This paper explores price effects caused by the expiration of derivatives in the cryptocurrency market. Applying different statistical tests (ANOVA, Mann–Whitney, and t-tests) and econometric methods (the modified cumulative abnormal return approach, regression analysis with dummy variables, and the trading simulation approach) to daily and weekly Bitcoin data over the period 2018–2021, the following hypotheses are tested: (H1) Expiration days create patterns in price behavior in the cryptocurrency market; and (H2) Price patterns can be exploited to generate abnormal profits from trading. The results suggest that expiration effects are only nominally present in the cryptocurrency market. There are differences in returns between expiration-related periods and average returns, but these differences are statistically insignificant. The only case in which an anomaly was detected was related to abnormally high returns during the week of expiration: returns during such weeks were positive in 65% of cases, and were on average 5 times higher than during usual weeks. Trading strategies based on this fact were able to generate results different from those of random trading, with a Sharpe ratio above 1. This is evidence in favor of the existence of a real price anomaly, which contradicts the efficient market hypothesis, and this could be implemented in the practice of traders and investors by creating trading strategies based on detected price effects or special technical analysis indicators to generate trading signals. For academics, these results might provide an opportunity to improve time series forecasting analysis in the case of Bitcoin.
Cryptocurrencies show tremendous growth by market capitalization, however Bitcoin cross-country holdings are still in question. The purpose of the paper is to show that inflation discontent with the rule of law failures can explain why residents of different countries are prone to cryptocurrency holdings. The level of financial development is also considered. A hypothesis is proposed for more complex and segmented motives of Bitcoin holdings, tested by the OLS method. Single- and multi-factor regressions with independent variables are used, which can validate cross-country Bitcoin holdings in terms of inflation discontent, quality of institutions and financial development. Regression results confirm the idea of more segmented motives to hold Bitcoins. First, the hedge against inflation motive is rooted in the institutional weakness of central banks, and the regression results show that inflation variables are the most significant. Second, the hedge against institutional risks of asset ownership motive, based on the lack of rule of law and the relevant variable, is best performing among other institutional variables. Third, it is wrong to neglect financial development. However, it only plays a role in interaction with better innovation performance, meaning that crypto investors try not only to diversify their portfolios, but also to profit from involving in a sector with promising technological perspectives. The main takeaway is that institutional factors help explain why people in countries with worsened inflation and institutional performance tend to hold a large fraction of Bitcoins in assets. Obviously, monetary and institutional fragility is underestimated in the general discussion about the nature of digital money.
In this chapter structures that generate yield in cryptofinance will be analyzed and related to leverage. While the majority of crypto-assets do not have intrinsic yields in and of themselves, similar to cash holdings of fiat currency, revolutionary innovation based on smart contracts, which enable decentralised finance, does generate return. Examples include lending or providing liquidity to an automated market maker on a decentralised exchange, as well as performing block formation in a proof of stake blockchain. On centralised exchanges, perpetual and finite duration futures can trade at a premium or discount to the spot market for extended periods with one side of the transaction earning a yield. Disparities in yield exist between products and venues as a result of market segmentation and risk profile differences. Cryptofinance was initially shunned by legacy finance and developed independently. This led to curious and imaginative adaptions, reminiscent of Darwin's finches, including stable coins for dollar transfers, perpetuals for leverage, and a new class of exchanges for trading and investment.
Purpose Perhaps the most popular pricing model among Bitcoin enthusiasts is the stock-to-flow (S2F) model. The model gained significant traction after successfully predicting the meteoric rise of Bitcoin prices from late 2020 to early 2021. This paper dissects the S2F model for Bitcoin empirically to determine its viability and investigate whether investors can profit from an S2F-based trading strategy. Design/methodology/approach This paper, dissects the S2F model for Bitcoin by putting it through a battery of tests to examine its design, characteristics, robustness and appropriateness. Findings Overall, this paper finds the S2F model to be insensitive to differing assumptions in the early stages of the model, alleviating concerns about data mining. This paper produces a dynamic S2F model with no peek-ahead bias and shows evidence that prediction accuracy increases over time. Finally, this paper shows that a dynamic trading strategy that goes long (short) when Bitcoin is undervalued (overvalued) according to S2F is far less profitable than a classic buy-and-hold strategy. Originality/value To the best of the authors’ knowledge, this is the first paper to analyze the S2F model in an academic setting by providing a rigorous assessment of the model's construction. This paper demonstrates how the model can be implemented realistically without the peek-ahead bias, creating a tool that can be used contemporaneously by investors.
The globally acknowledged accelerating crypto hype has put a lot of work on researchers and analysts. This new marvel of digital currencies requires a lot of attention, for it to become conventional worldwide. Virtual coins or cryptographic forms of money utilize encryption framework, so called the Block Chain technology, that manage the formation and supply of coins and exchanges must be recognized from a financial examination point of view. Subsequently, it is essential to inspect which social, money related & macroeconomic components decide its cost with a specific end goal to know the degree and outcomes of the economy. This paper aims to study different internal and external factors that affect cryptocurrencies’ prices. A sample of four digital coins with largest market capitalization has been selected. Daily price data from the years 2015 to 2020 of Bitcoin with other altcoins such as Ethereum, Ripple and Litecoin has been taken. Internal factors consist of demand and supply variables and also the attractiveness associated with its increasing hype. Other factors include KSE-100 Index (Karachi Stock Exchange), USD-PKR (Dollar to Pakistani Rupee) exchange rate and oil prices from PSO (Pakistan State Oil). ARDL analysis has been done to study the effect of these factors on the prices of crypto coins. Our analysis shows that circulating supply has a significant effect on Ethereum and Ripple prices in the long run. Attractiveness has been significant on the prices of Ethereum only.
The decentralization of cryptocurrency has decreased the level of central control, which has impacted international trade and ties. There is also an urgent need for a credible way of projecting the price of cryptocurrencies, which is currently unavailable. A novel method to predict cryptocurrency price is proposed in this paper, which makes use of deep learning techniques such as the recurrent neural network (RNN), gated recurrent unit (GRU), convolution 1D, and the long short-term memory (LSTM). This method considers a variety of factors such as market capitalization, volume, circulating supply, and maximum supply. It is more accurate at recognizing long-term relationships than the LSTM. Developed in Python, the proposed approach was tested on a range of real-world data sets. The findings demonstrate that the proposed method may be used to properly predict the price of cryptocurrencies.
Zeyd Boukhers, Azeddine Bouabdallah, Cong Yang, Jan Jürjens
Since Bitcoin first appeared on the scene in 2009, cryptocurrencies have become a worldwide phenomenon as important decentralized financial assets. Their decentralized nature, however, leads to notable volatility against traditional fiat currencies, making the task of accurately forecasting the crypto-fiat exchange rate complex. In this study, we examine the various independent factors that affect the Bitcoin-Dollar exchange rate's volatility. To this end, we propose CoMForE, a multimodal AdaBoost-LSTM ensemble model, which not only utilizes historical trading data but also incorporates public sentiments from related tweets, public interest demonstrated by search volumes, and blockchain hash-rate data. Our developed model goes a step further by predicting fluctuations in the overall cryptocurrency value distribution, thus increasing its value for investment decision-making. We have subjected this method to extensive testing via comprehensive experiments, thereby validating the importance of multimodal combination over exclusive reliance on trading data. Further experiments show that our method significantly surpasses existing forecasting tools and methodologies, demonstrating a 19.29% improvement. This result underscores the influence of external independent factors on cryptocurrency volatility.
Robo-advisor is one of the most prominent innovation in the wealth management industry, and its success in Indonesia has been evident in the case of Bibit. Therefore, wealth management companies need to employ Robo-Advisor to overcome their competition. This research aims to give recommendation on asset allocation method and asset class selection for Robo-Advisors in Indonesia using Sharpe Ratio Analysis. Then, the author will analyze the robo-advisor’s performance during equity market downturn. Finally, The Robo-Advisor’s actual performance will be tested in 2018, 2019, and 2020. The Sharpe ratio analysis result showed that Robo-Advisors seeking higher risk-adjusted return should choose mean-variance optimization over risk parity for asset allocation method, and the inclusion of gold and bitcoin in a portfolio of stock mutual fund and bond mutual fund increases the risk-adjusted return of the portfolio. The proposed robo-advisor’s portfolio protected investors from equity market downturn in 2011-2010 in 83,3% of the case. Finally, the proposed robo-advisor’s portfolio generated better return for the conservative, moderate and aggressive investor during 2018, 2019, and 2020 when compared to LQ45.
Kripto paralar artan dijitalleşme ve merkeziyetsiz finans düşüncesinin bir ürünü olarak ortaya çıkmış ve yeni projelerle birlikte büyümeye devam etmektedir. Kripto paralar, alım veya satım gibi her türlü işlemlerde kullanılmasının yanı sıra değişim aracı ve yatırım aracı olarak da kullanılabilmektedir. Ayrıca madencilik yoluyla söz konusu para birimi üretimi yapılabilmektedir. Kripto para denilince ilk olarak ortaya çıkan ve tüm kripto paraların öncüsü olarak kabul edilen Bitcoin, piyasa hacmi açısından da en yüksek kripto paradır. Bu bağlamda çalışmamızın amacı, piyasanın öncül parası olan Bitcoin ve kısaca “Altcoin” diye ifade ettiğimiz diğer kripto paralar arasındaki ilişkiyi incelemektir. İlişkiyi incelemek için zaman serisi analiz yöntemi kullanılarak 01.01.2018-31.12.2020 dönemi günlük veriler Trandingview ve Coinmarketcup aracılığıyla toplanmış ve Johansen eşbütünleşme testi, Vektör Hata Düzeltme Modeli (VECM) ve Granger nedensellik testi yapılmıştır. Johansen eşbütünleşme sonucuna göre ele alınan dönemlerde kullanılan değişkenler arasında uzun dönemli bir ilişki bulunmaktadır. Granger nedensellik sonucuna göre ise Cardona'dan Bitcoin'e, Bitcoin'den Etheryum'a ve Cardano'dan Binance Coin' doğru tek yönlü nedensellik tespit edilmiştir.
We employed linear and nonlinear error correction models (ECMs) to predict the log returns of Bitcoin (BTC). The linear ECM is the best model for predicting BTC compared to the neural network and autoregressive models in terms of RMSE, MAE, and MAPE. Using a linear ECM, we are able to understand how BTC is affected by other coins. In addition, we performed Granger-causality tests on fourteen cryptocurrencies.
Purpose This study aims to analyze the time-varying correlation between the cryptocurrency policy uncertainty (UCRY Policy) and cryptocurrency returns. More specifically, it analyzes whether these correlations vary according to the uncertainty attributable to salient events such as China banning ICOs, cryptocurrency exchanges attacks, Coronavirus (Covid-19) pandemic crisis and the United States (U.S.) Security and Exchange Commission’s (SEC’s) announcement about Ripple. Design/methodology/approach To measure the dynamic relationship, it uses the dynamic conditional correlation (DCC) model of Engle (2002) to consider time variation in UCRY Policy and cryptocurrency returns. The data set encompasses the weekly frequency data of the UCRY Policy and Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC), Ripple (XRP), Stellar (XLM), Dash (DASH), Monero (XMR) from 4 September 2016, to 21 February 2021. Findings Empirical findings indicate that the correlations between the UCRY Policy and the BTC, ETH, LTC, XRP, XLM, DASH and XMR returns are consistently negative. Thus, an increase in the volatility of the UCRY Policy can lead to a decrease in volatility for BTC, ETH, LTC, XRP, XLM, DASH and XMR returns. Besides, these findings indicate that the estimated DCC is not only time-varying but also substantially responsive to salient events, such as China banning ICOs, cryptocurrency exchanges attacks, the Covid-19 pandemic crisis and SEC’s announcement about Ripple. Besides, empirical findings show that cryptocurrency returns are adversely impacted by UCRY Policy during the salient events (China bans ICOs, the hack of cryptocurrency exchanges, Covid-19 crisis), suggesting their failure to act as a hedge or safe-haven asset. Originality/value To the best of the author’s knowledge, this study investigates the time-varying correlation between UCRY Policy and cryptocurrency returns. Besides, this study may be useful for new studies and fill a gap in the finance literature, due to the limited number of studies on the UCRY Policy in the finance literature.
This study attempts to answer the question: “Is the Cryptocurrency Policy Uncertainty (UCRY Policy) a determinant of the Bitcoin’s price (BTC)?”. Besides, this study uses these factors as explanatory variables for the BTC movements alongside the UCRY Policy and control variables such as the velocity of the Bitcoin in circulation (BC), the computational power of Bitcoin (HR), popularity (PO), and exchange rate (EX). In the study, December 30, 2013- February 21, 2021, was determined as the term and weekly data were investigated. The ARDL bounds testing method was used to determine the relationship between the variables. According to empirical findings, this study suggests that the UCRY Policy is essential to the BTC. There is a negative relationship between UCRY Policy and BTC. When UCRY Policy increases, BTC decreases, holding other variables constant. Besides, this study shows that control variables can be used as determinants of BTC. In long run, BC and HR have a significant, positive relationship with BTC. The EX has a significant, negative relationship with BTC. The PO has a significant, positive relationship with BTC in the short run. In addition, this study demonstrates that UCRY Policy can be used as a type of uncertainty index for Bitcoin.
This paper proposes a novel asymmetric jump model for modeling interactions in discontinuous movements in asset prices. Given the jump behavior and high volatility levels in cryptocurrency markets, we apply our model to cryptocurrencies to study the impact of various types of jumps occurring in one cryptocurrency’s price process on the discontinuity component of the realized volatility of other cryptocurrencies. Our model also allows us to assess the impact of co-jumps. Using high-frequency data to compute the daily realized volatility, we show that downside, upside, and small jumps observed in cryptocurrencies negatively affect the jump component of other cryptocurrencies’ realized volatility, while large jumps have the opposite effect. We further find significant asymmetric effects between small and large as well as between downside and upside jumps for several cryptocurrencies. Moreover, we find evidence of co-jumping behavior, which can trigger future jumps. The practical implications of our findings are also discussed. Finally, we extend our analysis to study the effects of jumps in mainstream financial assets on cryptocurrencies’ jump behavior and find that upside and downside jumps observed in the S&P 500 index negatively impact cryptocurrency jumps.