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3,636 results · page 31 of 152
Xiaochun Guo, Kun Guo, Shouyang Wang
No abstract is available for this record.
Farwa Batool
No abstract is available for this record.
Radovan Vojtko, Cyril Dujava
No abstract is available for this record.
Radu Lupu, Catalina Maria Popa
No abstract is available for this record.
Syed Jawad Hussain Shahzad, Elie Bouri, Larisa Yarovaya, Brian M. Lucey
No abstract is available for this record.
Damian Zięba
The aim of this study was to determine which type of Lévy motion fits the data of cryptocurrencies better, namely Alpha-Stable distribution or one of distributions from the family of generalized hyperbolic motions. The log-returns of 227 cryptocurrencies, standardized by the realized volatility estimated with the GARCH (1,1), were fitted to 11 types of distributions. The results show that the generalized hyperbolic motions fit the cryptocurrency data much more accurately than the Alpha-Stable distribution, similarly as in the case of TOP100 NASDAQ stocks. In the further stage of the analysis, it is shown how the distribution of cryptocurrency data varies over time, i.e. before, during, and after the 'boom-period' of 2017/2018.
BEN ABDALLAH Mohamed, TALBI Omar
The cryptocurrency market has experienced significant growth in international finance in recent years, attracting a large number of investors. This has led to a substantial increase in the overall trading volume[1], surpassing 1.2 trillion USD in July2023. Bitcoin in particular, has garnered significant interest from both advanced and emerging economies. Notably, Bitcoin is legal tender in El Salvador and the Central African Republic. The cryptocurrency market possesses distinct characteristics that set it apart from traditional markets such as exchanges, commodities, and equities. Its decentralization, facilitated by Blockchain technology, stands as a key differentiating factor. This technology enables anonymous trading of cryptocurrencies, with the identities of market participants, account holders, and electronic wallet managers remaining unknown. Consequently, there is an inherent ambiguity when it comes to characterizing the behavior of these investors or determining their preferred trading horizons. In this context, an important question arises: how do investors in the cryptocurrency market navigate price volatility? Furthermore, how do their trading strategies, which are inherently tied to their investment horizons, impact each other and consequently influence market prices? The heterogeneity hypothesis, which is a prevalent assumption in financial markets, is particularly relevant when exploring how investors in the cryptocurrency market navigate price volatility. The diverse trading strategies and approaches to handling price fluctuations among heterogeneous investors impact and influence the overall volatility and price dynamics of cryptocurrencies. The heterogeneity market hypothesis[2] refers to the existence of differences among investors or traders regarding their beliefs, information, risk preferences, investment strategies, and time horizons. In a heterogeneous market, investors, traders and financial institutions have varying views and expectations about the future performance of assets. These differences can manifest in various ways, such as market information access, market liquidity provision, trading strategies and market structure. While numerous empirical studies have explored the concept of market heterogeneity in relation to traditional assets such as those conducted by Müller et al. (1993;1997), Lux and Marchesi (2000), LeBaron (2000), Dacorogna et al. (2001) and Benhmad (2011), there is a paucity of research on this topic specifically focusing on cryptocurrencies. This paper aims to contribute to the existing literature that investigates the hypothesis of investor heterogeneity in traditional markets, including forex, stocks, and commodities. Specifically, we focus on the cryptocurrency market, which is predominantly dominated by Bitcoin. As of November 2021[3], Bitcoin holds the distinction of being the most actively traded cryptocurrency, with a market capitalization exceeding 1.2 trillion USD. Given its significance, Bitcoin garners substantial interest from investors across various categories. Therefore, this research seeks to uncover potential synergies among participants[4] in the cryptocurrency market and identify investor types and investment horizons prevalent in this market. By understanding these factors, we can gain insights into the dynamic volatility displayed by Bitcoin’s price. To tackle this issue, we investigate Bitcoin’s price volatility, analyzing causal relationships among short-term, medium and long-term traders by using wavelet transform to decompose volatility at different trading frequency scales[5] considered, then Granger causality test will be employed to determine if changes in one scale impact others. Furthermore, we extend this analysis by employing the nonlinear causality test proposed by Hmamouche. Y (2020). This nonlinear causality test goes beyond the limitations of the linear causality test and helps identifying potential nonlinear causal effects that may exist between the volatilities at different frequency scales, which could be overlooked by the linear causality test. The rest of this paper is organized as follows: Section 2 presents the theoretical framework relatively to the heterogeneity market hypothesis. Section 3 focuses on the empirical review. Section 4 and 5 turns to the data and methodology. Section 6 provides the empirical findings. Section 7 concludes.
Jun Deng, Huifeng Pan, Shuyu Zhang, Bin Zou
Bitcoin inverse futures are dominant derivative contracts traded in the cryptocurrency market. We aim to understand the mean-variance tradeoff of such contracts through quantitative studies. To this purpose, we derive explicit representations for the expectation and variance of the returns on Bitcoin inverse futures and obtain their first-order approximations. The empirical findings show that Bitcoin inverse futures are more (resp. less) risky than standard futures when the market is in backwardation (resp. contango). We further find that Bitcoin inverse futures bear higher downside risk, as measured by semi-deviation, than standard futures.
Wulf A. Kaal
No abstract is available for this record.
Soohyon Kim, Yeachan Bellman Hue
No abstract is available for this record.
Wenhao XIE, Guangxi Cao
No abstract is available for this record.
Illia Uzun, M. Lobachev
Abstract. This article presents an analysis of the historical dynamics of the cryptocurrency market based on time series data using the OHLCV dataset. The study presents the results of calculations of the main cryptocurrency market momentum technical indicators. Using intelligent computational methods, the paper assesses patterns and trends in the data of major cryptocurrencies. The study emphasizes the importance of technical analysis in understanding the volatile landscape of digital currencies. Key words: time-series, data analysis, cryptocurrency market, momentum indicators, technical analysis indicators, OHLCV.
Chulwoo Han, Byeongguk Kang, Jehyeon Ryu
No abstract is available for this record.
Grissa, Intissar, Abaoub, Ezzeddine
The main theme of this thesis lies in our attempt to contribute to explaining the Home Bias Puzzle (HBP) observed in international financial markets in the presence of Cryptocurrencies, within the framework of FinTech and more specifically in the context of Decentralized Finance (DeFi). Through a meticulous review of recent works on the question of international portfolio diversification, encompassing physico-financial assets such as Cryptocurrencies, technology stocks, classical stocks, currencies, commodities, and oil, we examined the issues of arbitrage and the strategy of choosing investment in domestic assets and/or choosing investment in international portfolio diversification. To empirically test our central issue, we validated four essays formulated as hypotheses: In the first essay on Efficiency and Volatility, we examined, through time series modeling, the impact of integrating cryptocurrencies into the investor's portfolio to verify our first hypothesis, namely the transmission of volatility shocks induced by this asset. The use of ARCH and GARCH modeling shows that the coefficients associated with them are close to unity, thus indicating a permanent effect of shocks on conditional variance. However, during the COVID-19 pandemic, Bitcoin was considered a safe haven asset. Also, relying on the econometric results of EGARCH and TARCH models, similar to Wang (2021), we show the existence of an excessive leverage effect on the volatility of future returns for Bitcoin (+26.50%) and Dogecoin (+65.07%). Furthermore, our study shows the absence of leverage for the other cryptocurrencies in the sample. Our second essay aims to validate the second hypothesis borrowed from industrial economics on the Integration-Segmentation-Diversification (ISD) triptych of asset portfolios, i.e., the relationship between goods and services markets and the capital market. To verify this hypothesis, we used a VAR (Vector AutoRegressive) modeling to analyze the causal time relationship between economic variables (real sphere) and financial variables (financial sphere) through standard tests (AIC) in the first stage and (SC) in the second stage. The results obtained show that price variations in the developed markets of the sample do not follow a common long-term trend. In this context, there would likely be an opportunity for diversification among developed markets, a product of financial liberalization (Attig.N. and al. (2023)). Thirdly, the empirical test of the existence of a Home Bias, our third essay and hypothesis were conducted over the period 2006-2021, with 640 observations. The determinants of the Home Bias Puzzle (HBP) were divided into seven panels: governance variables, macroeconomic variables, market size and microstructure variables, information asymmetry, familiarity and geography, Foreign Trade, and finally geopolitical variables. The econometric results we obtained are consistent with previous findings (Garg, Karmakar, M. and Paul, S., (2023); Lee, J. Lee, K. and Oh, F.D. (2023)). Finally, the last essay, reflecting hypothesis four on the relationship between Cryptocurrencies, Portfolio Diversification, and Behavioral Finance, highlights the superiority of the W. Sharpe (1964) performance index compared to other naive portfolio diversification strategies derived from the Mean-Variance approach by H. Markowitz (1952). Our results corroborate those obtained by Hachicha F., and al. (2023).
Santiago Alonso DÃaz, Adriana Garcia
No abstract is available for this record.
Ali Asare Nezhad, Ahmad Kalhor, Reshad Hosseini, Babak Nadjar Araabi · 5 authors
No abstract is available for this record.
Yu. V. Bezsmolnyi, Maksym Seniv
The work carried out a comparative analysis of scientific publications regarding the possibility of predicting the direction of the cryptocurrency exchange rate using the data of open numerical indicators, based on the results of which it can be concluded that due to the volatility of the cryptocurrency market and the need for accurate forecasting, there is a need to create an aggregated indicator that will take into account the current price exchange rate asset, parameters of simple indicators, trading volume, etc. In addition, this indicator will be a parameter for the application of a multi-criteria analysis model in the process of supporting decision-making for cryptocurrency trading. A software decision support system for cryptocurrency traders on the Trading View platform has also been developed, which allows the cryptocurrency trader to get the value of the current situation of the cryptocurrency market in the form of a value using the method of weighting coefficients and selected indicators. Among the selected indicators: RSI, MA, CCI, Stochastic Oscillator, OBV, ADX, CMF to determine the moment of opening a position, and Fibonacci Retracement, Ichimoku Cloud to determine the closing of positions. Taking into account all the indicators and the coefficients determined for them, the obtained range of values is from 0 % to 100 %. If the value of the indicator exceeds the threshold of 20 %, it means that it is necessary to inform the trader about a possible entry point. That is, a value of 20 % to 40 % is weak performance, 40 % to 60 % is medium performance, 60 % to 80 % is strong performance, and a value greater than 80 % will not be overlapped by new pyramiding values for a better overall indicator success rate. The value of the indicator determines the potential effectiveness of opening positions, and thanks to the RSI indicator, the direction of opening positions is determined. The direction of the position is divided into long and short. An indicator has been developed for the TradingView platform, which, unlike existing simple indicators, collects data from open access and calculates a potential point for opening a position. Obtaining the numerical value of a single indicator saves the trader time to review and analyze a collection of indicators and time to decide on opening a position, as the cryptocurrency market is known for its sudden volatility, where a decision must be made quickly.
Hubert Dichtl, Wolfgang Drobetz, Tizian Otto
No abstract is available for this record.
Andrea Andolfatto, Siddharth Naik, Lorenzo Schönleber
No abstract is available for this record.
Aida Mohagheghzadeh, Babak Amiri, Ahmad Makui
This paper addresses the imperative task of assessing and ranking cryptocurrencies, particularly pertinent in the context of the burgeoning popularity of public blockchains. The proliferation of available options necessitates a rigorous evaluation, prompting the formulation of a novel model grounded in both objective and subjective criteria. To contend with the challenge posed by the expanding landscape of public blockchains, ten discerning criteria are delineated, encompassing facets such as Technology, TPS, Market capitalization, GitHub fork, GitHub stars, Twitter followers, Twitter hashtags, trading volume, sentiment score, and the price range differential. Leveraging expert opinions, the pairwise impact of these criteria is ascertained, and the DEMATEL method is judiciously employed to derive their respective weights. Subsequently, the PROMETHEE method is harnessed to effectuate the ranking of 20 cryptocurrencies predicated on the identified criteria. Furthermore, the integration of LSTM enables the prediction of values for four predictable criteria, seamlessly incorporated into the PROMETHEE model to furnish rankings across diverse temporal intervals. The proposed model, thus, presents a holistic and pragmatic approach to inform investment decision-making within the dynamic cryptocurrency market. By embracing a comprehensive set of criteria and integrating predictive analytics, this model stands as a valuable contribution to the field, offering nuanced insights to stakeholders navigating the complexities of cryptocurrency investment.
Ziyue Su
In recent years, with the rapid development of science and technology today (especially blockchain techniques), a special currency cryptocurrency (also relatively known as digital currencies) was born in 2008 and has received a lot of attention. On account of its special nature and intrinsic, a large amount of investors often combine digital currencies and quantitative trading to make profits and earn extra returns based on the concepts. With this in mind, this study mainly describes the definition of digital currency and its development process and compares digital currency with ordinary currency to highlight its advantages and disadvantages. Subsequently, this research introduces the combined application of digital currency and quantitative transaction in general with some of the backtesting results. According to the analysis, this paper puts forward suggestions on the existing problems of digital currency and promotes its further development in the future. Overall, these results shed light on guiding further exploration of quantitative strategy designs for cryptocurrency.
G P Sanjana, J. Jaswith, K. Aashritha, Nirmal Keshari Swain · 5 authors
No abstract is available for this record.
Tran Le Nguyen, Lien Thi Kim Nguyen, Nhu-Tai Do
No abstract is available for this record.
Toshiko Matsui, William J. Knottenbelt
No abstract is available for this record.