Propósito. Esta investigación tiene como objetivo identificar la literatura científica existente en torno al valor y precio de los NFTs. Metodología. La metodología utilizada en el presente artículo consta de un análisis bibliométrico, se usa la base de datos Scopus desde la primera aparicion de un artículo hasta la actualidad. Hallazgos. Los resultados muestran que existe muy poca literatura científica entorno al valor y precio de los NFTs, se logró identificar ocho artículos, en donde solamente tres contribuyen en su totalidad a la descripción de las variables valor y precio, además se encuentran variables incidentes en el valor y precio de los NFTs lo que llevaría a tener el potencial de generar nuevo conocimiento en este ámbito, realizar propuestas teóricas en modelos de valuación y precio para estos activos. Originalidad.La investigación se realizó considerando solamente a la base de datos Scopus utilizando el software VosViewr, se recomienda para futuras investigaciones tomar en cuenta otras bases de datos.
Tether Limited has the sole authority to create (mint) and destroy (burn) Tether stablecoins (USDT). This paper investigates Bitcoin's response to USDT supply change events between 2014 and 2021 and identifies an interesting asymmetry between Bitcoin's responses to USDT minting and burning events. Bitcoin responds positively to USDT minting events over 5- to 30-minute event windows, but this response begins declining after 60 minutes. State-dependence is also demonstrated, with Bitcoin prices exhibiting a greater increase when the corresponding USDT minting event coincides with positive investor sentiment and is announced to the public by data service provider, Whale Alert, on Twitter.
This study examines the weak form of the efficient market hypothesis for Bitcoin using a feedforward neural network. Due to the increasing popularity of cryptocurrencies in recent years, the question has arisen, as to whether market inefficiencies could be exploited in Bitcoin. Several studies we refer to here discuss this topic in the context of Bitcoin using either statistical tests or machine learning methods, mostly relying exclusively on data from Bitcoin itself. Results regarding market efficiency vary from study to study. In this study, however, the focus is on applying various asset-related input features in a neural network. The aim is to investigate whether the prediction accuracy improves when adding equity stock indices (S&P 500, Russell 2000), currencies (EURUSD), 10 Year US Treasury Note Yield as well as Gold&Silver producers index (XAU), in addition to using Bitcoin returns as input feature. As expected, the results show that more features lead to higher training performance from 54.6% prediction accuracy with one feature to 61% with six features. On the test set, we observe that with our neural network methodology, adding additional asset classes, no increase in prediction accuracy is achieved. One feature set is able to partially outperform a buy-and-hold strategy, but the performance drops again as soon as another feature is added. This leads us to the partial conclusion that weak market inefficiencies for Bitcoin cannot be detected using neural networks and the given asset classes as input. Therefore, based on this study, we find evidence that the Bitcoin market is efficient in the sense of the efficient market hypothesis during the sample period. We encourage further research in this area, as much depends on the sample period chosen, the input features, the model architecture, and the hyperparameters.
Yeni finansal varlıklar ile klasik yatırım enstrümanları arasındaki ilişkilerin önemi artmaktadır. Çalışmada Diks ve Panchenko Doğrusal Olmayan Nedensellik Testi kullanarak Bitcoin, Ethereum fiyatları ve borsa endeksleri arasındaki nedensellik ilişkileri incelenmektedir. Çalışma dönemi covid-19 pandemisinin Türkiye’de ilan edildiği tarihten başlayarak 11/03/2020-06/04/2022 arasındaki günlük frekanstaki verileri kapsamaktadır. Böylece pandemi süreciyle birlikte küresel çaplı krizlerde kripto para birimleri ile küresel finans piyasaları arasındaki ilişkinin incelenmesi hedeflenmiştir. Çalışma literatürde özellikle Ethereum ile ilgili yapılmış çalışmaların kısıtlı olması sebebi ile diğer çalışmalardan ayrılmaktadır. Kullanılan doğrusal olmayan nedensellik analizi sonuçlarına bakıldığında, dünya borsa endeksi ile Bitcoin fiyatı arasında ve Asya borsa endeksi ile Ethereum fiyatı arasında çift yönlü nedensellik ilişkisi olduğu tespit edilmiştir. Ayrıca Bitcoin fiyatına Avrupa ve ABD borsa endekslerinden tek yönlü nedensellik olduğu görülmektedir. Aynı zamanda Bitcoin fiyatından da Asya borsa endeksine tek yönlü nedensellik ilişkisi mevcuttur. Ethereum fiyatından ise Avrupa borsa endeksine doğru tek yönlü nedensellik ilişkisi söz konusudur.
H. M. Tenkam, Jules C. Mba, Sutene Mwambetania Mwambi
This paper focuses on the selection and optimisation of a cryptoasset portfolio, using the K-means clustering algorithm and GARCH C-Vine copula model combined with the differential evolution algorithm. This integrated approach allows the construction of a diversified portfolio of eight cryptocurrencies and determines an optimal allocation strategy making it possible to minimize the conditional value-at-risk of the portfolio and maximise the return. Our results show that stablecoins such as True-USD are negatively correlated to the other cryptoassets in the portfolio and could therefore be a safe haven for crypto-investors during market turmoil. Our findings are in line with previous studies exhibiting stablecoins as potential diversifiers.
This study aims to analyze the causal relationship between electricity consumption, price and transaction volume of Bitcoin, which is the most important asset of the crypto money market in terms of both market capitalization and transaction volume. In this study, the Bitcoin electricity consumption variable is represented by Cambridge Bitcoin Electricity Consumption Index. As the data set, 1446 days of data between February 2017 and February 2021 were used. The causality relationship between the variables is analyzed using the Hatemi-J (2012) and Toda Yamamoto (1995) tests. In addition, this study is a rare study that examines the relationship between electricity and volume, together with the work done by Schinckus et al. (2020). According to the results of this study, the decrease in Bitcoin electricity consumption causes a decrease in the Bitcoin price. However, a negative relationship is detected Bitcoin electricity consumption and Bitcoin trade volume in this study, like the study by Schinckus et al. (2020), the relationship was found to be very weak.
Ruzita Abdul‐Rahim, Airil Khalid, Zulkefly Abdul Karim, Mamunur Rashid
This paper estimates the comovement between two leading cryptocurrencies and the G7 stock markets. It then attempts to explain the comovement with the rational investment theory by examining whether it is driven by market uncertainty measures, public attention to COVID-19, and the government’s containment and health responses to COVID-19. Wavelet Coherence heatmaps show that the stock-cryptocurrency comovements increase significantly and positively during the pandemic, indicating that cryptocurrencies lose their safe haven properties against stocks during the heightened market uncertainties. Over the longer investment horizons, Bitcoin reemerges as a safe haven or strong hedger while Ethereum’s properties weaken. Seemingly Unrelated Regression results reveal that the stock-cryptocurrency comovements are rationally explained by market uncertainties, government responses to COVID-19, and market fundamentals. However, the comovements are also driven by the fear of COVID-19 to a certain extent. Our findings offer valuable insights for investors considering cryptocurrencies to rebalance their equity portfolios during market distress. For policymakers, the Economic Policy Uncertainty (EPU) results suggest that government policies and regulatory frameworks can be used to regulate speculation and investment activities in the cryptocurrency market.
Purpose This paper analyzes the connectedness with network among the major cryptocurrencies, the G7 stock indexes and the gold price over the coronavirus disease 2019 (COVID-19) pandemic period, in 2020. Design/methodology/approach This study used a multivariate approach proposed by Diebold and Yilmaz (2009, 2012 and 2014). Findings For a stock index portfolio, the results of static connectedness showed a higher independence between the stock markets during the COVID-19 crisis. It is worth noting that in general, cryptocurrencies are diversifiers for a stock index portfolio, which enable to reduce volatility especially in the crisis period. Dynamic connectedness results do not significantly differ from those of the static connectedness, the authors just mention that the Bitcoin Gold becomes a net receiver. The scope of connectedness was maintained after the shock for most of the cryptocurrencies, except for the Dash and the Bitcoin Gold, which joined a previous level. In fact, the Bitcoin has always been the biggest net transmitter of volatility connectedness or spillovers during the crisis period. Maker is the biggest net-receiver of volatility from the global system. As for gold, the authors notice that it has remained a net receiver with a significant increase in the network reception during the crisis period, which confirms its safe haven. Originality/value Overall, the authors conclude that connectedness is shown to be conditional on the extent of economic and financial uncertainties marked by the propagation of the coronavirus while the Bitcoin Gold and Litecoin are the least receivers, leading to the conclusion that they can be diversifiers.
2017 is the year when the cryptocurrencies came into limelight with the primary trading in bitcoin being featured on a mainstream market in Chicago, Since then many people started investing into bitcoins or at least wanting to know about bitcoin, mostly including the people from the software industry. In this article it is clearly defined in a stepwise order using meaningful pictures and figures, from the very beginning of cryptocurrencies to the technology behind bitcoin, that is the blockchain technology. Most people do not trust in the security of trading in cryptocurrencies, but after understanding the working of Blockchain one would definitely believe in the technical security of using a cryptocurrency but the financial reasoning to investment in cryptocurrencies is a whole another story.
This article's motivation is to understand the volatile Bitcoin price increase. The objective is to develop price estimation methods. The methodology is to present five differential equation models estimated against the 23 July 2010-21 June 2021 Bitcoin data. The findings are that Gompertz growth fits the damped oscillations and lengthening cycles well, and tracks the early data better with the weighted least squares method. Gompertz growth combined with charged capacitor growth tracks the early data even better. Logistic growth is too slow to track the early data. Logistic growth combined with charged capacitor growth to some extent tracks the early data. Pure charged capacitor growth is unrealistic. The dates for the future bull market maxima depend to a low degree on the growth model carrying capacity approached asymptotically, assumed to match gold at $10 trillion, and to be 50 times higher. The implications for traders are to focus on the large standard deviations. Investors should understand the growth potential compared with other asset classes. Regulators should ensure financial stability by focusing on the fluctuations. Central banks should adjust the money supply while acknowledging. Bitcoin competition. Collective units should understand Bitcoin growth models to determine whether to accept Bitcoin transactions.
Research on cryptocurrencies has proliferated in recent years. Our research objective was to answer the question of whether macroeconomic news from the U.S. affects Bitcoin in the same way it affects other common investment assets such as gold, the S&P 500, 2-year Treasury bills, and 10-year Treasury bills. Following previous research, seven macroeconomic news announcements from the U.S. were selected, and an empirical analysis of the daily returns, volatility, and volume of the selected assets was conducted. The results show that while Bitcoin is the most volatile (i.e., riskiest) of all the assets, the expected direction of movement is visible after the official announcement of the macroeconomic news on that day, and is comparable to that of the 2-year Treasury bills. It is also evident that the trading volume of Bitcoin does not change, unlike other assets, suggesting that the price of Bitcoin is always moved by the same players, indicating the closed and, therefore, riskier nature of cryptocurrency markets. Finally, we found evidence that the impact of macroeconomic announcements on Bitcoin returns is stronger when the announcements are negative but, interestingly, the returns of Bitcoin, unlike those of other assets, are more volatile after positive announcements.
John W. Goodell, Ilan Alon, Laura Chiaramonte, Alberto Dreassi · 6 authors
We investigate the impact of BRICS regulatory announcements on cryptocurrency volatilities and returns. Results evidence risk substitutions after announcements moving from ETH, XRP and LTC to BTC and vice versa, with BTC having volatility reactions to regulatory announcements that differ from those of other cryptocurrencies. Bootstrap quantile regression indicates a stronger detrimental impact of announcements when BTC is currently manifesting lower volatility and higher daily returns. Robustness checks confirm our findings, as well as evidence that the cryptocurrencies in our sample are considerably more reactive to BRICS announcements than US Fed announcements, suggesting important linkages between emerging markets and cryptocurrencies.
The objective of this study is to use the South African financial markets (Johannesburg Stock Exchange or JSE and USD/ZAR) as a case study to understand the volatility spillover dynamics of Bitcoin as a digital asset. Methodologically, the study applies the exponential generalized autoregressive conditional heteroskedastic (EGARCH) model, followed by a robustness check by applying the time-varying conditional correlation multivariate GARCH (VCC-MGARCH) model. The study utilizes the data set for the period 2011 to 2019, a period before the COVID‑19 pandemic. The research outcome revealed three interesting observations. First, Bitcoin and the South African stock market are independent of each other. Second, there is a bidirectional shock transmission between Bitcoin and USD/ZAR in the mean returns only, but not variance. Lastly, results confirm the existence of a bidirectional volatility spillover in both the mean and variance between the JSE stock market and the USD/ZAR market. The study outcome should enlighten investors who may want to consider Bitcoin as a diversifier in their investment and portfolio strategies.
As a new type of electronic currency, bitcoin is more and more recognized and sought after by people, but its price fluctuation is more intense, the market has certain risks, and the price is difficult to be accurately predicted. The main purpose of this study is to use a deep learning integration method (SDAE-B) to predict the price of bitcoin. This method combines two technologies: one is an advanced deep neural network model, which is called stacking denoising autoencoders (SDAE). The SDAE method is used to simulate the nonlinear complex relationship between the bitcoin price and its influencing factors. The other is a powerful integration method called bootstrap aggregation (Bagging), which generates multiple datasets for training a set of basic models (SDAES). In the empirical study, this study compares the price sequence of bitcoin and selects the block size, hash rate, mining difficulty, number of transactions, market capitalization, Baidu and Google search volume, gold price, dollar index, and relevant major events as exogenous variables uses SDAE-B method to compare the price of bitcoin for prediction and uses the traditional machine learning method LSSVM and BP to compare the price of bitcoin for prediction. The prediction results are as follows: the MAPE of the SDAE-B prediction price is 0.016, the RMSE is 131.643, and the DA is 0.817. Compared with the other two methods, it has higher accuracy and lower error, and can well track the randomness and nonlinear characteristics of bitcoin price.
This study investigates the asymmetric shock transmission mechanisms between seven large cryptocurrencies and crude oil at different market conditions across time. Wavelet technique was used to decompose the daily return series of the assets into wavelet scales to capture trading horizons. We applied quantile regression (QR) and quantile-in-quantile Regression (QQR) on the decomposed series to capture the bear (bull) market conditions. Applying the QR, we found Ethereum, Steller, Ripple and Monero as hedges for oil market volatility at all market regimes from medium to long terms. The QR undermined the hedging properties of Bitcoin, Litecoin and Das, suggesting possible spread of market disruptions from these markets to crude oil market. We observe from QQR that the assets have negative influence on each other at bear market but positive influence at bull market across time, signifying hedging possibilities for both assets in bear market. The significance of our finding is strengthened by the recent rise in the market share of cryptocurrencies.
The vulnerability of solar power producers to sunshine fluctuations exposes them to the volumetric risk that future electricity generation may deviate from predicted generation. Weather derivatives have recently emerged as a tool for hedging the volumetric risks of these power producers. However, the state-of-the-art instruments have several shortcomings, contributing to their limited application in the industry. Therefore, novel solar radiation-based weather derivative smart contract arrangements on a blockchain marketplace are proposed to address some of the main limitations of traditional instruments. In this regard, the cash flow of solar generators is modelled to assess the weather elements causing its stochasticity. Using this information, novel smart contract arrangements on a blockchain marketplace with solar radiation days as the underlying weather index are developed and analytically valued. Thereafter, a suite of novel smart contract autonomous mechanisms compelling contracting parties to behave rationally and maintain an enduring arrangement is presented. Finally, a trading strategy based on the developed smart contract arrangements is proposed to minimize the power producers’ volatility risk. Results emanating from notional simulations indicate that the proposed approach could be more suitable for hedging the volumetric risks of solar power producers than traditional instruments.
In this paper, we take a forecasting perspective and compare the information content of a set of market risk factors, cryptocurrency-specific predictors, and sentiment variables for the returns of cryptocurrencies vs traditional asset classes. To this aim, we rely on a flexible dynamic econometric model that not only features time-varying coefficients, but also allows for the entire forecasting model to change over time to capture the time variation in the exposures of major digital currencies to the predictive variables. Besides, we investigate whether the inclusion of cryptocurrencies in an already diversified portfolio leads to additional economic gains. The main empirical results suggest that cryptocurrencies are not systematically predicted by stock market factors, precious metal commodities or supply factors. On the contrary, they display a time-varying but significant exposure to investors' attention. In addition, also because of a lack of predictability compared to traditional asset classes, cryptocurrencies lead to realized expected utility gains for a power utility investor.
Global economic markets are encountering apprehensions and susceptibilities after the pandemic of COVID-19. Investment patterns are becoming restrained because of uncertain global scenarios and reduced GDP worldwide. World economies are moving towards digital era and investors are becoming more open towards the newest forms of investments. Due to the uncertain scenarios, investors globally are looking forward to some lucrative forms of investments and cryptocurrencies are the ray of hope for global investors. The present study is attempt to explore the changing dynamics of cryptocurrencies with the market uncertainties. Volatility of five cryptocurrencies, namely Bitcoin, Ethereum, XRP, Chainlink and Bitcoin Cash are analysed using the Generalised AutoRegressive Conditional Heteroskedasticity Model. Results showed that the investors preferred taking cautious decisions and invested more in famous bitcoin rather than other cryptocurrencies.
Susilo Nur Aji Cokro Darsono, Wing‐Keung Wong, Tran Thai Ha Nguyen, Dyah Titis Kusuma Wardani
This study examines the effect of economic policy uncertainty (EPU) on sustainable investment returns by using panel data of stock market returns and the EPU index from twelve countries for the period from April 2015 to December 2020. In addition, precious metal prices, energy prices, and cryptocurrency prices are used as control variables. To do so, we investigate the impact of EPU, gold prices, oil prices, and Bitcoin prices on stock market returns by using the panel autoregressive distributed lag (ARDL) model to examine both the long-run correlation and short-run effect. Our findings show that EPU, gold prices, oil prices, and Bitcoin prices have a time-varying significant impact on sustainable stock market returns. We discovered that EPU has a significantly negative impact on the returns of the sustainable stocks in the markets over the long run. In contrast, the rise of the gold price, oil price, and Bitcoin price have a significantly positive impact on the returns of the sustainable stocks in the twelve sustainable markets in the long run. On the other hand, EPU in Singapore, Spain, the Netherlands, and Russia has a significant short-run impact on market returns in each country. Based on the findings, managers and investors in the sustainable stock markets are highly recommended to pay more attention to the volatility of EPU, gold prices, oil prices, and Bitcoin prices in the short run to control the risk of returns in the sustainable stock market. Furthermore, policymakers must closely monitor the movement of the EPU index, as it is a major driver of sustainable stock market returns.
Shusheng Ding, Tianxiang Cui, Xiangling Wu, Anna Min Du
A Central Bank Digital Currency (CBDC) launched by the Bank of England could enable businesses to directly make electronic payments. It can be argued that digital payment is helpful in supply chain management applications. However, the adoption of CBDC in the supply chain could bring new turbulence since the CBDC value may fluctuate. Therefore, this paper intends to optimize the production plan of manufacturing supply chain based on a volatility clustering model by reducing CBDC value uncertainty. We apply both GARCH model and machine learning model to depict the CBDC volatility clustering. Empirically, we employed Baltic Dry Index, Bitcoin and exchange rate as main variables with sample period from 2015 to 2021 to evaluate the performance of the two models. On this basis, we reveal that our machine learning model overwhelmingly outperforms the GARCH model. Consequently, our result implies that manufacturing companies’ performance can be strengthened through CBDC uncertainty reduction.