Bu çalışmada temel piyasalar arasındaki volatilite yayılımları Diebold ve Yılmaz (2012) tekniğiyle araştırılmıştır. Temel piyasaları temsilen MSCI dünya endeksi, ABD 2 yıllık devlet tahvil faizi, dolar endeksi, ons altın, brent petrol ve bitcoin kullanılmıştır. Çalışmada 2 Ocak 2015 – 29 Haziran 2021 dönemine ait günlük verilerden elde edilen volatiliteler kullanılmıştır. Çalışmada, temel piyasalar arasındaki volatilite yayılım endeksinin %30,9 olduğu, faiz ve MSCI dünya endeksinin volatilite yayıcısı buna karşın dolar endeksi, altın, petrol ve bitcoinin volatilite alıcısı oldukları, faizin temel piyasalarda önemli volatilite yayıcısı olduğu, bitcoinin temel piyasalarla volatilite ilişkisinin zayıf olduğu ve temel piyasalar arasındaki volatilite yayılımlarının COVID-19 sürecinde yükseldiği belirlenmiştir. Elde edilen sonuçlar, portföy yönetimi, risk yönetimi, yatırımlar, ekonomi yönetimleri açısından kullanılabilirlik taşımaktadır.
This study examines the volatility changes of 20 cryptocurrencies from January 2018 to May 2021 using sparse VHAR-MGARCH model. Our proposed model incorporates the high-dimensionality and time-varying conditional heterogeneity of cryptocurrency markets. We examined the time-varying spillover index, dynamic correlation structure, and connectivity between cryptocurrencies. Our empirical analysis clearly shows that there was a volatility shift on 13 March 2020, due to a market crash caused by COVID-19. This naturally divides the data into three periods: pre-crisis, during the crisis, and post-crisis regimes. The pre-crisis regime exhibited long-term cyclic fluctuations in the spillover index. However, after the market crash, the spillover index remained at a very high level with almost no interconnections between cryptocurrencies. The post-crisis regime showed quite a few irregular and sharp spikes in the spillover index, together with record-breaking prices and volumes.
Samuel Kwaku Agyei, Anokye M. Adam, Ahmed Bossman, Oliver Asiamah · 7 authors
We present a multi-scale and time-frequency analysis of the degree of integration and the lead-lag relationship between six cryptocurrencies (i.e., Bitcoin, Bitcoincash, Ethereum, Litecoin, Ripple, and Tether) and the cryptocurrency-implied volatility index (VCRIX). As a result, the wavelet techniques—bi-wavelet, partial wavelet, bivariate contemporary correlations (BCC), wavelet multiple correlations (WMC) and wavelet multiple cross-correlations (WMCC) are applied. Findings from the study provide that the interdependencies between the cryptocurrencies and VCRIX are high and mostly positive across investment horizons. Furthermore, the comovements between the cryptocurrencies designate long memory dynamics. The high comovements between cryptocurrencies are highly influenced by idiosyncratic shocks they possess rather than the VCRIX. In addition, the BCC and the WMC indicate that there is a high integration among all the cryptocurrencies. Categorically, the VCRIX could not lead or lag the interdependencies among the cryptocurrencies in the WMCC analysis. Findings from the study, therefore, divulge that investing in a single or few cryptocurrencies is highly risky due to the adverse impact of the VCRIX on individual cryptocurrencies. In general, investors should effectively hedge against volatilities in the cryptocurrency markets due to the significant predictive ability of VCRIX as an effective proxy.
Abstract We examine the interactions between stablecoins, Bitcoin, and a basket of altcoins to uncover whether stablecoins represent the investors’ demand for trading and investing into cryptoassets or rather play a role as boosting mechanisms during cryptomarkets price rallies. Using a set of instruments covering the standard cointegration framework as well as quantile-specific and non-linear causality tests, we argue that stablecoins mostly reflect an increasing demand for investing in cryptoassets rather than serve as a boosting mechanism for periods of extreme appreciation. We further discuss some specificities of 2017, even though the dynamic patterns remain very similar to the general behavior. Overall, we do not find support for claims about stablecoins being bubble boosters in the cryptoassets ecosystem.
The large-scale application of blockchain technology is an expected to be an inevitable trend. This study revolves around published papers and articles related to blockchain technology, relevance analysis and sorting through the retrieved documents with six core layers of blockchain: Application Layer, Contract Layer, Actuator Layer, Consensus Layer, Network Layer and Data Layer. Based on the analysis results, this study found that China's research is more towards the preference and application of landing and industry and smart cities with blockchain as the underlying technology. International research is more focused on the research of finance as the underlying technology of blockchain and tries to combine crypto assets with real industries, such as crypted assets and payment systems for traditional industries. This paper studies the impact of monetary entropy on cryptocurrencies in smart cities and uses the monetary entropy formula to measure the crypto-economic entropy. We use Kolmogorov entropy to describe the degree of chaos in the cryptocurrency market in a smart city. The study illustrates the current status of blockchain technology and applications from the perspective of cryptocurrency in a smart city. We find that smart cities and cryptocurrencies have a mutually reinforcing effect.
Cryptocurrencies can be considered as mathematical money. As the most famous cryptocurrency, the Bitcoin price forecasting model is one of the popular mathematical models in financial technology because of its large price fluctuations and complexity. This paper proposes a novel ensemble deep learning model to predict Bitcoin’s next 30 min prices by using price data, technical indicators and sentiment indexes, which integrates two kinds of neural networks, long short-term memory (LSTM) and gate recurrent unit (GRU), with stacking ensemble technique to improve the accuracy of decision. Because of the real-time updates of comments on social media, this paper uses social media texts instead of news websites as the source data of public opinion. It is processed by linguistic statistical method to form the sentiment indexes. Meanwhile, as a financial market forecasting model, the model selects the technical indicators as input as well. Real data from September 2017 to January 2021 is used to train and evaluate the model. The experimental results show that the near-real time prediction has a better performance, with a mean absolute error (MAE) 88.74% better than the daily prediction. The purpose of this work is to explain our solution and show that the ensemble method has better performance and can better help investors in making the right investment decision than other traditional models.
Explaining changes in bitcoin's price and predicting its future have been the foci of many research studies. In contrast, far less attention has been paid to the relationship between bitcoin's mining costs and its price. One popular notion is the cost of bitcoin creation provides a support level below which this cryptocurrency's price should never fall because if it did, mining would become unprofitable and threaten the maintenance of bitcoin's public ledger. Other research has used mining costs to explain or forecast bitcoin's price movements. Competing econometric analyses have debunked this idea, showing that changes in mining costs follow changes in bitcoin's price rather than preceding them, but the reason for this behavior remains unexplained in these analyses. This research aims to employ economic theory to explain why econometric studies have failed to predict bitcoin prices and why mining costs follow movements in bitcoin prices rather than precede them. We do so by explaining the chain of causality connecting a bitcoin's price to its mining costs.
Purpose This article unveils first the lead–lag structure between the confirmed cases of COVID-19 and financial markets, including the stock (DJI), cryptocurrency (Bitcoin) and commodities (crude oil, gold, copper and brent oil) compared to the financial stress index. Second, this paper assesses the role of Bitcoin as a hedge or diversifier by determining the efficient frontier with and without including Bitcoin before and during the COVID-19 pandemic. Design/methodology/approach The authors examine the lead–lag relationship between COVID-19 and financial market returns compared to the financial stress index and between all markets returns using the thermal optimal path model. Moreover, the authors estimate the efficient frontier of the portfolio with and without Bitcoin using the Bayesian approach. Findings Employing thermal optimal path model, the authors find that COVID-19 confirmed cases are leading returns prices of DJI, Bitcoin and crude oil, gold, copper and brent oil. Moreover, the authors find a strong lead–lag relationship between all financial market returns. By relying on the Bayesian approach, findings show when Bitcoin was included in the portfolio optimization before or during COVID-19 period; the Bayesian efficient frontier shifts to the left giving the investor a better risk return trade-off. Consequently, Bitcoin serves as a safe haven asset for the two sub-periods: pre-COVID-19 period and COVID-19 period. Practical implications Based on the above research conclusions, investors can use the number of COVID-19 confirmed cases to predict financial market dynamics. Similarly, the work is helpful for decision-makers who search for portfolio diversification opportunities, especially during health crisis. In addition, the results support the fact that Bitcoin is a safe haven asset that should be combined with commodities and stocks for better performance in portfolio optimization and hedging before and during COVID-19 periods. Originality/value This research thus adds value to the existing literature along four directions. First, the novelty of this study lies in the analysis of several financial markets (stock, cryptocurrencies and commodities)’ response to different pandemics and epidemics events, financial crises and natural disasters (Correia et al., 2020; Ma et al., 2020). Second, to the best of the authors' knowledge, this is the first study that examine the lead–lag relationship between COVID-19 and financial markets compared to financial stress index by employing the Thermal Optimal Path method. Third, it is a first endeavor to analyze the lead–lag interplay between the financial markets within a thermal optimal path method that can provide useful insights for the spillover effect studies in all countries and regions around the world. To check the robustness of our findings, the authors have employed financial stress index compared to COVID-19 confirmed cases. Fourth, this study tests whether Bitcoin is a hedge or diversifier given this current pandemic situation using the Bayesian approach.
Cryptocurrencies provide a natural setting to test for the existence of price bubbles using the local martingale theory of bubbles because cryptocurrencies have no cash flows. Using a robust statistical algorithm, we test for price bubbles in eight cryptocurrencies, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Bitcoin Cash (BCH), EOS (EOS), Monero (XMR), and Zcash (ZEC), from 1 January 2019 to 17 July 2019. The statistical test first estimates the cryptocurrencies’ volatilities as a function of the price level. Then, these estimates are extrapolated over the positive real line using power functions. Finally, these power functions underly a sequence of hypothesis tests for price bubbles that control for both Type I and Type II errors. Five of the eight currencies (BTC, BCH, EOS, XMR, ZEC) exhibit price bubbles, LTC does not, and the evidence for ETH and XRP is inconclusive. The paper provides strong evidence for the prevalence of bubbles in cryptocurrencies.
This research aims to evaluate whether dynamic portfolios consisting of bitcoin and LQ45 stocks outperform portfolios composed solely of LQ45 stocks, especially during the Covid-19 pandemic. Accordingly, we use the time-series data of eight stocks and bitcoin from January 1, 2020, to December 31, 2020. We then run the DCC-GARCH method to analyze better the dynamic correlation between assets and the abnormalities of stock return distributions. The findings demonstrate that bitcoin is negatively correlated with LQ45 stocks, and hence, it can be used to hedge against stock assets. Further, we measure the portfolio performance of bitcoin-hedged and unhedged stock portfolios using the Jensen Index, Treynor Index, Sharpe Index, Sortino Ratio, and Omega Ratio. These measures consistently indicate that bitcoin-hedged stocks outperform unhedged stocks. In sum, our study concludes that incorporating bitcoin into portfolio formation improves portfolio performance.
Purpose The paper provides new evidence for Bitcoin’s safe-haven property by examining the relationship between currency price, return and Bitcoin trading volume. Design/methodology/approach A unique dataset from a person-to-person (p2p) exchange is used to investigate association between Bitcoin trading volume and currency prices. Currency returns are used to identify local economic crises, the 8 crisis affected currencies are Venezuela Bolivar (VES), Iranian Rial (IRR), Ukrainian Hryvnia (UAH), Argentine Peso (ARS), Egyptian Pound (EGP), Nigerian Naira (NGN), Turkish Lira (TRY) and Kazakhstani Tenge (KZT). Findings The paper demonstrates that local economic crises are positively associated with increased Bitcoin trading. There is a negative association between trading volume and currency value (and return), suggesting low currency price and currency depreciation are accompanied with increased Bitcoin trading. The results not only hold for the crisis affected currencies but also currencies of advanced economies. Granger causality test also reinforces the negative association results. Originality/value The finding indicates some forms of flight-to-safety have occurred during local market crises when capital flight from domestic markets to Bitcoin, strengthening Bitcoin’s hedging asset status. However, total global trading volume declines after the start of the COVID pandemic, suggesting that Bitcoin is still regarded as a speculative asset. Overall, the findings show that Bitcoin is a hedging asset to protect against local currency depreciation, but not a safe-haven asset for the global crisis.
Tüm dünyayı etkisi altına alan merkeziyetsiz finans oluşumları ve yaşanan dönüşüm günümüzde birçok kişi tarafından ilgiyle karşılanmaktadır. Arkasında barındırdığı teknolojinin yenilikçi ve işlevsel olması, kripto paraların market hacimlerinin günden güne artması ve değerlenmesi, yatırımcıları bu alana çeken yegane faktörlerden bir tanesidir. Ticari alım satım işlemlerinin sağlanabilmesi için sadece bir adet akıllı cihaz ve internete ihtiyaç duyulması, ulaşılabilirlik konusunun da bir hayli kolay olmasını sağlamaktadır. Yoğunluklu olarak son dönemlerde finans dünyasının içindeki büyük otoritelere karşı bir baş kaldırı hareketi olarak adlandırılan bu dönüşüm, arkasında milyonlarca bireysel ve kurumsal yatırımcıyı barındırmaktadır. Bu çalışmada, Bitcoin ve hisse senedi piyasaları arasındaki karşılıklı ilişki incelenmiştir. Dünya üzerinde bulunan seçkin borsa endeksleri çalışmada kendisine yer bulmuştur. Çalışmada 01 Ocak 2013 - 31 Ekim 2021 yılları arasında haftalık veriler kullanılmış, VAR analizi yardımı ve Granger Nedensellik Testi aracılığı ile değişkenler arasındaki ilişki test edilmiştir. Analizden elde edilen bulgulara göre Granger nedensellik analizi sonuçlarına göre Bitcoin değişkeninden Dow Jones endeksine %5 anlamlılık düzeyinde tek yönlü bir ilişki olduğu tespit edilmiştir. Analizden elde edilen bir diğer bulgu ise %10 anlamlılık düzeyinde Bitcoin değişkeninden S&P500 endeksine tek yönlü bir nedensellik ilişkisi olduğu saptanmıştır.
In recent days, DeFi tokens have gained popularity as an investment option in the pandemic period and has gained a significant amount of investment. Cryptocurrency trading is a type of DeFi that has gained a lot of attention in the global market. The value of such currencies is increasing on a daily basis and peaked during the pandemic. One of these significant cryptocurrencies is the ether cryptocurrency, which ranks second only to the bitcoin cryptocurrency in terms of the value of a single coin. The ARIMA model will be used to forecast the price of ether. In this paper, an hourly forecast and a short term period forecast are performed. The forecast clearly shows that the ARIMA model performed better on log transformed data than on the original data. It’s also evident that COVID-19 pandemic has also aided the growth of ethereum when compared to previous year.
Mario Iván Contreras-Valdez, José Antonio Núñez Mora, Guillermo Benavides Perales
This study presents a multivariate study regarding Bitcoin and its interactions with other financial assets of different classes. This is done by adjusting a multivariate semi heavy-tailed distribution to portfolios containing indexes, currencies, and commodities and one cryptocurrency. Later, a rolling window is deployed to obtain the dynamic parameters of the distribution in a weekly basis. With a Markowitz specification problem, the optimal portfolio weights are computed dynamically using the parameters of the multivariate NIG distribution as inputs. The results provide evidence that correlations of Bitcoin with other assets may provide certain degree of diversification to portfolios; nevertheless, the high volatility of this asset makes it unpractical to employ in significant weights. This paper is relevant for researchers and practitioners as it provides a new tool to manage portfolios with cryptocurrencies and more reliable weights to the asset allocation.