Cryptocurrencies are attracting considerable attention around the world because of the various advantages that they offer. On the other hand, they also carry some inherent risks. Although monetary authorities broadly agree that cryptocurrencies do not engender an immediate threat to national and global financial systems, the future is full of unknowns. In this regard, drawing a framework based on the current drivers of demand for cryptocurrencies would help visualize the prospects for these assets and create a roadmap to avoid or manage any disruptive risks. This discussion paper aims to contribute to the literature by examining the key factors that will determine the future performance of cryptocurrencies. The main conclusion derived from the discussion is that national regulations will potentially affect the direction of cryptocurrencies, as well as the need for any special efforts in the domain of monetary policy
Renewable energies (REs) are trending as the technology to improve its efficacy moving at a fast pace. REs has been utilized in many domestic and industrial premises as a surplus energy. Trading of this surplus energy within a local market scenario, can help in meeting the demand requirements and can outlook a new scope of energy trading. This requires high security and transparency among the stake holders. Among various technologies, blockchain can be proved as a promising platform for a secure and transparent transaction. This paper comprises an extensive review of different blockchain based energy trading model and discusses various research scopes for mitigating of certain issues with ongoing research models to gain higher efficiency.
The most significant disturbance now affecting all economies and financial institutions is the digital transformation of economies. The world’s economy and financial institutions are digitizing at an unprecedented rate. Bitcoin is a devolved crypto-currency, or digital asset, that uses blockchain technology to expedite peer-to-peer financial transactions. Price volatility is one of the primary issues with decentralized cryptocurrencies, highlighting the need of examining the underlying price mechanism. Additionally, Bitcoin prices display non-stationary behaviour, meaning that their statistical distribution fluctuates over time. Bitcoin prices are stochastic, and no one set of characteristics can be used to forecast them completely. Nonetheless, academics have demonstrated varying degrees of effectiveness in estimating Bitcoin values using various feature sets. This article explains how to forecast Bitcoin price movements and prices using machine learning approaches. We intend to apply ARIMA, Facebook Prophet and XGBoost techniques for price prediction.
This study aims to explore the potential use of the cryptocurrency bitcoin as an investment instrument in Indonesia. The return obtained from bitcoin cryptocurrency is compared to other investment instruments, namely stock returns, gold and the rupiah exchange rate. The research period was carried out based on research data from 2011 to 2020. This study employee compares means test (t test) and analysis of variance (F test) on rate of return of bitcoin investment. The bitcoin return compare to the rate of return form the others investments instruments namely exchange rate, gold and stock. The study collected 120 data of each investments instruments: bitcoin, exchange rate, gold and stock from various of sources during 2011–2020. Then, we calculate the return and risk of individual investment instruments. The results showed that the bitcoin currency had the highest rate of return 18% with a standard deviation of 61% compared to exchange rate, gold and stock returns. While the rate of return for the others investment instruments showed less than 0.5% with standard deviation less than 5%. The rate of return bitcoin has significance difference compare to the rate of return of exchange rate, gold and stock. The study contribute for the investors who would like to invest on bitcoin. The investors should understand the characteristic of bitcoin in term of rate of returns and also the risk. This study also contributes to government of Indonesia on crypto currency development. The Indonesia government should adopt and regulate on crypto currency in the future to secure the investor and economic growth.
Purpose This paper aims to examine the frequency of co-movements and asymmetric dependencies between bitcoin (BTC), gold, Brent crude oil and the US economic policy uncertainty (EPU) index. Design/methodology/approach The authors use a wavelet approach and a quantile-on-quantile regression (QQR) method. Findings The results show a positive interdependence between BTC and commodity price returns at both medium and low frequencies over the sample period. In contrast, the dependence is negative between BTC and EPU index at both medium and low frequencies. Furthermore, the co-movements between markets are more pronounced during crises. The results show that strategic commodities and EPU index have the ability to predict BTC price returns at both medium- and long-terms. The QQR method reveals that higher gold returns tend to predict higher/lower BTC returns when the market is in a bullish/bearish state. Moreover, lower gold returns tend to predict lower (higher) BTC returns when the market is in a bearish (bullish) state (positive (negative) relationship). The lower Brent returns tend to predict higher/lower BTC returns when the market is in a bullish/bearish state. High Brent quantiles tend to predict the lower BTC returns in its extremely bearish states. Finally, higher and lower EPU changes tend to predict lower and higher BTC returns when the market is in a bearish/bullish state (negative relationship). Originality/value There is generally a lack of understanding of the linkages between BTC, gold, oil and uncertainty index across multiple frequencies. This is, as far as the authors know, the first attempt to apply both the wavelet approach and a QQR method to examine the multiscale linkages among markets under study. The findings should encourage the relevant policymakers to consider these co-movements which vary over time and in duration when setting up regulations that deem to enhance the market efficiency.
The current study investigates the connectedness between US COVID-19 news, Dowes Jones Index (DJI), green bonds, gold, and bitcoin prices for the period 22 January 2020–3 August 2021. The study has employed wavelet coherency, the continuous wavelet transform, and the wavelet-based Granger causality methods to obtain the dependence result. The continuous wavelet transform (CWT) analysis reveals that the United States equity market prices are extremely sensitive with regard to spreading coronavirus (USCOVID-19) news and changes in the oil price. Green bonds, gold, and bitcoin have minimal connectedness with the equity market, which might lead to the hedge and safe haven role of these assets during the COVID-19 crisis period. Lastly, very strong comovement was found between bitcoin and gold during the entire sample. The results of the present study offer a number of fresh and noticeable policy implications for international investors and asset managers.
Extending the time period of Goodell and Goutte (2020), we apply wavelet methods to daily data of COVID-19 world deaths and daily Bitcoin prices from December 31, 2019 to August 31, 2020. We confirm Goodell and Goutte (2020) for an extended period by evidencing a positive correlation between levels of COVID-19 and Bitcoin prices, suggesting Bitcoin as a safe haven investment. Investigations such as this are important to both scholars and policy-makers, as well as investment professionals interested in the financial implications of both COVID-19 and cryptocurrencies.
In this study, we compare the role and the safe-haven properties of bitcoin and gold against developed and emerging market indices during extreme market conditions as the COVID-19 crisis. We explore the effects of adding bitcoin and gold to an optimal portfolio by relying Sharpe ratio and genetic algorithm approach. We use a stochastic dominance approach to compare the performance of portfolios for each scenario. The results show that by adding bitcoin, the portfolio performance improves only during the sovereign debt crisis. However, during the non-crisis period and during the COVID-19 crisis, the portfolios with and without bitcoin do not dominate, this shows that bitcoin does not act as a safe-haven. However, our results affirm the safe-haven nature of gold during the COVID-19 crisis.
Çin, ilk SARS-CoV-2 (COVID-19) vakasını 31.12.2019 tarihinde Dünya Sağlık Örgütü'ne (DSÖ) bildirmiştir. Bununla birlikte söz konusu virüs kısa sürede Dünya'da 200'den fazla ülkeye yayılmıştır. 07.04.2021 tarihi itibariyle Dünyada 133 milyondan fazla vaka tespit edilirken, yaklaşık 2.9 milyon kişi hayatını kaybetmiştir. Pandemi koşullarında hükümetler vatandaşlarını korumak için farklı politikalar izlediler. Pandemi, bu ülkelerdeki sağlık sistemine ek olarak borsaları ve altın, petrol, kripto para gibi çeşitli küresel varlıkları da etkilemiştir. Bu çalışmada, 26.06.2018 - 07.04.2021 dönemi için piyasa değeri en yüksek olan kripto para birimlerinde fiyat balonlarının varlığının incelenmesi amaçlanmaktadır. Bu çalışma kapsamında, pandemi öncesi ve pandemi döneminde on kripto para birimindeki fiyat balonlarının araştırılması için SADF testi uygulanmıştır. Buna göre, on kripto para biriminden sekizinin fiyat balonuna sahip olduğu tespit edilmiştir. Pandemi öncesi dönemde en yüksek fiyat balonu sayısının toplam 84 işlem günü ile Binance Coin'de görüldüğü belirlenmiştir. Sırasıyla, Bitcoin, Chainlink (Bitcoin ile aynı gün sayısı), Litecoin ve Tether bu kripto para birimini takip etmiştir. Bununla birlikte, COVID-19 salgınında işlem günü bazında en yüksek fiyat balonu, toplam 230 gün ile Theta'da görülmüştür. Pandemi döneminde, Chainlink, Bitcoin, Ethereum, Cordano, Binance Coin ve Litecoin, Theta'yı sırasıyla 183, 140, 104, 94, 66 ve 28 işlem günü ile takip etmiştir. Fiyat balonlarının yaklaşık yüzde yetmiş beşi pandemi döneminde görülmüştür. Bu sonuçlar, kripto para birimlerinin yeni yatırımlar için spekülatif varlıklar olduğunu göstermektedir. Tüm analiz dönemi değerlendirildiğinde ise toplam 234 gün ile Chainlink'te en yüksek fiyat balonu tespit edilmiştir. Ayrıca, Bitcoin 131 gün ile aralıksız en uzun fiyat balonunu göstermiş, Theta ve Ethereum bu kripto parayı takip etmiştir.
David Opeoluwa Oyewola, Emmanuel Gbenga Dada, Juliana Ngozi Ndunagu, Daniel Eneojo Emmanuel
In the wake of recent pandemic of COVID-19, we explore its unprecedented impact on the demand and supply of cryptocurrencies’market using machine learning such as Naïve Bayes (NB), Decision Trees (C5), Decision Trees Bagging (BG), Support Vector Machine (SVM), Random Forest (RF), Multinomial Logistic Regression (MLR), Recurrent Neural Network (RNN), Long Short Term Memory and Noise Bagging (NBG). The study employed Noise filters to enhance the performance of Decision Trees Bagging named NBG. Dataset utilized for this analysis were obtained from the website of Coin Market Cap, including: Binance Coin (BCN), BitCoin Cash (BCH), BitCoin (BTC), BitCoinSV (BSV), Cardano (CDO), Chainlink (CLK), CryptoCoin (CCN), EOS (EOS), Ethereum (ETH), LiteCoin (LTC), Monero (MNO), Stellar (SLR), Tether (TTR), Tezos (TZS), XRP (XRP), and daily data collected from exchange markets platforms spans from 2nd January 2018 to 7th July 2020. Auto encoder was utilized for the labelling of the trading strategies buy-hold-sell.
The blockchain technology introduced by bitcoin, with its decentralised peer-to-peer network and cryptographic protocols, provides a public and accessible database of bitcoin transactions that have attracted interest from both economics and network science as an example of a complex evolving monetary network. Despite the known cryptographic guarantees present in the blockchain, there exists significant evidence of inconsistencies and suspicious behavior in the chain. In this paper, we examine the prevalence and evolution of two types of anomalies occurring in coinbase transactions in blockchain mining, which we reported on in earlier research. We further develop our techniques for investigating the impact of these anomalies on the blockchain transaction network, by building networks induced by anomalous coinbase transactions at regular intervals and calculating a range of network measures, including degree correlation and assortativity, as well as inequality in terms of wealth and anomaly ratio using the Gini coefficient. We obtain time series of network measures calculated over the full transaction network and three sub-networks. Inspecting trends in these time series allows us to identify a period in time with particularly strange transaction behavior. We then perform a frequency analysis of this time period to reveal several blocks of highly anomalous transactions. Our technique represents a novel way of using network science to detect and investigate cryptographic anomalies.
Khreshna Syuhada, Djoko Suprijanto, Arief Rachman Hakim
This paper aims to compare the safe-haven roles of gold and Bitcoin for energy commodities, including oils and petroleum, during COVID-19. Specifically, we examine the presence of reduction in downside risk after mixing gold/Bitcoin with such energy commodities. To do this, we account for dependence among energy commodities and gold/Bitcoin returns by applying a (vine) copula. The findings show that gold substantially reduces the downside risk of a portfolio containing any allocation to gold and energy commodities, indicating its safe-haven ability. In contrast, Bitcoin's safe-haven functionality is inconsistent since the downside risk reduction is achieved for Bitcoin's small allocation only.
Bu çalışmada, Bitcoin elektrik tüketiminin, Bitcoin üretiminde önde gelen seçili ülkelerin enerji piyasaları ile arasındaki ilişki araştırılmıştır. Bu amaç doğrultusunda 22.05.2017-10.02.2021 dönemleri arasında haftalık veriler kullanılarak; Cambridge Bitcoin Elektrik Tüketim Endeksi (CBECI) ile S&P 500, MOEX ve SSE enerji endeksleri arasındaki volatilite hareketleri incelenmiştir. CCC-GARCH modeliyle kurgulanan analizlerden elde edilen bulgular CBECI endeksinin; MOEX enerji endeksi ile arasında çift yönlü volatilite ilişkisi olduğunu, S&P 500 ve SSE enerji endeksleri ile arasında tek yönlü bir volatilite ilişkisi olduğunu göstermektedir. Bulgular Bitcoin elektrik tüketiminin, Rusya ve Çin’in enerji şirketi değerlemelerini etkilediği; ABD ve Rusya’nın enerji şirketi değerlemelerinden etkilendiği sonucuna ulaşılmaktadır.
With the emergence of a large number of virtual currencies represented by Bitcoin and Ethereum and the continuous rise of their prices, a large number of investors have been attracted to invest in them. The attention and research based on virtual currencies have aroused the wide concern of the social public. The price prediction of the virtual currencies is one of the research hotspots, obtaining and analyzing the historical data of virtual currency price for the prediction of future price. We find that some commonly used machine learning algorithms have a great deviation in the price prediction of virtual currency. To solve this problem, we propose a novel support vector regression (SVR) based on data segmentation, which can significantly improve the accuracy and effectiveness of price prediction of virtual currency. The experimental results show that our algorithm has obvious advantages over the traditional SVR algorithm and other eight classical machine learning algorithms.
In December 2017, two leading derivative exchanges, CBOE and CME, introduced the first regulated Bitcoin futures. Our aim is estimating their causal impact on Bitcoin volatility and trading volume. Employing a new causal approach, C-ARIMA, we find that the CME future triggered an increase in both outcomes. There is also evidence of a positive volume-volatility relationship and that the effect on volatility was partially due to the higher trading volumes induced by the launch of the contract. After controlling for the effect on volumes, we find that the CME instrument caused Bitcoin volatility to increase by more than double.
Bu çalışmada popülerliği son yıllarda artan kripto paralar sınıfında değerlendirilen Bitcoin ile Euro getirileri arasındaki volatilite etkileşimini incelemek için 02.02.2014-28.02.2021 dönemine ait günlük veriler kullanılmıştır. Değişkenlere ait getirilerin zaman içindeki hareketini incelemek için oluşturulan grafiklerden volatilite kümelenmesi tespit edilmiş ve çok değişkenli GARCH modelleri kullanılmıştır. Modellerden elde edilen sonuçlar karşılaştırılarak log-olabilirlik değeri en küçük(negatif olarak) bulunan BEKK-GARCH modeli uygun model kabul edilerek Euro ile Bitcoin arasında çift yönlü volatilite etkileşimi bulunmuştur. Ayrıca DCC-GARCH modeli sonuçlarına göre ise iki getiri arasında asimetri ilişkisi ve oranında pozitif, güçlü bir dinamik korelasyon tespit edilmiştir.