This study examined the relationship between cryptocurrency shocks and exchange rate behaviour in Nigeria. Selected cryptocurrencies for the study are Bitcoin, Ethereum, Litecoin, Ripple and Binance coin which are the most traded cryptocurrencies in Nigeria. Augmented Dickey-Fuller (ADF), Johansen Cointegration and Vector Autoregressive (VAR) tests were used to analyze the monthly data of exchange rate and selected cryptocurrencies for four years (45 months). The result of the cointegration test revealed the existence of a long-run relationship among the variables. ECM result showed that about 6% of the short-run disequilibrium are being corrected and integrated into the long-run equilibrium relationship. In addition, the Variance Decomposition result showed that Ripple has the highest variations to exchange rate in the short and long runs. The present value of exchange rate adjusts slightly to changes in cryptocurrency. Ripple and Bitcoin have the highest shocks on the exchange rate. Therefore, monetary authorities should give adequate attention to cryptocurrency transactions and make policy decisions on how to reduce the prevailing high exchange rate in Nigeria by integrating crypto transactions in their systems. Transaction in cryptocurrency is still at the early stage, especially in Nigeria; only five years data can be gotten on commonly traded cryptocurrencies in Nigeria. This is a limitation to the study in terms of the number of cryptocurrencies used in the study. More cryptocurrencies can be included in future studies.
Purpose This paper aims to examine the predictive power of the volume of Economic Uncertainty Related Queries and the Macroeconomic Uncertainty Index on the Bitcoin returns. Design/methodology/approach Data consists of 118 monthly observations from September 2010 to June 2020. Due to the departure of series from Gaussian distribution and the existence of outliers, the authors use the quantile analysis framework to investigate the persistency of the shocks, the long-run relationships and Granger causality among the variables. Findings This research provides several important findings. First, the substantial differences between conventional and quantile test results stress the importance of the method selection. Second, throughout the conditional distribution of the series, stochastic properties of the variables, long-run and the causal relationships between the variables might be significantly different. Third, rich information provided by the quantile framework might help the investors design better investment strategies. Originality/value This study differs from the previous research in terms of variable selection and econometric methodology. Therefore, it presents a more comprehensive framework that suggests implications for empirical researchers and Bitcoin investors.
In this paper, we investigate the relationship between the RavenPack news-based index associated with coronavirus outbreak (Panic, Sentiment, Infodemic, and Media Coverage) and returns of two commodities—Bitcoin and gold. We utilized the novel quantile-on-quantile approach to uncover the dependence between the news-based index associated with coronavirus outbreak and Bitcoin and gold returns. Our results reveal that the daily levels of positive and negative shocks in indices induced by pandemic news asymmetrically affect the Bearish and Bullish on Bitcoin and gold, and fear sentiment induced by coronavirus-related news plays a major role in driving the values of Bitcoin and gold more than other indices. We find that both commodities, Bitcoin and gold, can serve as a hedge against pandemic-related news. In general, the COVID-19 pandemic-related news encourages people to invest in gold and Bitcoin.
This paper mainly studies the market nonlinearity and the prediction model based on the intrinsic generation mechanism (chaos) of Bitcoin’s daily return’s volatility from June 27, 2013 to November 7, 2019 with an econophysics perspective, so as to avoid the forecasting model misspecification. Firstly, this paper studies the multifractal and chaotic nonlinear characteristics of Bitcoin volatility by using multifractal detrended fluctuation analysis (MFDFA) and largest Lyapunov exponent (LLE) methods. Then, from the perspective of nonlinearity, the measured values of multifractal and chaos show that the volatility of Bitcoin has short-term predictability. The study of chaos and multifractal dynamics in nonlinear systems is very important in terms of their predictability. The chaos signals may have short-term predictability, while multifractals and self-similarity can increase the likelihood of accurately predicting future sequences of these signals. Finally, we constructed a number of chaotic artificial neural network models to forecast the Bitcoin return’s volatility avoiding the model misspecification. The results show that chaotic artificial neural network models have good prediction effect by comparing these models with the existing Artificial Neural Network (ANN) models. This is because the chaotic artificial neural network models can extract hidden patterns and accurately model time series from potential signals, while the benchmark ANN models are based on Gaussian kernel local approximation of non-stationary signals, so they cannot approach the global model with chaotic characteristics. At the same time, the multifractal parameters are further mined to obtain more market information to guide financial practice. These above findings matter for investors (especially for investors in quantitative trading) as well as effective supervision of financial institutions by government.
Market traders buy and sell volatile assets frequently, with a goal to maximize their total return. We have been asked to develop a model that uses only the past stream of daily prices to date to determine each day if the trader should buy, hold, or sell their assets in their portfolio. The assets that can be traded are Bitcoin and gold. We will start with $1000 on 9/11/2016 and try to maximize the total return until 9/10/2021. We will start from forecasting prices and developing trading strategies. In terms of price prediction, we use MSE and Trend_Acc as indicators, and use XGBoost, a representative strong learning algorithm in traditional machine learning, and LSTM, which is good at time series prediction in deep learning, to fit and forecast the data respectively. At first glance, the curve fitting MSE are satisfactory , but, a closer look reveals that the model either firmly remembers the data of the training set, resulting in a lack of generalization ability for unknown data (XGBoost), or tends to take the previous day's results as the predicted results, resulting in a significant lag in the prediction curve (LSTM), all of which are reflected in the models' poor performance in predicting whether prices will rise or fall in the future. In our view, since a large number and complexity of factors affecting prices, it is unrealistic to predict future prices accurately from past prices alone, unless we can get rid of the limitation of the problem, use additional data to assist the prediction, or use all the data as a training set for fitting, we cannot achieve good results in the prediction, but such behavior is inconsistent with our original intention. We established restricted trading model based on composite index judgment. The model not only applies the traditional economic Relative Strength Index and Stochastics Oscillator Index, but also introduces the K-Lipschitz limitation in deep learning into the model. The model dynamically adjusts each transaction strategy according to the changes of working capital and total assets, purchase cost, selling profit and other factors, and the total income of the model is $132433.1. In the horizontal comparison, the profit of our model is more than 39.6%-283.6% than that of the traditional moving average strategy and RSI-STC strategy, and 51.3% higher than that of the random walk model using Montmarlowe algorithm. In addition, we collected the transaction data of bitcoin and gold from 2012-01-01 to 2022-02-21, and applied the model to the historical data, and received good returs. For example, from 2012-1-1 to 2022-2-21, the return was $8418074.9. It is proved that the model has high generalization performance and strong stability. To test the sensitivity of the model to transaction costs, we also analyze the changes in trading strategies that should occur when fees rise. The analysis shows that the traders' single trading volume decreases first and then increases with the increase of the commission fee. The results of sensitivity analysis show that the return change is less than 3%, which proves the robustness of the model. Finally, we made a memo to summarize our work.
Inzamam Ul Haq, Apichit Maneengam, Supat Chupradit, Chunhui Huo
This article aims to explore the co-movement of daily returns among S&P green bonds (GB/GBs), the top five sustainable cryptocurrencies, Bitcoin, the Dow Jones Sustainability World Index (DJSWI) and the Dow Jones Sustainability Emerging Market Index (DJSEMI) to determine whether GBs, Bitcoin and sustainable cryptocurrencies are truly sustainable; in addition, it investigates hedging and diversification opportunities. Using a partial wavelet coherence framework to capture the bivariate co-movement, our findings show strong (weak) positive co-movements among GB (sustainable cryptocurrencies) and DJSWI returns, where GBs (sustainable cryptocurrencies) have a heterogeneous leading role in the short-term and long-term horizons. Results indicate moderate positive (negative) co-movement among GBs and sustainable cryptocurrencies (Bitcoin) and DJSWI in the short run (long run). Overall, the results show GB (sustainable cryptocurrencies) acts as a diversifier for Bitcoin and sustainable cryptocurrencies in most cases (DJSWI). However, increasing Bitcoin returns adversely impacts the DJSWI in the long run. Findings are equally imperative for green investors, crypto traders and policymakers, where investors and traders can earn financial and social returns, and policy-makers can deploy suitable policies for the development of sustainable cryptocurrency mining processes. The role of Bitcoin is alarming for the United Nations Sustainable Development Goals and global greener economy.
Bitcoin, with its ever-growing popularity, has demonstrated extreme price volatility since its origin. This volatility, together with its decentralised nature, make Bitcoin highly subjective to speculative trading as compared to more traditional assets. In this paper, we propose a multimodal model for predicting extreme price fluctuations. This model takes as input a variety of correlated assets, technical indicators, as well as Twitter content. In an in-depth study, we explore whether social media discussions from the general public on Bitcoin have predictive power for extreme price movements. A dataset of 5,000 tweets per day containing the keyword `Bitcoin' was collected from 2015 to 2021. This dataset, called PreBit, is made available online. In our hybrid model, we use sentence-level FinBERT embeddings, pretrained on financial lexicons, so as to capture the full contents of the tweets and feed it to the model in an understandable way. By combining these embeddings with a Convolutional Neural Network, we built a predictive model for significant market movements. The final multimodal ensemble model includes this NLP model together with a model based on candlestick data, technical indicators and correlated asset prices. In an ablation study, we explore the contribution of the individual modalities. Finally, we propose and backtest a trading strategy based on the predictions of our models with varying prediction threshold and show that it can used to build a profitable trading strategy with a reduced risk over a `hold' or moving average strategy.
This paper aims to investigate and measure Bitcoin and the five largest stablecoin market volatilities by incorporating various range-based volatility estimators to the BEKK- GARCH and Copula-DCC-GARCH models. Specifically, we further measure Bitcoins’ volatility related to five major stablecoins and examine the connectedness between Bitcoin and the stablecoins. Our empirical findings document that the connectedness between Bitcoin and stablecoin market volatility behaviors exhibits the presence of stable interconnection. This study is of particular importance since it is crucial for market participation in the ongoing crypto assets to be informed about both the volatility patterns of major cryptocurrencies and the relative volatility of Bitcoin against the stablecoin markets. Eventually, we find that there is no systematic evidence for the various parity deviations of the stablecoins that are profoundly impacted by Bitcoin volatility. Thus, Bitcoin and the largest stablecoin Tether could stabilize together. However, Bitcoin shall not be generalized to other stablecoins in terms of stability results.
The fame of cryptocurrencies soars in 2017 because of a few consecutive months of the exponential development of their market capitalization. Even though machine learning has been fruitful in anticipating stock market costs through a large group of various time series models, its application in foreseeing cryptocurrency costs has been very prohibitive. The reason behind this is clear as the costs of cryptocurrencies rely upon a ton of factors like technological progress, internal competition, pressure on the markets to deliver, economic problems, security issues, political factors and so on Their high volatility prompts the incredible capability of high benefit if savvy designing systems are taken. Sadly, because of their absence of lists, cryptocurrencies are somewhat capricious contrasted with traditional financial predictions like stock market predictions. The proposed paper describes how Cryptocurrency works, its use, legal prospect, security and what is the technology behind it
The aim of this study is to gauge the impact of global economic policy uncertainty and natural resource prices, that is, oil prices and gold prices, on Bitcoin returns by using monthly data spanning from May 2013 to December 2021. The study applies ARDL and nonlinear ARDL for evaluating the symmetric and asymmetric effects of Global Economic Uncertainty (GU), oil price (O), and natural gas price on Bitcoin volatility investigated by using the ARCH-GARCH-ERAGCH and non-granger causality test. ARDL model estimation establishes a long-run cointegration between GU, O, G, and Bitcoin. Moreover, GU and oil price exhibits a negative association with Bitcoin and positive influences running from gold price shock to Bitcoin in the long run. NARDL results ascertain the long-run asymmetric relations between GU, oil price, gold price (G), and Bitcoin return. Furthermore, GU’s asymmetric effect and positive shock in gold price negatively linked to Bitcoin return in the long run, whereas asymmetric shock in oil price and negative shocks in gold price established a positive linkage with Bitcoin. The results of ARCH effects disclose the volatility persistence in the variables. The causality test reveals that the feedback hypothesis explains the causal effects between GU and Bitcoin and unidirectional causality running from Bitcoin to gold price and oil price to Bitcoin.
Cryptocurrencies are growing rapidly, with various altcoin being introduced recently, despite the fact that the market is very volatile, cryptocurrency now holds trillions of dollars in the market and has plenty of platforms for trading and owning cryptocurrencies, like Binance, Coinbase, and others. In particular, Bitcoin has caught the atten-tion of many people over the year with a current market cap. of 731.56 billion dollars circulating in the market. One of the major problems in cryptocurrencies is volatility, and often the prices can vary due to the external events that trigger the market. That is, Twitter sentiment. The objective of the article is to investigate people’s opinion about the cryptocurrency market on social media using collected tweets for 2 popular hashtags of Bitcoin and investigating the tweets using sentiment analysis. The study found that sentiment scores could be related to observed price fluctuations.
Tamara Zuvela, Sara Lazarevic, Sofija Djordjevic, Marko Arsenović · 5 authors
Cryptocurrency is a type of digital or virtual currency that uses cryptography to secure and verify transactions as well as to control the creation of new units, it uses Blockchain properties for the same. Blockchain is a decentralized digital ledger technology that records transactions securely and transparently. Blockchain technology and cryptocurrency are closely connected. Cryptocurrencies rely on blockchain technology to operate, as blockchain serves as the decentralized ledger that records all transactions and ensures their security and transparency. [7] As the internet becomes more accessible and convenient, an increasing number of people and organizations are turning to digital transactions. Digital payment systems are significantly faster, less expensive, and more efficient. As a result, it's not unexpected that innovative digital payment system types are quickly emerging. No other approach even comes close to the colossus that is cryptocurrencies. Predicting cryptocurrency prices can be useful for a variety of reasons. For traders and investors, predicting cryptocurrency prices can help them make informed decisions about when to buy or sell cryptocurrencies, maximizing their profits or minimizing their losses. For prediction, the algorithms used are GRU (gated recurrent unit), LSTM (longshort-term memory), and Bi-LSTM (Bi-directional long-short-term memory) algorithms to predict the future price of a cryptocurrency. An ensemble model is also created using the three models, and prices could be accurately predicted using these models and displaying the obtained results.
Bu çalışmanın amacı Bitcoin’in emtialar için çeşitlendirici rolünün ve emtialarla etkileşiminin incelenmesidir. İnceleme kapsamında Bitcoin, altın, gümüş, emtia endeksi, ham petrol ve enerji emtiaları endeksi değişkenlerinden oluşan 17.09.2014 - 24.11.2021 dönemini kapsayan günlük veri seti Garman-Klass serilerine dönüştürülmüş ve dinamik koşullu korelasyon modelleri uygulanmıştır. Uygulama sonucunda Bitcoin ile emtialar arasındaki etkileşimi test etmek için en uygun modelin cDCC-GARCH olduğu gözlenmiş ve Bitcoin ile emtialar (gümüş hariç) arasındaki etkileşimin negatif yönlü; emtiaların kendi aralarındaki etkileşimin pozitif yönlü olduğu tespit edilmiştir. Bulgular, Bitcoin’in emtialar için (gümüş hariç) diğer emtialara göre daha iyi bir çeşitlendirici olduğunu ve Bitcoin’in emtia bulunduran portföye dahil edildiğinde hedge etme görevi üstlendiğini göstermektedir.
Venture capital led by Bitcoin and gold has become increasingly popular in the past several years, so the research of cryptocurrencies (such as Bitcoin) becomes deeper and deeper. Many researchers have studied the collaborative investment of bitcoin and gold, which is an expective portfolio. In this paper, the authors constructed a systematic model, achieving the combination among prediction, making strategies, solving profits, and evaluation. All the study in this paper is based on the given data and constructed model with accurate references.In this paper, the authors selected the long short-term memory model (LSTM) as the basis, then designed two models called the gold price prediction model (GPPM) and the Bitcoin price prediction model (BPPM) to estimate the price of both gold and Bitcoin, standing as a trader, not a “god economist”. The error analysis shows a good performance of GPPM and BPPM, and it gives the authors confidence to make strategies and calculate final profits (investment worth).Unambiguously, the final goal of this question is to maximize the total assets (profits), so the author set up a single objective optimization model (SOOM) called the trading strategy model (TSM). The total constraint conditions are divided into six directions, including the basic trading conditions, the evaluation of financial risk, and the difference between gold and Bitcoin. Additionally, the costumers with different trading risk tolerance will acquire different assets finally, which indicates that the prudent policy generally can lead to a better result. After calculation, the asset on 2021/9/10 is about 1.59×108 USD, a considerable number.The evaluation of TSM has two parts, one is the disturbance test. This test randomly sets that several days’ trading does not occur, then has a comparison between the original model prices and the prices after disturbance. The result proves that the strategy predicted by TSM is the best strategy. The result of the sensitivity test in section 4 finds the polynomial relationship between the assets and the transaction costs. Under current conditions, the final assets will decrease by 4.2% if the transaction costs of gold increase by 1%, and will increase by 2.1% if the transaction costs of bitcoin increase by 1%.Finally, the authors wrote a memorandum for different customers & traders. We sincerely hope the memorandum can help them in the near future.
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.
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.