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4,843 results · page 140 of 202
Oliver Borgards, Robert Czudaj
No abstract is available for this record.
Shaista Wasiuzzaman, Hajah Siti Wardah Haji Abdul Rahman
No abstract is available for this record.
Elie Bouri, Tareq Saeed, Xuan Vinh Vo, David Roubaud
No abstract is available for this record.
Vasileios Siakoulis, Αναστάσιος Πετρόπουλος, Panagiotis Lazaris
The recent surge in Bitcoin price performance has attracted significant attention from both the market and academic researchers. This paper constitutes the first principled attempt to determine market risk own-funds requirements for Bitcoin. To this end, we examine price microstructure of the USD per bitcoin, and compare to other financial variables, as a proxy toward classifying Bitcoin into the appropriate risk-class. Using the outcomes of this analysis, we classify and quantify the entailed risk from a market risk minimum capital requirements perspective. To perform the prescribed analysis, we introduce a novel methodological paradigm, which adopts bleeding-edge concepts from the field of Data Science and Machine Learning.
Gianna Figà‐Talamanca, Sergio M. Focardi, Marco Patacca
Abstract In this paper, we apply dynamic factor analysis to model the joint behaviour of Bitcoin, Ethereum, Litecoin and Monero, as a representative basket of the cryptocurrencies asset class. The empirical results suggest that the basket price is suitably described by a model with two dynamic factors. More precisely, we detect one integrated and one stationary factor until the end of August 2019 and two integrated factors afterwards. Based on this evidence, we define a multiple long-short trading strategy which proves profitable when the second factor is stationary.
Khaled Mokni
No abstract is available for this record.
Chi‐Wei Su, Meng Qin, Xiaolei Zhang, Ran Tao · 5 authors
This paper probes the interrelationship between Bitcoin price (BP) and the U.S. partisan conflict (PC) by performing the bootstrap full- and sub-sample Granger causality tests. The positive influence from PC to BP reveals that Bitcoin can be considered as a tool to avoid the uncertainty caused by the rise in PC. However, this view cannot be supported by the negative impact, the major reason is that the burst of bubble undermines the hedging ability of Bitcoin. The above results are inconsistent with the intertemporal capital asset pricing model (ICAPM), underlining that high PC may drive BP to rise, in order to compensate for the losses and costs from factionalism. Conversely, BP has a negative impact on PC, suggesting that the U.S. political situation can be reflected by the Bitcoin market. Under the circumstance of the fiercer factionalism in the U.S., this investigation can benefit investors and related authorities.
Shinji Kakinaka, Ken Umeno
Asymmetric relationship between price and volatility is a prominent feature of the financial market time series. This paper explores the price–volatility nexus in cryptocurrency markets and investigates the presence of asymmetric volatility effect between uptrend (bull) and downtrend (bear) regimes. The conventional GARCH-class models have shown that in cryptocurrency markets, asymmetric reactions of volatility to returns differ from those of other traditional financial assets. We address this issue from a viewpoint of fractal analysis, which can cover the nonlinear interactions and the self-similarity properties widely acknowledged in the field of econophysics. The asymmetric cross-correlations between price and volatility for Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC) during the period from June 1, 2016 to December 28, 2020 are investigated using the MF-ADCCA method and quantified via the asymmetric DCCA coefficient. The approaches take into account the nonlinearity and asymmetric multifractal scaling properties, providing new insights in investigating the relationships in a dynamical way. We find that cross-correlations are stronger in downtrend markets than in uptrend markets for maturing BTC and ETH. In contrast, for XRP and LTC, inverted reactions are present where cross-correlations are stronger in uptrend markets.
Piyachart Phiromswad, Pattanaporn Chatjuthamard, Sirimon Treepongkaruna, Sabin Srivannaboon
This paper empirically examines jumps and cojumps of both major and minor cryptocurrencies. Understanding the nature of their jumps and cojumps plays an important role in risk management, asset allocation and pricing of derivatives. We find that all cryptocurrencies display significant jumps. Furthermore, minor cryptocurrencies appear to have significantly higher jump intensity and jump size than major cryptocurrencies. Finally, we find that cojumps of the Thai stock market index and minor cryptocurrencies have a greater intensity than that of major cryptocurrencies.
Syed Jawad Hussain Shahzad, Faruk Balli, Muhammad Abubakr Naeem, Mudassar Hasan · 5 authors
No abstract is available for this record.
Mohammed Sawkat Hossain
Purpose The authors make a fundamental initial effort to conduct a systematic review analysis on “cryptocurrency,” mainly to analyze the way it has been changing the “stereotype” financial transactions, and also identify the probable unexplored research avenues on this innovative investment regime. The study aims to draw the landscape of the current state, prospects, challenges, trends and possible agendas of cryptocurrency in the global market. Design/methodology/approach Using a quali-quantitative approach widely known as meta-literature review, the synthesis analysis on “cryptocurrency” is conducted. Methodologically, the authors review and analyze the most recent and relevant papers preferably published between 2016 and 2020 in leading business and finance journals of ISI Web of Science (ISI WOS) through bibliometric analysis particularly coupled with content analysis. Findings The findings of the meta-analysis summarize the relevant stylized facts of the cryptocurrency market: distinctive features of blockchain technology, decentralized payment method, low-cost facility, ensuring pseudo-anonymity, independence from central authority, double spending attack protection, organic and instantaneous nature, among others. In addition, the analysis identified several future research regimes: pricing model, prospect of investment regime, hedging properties, volatility dynamics, information asymmetry, underlying risk factors and bubble-like nature in global cryptocurrency market. Practical implications This academic novelty significantly contributes to enhance our knowledge on the current state-of-the-art of digital finance, outlines the research agenda and eventually provides important investment implications for financial managers, research analysts, investors, market practitioners, regulatory compliance professionals and policymakers. Therefore, the findings shed the lights on new investment opportunity in the global market. Originality/value Cryptocurrency, virtual currency or digital asset having cryptography for idiosyncratic security features, seems to be a persistent paradigm shift in the digitalized financial system. Despite the continuing growth, the academic research on cryptocurrency is still at nascent stage, particularly because researchers did not deeply draw attention at this financial innovation. In addition, the authors argue that none of the earlier studies yet conducted a meta-analysis on this latest investment regime. Therefore, this review study is the initial attempt to fill up the gap in the finance literature.
Necip İhsan Arıkan
Technically cryptocurrencies often have Distributed Ledger Technology (DLT) and encryption based on infrastructure called blockchain that allows all nodes to verify the validity of a transaction. In terms of monetary theory, cryptocurrencies are currently the most developed virtual currencies that cannot perform all the basic functions of money such as the account, exchange and capital accumulation.The price of cryptocurrency is based on supply and demand, without an intervention of a central authority. Dynamics that affect the value of cryptocurrencies can be classified as internal and external variables. The internal dynamics of cryptocurrencies have been examined under the headings of economic infrastructure and technological infrastructure. External factors that are effective in determining the value are observed as popularity, security, volume, inflation, tax, crypto exchange accidents, perception, speculations / manipulations and news.
Nazan Şak
Kripto paralar, teknolojideki ilerlemeler ile birlikte ilk ortaya çıktığı günden itibaren hızlı bir şekilde gelişme göstererek işlem görmeye başlamıştır. Matematiksel algoritmalar kullanılarak özel şifreleme mekanizmalarıyla blok zincir (blockchain) olarak adlandırılan sistemler ile üretilen kripto paralar içinde Bitcoin, en yüksek piyasa değerine ve işlem hacmine sahip sanal paradır. Zamanla Bitcoin’e alternatif birçok sanal para da bu sistem içinde yer almaya başlamıştır. Bu çalışmada, son dönemde diğer yatırım araçlarına alternatif olarak görülen kripto paralardan piyasa değeri olarak ilk 30 içinde yer alan ve ilgili dönemde verisine ulaşılabilen 13 kripto para kullanılmıştır. Pozitif ve negatif şokların yaşandığı kazandıran ve kaybettiren dönemlerde bu paralar arasındaki ilişki, Hatemi-J asimetrik nedensellik testiyle incelenmiştir. Bu amaçla, Bitcoin, Ethereum, Ripple, Bitcoin cash, Litecoin, Eos, Binance coin, Stellar, Monero, Dash, Ethereum classic, Neo ve Zcash kripto paralarının 26.7.2017-27.2.2020 tarihleri arasındaki günlük kapanış fiyatları verileri kullanılmıştır. Yapılan analiz sonucunda özellikle kazandıran dönemlerde kişilerin yatırım araçlarını çeşitlendirebildiği; kaybettiren dönemlerde ise daha az riskli olarak görülen kripto paralara yatırım yaptığı gözlenmiştir. Negatif şok dönemlerinde en çok tercih edilen kripto para Ripple, Binance coin, Bitcoin cash ve Monero iken; pozitif şok dönemlerinde Bitcoin, Ripple, Binance coin, Dash ve Bitcoin cash’dir.
Murat AKBALIK, Nicholas Apergis, Melis Zeren, Ömer Sarıgül
The paper investigates the impact of Bitcoin volatility on international capital inflows through the methodology of an AR(1)-CGARCH model across a global panel of 132 countries, as well as across different regions, i.e. Asia, European Union (EU), America (including the US, Canada and Latin American countries), and Africa. The findings document that there is a strong impact of Bitcoin volatility on global international capital inflows, as well as in the cases of the American and Asian cases. However, the results document a statistically insignificant effect for the cases of the EU and African countries.
Ayben Koy, Mustafa YAMAN, Sefa Mete
Blok zincir sisteminde işlem gören en yeni inovatif finansal ürünlerden biri olan kripto paralar, yatırımcılardan yüksek ilgi görmektedir. Kripto para piyasasının en yüksek işlem hacimli ürünü Bitcoin (BTC), gösterdiği yüksek oynaklıklar ve spekülatif fiyat balonları ile de ön plana çıkmıştır. BTC’nin volatilite yapısında ABD borsa endeks getirilerinin varlığını araştıran bu çalışma, 10.03.2016 – 11.06.2019 dönemindeki günlük verileri kapsar. Genelleştirilmiş Otoregresif Koşullu Değişen Varyans modellerinden GARCH, EGARCH ve TARCH modellerinin kullanıldığı çalışmada, SP500, Nasdaq100 ve Dow Jones Industrial varyans değişkeni olarak kullanılmıştır. Bulgular, (1) her üç endeksin de BTC’in volatilitesini açıklamada anlamlı olduğu, (2) borsa endeksleri ile geliştirilmiş modellerin, GARCH, EGARCH ve TARCH modellerinin tamamında benzer temel modelden daha güçlü olduğu ve (3) endekslerle geliştirilmiş EGARCH modelinin ise en güçlü model olduğunu göstermektedir
Kim Hung Pho, Sel Ly, Richard Lu, Thị Hồng Vân Hoàng · 5 authors
No abstract is available for this record.
Viviane Y. Naïmy, Omar Haddad, Gema Fernández‐Avilés, Rim El Khoury
This paper provides a thorough overview and further clarification surrounding the volatility behavior of the major six cryptocurrencies (Bitcoin, Ripple, Litecoin, Monero, Dash and Dogecoin) with respect to world currencies (Euro, British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen), the relative performance of diverse GARCH-type specifications namely the SGARCH, IGARCH (1,1), EGARCH (1,1), GJR-GARCH (1,1), APARCH (1,1), TGARCH (1,1) and CGARCH (1,1), and the forecasting performance of the Value at Risk measure. The sampled period extends from October 13th 2015 till November 18th 2019. The findings evidenced the superiority of the IGARCH model, in both the in-sample and the out-of-sample contexts, when it deals with forecasting the volatility of world currencies, namely the British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen. The CGARCH alternative modeled the Euro almost perfectly during both periods. Advanced GARCH models better depicted asymmetries in cryptocurrencies' volatility and revealed persistence and "intensifying" levels in their volatility. The IGARCH was the best performing model for Monero. As for the remaining cryptocurrencies, the GJR-GARCH model proved to be superior during the in-sample period while the CGARCH and TGARCH specifications were the optimal ones in the out-of-sample interval. The VaR forecasting performance is enhanced with the use of the asymmetric GARCH models. The VaR results provided a very accurate measure in determining the level of downside risk exposing the selected exchange currencies at all confidence levels. However, the outcomes were far from being uniform for the selected cryptocurrencies: convincing for Dash and Dogcoin, acceptable for Litecoin and Monero and unconvincing for Bitcoin and Ripple, where the (optimal) model was not rejected only at the 99% confidence level.
Elie Bouri, David Gabauer, Rangan Gupta, Aviral Kumar Tiwari
No abstract is available for this record.
Ángeles Cebrián-Hernández, Enrique Jiménez-Rodríguez
Since the launch of Bitcoin, there has been a lot of controversy surrounding what asset class it is. Several authors recognize the potential of cryptocurrencies but also certain deviations with respect to the functions of a conventional currency. Instead, Bitcoin’s diversifying factor and its high return potential have generated the attention of portfolio managers. In this context, understanding how its volatility is explained is a critical element of investor decision-making. By modeling the volatility of classic assets, nonlinear models such as Generalized Autoregressive Conditional Heteroskedasticity (GARCH) offer suitable results. Therefore, taking GARCH(1,1) as a reference point, the main aim of this study is to model and assess the relationship between the Bitcoin volatility and key financial environment variables through a Conditional Correlation (CC) Multivariate GARCH (MGARCH) approach. For this, several commodities, exchange rates, stock market indices, and company stocks linked to cryptocurrencies have been tested. The results obtained show certain heterogeneity in the fit of the different variables, highlighting the uncorrelation with respect to traditional safe haven assets such as gold and oil. Focusing on the CC-MGARCH model, a better behavior of the dynamic conditional correlation is found compared to the constant.
Ioannis E. Livieris, Niki Kiriakidou, Stavros Stavroyiannis, Panagiotis Pintelas
Nowadays, cryptocurrencies are established and widely recognized as an alternative exchange currency method. They have infiltrated most financial transactions and as a result cryptocurrency trade is generally considered one of the most popular and promising types of profitable investments. Nevertheless, this constantly increasing financial market is characterized by significant volatility and strong price fluctuations over a short-time period therefore, the development of an accurate and reliable forecasting model is considered essential for portfolio management and optimization. In this research, we propose a multiple-input deep neural network model for the prediction of cryptocurrency price and movement. The proposed forecasting model utilizes as inputs different cryptocurrency data and handles them independently in order to exploit useful information from each cryptocurrency separately. An extensive empirical study was performed using three consecutive years of cryptocurrency data from three cryptocurrencies with the highest market capitalization i.e., Bitcoin (BTC), Etherium (ETH), and Ripple (XRP). The detailed experimental analysis revealed that the proposed model has the ability to efficiently exploit mixed cryptocurrency data, reduces overfitting and decreases the computational cost in comparison with traditional fully-connected deep neural networks.
Zhenghui Li, Hao Dong, Christos Floros, Athanasios Charemis · 5 authors
The article aims to explore the heterogeneous feature in the determination of Bitcoin volatility using a Markov regime-switching model and test its forecasting ability. The forecasting methodology of the risk measurement of Bitcoin’s returns is based on the Conditional Autoregressive Value at Risk models (CAViaR) approach. Our results show that Bitcoin’s volatility is significantly related to the volatility of the crypto-asset’s return and the main determinants of volatility are speculation, investor attention, market interoperability and the interaction between speculation and market interoperability. In addition, we present evidence that investors’ attention is the main source of volatility. Speculation and the interaction term are related in a “U-shaped” form, whereas investor attention and market interoperability show a linear trend on the volatility of Bitcoin.
Navleen Kaur, Supriya Lamba Sahdev, Gurinder Singh, Ashna Garg
The paper provides a detailed understanding about what exactly Bitcoin (BTC) is. It also answers the question about where and how to use bitcoin as a consumer. The paper elaborates about how bitcoin can be earned and generated along with an ease to understand explanation about its technology. The objective of the paper is to identify whether BTC is still a feasible asset to make an investment in as compared with other existing securities and assets for both short term and long-term investors in the global market. The purpose is to figure out if bitcoin has potential in 2020 and coming years and will it yield high returns even after it has seen a price drop in 2019. Bitcoin became popular when its price peaked to almost $20000 in 2017. Global investors not only look at BTC in short term speculative prospective but also as a long-term investment asset hence it is called as the Digital Gold. BTC is also considered to be highly volatile in nature which is why studying the risk factor involved in investing in it becomes very important. Both mean variance approach and correlation approaches are used to evaluate the risk involved in BTC as an asset and comparison is made with other popular global assets to see if investing in Bitcoin is an ideal option or not.
Νikolaos Kyriazis
This paper conducts a review on theoretical and empirical findings on the increasingly popular measure of trade policy uncertainty (TPU) in economics and finance. Moreover, an empirical investigation takes place in order to find the impact that TPU exerts on Bitcoin market values by employing a spectrum of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) specifications. Existing studies support that trade policy uncertainty leads to lower-quality and more expensive products and weak participation in international trade. Moreover, it contributes to lower democratic sentiment, hesitant internal migration and lesser socio-economic mobility and higher fluctuations in profitable assets. Moreover, our econometric findings reveal that TPU positively affects Bitcoin prices while crude oil values negatively influence this major cryptocurrency. Thereby, higher trade policy uncertainty is found to increase demand and favorite investments into risky assets in order to ameliorate the risk-return trade-off in investors’ portfolios. This study provides a compass for investing during turmoil due to trade wars and tariffs.
Amanjot Singh
No abstract is available for this record.