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2,335 results · page 33 of 98
Hao Fan, Yahua Xu, Elie Bouri, Pingping Zeng
No abstract is available for this record.
Jan Šíla, Evžen Kočenda, Ladislav Krištoufek, Jiří Kukačka
No abstract is available for this record.
Jan Prüser
Abstract The high returns of cryptocurrencies have attracted many investors in recent years. At the same time the evolution of cryptocurrencies is characterized by extreme volatility. For investors, it is therefore key to gauge the risks related to an investment in cryptocurrencies. We provide a comparison of several GARCH and stochastic volatility models for forecasting the risk of cryptocurrencies over the out-of-sample period from 28.09.2018 to 28.02.2023. It turns out that the widely used GARCH(1,1) does not provide accurate risk predictions. In contrast, adding t -distributed innovations or allowing for regime changes improves the accuracy in both model classes. Finally, we consider a Bayesian decision-guided approach with discount learning to combine the different models and provide robust evidence that combining the model predictions leads to accurate combined risk predictions.
Leyi Zhang
Quantitative trading plays a pivotal role in financial markets. Over the past decade, quantitative trading has made remarkable improvements. Due to instability and nonlinearity in financial markets, it is still challenging to formulate high-return trading strategies to address the problem of long-t
Ke Xu, Yu‐Lun Chen, Bo Liu, Jian Chen
Abstract Price discovery studies of a single asset traded in multiple markets have traditionally focused on assessing the relative price discovery contribution of each market. However, in this paper, we demonstrate that the overall price discovery across all markets can undergo changes even when the relative price discovery of each market remains constant. We propose that this overall change in price discovery can be effectively captured by the fractional parameter in the fractionally cointegrated vector autoregressive (FCVAR) model. In contrast, the widely used cointegrated vector autoregressive (CVAR) model fails to account for this dynamic in overall price discovery. Through a combination of simulation exercises and empirical applications, we show that the FCVAR approach outperforms the CVAR model not only in evaluating the relative price discovery contributions but also, more importantly, in providing a comprehensive measurement of overall price discovery.
Juan Ignacio Ibañez
No abstract is available for this record.
Myriam Ben Osman, Emilios Galariotis, Khaled Guesmi, Haykel Hamdi · 5 authors
No abstract is available for this record.
Katarzyna Kryńska, Robert Ślepaczuk
No abstract is available for this record.
Mehmet Canayaz, Charles Cao, Giang Nguyen, Qiang Wang
No abstract is available for this record.
Milan Fičura
No abstract is available for this record.
Adnan Branković
Due to economic uncertainty and the financial crisis of 2008, a desire for an unregulated currency arose, leading to the invention of Bitcoin. Using a pseudonym called Satoshi Nakamoto, Bitcoin was created in 2009, anonymously or by a group of unknown individuals. Since Bitcoin has been the most valuable cryptocurrency in recent years, its prices have fluctuated dramatically, making it difficult to predict their prices. Investors, businesses, risk managers, and market analysts can all benefit from being able to predict Bitcoin prices. By using the Bitcoin transaction data obtained from the Bitstamp website in this study, several different Machine Learning models are employed to determine the most accurate model for predicting Bitcoin prices. These models are based on 1-minute interval exchange rates in USD from January 1, 2012, to January 8, 2022. Analysis was performed primarily with Python, but it was also used and Hadoop, a distributed data storage and processing framework that uses the map-reduce programming model to allow efficient parallel processing of Big Data. Based on the results of our research, comprising three experiments, autoregres-sive-integrated moving average (ARIMA) makes the most accurate prediction of Bitcoin prices, with a 95.98% success rate.
Negar Fazlollahi, Saeed Ebrahimijam
No abstract is available for this record.
Eric M. Kimani, Anthony Ngunyi, Joseph Mung’atu
Cryptocurrencies are considered to be among the most disruptive innovations done in the financial sector within the last decade. It is a digital asset that is designed to serve as a medium of exchange using cryptography. Financial modeling of cryptocurrencies is needed in order to determine the presence of dependence between currencies. Copulas functions assist in modeling dependency structure by making it possible to separate marginal distributions of a given multivariate distribution. The purpose of the study was to model dependencies of cryptocurrencies using copula Garch. The study proposed the use of copula Garch model to model the dependence of cryptocurrency price data. Bivariate copula was extended to Bivariate Copula Garch in order to model prices and measure the cryptocurrency dependence. Prices of the four cryptocurrencies (Bitcoin, Binance, Litecoin and Dogecoin) were analyzed to establish whether there exists any dependency. The results showed standard Garch (1,1) under the highly flexible ARMA-GARCH model was appropriate to identify the true patterns of index returns. Fitting the copula standard Garch (1,1) model to the currencies, it was observed that the pair Litecoin and Bitcoin has the highest tail dependence among the selected cryptocurrencies, which implies that change in prices of Litecoin will influence the prices of Bitcoin and vice versa is true. Optimization of the cryptocurrencies showed that Dogecoin has the best optimization. The results of this study indicate that investing on Dogecoin significantly reduces risk irrespective of significant correlation among Litecoin, Bitcoin and Binance. Standard Garch (1,1) is the best in identifying dependence between the cryptocurrencies.
Viviane de Senna, Adriano Mendonça Souza
ABSTRACT Cryptocurrencies are assets with transactions managed by new methods compared to traditional transactions mediated by Stock Exchanges. The insertion of these assets can change the economic system. The objective of the study is to analyze a set of articles published in international databases of scientific content on cryptocurrencies and the relations with the Stock Exchanges to understand the evolution of the theme over time. The consultation was carried out in the Scopus and Web of Science databases, where 196 articles were analyzed, these indicated learning algorithms, electronic trading, financial and digital markets thematic evolution. The main studies focused on investigating the behavior of cryptocurrencies in the face of market variables, cryptocurrencies as a safe haven or diversification, analysis of prices and the impact of emotional value on cryptocurrencies. The most relevant articles, the citations and co-citations network of these, provided insights into not yet known literature, such authors are Baur et al., 2018; Ji et al., 2020; Peng et al., 2018; Symitsi & Chalvatzis, 2019; Urquhart, 2017.
Juliane Proelss, Stéphane Sévigny, Denis Schweizer
No abstract is available for this record.
Kristof Lommers, Jack Kim, Boris Skidan, Viktor Smits
No abstract is available for this record.
Bao Doan, Dulani Jayasuriya, John B. Lee, Jonathan J. Reeves
In this study, we analyse systematic risk associated with the two leading cryptocurrencies - Bitcoin and Ethereum, from 2015 to 2023. Our findings show a significant escalation in the systematic risk levels, with beta estimates rising from 0.032 to 0.834 for Bitcoin, and from 0.087 to 1.003 for Ethereum. This hike in risk levels has dramatically reduced the diversification benefits of cryptocurrency that were documented in prior studies. In addition, we also identify increased autocorrelation of cryptocurrency systematic risk.
Olga Klein, Roman Kozhan, Ganesh Viswanath-Natraj, Junxuan Wang
No abstract is available for this record.
Sinda Hadhri
No abstract is available for this record.
Kuo‐Shing Chen, J. Jimmy Yang
This study unveils the unique properties of crypto assets and investigates the dynamic connectedness between six prominent stablecoins and Bitcoin in comparison with Bitcoin/stablecoin pairs involving portfolio management. Empirically, using the DCC-GJR-GARCH and partial wavelet coherence approaches, we show that Bitcoin provides greater diversification potential benefits compared to stablecoins during the COVID-19 crisis. The evidence suggests that Bitcoin, as the dominant cryptoasset, can serve as a suitable asset for portfolio diversification against stablecoins. Besides, we evaluate potential hedging benefits of cryptocurrencies for market participants and find that stablecoins are poor hedging products in most of the cases considered. The optimal portfolio for the hedging strategy involving a mix of Bitcoin and stablecoins reveals that the weights assigned to stablecoins are lower than those for Bitcoin. In particular, our results provide timely implications for market participants whose crypto portfolios include stablecoins, especially after Terra’s collapse.
Haibao Wen, Shengmin Sun, Tao Huang, Dunhui Xiao
In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry . While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets – where misleading practices like fake ratings are rampant – remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions. Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior , preserving the reputation system’s foundational sustainability . Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system’s privacy facets, rendering it particularly influential in the ever-evolving digital domain.
An Pham Ngoc Nguyen, Martin Crane, Marija Bezbradica
Abstract The Cryptocurrency Volatility Index (CVI index) has been introduced to estimate the 30-day future volatility of the cryptocurrency market. In this article, we introduce a new Deep Neural Network with an attention mechanism to forecast future values of this index. We then look at the stability and performance of our proposed model against the benchmark models widely used for time series prediction. The results show that our proposed model performs well when compared to popular methods such as traditional Long Short Term Memory, Temporal Convolution Network, and other statistical methods like Simple Moving Average, Random Forest and Support Vector Regression. Furthermore, we show that the well-known Simple Moving Average method, while it has its own advantages, has the weak spot when dealing with time series with large fluctuations.
Joe Maristela
No abstract is available for this record.
Nihar Patel, Nakul Vasani, Nilesh Kumar Jadav, Rajesh Gupta · 8 authors
<abstract><p>In this paper, a distributed machine-learning strategy, i.e., federated learning (FL), is used to enable the artificial intelligence (AI) model to be trained on dispersed data sources. The paper is specifically meant to forecast cryptocurrency prices, where a long short-term memory (LSTM)-based FL network is used. The proposed framework, i.e., <italic>F-LSTM</italic> utilizes FL, due to which different devices are trained on distributed databases that protect the user privacy. Sensitive data is protected by staying private and secure by sharing only model parameters (weights) with the central server. To assess the effectiveness of <italic>F-LSTM</italic>, we ran different empirical simulations. Our findings demonstrate that <italic>F-LSTM</italic> outperforms conventional approaches and machine learning techniques by achieving a loss minimal of $ 2.3 \times 10^{-4} $. Furthermore, the <italic>F-LSTM</italic> uses substantially less memory and roughly half the CPU compared to a solely centralized approach. In comparison to a centralized model, the <italic>F-LSTM</italic> requires significantly less time for training and computing. The use of both FL and LSTM networks is responsible for the higher performance of our suggested model (<italic>F-LSTM</italic>). In terms of data privacy and accuracy, <italic>F-LSTM</italic> addresses the shortcomings of conventional approaches and machine learning models, and it has the potential to transform the field of cryptocurrency price prediction.</p></abstract>