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.
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.
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.
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.
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.
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.
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.
Leonardo H.S. Fernandes, Elie Bouri, JOSÉ W. L. SILVA, Lucian Bejan · 5 authors
We examine the price disorder and market efficiency of five cryptocurrencies (Bitcoin, BNB, Cardano, Ethereum, and XRP) before and during COVID-19 pandemic period. Using permutation entropy and Fisher information measure (FIM), we construct the Shannon-Fisher causality plane (SFCP) to map these cryptocurrencies and their respective locations in a two-dimensional plane and then apply sliding time window approach to study the temporal evolution of efficiency. All cryptocurrencies exhibit high but slightly varying informational efficiency during both periods. Cardano is the most efficient. These results might point to the increasing maturity and lower potential for price predictability, which matter to cryp-tocurrencies usage for liquidity risk diversification strategy.
The majority of NFTs utilize the Ethereum blockchain platform to facilitate smart contracts. In this paper, we execute various econometric analyses to determine if this technical dependence induces a financial linkage to the risk, return, and prices of assets. For robustness, we also test the same relation between Bitcoin and NFTs. Empirical analyses are conducted through SADF bubbles test, DCC-GARCH time-varying correlation analysis, Bootstrap causality tests and spillover analysis. According to the results of various price, return, and volatility analyses, we find that NFTs do not demonstrate idiosyncratic features in their price developments and thus they cannot be considered as a separate asset class. Additionally, NFTs do not possess a specific financial linkage with Ethereum from using its infrastructure. Finally, we suggest NFT investors use alternative financial instruments, rather than Ether and Bitcoin in portfolio diversification, due to the presence of significant time-varying relationships and interactions.
Sanal Kripto Para kavramı Bitcoin ile birlikte 2009 yılında dikkat çekmeye başlamış ve özellikle 2013 yılındaki fiyat artışı ile popülaritesi artmıştır. Sanal Kripto Para birimlerinin ilki ve öncüsü olan Bitcoin ile 2. En büyük piyasa değerine sahip olan Ethereum tasarımsal yapıları ve amaçları bakımından birbirlerinden oldukça farklıdır. Sanal bir para birimi olması için tasarlanan Bitcoin ile üzerinde akıllı kontratlar çalışmasına olanak vermek için tasarlanan Ethereum birçok alanda olduğu gibi iktisat alanında da oldukça dikkat çekmiş ve literatürde birçok çalışmaya konu olmuştur. Bu çalışmanın amacı Türkiye GSYH’sı, M2 Tanımı ile para arzı ve tüketici güven endeksinin Bitcoin ve Ethereum fiyatına olan etkilerini karşılaştırmalı olarak tahmin etmektir. Bu amaçla, Bitcoin ve Etherum’un Türk Lirası cinsinden fiyatlarını bağımlı değişken alan iki farklı model kurulmuştur. Kurulan modeller, Ocak 2016 ile Aralık 2020 dönemini kapsayan aylık veriler kullanılarak, zaman serisi analizi kapsamında, Johansen Eşbütünleşme Testi ve Tam Uyarlanmış En Küçük Kareler Yöntemi (FMOLS) kullanılarak uzun dönemde sınanmıştır. Yapılan analizin sonucunda hem Bitcoin hem de Ethereum’un Türk lirası cinsinden fiyatları, GSYH ve tüketici güven endeksi ile pozitif ilişkili, M2 para arzı ile negatif ilişkili bulunmuştur. Çalışmanın bulgularından bir diğeri ise toplam piyasa değeri Bitcoin’e göre daha düşük olan Ethereum’un tüm değişkenlerden daha çok etkilendiğidir. Elde edilen sonuçlar bu çalışmanın türetildiği Yüksek Lisans Tezindeki bulgular ile örtüşmekte ve birbirini desteklemektedir.
COVID-19'un başlangıcı, 2020'nin belirleyici olayı haline geldi ve kripto para birimleri de dahil olmak üzere tüm dünyadaki finansal piyasaları etkiledi. Bu süreçte, altın ve diğer emtialar gibi güvenli bir liman olarak görülmeye başlanan kripto para birimlerine ve diğer dijital varlıklara yatırıma olan ilgi arttı. Bu amaçla bu çalışmada Covid-19 sürecinde, altın ve petrol fiyatlarında meydana gelen şokların Bitcoin fiyatları üzerindeki asimetrik etkisi incelenmiştir. Çalışmada Doğrusal Olmayan Gecikmesi Dağıtılmış Otoregresif (NARDL) analiz yöntemi kullanılmıştır. Analizin sonucunda, uzun vadede altın fiyatlarında meydana gelen negatif şokların Bitcoin fiyatlarını olumlu etkilediği, petrol fiyatlarında meydana gelen negatif şokların ise Bitcoin fiyatlarını olumsuz etkilediği sonucuna ulaşılmıştır. Uzun vadede altın ve petrol fiyatlarında meydana gelen pozitif şokların ise Bitcoin fiyatları üzerinde istatistiki olarak anlamlı bir etkisinin olmadığı görülmüştür. Kısa vadede ise hem altın hem de petrol fiyatlarında meydana gelen pozitif şokların Bitcoin fiyatlarını olumlu etkilediği, negatif şokların ise olumsuz etkilediği tespit edilmiştir. Sonuç olarak, Bitcoin’in küresel yatırımcılar için finansal çeşitlendirmede ideal olabileceği ve yeni bir sanal altın olarak piyasalardaki yerini alabileceği görülmüştür.
In the last decade as a result of the changes in business landscape new payment systems have evolved. Some of the Consumers, business stakeholders, investors and individuals turned to different types of payment systems and virtual currencies for various reasons. Peer to peer architectured Bitcoin which uses a blockchain mechanism is one of these approaches that found place in our lives. In this study, a leading indicator focused data mining methodology has been followed in analyzing Bitcoin market value and bitcoin valuation. Several classification and clustering algorithms applied to the data following a literature review, pre-processing of the data and conceptual framework formation. Finaly performances of these supervised and unsupervised machine learning techniques with rules discovered have been compared, assessed and presented for this type of problem and research domains.
Bu çalışma 2011 yılından itibaren temel olarak “belirsizlik” ve “ekonomi” anahtar kelimelerini içeren tweetlerin baz alınarak oluşturulduğu Twitter Bazlı Belirsizlik Endeksinin, son yılların gözde yatırım araçlarından olan kripto paraların volatilitesine etkisini incelemeyi amaçlamaktadır. Piyasa değeri en yüksek, Binance, Bitcoin, Cardano, Ethereum, Ripple ve Tether kripto paralar 18/01/2018- 11/07/2021 dönemi için günlük verilerle ARCH-GARCH ailesi modelleri ile incelenmiştir. Çalışmada öncelikle ortalama denklemi oluşturulan modellerin ARCH-GARCH modellerine uygunluğu sınanmış ve incelenen dönemde Bitcoin ve Ethereum için ARCH etkisinin olmadığı ancak Binance, Cardano, Ripple ve Tether için volatilite modellerinin kullanımının uygun olduğu bulgusu elde edilmiştir. Binance için GARCH (1,1), Cardano için GARCH-M (1,1), Ripple için ARCH (2) modeli volatiliteyi en iyi yakalayan model olarak seçilmiştir. Twitter Bazlı Belirsizlik Endeksinin bu modellerin hepsinde istatistiki olarak anlamlı ve pozitif bir etkiye sahip olduğu tespit edilmiştir. Bu sonuçlara göre bir sosyal medya platformu olan Twitter’da yer alan belirsizlik ve ekonomi içerikli tweetlerin kripto varlıkların volatilitesini etkilediğini söylemek mümkündür.
Sanjib Kumar Nayak, Sarat Chandra Nayak, Subhranginee Das
Artificial neural networks (ANNs) are suitable procedures for predicting financial time series (FTS). Cryptocurrencies are good investment assets; therefore, the effective prediction of cryptocurrencies has become a trending area of research. Capturing inherent uncertainties associated with cryptocurrency FTS with conventional methods is difficult. Though ANNs are the better alternative, fixing the optimal parameters of ANNs is a tedious job. This article develops a hybrid ANN through Rao algorithm (RA + ANN) for the effective prediction of six popular cryptocurrencies such as Bitcoin, Litecoin, Ethereum, CMC 200, Tether, and Ripple. Six comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in a similar way. All these models are evaluated through the mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated the lowest MAPE and ARV values, statistically different as compared with existing methods mentioned above, and hence can be recommended as a potential financial instrument for predicting cryptocurrencies.
Coronavirus (COVID-19), which emerged as an epidemic in China in December 2019, has been recognized as a pandemic by the World Health Organization as of March 2020. Events regarding the coronavirus shocked the markets and were seen as a threat to the markets. In this context, this study aims to examine the effect of the COVID-19 on Bitcoin prices and precious metals which are seen as low-risk assets in global markets. In the study, the causality relationship between the daily number of COVID-19 cases approved by the WHO and Google trends, and the price series of Bitcoin, Gold, Silver, Platinum, Palladium was investigated to determine the effects of the developments in the course of the epidemic on the prices of Bitcoin and precious metals. Toda-Yamamoto causality test was performed in the study where daily data were used between 19.01.2020-31.03.2021. According to the findings, a causality relationship could not be determined between the number of COVID-19 cases with Bitcoin and precious metals while it was observed that the recognition of COVID-19 has a very strong causal effect on Bitcoin prices and the prices of other precious metals except silver. In addition, a reciprocal causality relationship has been identified between the confirmed COVID-19 cases and the recognition of COVID-19.
Bu çalışmanın amacı, en fazla ilgi gören kripto para birimleri arasında yer alan Bitcoin ile gelişmekte olan piyasalar arasında önde gelen Borsa İstanbul (BİST) endekslerinden BİST 100 (XU100), BİST Mali (XUMAL) ve BİST Teknoloji (XUTEK) endeksleri arasındaki ilişkilerin incelenmesi olarak belirlenmiştir. Bu amaçla çalışma kapsamında Borsa İstanbul 100 fiyat endeksi, Borsa İstanbul Mali fiyat endeksi ve Borsa İstanbul Teknoloji fiyat endeks ile Bitcoin arasındaki kısa ve uzun dönemli ilişki ARDL sınır testi yaklaşımı ile incelenmiştir. Elde edilen bulgular, Bitcoin fiyatı ile Borsa İstanbul Mali Endeksi arasında uzun dönem bir ilişkinin var olduğunu göstermiştir. Ancak, Bitcoin fiyatı ile diğer endeks fiyatları arasında uzun dönemli bir ilişkinin varlığına yönelik herhangi bir bulguya ulaşılamamıştır. Elde edilen kısa dönem bulgular ise Bitcoin fiyatı ile Borsa İstanbul Mali fiyat endeksi arasında herhangi anlamlı bir ilişkinin bulunmadığını göstermektedir.