and collateral assets like Bitcoin in amplifying the price crash. Using a Vector Logistic Smooth Transition AutoRegressive (VLSTAR) model, we analyze daily data from October 2020 to November 2022 to uncover how exogenous volumes influenced LUNA’s price trajectory during the crisis. Our findings reveal that high trading volumes, particularly during regime two (the post-depeg period), significantly exacerbated the price decline, validating the impact of large-scale liquidations on LUNA’s price path. Additionally, Bitcoin volumes played a critical role in destabilizing the system, confirming that the liquidity of underlying collateral assets is pivotal in maintaining price stability. These insights contribute to understanding the systemic vulnerabilities in algorithmic stablecoins and offer implications for future stablecoin design and risk management strategies. They are relevant for investors, policymakers, and researchers seeking to be aware of market volatility and prevent future crises in stablecoin ecosystems. Full article

Since cryptocurrencies are among the most extensively traded financial instruments globally, predicting their price has become a crucial topic for investors. Our dataset, which includes fluctuations in Bitcoin’s hourly prices from 15 May 2018 to 19 January 2024, was gathered from Crypto Data Download. It is made up of over 50,000 hourly data points that provide a detailed view of the price behavior of Bitcoin over a five-year period. In this study, we used potent algorithms, including gradient descent, attention mechanisms, long short-term memory (LSTM), and artificial neural networks (ANNs). Furthermore, to estimate the price of Bitcoin, we first merged two deep learning algorithms, LSTM and attention mechanisms, and then combined LSTM-Attention with gradient-specific optimization to increase our model’s performance. Then we integrated ANN-LSTM and included gradient-specific optimization for the same reason. Our results show that the hybrid model with gradient-specific optimization can be used to anticipate Bitcoin values with better accuracy. Indeed, the hybrid model combines the best features of both approaches, and gradient-specific optimization improves predictive performance through frequent analysis of pricing data changes. Full article

Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies. Full article

In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing their probability of death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, the daily range incorporates all the information provided in traditional daily datasets, including the open-high-low-close (OHLC) prices for each asset. We evaluated the accuracy of the probability of death estimated with the daily range against various forecasting models, including credit scoring models, machine learning models, and time-series-based models. Our study considered different definitions of “dead coins” and various forecasting horizons. Our results indicate that credit scoring models and machine learning methods incorporating lagged trading volumes and online searches were the best models for short-term horizons up to 30 days. Conversely, time-series models using the daily range were more appropriate for longer term forecasts, up to one year. Additionally, our analysis revealed that the models using the daily range signaled, far in advance, the weakened credit position of the crypto derivatives trading platform FTX, which filed for Chapter 11 bankruptcy protection in the United States on 11 November 2022. Full article

The article is time relevant, since a number of countries, such as China and Russia, started pilot testing their digital currencies in 2020, due to the necessity of contactless means of payment during the coronavirus pandemic. The purpose of this research is to revisit the phenomenon of the virtual money. What is new here is that this is one of the first papers concentrated on a digital currency for a group of countries. The article offers an econometric representation of how the BRICS (Brazil, Russia, India, China and South Africa) currency may be utilized when hypothetically coined on a crypto-exchange of the BRICS monetary union. This research contains data condensed in a table and graphical form. The major idea of this article is that only a digital unit of account for a group of countries such as the BRICS, unlike a cryptocurrency, may help create a sustainable financial stability environment and solid monetary infrastructure. The author conducts a detailed analysis of a digital currency compared to a cryptocurrency. The hypothesis is that a shared digital currency for the BRICS may promote financial risk diversification through a risk-sharing mechanism. The author’s results include a formula that may provide a way of calculating the quantity of the BRICS’ digital currency, as well as a simulated representation of a would-be BRICS currency’s dynamics. The practical significance of this paper is that the proposed BRICS digital currency can find its use in investment portfolios as an asset. This asset may provide stable returns and benefit from the growth prospects of the BRICS economies as ones of the most rapidly developing markets in the world. Potential investors in the currency of the union may profit from the abundance of natural resources of Brazil, Russia, and South Africa in terms of energy and other minerals offered at the best world market prices, as well as the technology, labor, and durable goods of India and China priced at competitive valuations. The assets expressed in the BRICS currency have the potential of growing over the years, so a dollar invested today may turn an enormous return on investment within this decade, unlike stagnant markets in Europe, Japan, and the US. The author proves that a cryptocurrency cannot serve a shared currency function for the BRICS, and it stresses the very significance of circulating the shared digital currency in particular. Finally, the author simulates the dynamics of the BRICS’ digital currency and proposes an approach to calculating its exchange rate relative to some of the leading currencies in the international monetary system. Full article

Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis task. Researchers have proposed predictors based on statistical, machine learning (ML), and deep learning (DL) approaches, but the literature is limited. Indeed, it is narrow because it focuses on predicting only the prices of the few most famous cryptos. In addition, it is scattered because it compares different models on different cryptos inconsistently, and it lacks generality because solutions are overly complex and hard to reproduce in practice. The main goal of this paper is to provide a comparison framework that overcomes these limitations. We use this framework to run extensive experiments where we compare the performances of widely used statistical, ML, and DL approaches in the literature for predicting the price of five popular cryptocurrencies, i.e., XRP, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), and Monero (XMR). To the best of our knowledge, we are also the first to propose using the temporal fusion transformer (TFT) on this task. Moreover, we extend our investigation to hybrid models and ensembles to assess whether combining single models boosts prediction accuracy. Our evaluation shows that DL approaches are the best predictors, particularly the LSTM, and this is consistently true across all the cryptos examined. LSTM reaches an average RMSE of $0.0222$ and MAE of $0.0173$, respectively, $2.7\%$ and $1.7\%$ better than the second-best model. To ensure reproducibility and stimulate future research contribution, we share the dataset and the code of the experiments. Full article

The purpose of this study is to investigate the salient factors that influence Indonesian cryptocurrency owners in making their investment decision. This study employs intergroup bias, subjective norms, overborrowing, and spending control to explain cryptocurrency investment behavior. The questionnaire was collected from 309 respondents from the five largest internet user areas: Jakarta, Surabaya, Bandung, Semarang, and Medan. This study executes the research framework using binary logistic regression. The results reveal that intergroup bias and overborrowing are the most impulsive factors contributing to the cryptocurrency investment decisions over the past year. Furthermore, after November 2021, Indonesian crypto owners are more irrational in a bearish period since their investment decisions are driven by their desire to be accepted in the social group. Moreover, when they have overindebtedness, instead of solving their debt problems, they prefer to spend their money on cryptocurrency investments. The subjective norms’ influencers suggest that crypto owners not invest when the cryptocurrency price is sharply declining. The findings contribute to the dual-systems perspective and social contagion theories, enriching the empirical study regarding investment behavior. Full article

This paper examines the trading performances of several technical oscillators created using crypto-asset pricing methods for short-term bitcoin trading. Seven pricing models proposed in the professional and academic literature were transformed into oscillators, and two thresholds were introduced to create buy and sell signals. The empirical back-testing analysis showed that some of these methods proved to be profitable with good Sharpe ratios and limited max drawdowns. However, the trading performances of almost all methods significantly worsened after 2017, thus indirectly confirming an increasing financial literature that showed that the introduction of bitcoin futures in 2017 improved the efficiency of bitcoin markets. Full article

The capacity of highways has been an ever-present constraint in the 21st century, bringing about the issue of safety with greater likelihoods of traffic accidents occurring. Furthermore, recent global oil prices have inflated to record levels. A potential solution lies in vehicular platooning, which has been garnering attention, but its deployment is uncommon due to cyber security concerns. One particular concern is a Sybil attack, by which the admission of fake virtual vehicles into the platoon allows malicious actors to wreak havoc on the platoon itself. In this paper, we propose a secure management scheme for platoons that can protect major events that occur in the platoon operations against Sybil attacks. Both vehicle identity and message exchanged are authenticated by adopting key exchange, digital signature and encryption schemes based on elliptic curve cryptography (ECC). Noteworthy features of the scheme include providing perfect forward secrecy and both group forward and backward secrecy to preserve the privacy of vehicles and platoons. Typical malicious attacks such as replay and man-in-the-middle attacks for example can also be resisted. A formal evaluation of the security functionality of the scheme by the Canetti–Krawczyk (CK) adversary and the random oracle model as well as a brief computational verification by CryptoVerif were conducted. Finally, the performance of the proposed scheme was evaluated to show its time and space efficiency. Full article