Volatility is an important concept for identifying and predicting the risk of financial products. The aim of the study is to determine the most appropriate discrete model for the volatility of Bitcoin returns using the discrete-time GARCH model and its extensions and compare it with the Lévy driven continuous-time GARCH model. For this purpose, the volatility of Bitcoin returns is modeled using daily data of Bitcoin / United States Dolar exchange rate. By comparing discrete-time models according to information criteria and likelihood values, the All-GARCH model with Johnson's-SU innovations is found to be the most adequate model. The persistence of the volatility and half-life of the volatility of the returns are calculated according to the estimation of the discrete model. This discrete model has been compared with the continuous model in which the Lévy increments are derived from the compound Poisson process using various error measurements. As a conclusion, it is found that the continuous-time GARCH model shows a better performance to predict the volatility.
Amaç: Bu çalışmanın amacı, Bitcoin ve Ons arasındaki volatilite aktarımını incelemektir. Bu nedenle, yatırımcılar riskten korunmak için portföylerinde Bitcoin’e yer vermeli mi ve Bitcoin Ons’a alternatif bir yatırım aracı mı konuları araştırılmıştır. Tasarım/Yöntem: Araştırmada öncelikle değişkenler getiri serisine çevrilmiş ve birim kök testleri sınanmıştır. Daha sonra, Bitcoin ve ONS arasındaki ilişki çok değişkenli stokasitik volatilite metodu ile incelenmiştir. Eviews9 ve WinBUGS14 paket programları yardımı ile analizler yapılmıştır. Bulgular: Analiz sonuçlarına göre, Bitcoin ve Ons değişkenlerinde meydana gelen şokların kalıcı etkiye sahip olduğu saptanmıştır. Bitcoin’den Ons’a doğru tek yönlü volatilite aktarımı olduğu tespit edilmiştir. Ayrıca Bitcoin’den Ons’a doğru gerçekleşen volatilite aktarımının pozitif olduğu belirlenmiştir. Sınırlılıklar: Çalışmada, 03.02.2012–13.01.2022 dönem aralığının alınması ve sadece iki değişkenin kullanılması araştırımın sınırlılıklarıdır. Ayrıca bu tarih aralığının alınmasının nedeni 2012 dönemi öncesi Bitcoin verisine ulaşılamaması ve analizlerin 2022 yılı Ocak ayında yapılmasıdır. Özgünlük/Değer: Çalışmanın diğer çalışmalardan ayrılan özelliği, Çok Değişkenli Stokastik Volatilite Metodu ile analizlerin yapılmasıdır. Ayrıca bu konuda literatürde çok çalışma olmaması ve literatüre katkı sunulması hedeflenmektedir.
The paper investigates the empirical verification of the efficacy of investment diversification using the main stock exchange indices in the Eurozone countries and Bitcoin. The paper also investigates whether and to what extent it is desirable for institutional investors, in addition to traditional financial instruments, to invest in Bitcoin. The aim of the research is to examine whether it is justified and to what extent to include Bitcoin in the portfolio of an institutional investor. Through this research, an attempt is made to find an answer to the research question: “What share of Bitcoin in the portfolio structure is justified, taking into account the ratio of return and risk”? The analysis includes data on the daily movement of selected action indices as well as the movement of Bitcoin. The methodology involves the analysis of high-frequency data, given that daily trading data were used. The results show that it is justified to include Bitcoin in the portfolio structure. Also, the results show which share of Bitcoin in the portfolio is justified from the aspect of institutional investors. The data used in the analysis cover the period from 2019 and 2020. Two portfolios have been created, one without Bitcoin and the other with Bitcoin. The goal in optimization for both portfolios is to minimize risk. The observed period of the analysis is characterized by the crisis caused by the coronary virus pandemic and the period of active bitcoin trading. The results of the research show that Bitcoin is a good source of diversification in a portfolio that contains traditional financial instruments, both for an investor who is not prone to risk, and for those investors who have a greater appetite for risk. The conclusion is that the rational behavior of institutional investors requires consideration of investing in Bitcoin using the Markowitz model. However, given the high degree of volatility, investors should be careful when making decisions about including Bitcoin in their investment portfolio. Bitcoin is an extremely volatile instrument. Given that it is a speculative and highly volatile financial instrument, investors have different views on Bitcoin. First in terms of defining this cryptocurrency and then in terms of including this instrument in the investment portfolio. By including Bitcoin in the investment portfolio, the goal of diversification has been achieved. This is to reduce the risk of the institutional investor to a minimum. In practice, this means that it is possible to create a portfolio that carries an acceptable level of risk with the desired level of return. Given that Bitcoin is an extremely volatile and consequently - risky instrument, the expected return is also - high. The results of the research show that the cryptocurrency Bitcoin can serve as a desirable instrument for diversification of the investment portfolio when looking at a portfolio that includes stock indices. The results suggest that it is desirable to include in the structure of the portfolio a certain share of Bitcoin, about 6%.
This work investigates the factors determining the Kazakh energy crisis which occurred in the second half of 2021. From the correlation observed among some data gathered to the purpose of the analysis, the relevant role played in this by cryptocurrency mining factories is identified. Beginning from June 2021, a massive number of them were relocated to Kazakhstan from the Popular Republic of China (PRC) because of normative restrictions introduced by the latter. The work also develops a reflection aimed at understanding the economic and environmental impact which has been produced by this relocation. The descriptive analysis will proceed as follows: the first section of the article will focus on the regulation of cryptocurrencies; the second section will focus on final electricity consumption and sup-porting empirical evidence and is closely related to the third and last section; the latter will focus on primary macro-economic indicators in relation to the increase in CO2 emissions in the Kazakh republic. To this end, it is useful to demonstrate a correlation between the energy crisis, the transfer of cryptocurrency mining to Kazakhstan, and to fuel the discussion regarding the need for a supranational institution with the aim of codifying a common international legislation, thus reinforcing the efforts made so far in this direction. Present and future implications and scenarios de-rived by the analysis are also introduced.
In this study, we explore the research published from 2009 to 2021 and summarize what extant literature has contributed in the last decade to the analysis of volatility and risk management in cryptocurrency investment. Our samples include papers published in journals ranked across different fields in ABS ranked journals. We conduct a bibliometric analysis using VOSviewer software and perform a literature review. Our findings are presented in terms of methodologies used to model cryptocurrencies’ volatility and also according to their main findings pertaining to volatility and risk management in those assets and using them in portfolio management. Our research indicates that the models that consider the Markov-switching regime seem to be more consensual among the authors, and that the best machine learning technique performances are hybrid models that consider the support vector machines (SVM). We also argue that the predictability of volatility, risk reduction, and level of speculation in the cryptocurrency market are improved by the leverage effects and the volatility persistence.
In this study, the dependence between Bitcoin (BTC) and economic policy uncertainty (EPU) of USA and China is estimated by applying the latest methodology of quantile cross-spectral dependence. Daily data comprising a total of 1947 observations and covering the period of 1 October 2013 to 31 January 2019 are used in this study. The findings indicate that a positive return interdependence between BTC and EPU is high in the short term, and this dependence decreases as investment horizons increase from weekly to yearly. The information on the time-varying and time–frequency structure of interdependence is also extracted by applying wavelet coherence analysis. The estimated results of wavelet coherence suggest that the correlation between BTC and EPU is positive during a short-term investment horizon. Finally, the frequency domain Breitung and Candelon causality test is applied, and results show the evidence of insignificant causality between Bitcoin and EPU. Overall, the findings highlight the diversification benefits of Bitcoin during the period of uncertainty.
The impact of Bitcoin-related Google queries, Facebook likes, reposts and comments on Bitcoin price is analyzed with the help of ARDL and GARCH models. Our results have led us to the following conclusions. Firstly, a sharp increase in Bitcoin’s popularity or hype, which manifested itself through a rise in the number of Bitcoin-related Google queries, has resulted in an increase in Bitcoin price. This effect corresponds to the description of the ‘collective hysteria’ that spread in the online community and was triggered by the increasing volatility of the Bitcoin market. Secondly, we found that Bitcoin’s popularity among ordinary Internet users has a positive impact in low-volatile and highly volatile rising markets but a negative one in a highly volatile falling market. Thirdly, Bitcoin’s popularity among informed Internet users has a negative impact on Bitcoin price in a period of low volatility. Fourthly, uninformed users’ trust in Bitcoin has a positive influence on Bitcoin price in low-volatile and highly volatile falling markets. Finally, the main factors that shape the Bitcoin market are trust and popularity.
Son dönemde para piyasalarında teknolojinin beraberinde getirdiği yeniliklerden dijital paralara ilgi artmaktadır. Gerek kaldıraçlı işlem yapılabilmesi gerek kısa sürede kazancı vadediyor oluşu, gerekse de alım-satım kolaylığı sebebiyle popülaritesi giderek artmaktadır. Bu çalışmada kripto paralar arasında en yüksek hacime sahip olması hasebiyle Bitcoin ve finansal değişkenlerden BIST100 endeksi arasındaki ilişkinin tespit edilmesi amaçlanmıştır. Bu doğrultuda 15.04.2011 ile 25.06.2021 tarihleri arası günlük veriler kullanılarak bu ilişki Eviews11 paket programında analiz edilmiştir. Bu amaçla analizin ilk aşamasında değişkenlerin birim kök içerip içermediği geleneksel birim kök testleri ile sınanmıştır. Daha sonra seriler arasında eşbütünleşme ilişkisini test etmek için Engel-Granger Eş Bütünleşme Analizi ve nedensellik testleri olarak Engel-Granger Nedensellik Testi, Toda-Yamamoto Nedensellik Testleri kullanılmıştır. Yapılan bu analizler ışığında eş bütünleşme testinin sonucuna göre Bitcoin-Bıst100 endeksi arasındaki ilişkinin eş bütünleşik olduğu tespit edilmiştir Engel-Granger Nedensellik testi BIST100 endeksinden Bitcoin fiyatlarına doğru iki yönlü nedensellik ilişkisi olduğunu doğrularken Toda-Yamamoto Nedensellik testi sonuçlarına göre ise Bıst100 endeksinden Bitcoin fiyatlarına doğru %5 anlamlılık düzeyinde anlamlı olduğu ve tek yönlü Toda-Yamamoto nedensellik ilişkisi görülmüştür. Son olarak çalışmanın sonuç bölümünde bütün bu bulgular önerilerle birlikte değerlendirilmiştir.
The present study investigates the efficiency of the Bitcoin futures in the price discovery process by assessing the lead-lag relationship between the futures and spot prices of Bitcoin. The study tests whether the Bitcoin futures market is leading the price discovery mechanism for the Bitcoin spot market. The study considers daily closing prices of both Bitcoin spot and future indices from December 12, 2017 to December 31, 2020. The stationarity of the two time-series variables is tested using Augmented Dickey-Fuller test while the long-run co-integrating relationship is tested using Johansen Co-integration test. To test the long-run causality, the Error Correction Mechanism framework (ECM) is used while the Wald test is applied to assess the short-run causality between the Bitcoin future and spot prices. The results of trace and max-eigen statistics indicate that there is long term co-integrating relationship between Bitcoin futures and Bitcoin spot markets. The negative significant coefficient of error correction term indicates that there is long-run causality from the Bitcoin futures towards the Bitcoin spot market. The significant Chi-square test statistics of the Wald test suggest that there is short-run causality from the Bitcoin futures towards the Bitcoin spot market. This shows that the Bitcoin futures market is acting as a leading indicator and the Bitcoin spot market as a lagging indicator. Thus, it is concluded that the price discovery is taking place between Bitcoin futures and the Bitcoin spot market. With the entrance of the new information in the cryptocurrency market, it is first observed in the Bitcoin futures followed by the Bitcoin spot prices.
Juan Piñeiro Chousa, Ángeles López Cabarcos, Aleksandar Šević, Isaac González-López
After the so-called “crypto-winter”, decentralised finance (DeFi) is reviving interest in cryptocurrency amongst the scientific community, public and private institutions, and investors. DeFi is a novel disruptive process that promotes the use of blockchain technology for creating and issuing all kinds of financial products and services. This study aimed to measure the relationship amongst the returns of DeFi tokens, other traditional assets, and user-generated content. While the relationship between other crypto assets and traditional assets has been researched, this has not been done on DeFi assets. This study uses a logit-probit model over a database comprising the daily returns of 13 DeFi, VIX, S&P GSCI Crude Oil Index, and S&P GSCI Gold Index, and the daily variation in DeFi mentions in Telegram chats and Twitter. The results show that all variables except the S&P GSCI crude oil index returns and the daily variation in Twitter mentions were significant. This suggests that DeFi acts, similar to other crypto assets, as a safe haven. This study contributes to the literature on decentralised finance tokens as investment assets, which requires much more research.
Cryptocurrencies are gaining more popularity due to their security, making counterfeits impossible. However, these digital currencies have been criticized for creating a large carbon footprint due to their algorithmic complexity and decentralized system design for proof of work and mining. We hypothesize that the carbon footprint of cryptocurrency transactions has a higher dependency on carbon-rich fuel sources than green or renewable fuel sources. We provide a machine learning framework to model such transactions and correlate them with the electricity generation patterns to estimate and analyze their carbon cost.
Samuel Asumadu Sarkodie, Maruf Yakubu Ahmed, Thomas Leirvik
The environmental sustainability of bitcoin is making waves in the empirical literature, yet, no study has thus far examined the financial determinants of bitcoin energy consumption and carbon footprint. Here, we use novel estimation methods comprising dynamic ARDL simulations and general-to-specific VAR to examine steady-state effects, cumulative impulse-response, and counterfactual shocks of bitcoin trade volume on bitcoin energy bitcoin carbon footprint to ensure genuine causal inferences. We observed an increase in bitcoin trade volume spur both carbon and energy footprint by 24% in the long-run, whereas a dynamic shock in trade volume escalates bitcoin energy and carbon footprint by 46.54%.
Assets with tail risk may produce a suboptimal portfolio under mean-variance optimization when asset returns are not normally distributed. We provide a new Monte Carlo simulation method for computing and attaching tails to observed empirical return distributions. We find that a combination of stochastic optimization and the new method for simulating tails in returns with expected shortfall utility function produces optimal portfolios that have better return and risk characteristics than those of mean-variance optimal portfolios. Results from this study suggest that bitcoin can be a diversifier in a multi-asset portfolio when optimization takes all moments of return into consideration.
Abstract In this paper, we provide an in-depth analysis of the herding nature in the cryptocurrency market. We use the first 200 crypto coins data ranked based on market capitalization on January 1, 2020, to show the analysis. We illustrate the crypto investors' herding nature and intensity in different terms (by using daily, weekly, and monthly frequency data) and various states (high vs. low EPU states and high vs. low VIX states). We also demonstrate the magnitude of the herding effect on the next day's market returns in the cryptocurrency market.
As the popularity of Bitcoin among finance communities continues, research on the relationship between Bitcoin and conventional commodities is becoming increasingly important. However, to date the literature has not yet found clear evidence of the dynamics of informational linkages between Bitcoin and the crude oil markets. This paper considers both static and time-varying information transmission of volatility, skewness and kurtosis, between two popular Bitcoin markets, Bitstamp and ItBit, and two major crude oil markets, WTI and Brent crude oil. A two-state regime-switching model is employed to estimate higher order moments and Legendre polynomials are applied to specify the time variations of spillovers. In addition, the latent impacts of the recent US–China trade war on spillovers are also explored. We conclude that crude oil is an information transmitter while Bitcoin is an information receiver in terms of the static and time-varying between-market transmission of three types of risk. The effect is affected by the occurrence of the trade war such that the information content of Bitcoin is enhanced once it begins. Moreover, Bitcoin is a diversifier for oil risk and this effect becomes more pronounced in the post-trade war period.
Purpose – the main aim of this article is to identify cryptocurrencies suitable for investment and portfolio diversification. Research methodology – the methodology of empirical research includes methods of scientific literature analysis, statistical data analysis, multicriteria evaluation, correlation analysis. Findings – Bitcoin is the leading cryptocurrency, but this result could have been due to an exceptionally high market capitalization. Based on the results of the analysis, the inclusion of Bitcoin, Etherium and Dogecoin in the investment portfolio of S&P500, Euro Stoxx 50, DAX and CAC 40 indexes could be considered. Terra could be an interesting investment when considering the benefits of diversification. Research limitations – based on the results of the study, the inclusion of all studied cryptocurrencies in the investment portfolio could be considered in order to diversify the portfolio, taking into account their investment attractiveness. Practical implications – Cryptocurrencies attract investors not only because of the returns they receive, but also because of the absence of intermediaries, which allows them to reduce transaction costs. High returns are associated with high risks, so it is necessary to conduct as much research as possible to identify the benefits of cryptocurrencies and to find risk management strategies. One such benefit of cryptocurrencies highlighted in research is diversification. Originality/Value – the novelty of the study lies in evaluation of 10 selected cryptocurrencies according to different criteria using a multi-criteria valuation method to identify cryptocurrencies that are non-correlated or weakly correlated with traditional assets and the most suitable for investment and for portfolio diversification.
This paper examines the diversification role of socially responsible investments (SRI) during the COVID-19 pandemic. To assess the contribution to risk diversification and improved financial performance of SRI we analyze the effect of including clean energy equities in portfolios of conventional equities and other assets commonly considered as safe havens. We construct minimum variance portfolios for different rebalancing frequencies and by considering or restricting short positions. Two approaches are applied: AR-GARCH models to fit the marginal distributions of individual assets and DCC skew Student copula specifications to model the conditional dependencies among pairs via the Kendall's tau correlation measure. We provide evidence of the important role that SRI have played in diversifying and improving the financial performance of portfolios based on different securities such as traditional equities, Treasury bonds, gold, crude oil and Bitcoin.
Most financial signals show time dependency that, combined with noisy and extreme events, poses serious problems in the parameter estimations of statistical models. Moreover, when addressing asset pricing, portfolio selection, and investment strategies, accurate estimates of the relationship among assets are as necessary as are delicate in a time-dependent context. In this regard, fundamental tools that increasingly attract research interests are precision matrix and graphical models, which are able to obtain insights into the joint evolution of financial quantities. In this paper, we present a robust divergence estimator for a time-varying precision matrix that can manage both the extreme events and time-dependency that affect financial time series. Furthermore, we provide an algorithm to handle parameter estimations that uses the "maximization-minimization" approach. We apply the methodology to synthetic data to test its performances. Then, we consider the cryptocurrency market as a real data application, given its remarkable suitability for the proposed method because of its volatile and unregulated nature.