\nAmaç – Çalışmada önemli fiyat dalgalanmalarına sahip kripto paralardan en yüksek işlem hacmine sahip olan Bitcoin’in volatilite dinamiklerini tespit etmek için Bitcoin getirilerinin yükseliş/kazandıran ve düşüş/kaybettiren rejimleri, rejim geçiş olasılıkları ve rejimde kalma sürelerinin tespit edilmesi amaçlanmıştır. Yöntem – Çalışmada Bitcoin getirilerinin yükseliş/kazandıran ve düşüş/kaybettiren rejimleri, rejim geçiş olasılıkları ve rejimde kalma süreleri hem değişimlerin hem de rejim geçiş olasılıklarının hesaplanmasına imkan veren Markov Rejim Değişim Modeli kullanılmıştır. Bulgular – Çalışma kapsamında çalışmaya konu periyotta Bitcoin getiri serisi için en uygun modelin üç rejimli MSIH(3)-AR(1) modeli olduğu tespit edilmiştir. Modele ilişkin analizler yapıldığında ise söz konusu modelin doğrusal modele göre daha güçlü sonuçlar verdiği görülmektedir. Üç rejimden oluşan modelde katsayısı negatif olan rejim 1 daralma rejimi dönemini, katsayıları pozitif olan rejim 2 geçiş ve rejim 3 ise genişleme rejimi dönemini göstermektedir. Bitcoin getiri serisinin bir rejimdeyken bir sonraki dönemde aynı rejimde kalma olasılıkları yüksek iken bir sonraki dönemde özellikle rejim 1’den diğer rejimlere, diğer rejimlerden ise rejim 1’e geçiş olasılıklarının düşük olduğu tespit edilmiştir. Tartışma – Çalışma kapsamında ele alınan dönem için Bitcoin getirilerinin rejim kalıcılığının yüksek olduğu tespit edilmiştir. Bu bağlamda yatırımcıların, Bitcoin getirilerinin incelenen dönemde hangi rejimde olduğunu bilmesi durumunda, bir sonraki dönemde bu rejimde kalma olasılığını tahminini yaparak yatırım kararını buna göre verme imkanı bulunmaktadır. Bununla birlikte ortalama rejimlerde kalma sürelerinin düşük olduğu göz önüne alındığında özellikle Bitcoin’i portföylerinde bulunduran aktif yatırımcıların sürekli olarak bu aracın rejim değişimlerini takip etmesi durumunda portföylerinin faydasını arttırma imkanı yakalayacağı görülmektedir.\n
Raja Nabeel‐Ud‐Din Jalal, Ilan Alon, Andrea Paltrinieri
Within a decade, cryptocurrencies have captured significant attention. After Bitcoin's emergence in 2008, new cryptocurrencies started to enter the financial market. We use bibliometric analysis to explore the cryptocurrency literature in the areas of business and management. We review and analyze 464 research articles through bibliometric measurements and social network analysis using Biblioshiny in R. Our study highlights the most influential authors, institutions, countries, and studies. Using the results of the social network analysis of the authors and countries, we illustrate the co-authorship and collaboration among authors from various countries at the institutional level and demonstrate how they have expanded the knowledge in this area. We identify four streams in the current cryptocurrency literature: (i) the determinants of cryptocurrency returns, (ii) the efficiency of cryptocurrencies, (iii) tests of portfolio diversification and sheep flock behaviour and (iv) the regulation, governance, and socio-economic impact of cryptocurrencies. Finally, we present an agenda for future research in the field.
The worth of digital currencies is increasing due to its proposed advantages and profits. Though decentralized, these digital currencies can be bought with digital wallets using cryptocurrency platform. Efficient Market Hypothesis (EMH) suggests fundamentals for understanding of financial markets however the opponents believe that this theory is incompetent in explaining the functioning of the markets. EMH is not a perfect model nevertheless it provides a concrete base for the analysis of capital markets. EMH’s weak version is utilized for this study. This research compares three top cryptocurrencies- Bitcoin, Ethereum and Litecoin to analyze their long-range memory effect to check the market efficiency and also to estimate the volatility for further investments in different cryptocurrencies. Generalized Hurst exponent methodology is applied to examine long range memory in selected cryptocurrencies market. Daily data from 17th September 2015 till 17th October 2018 is used in this study. It was found that: (i) Long memory exists in the selected cryptocurrencies; (ii) Ethereum market is more persistent than Bitcoin and Litecoin as its Hurst exponent is more than the other cryptocurrencies. These findings can be a source of assistance for the policy makers and investors while making prudent decisions regarding investment in emerging cryptocurrencies market.
Abstract We propose an equilibrium valuation model for bitcoin options by extending Cao. Bitcoin is interpreted as a foreign currency in a small open economy where money supply and aggregate dividend are exogenous. The equilibrium bitcoin prices increase with diffusive and jump risks of these two exogenous factors. Analytical option pricing formulas are obtained with Merton's model as a special case. Static analysis reveals that a bitcoin call (put) option value increases (decreases) with the money supply growth rate. Numerical analysis shows that all risks lead to a positive premium in option prices relative to the Black–Scholes model.
Mohamed Fakhfekh, Ahmed Jeribi, Ahmed Ghorbel, Néjib Hachicha
Purpose In a first place, the present paper is designed to examine the dynamic correlations persistent between five cryptocurrencies, WTI, Gold, VIX and four stock markets (SP500, FTSE, NIKKEI and MSCIEM). In a second place, it investigates the relevant optimal hedging strategy. Design/methodology/approach Empirically, the authors examine how WTI, Gold, VIX and five cryptocurrencies can be applicable to hedge the four stock markets. Three variants of multivariate GARCH models (DCC, ADCC and GO-GARCH) are implemented to estimate dynamic optimal hedge ratios. Findings The reached findings prove that both of the Bitcoin and Gold turn out to display remarkable hedging commodity features, while the other assets appear to demonstrate a rather noticeable disposition to act as diversifiers. Moreover, the results show that the VIX turns out to stand as the most effectively appropriate instrument, fit for hedging the stock market indices various related refits. Furthermore, the results prove that the hedging strategy instrument was indifferent for FTSE and NIKKEI stock while for the American and emerging markets, the hedging strategy was reversed from the pre-cryptocurrency crash to the during cryptocurrency crash period. Originality/value The first paper's empirical contribution lies in analyzing emerging cross-hedge ratios with financial assets and compare hedging effectiveness within the period of crash and the period before Bitcoin crash as well as the sensitivity of results to refits choose to compare between short term hedging strategy and long-term one.
We investigated the differential impacts of a new Twitter-based Market Uncertainty index (TMU) and variables for Bitcoin before and during the COVID-19 pandemic. Results showed that TMU is a leading indicator of Bitcoin returns only during the pandemic, and the effect of the TMU on Bitcoin’s conditional volatility is significantly greater during the pandemic. Furthermore, during the pandemic, the uncertainty content of people’s tweets is impacted by the highly salient Bitcoin market. Taken together, our results suggest that the information contained in virtual communities such as Twitter have a much larger impact on cryptocurrency markets following COVID-19.
This paper focuses on forecasting the price of Bitcoin, motivated by its market growth and the recent interest of market participants and academics. We deploy six machine learning algorithms (e.g., Artificial Neural Network, Support Vector Machine, Random Forest, k-Nearest Neighbours, AdaBoost, Ridge regression), without deciding a priori which one is the ‘best’ model. The main contribution is to use these data analytics techniques with great caution in the parameterization, instead of classical parametric modelings (AR), to disentangle the non-stationary behavior of the data. As soon as Bitcoin is also used for diversification in portfolios, we need to investigate its interactions with stocks, bonds, foreign exchange, and commodities. We identify that other cryptocurrencies convey enough information to explain the daily variation of Bitcoin’s spot and futures prices. Forecasting results point to the segmentation of Bitcoin concerning alternative assets. Finally, trading strategies are implemented.
Christoph J. Börner, Ingo Hoffmann, Jonas Krettek, Tim Schmitz
Abstract Cryptocurrencies (CCs) have become increasingly interesting for institutional investors’ strategic asset allocation and will therefore be a fixed component of professional portfolios in the future. However, this asset class differs from established assets primarily in that it has a higher standard deviation and tail risk. The question then arises whether CCs with similar statistical key figures exist. On this basis, a core market incorporating CCs with comparable properties enables the implementation of a tracking error approach. A prerequisite for this is the segmentation of the CC market into a core and a satellite, with the latter comprising the accumulation of the residual CCs remaining in the complement. Using a concrete example, we segment the CC market into these components based on modern methods from image/pattern recognition.
Christoph J. Börner, Ingo Hoffmann, Lars M. Kürzinger, Tim Schmitz
This paper evaluates and assesses the risk associated with capital allocation in cryptocurrencies (CCs). In this regard, we take a basket of 27 CCs and the CC index EWCI$^-$ into account. After considering a series of statistical tests we find the stable distribution (SDI) to be the most appropriate to model the body of CCs returns. However, as we find the SDI to possess less favorable properties in the tail area for high quantiles, the generalized Pareto distribution is adapted for a more precise risk assessment. We use a combination of both distributions to calculate the Value at Risk and the Conditional Value at Risk, indicating two subgroups of CCs with differing risk characteristics.
This article employs a Markovian regime-switching autoregressive approach to examine triangular arbitrage across forex and cryptocurrency markets during the coronavirus disease 2019 crisis. The findings suggest the following: (1) profitable triangular arbitrage tends to occur in the turbulent period during the crisis, significantly outperforming cryptocurrency investments; and (2) the persistent profitability of triangular arbitrage ensues from strong memory of high returns, low risk, and shock response to global quantitative monetary easing policy. Regulatory authorities should consolidate cryptocurrency supervision systems and establish cross-border coordination mechanisms to stabilize exchange rates and enhance market efficiency.
Purpose The purpose of this study is to investigate volatility connectedness between major cryptocurrencies by the virtue of market capitalization. In this context, this paper implements the frequency connectedness approach of Barunik and Krehlik (2018) and to measure short-, medium- and long-term connectedness between realized volatilities of cryptocurrencies. Additionally, this paper analyzes network graphs of directional TO/FROM spillovers before and after the announcement of the COVID-19 pandemic by the World Health Organization. Design/methodology/approach In this study, we examine the volatility connectedness among eight major cryptocurrencies by the virtue of market capitalization by using the frequency connectedness approach over the period July 26, 2017 and October 28, 2020. To this end, this paper computes short-, medium- and long-cycle overall spillover indexes on different frequency bands. All indexes properly capture well-known events such as the 2018 cryptocurrency market crash and COVID-19 pandemic and markedly surge around these incidents. Furthermore, owing to notably increased volatilities after the official announcement of the COVID-19 pandemic, this paper concentrates on network connectedness of volatility spillovers for two distinct periods, July 26, 2017–March 10, 2020 and March 11, 2020–October 28, 2020, respectively. In line with the related studies, major cryptocurrencies stand at the epicenter of the connectedness network and directional volatility spillovers dramatically intensify based on the network analysis. Findings Overall spillover indexes have fluctuated between 54% and 92% in May 2018 and April 2020. The indexes gradually escalated till November 9, 2018 and surpassed their average values (71.92%, 73.66% and 74.23%, respectively). Overall spillover indexes dramatically plummeted till January 2019 and reached their troughs (54.04%, 57.81% and 57.81%, respectively). Etherium catalyst the highest sum of volatility spillovers to other cryptocurrencies (94.2%) and is followed by Litecoin (79.8%) and Bitcoin (76.4%) before the COVID-19 announcement, whereas Litecoin becomes the largest transmitter of total volatility (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%). Except for Etherium, the magnitudes of total volatility spillovers from each cryptocurrency notably increase after – COVID-19 announcement period. The medium-cycle network topology of pairwise spillovers indicates that the largest transmitter of total volatility spillover is Litecoin (89.5%) and followed by Bitcoin (89.3%) and Etherium (88.9%) before the COVID-19 announcement. Etherium keeps its leading role of transmitting the highest sum of volatility spillovers (89.4%), followed by Bitcoin (88.9%) and Litecoin (88.2%) after the COVID-19 announcement. The largest transmitter of total volatility spillovers is Etherium (95.7%), followed by Litecoin (81.2%) and Binance Coin (75.5%) for the long-cycle connectedness network in the before-COVID-19 announcement period. These nodes keep their leading roles in propagating volatility spillover in the latter period with the following sum of spillovers (Etherium-89.5%, Bitcoin-88.9% and Litecoin-88.1%, respectively). Research limitations/implications The study can be extended by including more cryptocurrencies and high-frequency data. Originality/value The study is original and contributes to the extant literature threefold. First, this paper identifies connectedness between major cryptocurrencies on different frequency bands by using a novel methodology. Second, this paper estimates volatility connectedness between major cryptocurrencies before and after the announcement of the COVID-19 pandemic and thereby to concentrate on its impact on the cryptocurrency market. Third, this paper plots network graphs of volatility connectedness and herewith picture the intensification of cryptocurrencies due to a major financial distress event.
To date, cryptocurrency prices are volatile and many cryptocurrency developers have adopted ad hoc approaches to stabilize their cryptocurrency price. When these currencies are not 100% backed by other valued assets, part of their price volatility may arise from self-fulfilling expectations of a speculative attack (as in Obstfeld (1996)). We show that an exchange rate policy, which is less than 100% backed and dynamically adjusts in response to traders’ conversion demand eliminates speculative attacks while, under some conditions, preserving much of the desired exchange rate stability. This dynamic exchange rate policy admits a great deal of discretion to and requires commitment by the party implementing the policy. We demonstrate how to implement this policy using the Ethereum network—a smart contract blockchain environment—and how this implementation yields commitment to the policy.
Alim Al Ayub Ahmed, Harish Paruchuri, Siddhartha Vadlamudi, Apoorva Ganapathy
Digital finance is assuming a significant part in the arrangement of financial services all over the world. Fast growth with digitalization, data analysis, and computing capacities allows for a whole new scope of financial services and transactions. This financial development empowered by digital financial technology (Fintech) has pulled in a ton of attention, as it could offer some potential for economic growth and development. As a part of the Fintech environment, cryptography has started to grow quickly and digital assets are acquiring in favorability among financial bankers and investors. Human behavior as they engage with financial activities is personally associated with the noticed market elements. However, with many existing theories and studies on the fundamental motivations of the conduct of people in financial frameworks, there is still restricted experimental derivation of the behavioral conduct of the financial agents from a definite market analysis. Cryptocurrency technology has given a map to this analysis with its voluminous data and its transparency of financial transactions. It has empowered us to perform inference on the personal conduct standards of users in the market, which we analyze in the bitcoin and ethereum cryptocurrency markets. In our study, we initially decide different properties of the cryptography users by complex network analysis. Financial cryptography is a difficult subject that necessitates abilities from a variety of seemingly unrelated fields. There is a serious risk that attempts to establish Financial Cryptography frameworks would simplify or omit key disciplines because they are caught between central banking and cryptography. This paper discusses research that attempts to limit the scope of Financial Cryptography. This model should assist the project, administrative, and requirements personnel by classifying each discipline into a seven-layer model of basic nature, where the link between each adjoining layer is evident. While this model is shown as effective, all models have cutoff points. This one does not present a design system or a protocol agenda. Furthermore, given the model's initial adaptation and the field, it should be viewed as a suggestion of complexity rather than a definitive approach.