Anwar Hasan Abdullah Othman, Adam Abdullah, Razali Haron
As crypto-currencies hold dual nature of a medium of exchange (currency) and an investment asset, some questions may arise about the potentiality of including crypto-currencies as liquid investment asset in financial institutions particularly in the banking sector to enhance their liquidity risk management and improve their portfolio diversification investment strategy. The objective of this study therefore is to examine the characteristics of Bitcoin currency based on the requirements of High-Quality Liquid Assets (HQLA) standards of Basel III and compare its volatility structure with other traditional asset classes that are already recommended by Basle III as HQLA. The study utilizes both descriptive and quantitative analysis using the GARCH family models to examine the volatility structures of these assets. The findings show that Bitcoin currency holds the same characteristics of HQLA, however; the risk of legality and recognition is still under consideration by legal authorities around the world and this risk will be eradicated in the future as crypto-currencies derive their legality from their real intrinsic value, multi-economic usefulness and not by law as in the case of fiat money currency. Furthermore, the symmetric volatility structure analysis shows the continuing persistence of volatility and predictability behavior in return series of Bitcoin currency and other- traditional asset classes in the U.S. market. However, Bitcoin’s stability has gradually improved over time. With regard to the asymmetric informative response, Bitcoin returns respond more to negative shock but it has no statistical significance, thus suggesting the lack of leveraging effect in Bitcoin market but this effect was found to be statistically persistent in other traditional asset class markets. In addition, Bitcoin returns show very low correlation with other traditional asset classes. All these imply that Bitcoin is a potential candidate as a hedge and asset diversifier, which is recommended to be included in the HQLA. This study provides some support to recent theoretical work on crypto asset return behaviour and liquidity risk management. The findings provide appropriate information about Bitcoin asset behaviour compared to other traditional asset classes which will enable them to make the right investment decision with regard to hedging, diversification and liquidity risk management. The findings of this study may assist in evaluating the suitability of including crypto assets into HQLA to improve the liquidity requirement standards and ensure that banks have an adequate amount of HQLA specifically during times of financial turmoil.
Thousands of cryptocurrencies have been issued and publicly exchanged since Bitcoin was invented in 2008. The total cryptocurrency market value exceeds 300 billion US dollars as of 2019. This paper analyzes the prices, volumes, blockchain transactions, coin difficulties and public opinion popularities of 3607 actively exchanged cryptocurrencies. We aim to reveal and explain the homogeneity, i.e., the strong correlation of market performance, and the heterogeneity, i.e., the imbalance of popularities and sophistications, of the cryptocurrencies.
Forecasting exchange rates is difficult because financial time-series data is too complicated to analyze. In traditional financial studies, economic models and statistic approaches were widely used for predicting exchange rates. Recently, machine learning and deep learning techniques have played increasingly important roles in financial technology studies. This study adopts a deep learning technique called relation networks (RNs) to predict the exchange rates of fiat currencies and cryptocurrencies. To discover the relationship among different currencies, the concept of visual question answering (VQA) is applied in RNs. We also propose a specially designed architecture for the feature extraction stage to consider both spatial and temporal relationships simultaneously. The experimental results show that the proposed approach can achieve higher prediction performance for cryptocurrencies with approximately 65% accuracy rate. We aim to improve traditional approaches and construct a model using the concept of VQA based on RNs to optimize the prediction performance between fiat currencies and cryptocurrencies.
<ns3:p>Before 2008, one thing that could be said about the derivatives market was that they were anything but regulated. Nowadays, the same thing can be said about the current financial innovation: cryptocurrencies. However, there are many private law institutions which have not been regulated for centuries, yet they did not trigger a worldwide financial crisis. The reason for that is that the financial crisis constituted a complex phenomenon caused by many factors, occurring jointly and affecting the derivatives market in unison, not just by lack of regulation. This paper will elaborate whether factors which, in the opinion of the author, caused the recent financial crisis, currently exist, or can occur in the future, in the cryptocurrency market. Such a study will allow a conclusion to be drawn as to whether cryptocurrencies are capable of triggering a similar financial crisis as derivatives did before their current partial regulation.</ns3:p>
Cryptocurrency is decentralized and electronic alternative of currency. Bitcoin is the best example of this type of currency, and after bitcoin hundreds of such currencies were launched in market. Cryptocurrency is more frequently used, as it is theft proof, accessible anywhere & anytime. By using cryptocurrency the settlement of money is instant. Portfolio management is a technique through which a person decides how to allocate the resources. It helps in making better decisions and in minimizing risks of loss. Cryptocurrency and portfolio management are mostly used in financial sectors like banking and stocks. This paper reviews about the use of cryptocurrency and portfolio management with its associated challenges.
Abstract One important characteristic of cryptocurrencies has been their high and erratic volatility. To represent this complicated behavior, recent studies have emphasized the use of autoregressive models frequently concluding that generalized autoregressive conditional heteroskedasticity (GARCH) models are the most adequate to overcome the limitations of conventional standard deviation estimates. Some studies have expanded this approach including jumps into the modeling. Following this line of research, and extending previous research, our study analyzes the volatility of Bitcoin employing and comparing some symmetric and asymmetric GARCH model extensions (threshold ARCH (TARCH), exponential GARCH (EGARCH), asymmetric power ARCH (APARCH), component GARCH (CGARCH), and asymmetric component GARCH (ACGARCH)), under two distributions (normal and generalized error). Additionally, because linear GARCH models can produce biased results if the series exhibit structural changes, once the conditional volatility has been modeled, we identify the best fitting GARCH model applying a Markov switching model to test whether Bitcoin volatility evolves according to two different regimes: high volatility and low volatility. The period of study includes daily series from July 16, 2010 (the earliest date available) to January 24, 2019. Findings reveal that EGARCH model under generalized error distribution provides the best fit to model Bitcoin conditional volatility. According to the Markov switching autoregressive (MS-AR) Bitcoin’s conditional volatility displays two regimes: high volatility and low volatility.
This study is confined in analysing how the economic policy uncertainty (EPU) effects affect exchange rates on cryptocurrency assets in times of financial turbulence characterized by low confidence in the financial stock markets, and tranquil periods where the financial stock markets behave smoothly. Our research employs the D-Vine pair-copula method on daily selected cryptocurrency (Bitcoin, Ethereum and Ripple) prices within the period of the 10 August 2016 to the 23 February 2018. Our findings document the presence of the dependence between the US EPU and cryptocurrencies and indicate a significant correlation with Ethereum which exhibits a much better return.
Nowadays Bitcoin as cryptocurrency takes a significant place on the global financial markets. This paper analyzes the Bitcoin closing prices and traded volume during the period from December 28, 2013 to January 22, 2019. This period is known as a period with rapid increasing of the Bitcoin closing prices, mainly in the second half of the year 2017. The aim of this paper is twofold. First, we compute the Hurst coefficient to discover the close price dynamics and traded volume using a fractal point of view. We have discovered an anti-persistent behavior in the traded volume and random character of bitcoin closing prices. Second, we propose an analysis of the relationship between the close prices and traded volume. Our findings show how changes in the high-price period differ from changes in the low-price period. We also found that high prices caused investors to be afraid to trade due to possible rapid decrease in bitcoin closing prices.
This study examines changes in the value of Bitcoin after the BREXIT and US Presidential elections in 2016 to determine whether it and other cryptocurrencies experienced value changes associated with the vote outcomes. For comparative purposes, Bitcoin is also compared to currencies such as the Great Britain Pound, Mexican Peso and Special Drawing Rights to determine whether Bitcoin values change like currencies. Some evidence showed changes in the value of Bitcoin were weakly similar to store-of-wealth commodities like gold and silver. Overall, the election-window volatility of cryptocurrencies was much higher than the preceding one-year average for the BREXIT vote but less volatile than the one-year average for the US election vote.
Daniel Cerecedo Hernández, Carlos Armando Franco Ruiz, Mario Iván Contreras-Valdez, Jovan Axel Franco Ruiz
El objetivo de esta investigación es analizar la presencia de burbujas financieras o un comportamiento explosivo en cuatro criptomonedas: Ethereum, Ripple, Bitcoin Cash y EOS. La selección de los activos se basó en la capitalización de mercado. La metodología implementada fue una prueba simple y generalizada (SADF y GSADF) de una variación de la prueba aumentada de Dickey-Fuller propuesta por Phillips et al. (2011, 2015). Encontramos diez, siete, seis y siete comportamientos exuberantes en los activos mencionados, respectivamente. Esta metodología ha sido en gran parte inexplorada y podría emplearse de manera estándar en el sector financiero para cualquier otro activo. Esta es la primera investigación que detecta este tipo de comportamiento para un grupo de criptomonedas con frecuencia diaria. Con el presente trabajo y el artículo de Li et al. (2018), el 68,47% del mercado ha sido analizado bajo la metodología. En consecuencia, este comportamiento podría estar disperso en todo el sector.
Carol Alexander, Jaehyuk Choi, Heungju Park, Sungbin Sohn
Abstract BitMEX is the largest unregulated bitcoin derivatives exchange, listing contracts suitable for leverage trading and hedging. Using minute‐by‐minute data, we examine its price discovery and hedging effectiveness. We find that BitMEX derivatives lead prices on major bitcoin spot exchanges. Bid–ask spreads, interexchange spreads, and relative trading volumes are important determinants of price discovery. Further analysis shows that BitMEX derivatives have positive net spillover effects, are informationally more efficient than bitcoin spot prices, and serve as effective hedges against spot price volatility. Our evidence suggests that regulators prioritize the investigation of the legitimacy of BitMEX and its contracts.
Blockchain is one of the growing technologies used for financial management systems. Financial data must be kept secure otherwise it can create a huge loss. So, whenever security features or technologies are developed must keep financial security as a priority. Stock market management is another area of finance sector that works on two concepts, that is, minimize the risk and maximize the profit. In this chapter, the authors discuss how blockchain technology is used for stock market analysis. Mainly blockchain will help us to make optimal stock exchanges through automation and decentralization. Stock market across the globe is rapidly using blockchain technology for the market transaction. Some of the country is still preparing themselves to use the blockchain technology. This technology offers huge potential for tracing securities lending, margin financing, and surveillance of system risk.
Valerio Celeste, Shaen Corbet, Constantin Gurdgiev
The substantial volatility and growth in cryptocurrencies valuations between 2009 and the end of 2017 strongly suggest that both long memory and price volatility and return spillovers should be present in these assets’ dynamics. To date, literature on the major cryptocurrencies price processes does not address jointly and comprehensively their fractal properties, long memory and wavelet analysis, that could robustly confirm the presence of fractal dynamics in their prices, and confirm or deny the validity of the Fractal Market Hypothesis as being applicable to the cryptocurrencies. This research shows that Bitcoin prices exhibit long term memory, although its trend has been reducing overtime. In fact, assessing Bitcoin, Ethereum and Ripple across the period between 2016 and 2017, focusing solely on the period prior to the crash of 2018, we can conclude that Bitcoin was better described by a random walk, showing signs of markets maturity emerging, in contrast, other cryptocurrencies such as Ethereum and Ripple present evidence of a growing underlying memory behaviour.
Muhammad Saad, Jinchun Choi, DaeHun Nyang, Joongheon Kim · 5 authors
Recently, the Blockchain-based cryptocurrency market witnessed enormous growth. Bitcoin, the leading cryptocurrency, reached all-time highs many times over the year leading to speculations to explain the trend in its growth. In this article, we study Bitcoin and Ethereum and explore features in their network that explain their price hikes. We gather data and analyze user and network activity that highly impact the price of these cryptocurrencies. We monitor the change in the activities over time and relate them to economic theories. We identify key network features that help us to determine the demand and supply dynamics in a cryptocurrency. Finally, we use machine learning methods to construct models that predict Bitcoin price. Based on our experimental results using two large datasets for validation, we confirm that our approach provides an accuracy of up to 99% for Bitcoin and Ethereum price prediction in both instances.
Iqbal Thonse Hawaldar, T M Rajesha, Lolita Jane Dsouza
This study examines the weak form of efficiency of the exchange rate of cryptocurrencies against US Dollar. The study is based on the exchange rate of Bitcoin and Litecoin against US Dollar from 2013 to 2017. The data is tested for heteroscedasticity, and the efficiency of these coin market is analysed using unit root and stationary tests such as Augmented Dickey Fuller (ADF) test, Philips Perron (PP) test and Kwiatkowski Phillips Schmidt Shin (KPSS) tests. The results of the study reveal that the Bitcoin and Litecoin exchange rate exhibit a random walk. Speculation helps for the rapid growth of these currency markets. It is advisable for the investors to invest for short term to get higher returns rather than for the long term. Investment in cryptocurrencies involves market shocks due to its unpredictable nature.
In this paper, we study forecasting problems of Bitcoin-realized volatility computed on data from the largest crypto exchange—Binance. Given the unique features of the crypto asset market, we find that conventional regression models exhibit strong model specification uncertainty. To circumvent this issue, we suggest using least squares model-averaging methods to model and forecast Bitcoin volatility. The empirical results demonstrate that least squares model-averaging methods in general outperform many other conventional regression models that ignore specification uncertainty.
A digital currency in which encryption techniques are used to regulate the generation of units of currency and verify the transfer of funds, operating independently of a central bank. Therefore, Bitcoin is a form of digital currency that was designed by Satoshi Nakamoto (an unknown author of Bitcoin white paper 2008) and since then it has able to generate a considerable attention from investors due to its decentralized characteristics and the technology (block-chain) behind it. Bitcoin is a form of digital peer-to-peer currency system where transactions take place without a central bank. The transactions are verified by the nodes of the network and recorded in the Blockchain. Since the popularization of Bitcoin, this technology has caught attention of several technology companies who started to do research on the applications and opportunities of this technology. In this paper, an attempt has been made to capture the time varying variance of most prominent Cryptocurrency Bitcoin with world’s top traded currencies such as USD, GBP, Euro, Yen and CHF. In order to realise the stated objectives the researchers have collected the data from Prowess and Yahoo finance database from September 2013 till March 2018. In the first phase the collected data has been for normality and stationarity. Bitcoin was modelled for GARCH and EGARCH tests to capture the time varying volatility and leverage effect. Later the Johansen cointegration test has been conducted to find out the existence of cointegration between the top global currencies with Bitcoin. In the last phase the VECM has been run to capture the both long run and short relationship between Bitcoin and top five traded currencies. In the last phase Variance Decomposition has been run to capture the variance explained by the prominent global currencies on Bitcoin. Both USD and GBP share long run relationship with Bitcoin. Finally, the results have been compared with the possible evidence.