Blockchain Papers

Zhongbao Zhou, Zhengyang Song, Helu Xiao, Tiantian Ren

No abstract is available for this record.

Zynobia Barson, Peterson Owusu, Anokye M. Adam, Emmanuel Asafo‐Adjei

We employ a frequency‐dependent asymmetric and causality analysis to investigate the connectedness between gold and cryptocurrencies during the COVID‐19 pandemic. Hence, the variational mode decomposition‐based quantile regression is utilised. Findings from the study divulge that the variational mode functions at the lower quantiles are mostly significant and negative indicating that gold acts as a safe haven, a diversifier at most market conditions with insignificant coefficients, and a hedge at normal market conditions for most cryptocurrencies at various investment horizons. Particularly, hedging benefits mostly occur in the short‐ and medium‐term for Bitcoin and Ripple, as well as Bitcoin and Dogecoin in the long‐term with gold. This implies that there is high persistence in the hedging properties of gold with Bitcoin, followed by Ripple. We notice more significant relationship between gold and some cryptocurrencies in the long‐term of the COVID‐19 pandemic relative to the medium‐term emphasising the delayed responses of prices to information. Investors are recommended to be observant and mindful of investing in these markets due to the different dynamics.

Zaghum Umar, Afsheen Abrar, Adam Zaremba, Тамара Теплова · 5 authors

No abstract is available for this record.

Yizhi Wang

The aim of this study is to investigate the volatility spillover connectedness between NFTs attention and financial markets. This paper firstly proposes a new direct proxy for the public’s attention in the NFT market: the non-fungible tokens attention index (NFTsAI), based on 590m news stories from the LexisNexis News & Business database and applies the historical decomposition to assess the historical variations of the NFTsAI. Then the empirical analysis is performed via a TVP-VAR volatility spillover connectedness model. The empirical results show that NFTsAI indicates NFT markets are dominated by cryptocurrency, DeFi, equity, bond, commodity, F.X. and gold markets. And NFT markets are volatility spillover receivers. In addition, NFT assets could impede financial contagion and have significant diversification benefits. Employing a panel pooled OLS regression model as a supplementary analysis and a GARCH-MIDAS model as a robustness test. This study reveals that NFTsAI has sufficient power to explain the return of NFT assets from a fixed effect perspective, and NFTsAI contains useful forecasting information for both short and long-term volatility of NFT markets, separately. The new NFTsAI and the empirical findings contain useful insights for risk-averse investors, portfolio managers, institutional investors, academics and financial policy regulators.

Carol Alexander, Arben Imeraj

We analyse robust dynamic delta hedging of bitcoin options using a set of smile-implied and other smile-adjusted deltas that are either model-free, in the sense that they are the same for every scale-invariant stochastic and/or local volatility model, or they are based on simple regime-dependent parameterisations of local volatility. These deltas are popular with option market makers in traditional assets because they are very easy to implement. Previous empirical research on dynamic delta hedging is based solely on equity index options, but analysis of our unique data on hourly historical bitcoin option prices reveals that bitcoin implied volatility curves behave very differently from those of equity index options. For call and put options with a wide range of moneyness and with synthetic constant maturities of 10, 20 and 30 days, we compare the dynamic hedging performance of different smile-adjusted deltas over two one-year periods. We also examine the use of the perpetual contract rather than the standard futures as hedging instrument because the basis risk for the perpetual is very much smaller than it is for calendar futures. Results are presented as testable statistics of hedging error variance ratios. In certain periods the use of smile-implied hedge ratios can significantly out-perform the simple Black–Scholes delta hedge, especially when using the perpetual swap as hedging instrument, where efficiency gains can exceed 30% for out-of-the-money puts, and reach an average of 15% when hedging short-term out-of-the money calls during periods when the implied volatility curve slopes upwards. The advantage of using the perpetual contract is especially evident during 2021, for the longer-term contracts for which the basis is still rather large.

Elie Bouri, Ladislav Krištoufek, Nehmé Azoury

Interactions between stock and cryptocurrency markets have experienced shifts and changes in their dynamics. In this paper, we study the connection between S&P500 and Bitcoin in higher-order moments, specifically up to the fourth conditional moment, utilizing the time-scale perspective of the wavelet coherence analysis. Using data from 19 August 2011 to 14 January 2022, the results show that the co-movement between Bitcoin and S&P500 is moment-dependent and varies across time and frequency. There is very weak or even non-existent connection between the two markets before 2018. Starting 2018, but mostly 2019 onwards, the interconnections emerge. The co-movements between the volatility of Bitcoin and S&P500 intensified around the COVID-19 outbreak, especially at mid-term scales. For skewness and kurtosis, the co-movement is stronger and more significant at mid- and long-term scales. A partial-wavelet coherence analysis underlines the intermediating role of economic policy uncertainty (EPU) in provoking the Bitcoin-S&P500 nexus. These results reflect the co-movement between US stock and Bitcoin markets beyond the second moment of return distribution and across time scales, suggesting the relevance and importance of considering fat tails and return asymmetry when jointly considering US equity-Bitcoin trading or investments and the policy formulation for the sake of US market stability.

Donglian Ma, Hisashi Tanizaki

Abstract In this study, an investigation is conducted into the phenomenon of price clustering in Bitcoin (BTC) denominated in the Japanese yen (JPY). It answers two questions using tick-by-tick data. The first is whether price clustering exists in BTC/JPY transactions, and the other is how the scale of price clustering varies throughout a trading day. With the assistance of statistical measures, the last two digits of BTC price were discovered to cluster at the numbers that end with ’00’. In addition, the scales of BTC/JPY clustering at ’00’ tended to decline at the specific hour intervals. This study contributes to the emerging literature on price clustering and investor behavior.

Nusret Cakici, Syed Jawad Hussain Shahzad, Barbara Będowska-Sójka, Adam Zaremba

No abstract is available for this record.

Barbara Będowska-Sójka, Agata Kliber

The article aims to verify whether cryptocurrencies can hedge extreme price movements in Brent crude oil. The COVID-19 pandemic revealed that oil prices are heavily influenced by economic uncertainty and the mobility factor. We analyse Brent crude oil prices from February 10, 2020, to February 10, 2022. We consider Bitcoin, BNB, Ether, Tether, and USD Coin, the top five cryptocurrencies by market capitalization, as possible hedges. We explore their potential to protect oil investments using two approaches. The first focuses on price movement, and the second one on minimizing portfolio volatility. We use three modelling techniques: asymmetric causality in prices, a threshold vector-autoregressive model for returns, and dynamic conditional correlation analysis. We show that while stablecoins provide the best protection against downward movements in oil prices, they do not reduce investment volatility.

Carlos Trucíos, James W. Taylor

Abstract Several procedures to forecast daily risk measures in cryptocurrency markets have been recently implemented in the literature. Among them, long‐memory processes, procedures taking into account the presence of extreme observations, procedures that include more than a single regime, and quantile regression‐based models have performed substantially better than standard methods in terms of forecasting risk measures. Those procedures are revisited in this paper, and their value at risk and expected shortfall forecasting performance are evaluated using recent Bitcoin and Ethereum data that include periods of turbulence due to the COVID‐19 pandemic, the third halving of Bitcoin, and the Lexia class action. Additionally, in order to mitigate the influence of model misspecification and enhance the forecasting performance obtained by individual models, we evaluate the use of several forecast combining strategies. Our results, based on a comprehensive backtesting exercise, reveal that, for Bitcoin, there is no single procedure outperforming all other models, but for Ethereum, there is evidence showing that the GAS model is a suitable alternative for forecasting both risk measures. We found that the combining methods were not able to outperform the better of the individual models.

Nisarg Patel, Raj Parekh, Nihar Thakkar, Rajesh Gupta · 8 authors

Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However,Dashcoin is an individual cryptocurrency, but it is derived fromBitcoinandLitecoin. The change inBitcoinandLitecoinprices affects theDashcoin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction ofDashcoin through the past days’ prices ofDash,Litecoin, andBitcoinas they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations.

Peng‐Fei Dai, John W. Goodell, Luu Duc Toan Huynh, Zhifeng Liu · 5 authors

Abstract We evidence that cryptocurrencies have a higher probability of crashes than equity indices, although such crashes are of shorter duration. Commonality of crash risk between cryptocurrency and equity markets occur in approximately 80% of the periods examined. Further, recently evolved cryptocurrency uncertainty indices are more relevant for predicting co‐crash behavior than economic policy uncertainty. Results are consistent with cryptocurrencies being a growing source of financial instability.

Jiří Kukačka, Ladislav Krištoufek

Abstract The driving forces behind cryptoassets’ price dynamics are often perceived as being dominated by speculative factors and inherent bubble-bust episodes. Fundamental components are believed to have a weak, if any, role in the price-formation process. This study examines five cryptoassets with different backgrounds, namely Bitcoin, Ethereum, Litecoin, XRP, and Dogecoin between 2016 and 2022. It utilizes the cusp catastrophe model to connect the fundamental and speculative drivers with possible price bifurcation characteristics of market collapse events. The findings show that the price and return dynamics of all the studied assets, except for Dogecoin, emerge from complex interactions between fundamental and speculative components, including episodes of price bifurcations. Bitcoin shows the strongest fundamentals, with on-chain activity and economic factors driving the fundamental part of the dynamics. Investor attention and off-chain activity drive the speculative component for all studied assets. Among the fundamental drivers, the analyzed cryptoassets present their coin-specific factors, which can be tracked to their protocol specifics and are economically sound.

Anantha Divakaruni, Peter Zimmerman

The Lightning Network (LN) is a means of netting Bitcoin payments outside the blockchain. We find a significant association between LN adoption and reduced blockchain congestion, suggesting that the LN has helped improve the efficiency of Bitcoin as a means of payment. This improvement cannot be explained by other factors, such as changes in demand or the adoption of SegWit. We find mixed evidence on whether increased centralization in the Lightning Network has improved its efficiency. Our findings have implications for the future of cryptocurrencies as a means of payment and their environmental footprint.

Delia Elena Diaconaşu, Seyed Mehdian, Ovidiu Stoica

As an emerging digital asset, Bitcoin has been traded for more than a decade, reaching an impressively high market capitalization and continuing to expand its volume of trading at a rapid pace. Many countries have legalized or are considering legalizing a trading platform for this asset, and a set of companies worldwide accept it as a medium of exchange. As a result of this expansion, many studies in finance literature have focused on studying the efficiency of this cryptocurrency. In line with this literature, this paper investigates, using the abnormal returns and abnormal trading volumes methodologies, the dynamics of investors' reaction to the arrival of unexpected favorable and unfavorable information regarding the Bitcoin market in the context of the three famous hypotheses: the overreaction, the uncertain information, and the efficient market hypotheses. Overall, we find evidence confirming that the Bitcoin market tends to mature over time. More precisely, over the entire analyzed period, investors behave in accordance with the predictions of the uncertain information hypothesis when positive and negative events occur. However, splitting the timespan into sub-periods provides interesting insights. Remarkably in this respect is the fact that starting with the second sub-period, the response of investors in the Bitcoin market supports, in a moderate manner, the postulate of the efficient market hypothesis when favorable events are addressed. Moreover, our findings reveal that during the pandemic period, the efficiency of Bitcoin has increased, thus turning this stressful period into an advantage for this cryptocurrency. This improved market efficiency is also supported by the abnormal trading volume analysis.

Afees A. Salisu, Umar B. Ndako, Xuan Vinh Vo

No abstract is available for this record.

Sean Wilkoff, Serhat Yildiz

No abstract is available for this record.

Elli Kraizberg

Abstract The viability of exponentially growing non-fungible token (NFT) market is evaluated by identifying potential value-generating mechanisms that can be rationalized. After identifying the value-generating mechanisms underlying the positive values of NFTs, this study establishes a pricing model for NFTs that follows a continuous-time financial framework. As NFTs are claimed to securitize “ownership rights short of use”, and as such they may potentially serve as a substitute for the need to rely replace the reliance on the legal protection provided by intellectual property rights (IPRs). Considering this issue, this study evaluates the likelihood that NFTs will replace existing mechanisms that protect producers’ rightful claim to use their assets or the need to apply the legal code that governs IPRs. The financial condition for this potential shift is derived for a category of assets whose use or consumption does not reduce supply as the notion of scarcity does not apply.

Yu Wei, Yizhi Wang, Brian M. Lucey, Samuel A. Vigne

Several common properties shared by cryptocurrencies and precious metals, such as safe haven, hedge and diversification for risk assets, have been widely discussed since Bitcoin was created in 2008. However, no studies have explored whether cryptocurrency market uncertainties can help to explain and forecast volatilities in precious metal markets. By using the GARCH-MIDAS model incorporating cryptocurrency policy and price uncertainty, as well as several other commonly used uncertainty measures, this paper compares the in-sample impacts and out-of-sample predictive abilities of these uncertainties on volatility forecasts of COMEX gold and silver futures markets. The in-sample results demonstrate the significant impacts of cryptocurrency uncertainty on the volatilities of precious metal futures markets, and the out-of-sample evidence further confirms the superior predictive power of cryptocurrency uncertainty on volatility forecasting of the precious metal market. Our conclusions are robust through various model evaluation approaches based not only on predicting errors but also on forecasting directions across different forecasting time horizons.

Syed Abul Basher, Perry Sadorsky

Bitcoin has grown in popularity and has now attracted the attention of individual and institutional investors. Accurate Bitcoin price direction forecasts are important for determining the trend in Bitcoin prices and asset allocation. This paper addresses several unanswered questions. How important are business cycle variables like interest rates, inflation, and market volatility for forecasting Bitcoin prices? Does the importance of these variables change across time? Are the most important macroeconomic variables for forecasting Bitcoin prices the same as those for gold prices? To answer these questions, we utilize tree-based machine learning classifiers, along with traditional logit econometric models. The analysis reveals several important findings. First, random forests predict Bitcoin and gold price directions with a higher degree of accuracy than logit models. Prediction accuracy for bagging and random forests is between 75% and 80% for a five-day prediction. For 10-day to 20-day forecasts bagging and random forests record accuracies greater than 85%. Second, technical indicators are the most important features for predicting Bitcoin and gold price direction, suggesting some degree of market inefficiency. Third, oil price volatility is important for predicting Bitcoin and gold prices indicating that Bitcoin is a substitute for gold in diversifying this type of volatility. By comparison, gold prices are more influenced by inflation than Bitcoin prices, indicating that gold can be used as a hedge or diversification asset against inflation.

Jiqian Wang, Feng Ma, Elie Bouri, Yangli Guo

Abstract Academic research relies heavily on exogenous drivers to improve the forecasting accuracy of Bitcoin volatility. The present study provides additional insight into the role of both macroeconomic and technical indicators in forecasting the realized volatility of Bitcoin. Using 17 famous macroeconomic variables and 18 technical indicators between December 2011 and April 2021, the results reveal that the shrinkage methods, including elastic net and LASSO, can powerfully extract predictive information from macroeconomic and technical indicators. We further investigate the forecasting power of macroeconomic factors and technical indicators in terms of variable selection, business cycle, and volatility levels, and the results show strong evidence that the macroeconomic indicators (namely, S&P 500 realized volatility, global real economic activity index, and trade‐weighted USD index return) are the most frequently selected by shrinkage method, suggesting that their ability to forecast Bitcoin volatility is stronger than that of technical indicators. However, technical indicators are more powerful in forecasting Bitcoin volatility during the low volatility state.

Andrés García-Medina, Ester Aguayo-Moreno

No abstract is available for this record.

Gyeongho Kim, Dong-Hyun Shin, Jae Gyeong Choi, Sunghoon Lim

Cryptocurrency has recently attracted substantial interest from investors due to its underlying philosophy of decentralization and transparency. Considering cryptocurrency’s volatility and unique characteristics, accurate price prediction is essential for developing successful investment strategies. To this end, the authors of this work propose a novel framework that predicts the price of Bitcoin (BTC), a dominant cryptocurrency. For stable prediction performance in unseen price range, the change point detection technique is employed. In particular, it is used to segment time-series data so that normalization can be separately conducted based on segmentation. In addition, on-chain data, the unique records listed on the blockchain that are inherent in cryptocurrencies, are collected and utilized as input variables to predict prices. Furthermore, this work proposes self-attention-based multiple long short-term memory (SAM-LSTM), which consists of multiple LSTM modules for on-chain variable groups and the attention mechanism, for the prediction model. Experiments with real-world BTC price data and various method setups have proven the proposed framework’s effectiveness in BTC price prediction. The results are promising, with the highest MAE, RMSE, MSE, and MAPE values of 0.3462, 0.5035, 0.2536, and 1.3251, respectively.

Huaigang Long, Ender Demir, Barbara Będowska-Sójka, Adam Zaremba · 5 authors

We examine the role of geopolitical risk in the cross-sectional pricing of cryptocurrencies. We calculate cryptocurrency exposure to changes in the geopolitical risk index and document that coins with the lowest geopolitical beta outperform those with high geopolitical beta. Our findings suggest that risk-averse investors require additional compensation as motivation to hold cryptocurrencies with low and negative geopolitical betas, and they are willing to pay a premium for assets with high and positive geopolitical betas. The effect cannot be explained by known return predictors and is robust to many considerations.