Blockchain teknolojisinin aracısız veri/para transferi gerçekleştirmesi ile tüm çevrelerin gündemine gelen Bitcoin, birçok yatırımcının ilgi odağı olmuş ve Bitcoin ’in popüler olması ile birçok kripto para piyasaya sürülmüştür. Kripto paralarda fiyat volatilitesinin yüksekliği hızlı para kazanma arzusu içinde olan ve risk iştahı yüksek olan yatırımcıları fiyat tahminlemesi ve fiyatları etkileyen değişkenlerin belirlenmesi noktasında analiz yapmaya itmiştir. Bu çalışmanın amacı Aralık 2017 itibari ile değeri yaklaşık 20.000 ABD Dolara ulaşan ve yüksek volatilitesi ile yatırımcıların sürekli gündeminde olan Bitcoin fiyatına etki eden faktörlerin belirlenmesidir. Bu amaçla çalışmada literatürde kullanılan değişkenlere (Altın ve ABD Dolar) ek olarak küresel risklerin (Finansal Baskı Endeksi ve Jeopolitik Risk Endeksi) etkisi de ölçülmeye çalışılmıştır. Çalışama da Bitcoin fiyatı üzerine etki etmesi muhtemel değişkenler Çok Değişkenli Uyarlanabilir Regresyon Uzanımları-MARS yöntemi ile analiz edilmiştir. Çalışmada kullanılan veriler 2012/1-2019/11 yılları arasında aylık verilerden oluşmaktadır. Çalışmanın sonucunda kullanılan tüm bağımsız değişkenlerin belirli şartlar altında Bitcoin fiyatına etki edebileceği sonucuna ulaşılmıştır.
Purpose To show that when volume of trades is taken into consideration, Bitcoin does not seem as volatile as it claimed. Further, to study the relationship between Bitcoin trading volume, volatility and returns, and the asymmetry in response to economic information for the period from July 2010 to November 2017. Design/methodology/approach Comparison of Bitcoin price volatility with that of six currencies and gold. We repeat the analysis using returns divided by volume. We examine the relationship between volume, returns and volatility, and the asymmetry of the reaction of the volatility to economic news using asymmetric models (EGARCH) run for four meaningful distinct time periods/subsamples. Findings Positive and significant relationship between (1) volume and volatility after 2013 (year Bitcoin became popular) and (2) volume and returns before the Mt. Gox hack. During the euphoric period, starting at the beginning of 2013 until the Mt. Gox hack, unexpected increases in Bitcoin returns increased Bitcoin volatility more than unexpected, equally sized decreases (asymmetry). Originality/value We take into consideration the volume of trades to show that Bitcoin volatility seems high because of the low volume of trades. We study an extended time period, not covered by other studies. We divide our sample into four meaningful time periods based on important events in Bitcoin market history. This is important for a new market such as the Bitcoin market; the relationships under study are very important in markets where participants rely on technical analysis in the absence of reliable fundamental methodology to measure the intrinsic value of the asset.
Abstract The research seeks to contribute to Bitcoin pricing analysis based on the dynamics between variables of attractiveness and the value of the digital currency. Using the error correction model, the relationship between the price of the virtual currency, Bitcoin, and the number of Google searches that used the terms bitcoin , bitcoin crash and crisis between December 2012 and February 2018 is analyzed. The study also applied the same analysis to prices of Bitcoin denominated in different sovereign currencies traded during the same period. The Johansen (J Econ Dyn Control 12:231-254, 1988) test demonstrates that the price and number of searches on Google for the first two terms are cointegrated. This research indicates that there are strong short-term and long-term dynamics among attractiveness factors, suggesting that an increase in worldwide interest in Bitcoin is usually preceded by a price increase. In contrast, an increase in market mistrust over a collapse of the currency, as measured by the term bitcoin crash , is followed by a fall in price. Intense world economic crisis events appear to have a strong impact on interest in the virtual currency. This study demonstrates that during a worldwide crisis Bitcoin becomes an alternative investment, increasing its price. Based on it, bitcoin may be used as a safe haven by the financial market and its intrinsic characteristics might help the investors and governments to find new mechanisms to deal with monetary transactions.
Cryptocurrencies have recently captured the interest of the econometric literature, with several works trying to address the existence of bubbles in the price dynamics of Bitcoins and other cryptoassets. Extremely rapid price accelerations, often referred to as explosive behaviors, followed by drastic drops pose high risks to investors. From a risk management perspective, testing the explosiveness of individual cryptocurrency time series is not the only crucial issue. Investigating co-explosivity in the cryptoassets, i.e., whether explosivity in one cryptocurrency leads to explosivity in other cryptocurrencies, allows indeed to take into account possible shock propagation channels and improve the prediction of market collapses. To this aim, our paper investigates the relationships between the explosive behaviors of cryptocurrencies through a unit root testing approach.
Abstract The primary purpose of this paper is to investigate whether a novel Markov regime‐switching mixed‐data sampling (MRS‐MIADS) model we design can improve the prediction accuracy of the realized variance (RV) of Bitcoin. Moreover, to verify whether the importance of jumps for RV forecasting changes over time, we extend the standard MIDAS model to characterize two volatility regimes and introduce a jump‐driven time‐varying transition probability between the two regimes. Our results suggest that the proposed novel MRS‐MIDAS model exhibits statistically significant improvement for forecasting the RV of Bitcoin. In addition, we find that jump occurrences significantly increase the persistence of the high‐volatility regime and switch between high‐ and low‐volatility regimes. A wide range of checks confirm the robustness of our results. Finally, the proposed model shows significant improvement for 2‐week and 1‐month horizon forecasts.
Shuyu Zhang, Dunli Zhang, Jianming Zheng, Walter Aerts
Abstract Policy uncertainty created by regulatory authorities regarding the blockchain matters for the initial coin offering (ICO) market. Using an ICO dataset from four major cryptocurrency exchanges, we find that higher policy uncertainty regarding the blockchain leads to a lower return and a lower trading volume of an ICO on its first trading day. Although ICOs are decentralised by design and less vulnerable to direct regulatory intervention, we conclude that blockchain policy uncertainty constitutes an important component of the information environment of ICO ventures.
Roy Cerqueti, Massimiliano Giacalone, Raffaele Mattera
Recently, cryptocurrencies have attracted a growing interest from investors, practitioners and researchers. Nevertheless, few studies have focused on the predictability of them. In this paper we propose a new and comprehensive study about cryptocurrency market, evaluating the forecasting performance for three of the most important cryptocurrencies (Bitcoin, Ethereum and Litecoin) in terms of market capitalization. At this aim, we consider non-Gaussian GARCH volatility models, which form a class of stochastic recursive systems commonly adopted for financial predictions. Results show that the best specification and forecasting accuracy are achieved under the Skewed Generalized Error Distribution when Bitcoin/USD and Litecoin/USD exchange rates are considered, while the best performances are obtained for skewed Distribution in the case of Ethereum/USD exchange rate. The obtain findings state the effectiveness -- in terms of prediction performance -- of relaxing the normality assumption and considering skewed distributions.
Anwar Hasan Abdullah Othman, Syed Musa Alhabshi, Salina Kassim, Adam Abdullah · 5 authors
Purpose This study uses the autoregressive distributed lag model (ARDL) econometric approach to investigate empirically the effects of cryptocurrencies, the gold standard and traditional fiat money on global income inequality measured based on the Gini coefficient, and various ratios of income inequality distribution such as top 1 per cent, top 10 per cent, top 40 per cent and top 50 per cent. Design/methodology/approach The study uses the ARDL econometric approach. Findings The findings indicated that cryptocurrency and gold standard monetary systems contributed significantly to reducing global inequality of income and wealth distribution. Conversely, the traditional fiat money system contributes positively to global income and wealth inequality while also contributing significantly to their fluctuation. Practical implications This suggests that the fiat monetary system results in the coercive redistribution of income and wealth if governments pursue a social welfare policy. They must resolve this conflict between the current fiat monetary system and social policy by opting for an alternative monetary system such as cryptocurrency or gold standard. These alternative monetary systems offer the promise of resolving the income and wealth inequality associated with the traditional monetary system which are accompanied with the channels of inflation, lack of financial inclusion and debt creation, and to offer a more sustainable financial system. Originality/value The study recommends that monetary policy must be revisited to account for its direct effect on income and wealth redistribution to achieve social welfare goals.
Bitcoin is the digital currency of the digital economy. This article is an attempt to reveal the effects of policy uncertainty on Bitcoin returns with economic policy uncertainty (EPU) in the US, the UK, Japan , China, and Hong Kong . Furthermore, we also present the results of monetary policy uncertainty (MPU) on the Bitcoin market. The robust estimations from the quantile regression and Markov regime-switching model show that Bitcoin returns are affected by EPU. One of the essential findings is that Bitcoin returns are more responsive to EPU in the US, China, and Japan. In the US and Japan, uncertainty has a negative effect on the Bitcoin market whereas in China it has a positive effect. Global MPU uncertainty is also significant in explaining Bitcoin exchange rates. Moreover, the Bitcoin market is negatively affected by uncertainty in Federal Open Market Committee (FOMC), the gross domestic product, and other macroeconomic data. Uncertainty in the equity market and Bitcoin returns are negatively associated.
In this paper, we analyze the time-series of minute price returns on the Bitcoin market through the statistical models of generalized autoregressive conditional heteroskedasticity (GARCH) family. Several mathematical models have been proposed in finance, to model the dynamics of price returns, each of them introducing a different perspective on the problem, but none without shortcomings. We combine an approach that uses historical values of returns and their volatilities - GARCH family of models, with a so-called "Mixture of Distribution Hypothesis", which states that the dynamics of price returns are governed by the information flow about the market. Using time-series of Bitcoin-related tweets and volume of transactions as external information, we test for improvement in volatility prediction of several GARCH model variants on a minute level Bitcoin price time series. Statistical tests show that the simplest GARCH(1,1) reacts the best to the addition of external signal to model volatility process on out-of-sample data.
F. N. M. de Sousa Filho, J. N. Silva, Mário Augusto Bertella, Edgardo Brigatti
In this paper, we explore some stylized facts in the Bitcoin market using the BTC-USD exchange rate time series of historical intraday data from 2013 to 2018. Despite Bitcoin presents some very peculiar idiosyncrasies, like the absence of macroeconomic fundamentals or connections with underlying asset or benchmark, a clear asymmetry between demand and supply and the presence of inefficiency in the form of very strong arbitrage opportunity, all these elements seem to be marginal in the definition of the structural statistical properties of this virtual financial asset, which result to be analogous to general individual stocks or indices. In contrast, we find some clear differences, compared to fiat money exchange rates time series, in the values of the linear autocorrelation and, more surprisingly, in the presence of the leverage effect. We also explore the dynamics of correlations, monitoring the shifts in the evolution of the Bitcoin market. This analysis is able to distinguish between two different regimes: a stochastic process with weaker memory signatures and closer to Gaussianity between the Mt. Gox incident and the late 2015, and a dynamics with relevant correlations and strong deviations from Gaussianity before and after this interval.
Muhammad Abubakr Naeem, Mudassar Hasan, Muhammad Arif, Syed Jawad Hussain Shahzad
We compare the hedging, safe-haven, and diversification potential of gold and Bitcoin for different investment styles and industry portfolios in the United States. We find that gold is at least a weak hedge for the style and industry portfolios except for utilities, energy, and telecom. The hedging potential of gold is comparatively higher for large-cap portfolios, whereas Bitcoin offers minimal hedging effectiveness. However, Bitcoin shows hedging potential for the noncyclical industries. Although investors need a higher amount of investment to hedge the downside risk using gold, it still is a superior hedging instrument compared with Bitcoin. Finally, the analysis using the conditional diversification approach shows that gold is a superior and stable diversifier for style and industry portfolios. Overall, our findings provide evidence of superior safe-haven and hedging potential of gold over Bitcoin.
Bitcoin being a safe-haven asset is one of the traditional stories in the cryptocurrency community. However, during its existence and relevant presence, i.e., approximately since 2013, there has been no severe situation on the financial markets globally to prove or disprove this story until the COVID-19 pandemic. We study the quantile correlations of Bitcoin and two benchmarks—the S&P 500 and VIX—and make comparison with gold as the traditional safe-haven asset. The Bitcoin safe haven story is shown and discussed to be unsubstantiated and far-fetched, while gold comes out as a clear winner in this contest even when a broader cryptocurrency index (CRIX) is considered.
This paper compares a number of stochastic volatility (SV) models for modeling and predicting the volatility of the four most capitalized cryptocurrencies (Bitcoin, Ethereum, Ripple, and Litecoin). The standard SV model, models with heavy-tails and moving average innovations, models with jumps, leverage effects and volatility in mean were considered. The Bayes factor for model fit was largely in favor of the heavy-tailed SV model. The forecasting performance of this model was also found superior than the other competing models. Overall, the findings of this study suggest using the heavy-tailed stochastic volatility model for modeling and forecasting the volatility of cryptocurrencies.
Bitcoin being a safe haven asset is one of the traditional stories in the cryptocurrency community. However, during its existence and relevant presence, i.e. approximately since 2013, there has been no severe situation on the financial markets globally to prove or disprove this story until the COVID-19 pandemics. We study the quantile correlations of Bitcoin and two benchmarks -- S\&P500 and VIX -- and we make comparison with gold as the traditional safe haven asset. The Bitcoin safe haven story is shown and discussed to be unsubstantiated and far-fetched, while gold comes out as a clear winner in this contest.
For the past couple of years, Machine learning and trading helped by artificial intelligence has drawn growing interest. Here, the approach is used to test the hypothesis that the inefficiency of cryptocurrency industry can be exploited in order to produce anomalous revenue. For the duration between Nov. 2015 and Apr. 2018, daily data for 1, 681 crypto currencies were analyzed. Simple trade techniques supported by state-of -the-art machine learning algorithms are seen to outperform the traditional benchmarks. The results obtained imply that non-trivial, but fundamentally simple, algorithmic processes will help to predict the short-term future of the cryptocurrency market. The popularity of cryptocurrencies had skyrocketed in 2017 due to several consecutive months of super-exponential growth of market capitalization. There are over 1,500 currently recorded cryptocurrencies actively trading today with the cryptocurrencies sitting on more than $300 billion [2], and a total market capitalization of over $800 billion in January 2018. According to a recent survey, between 2.9 and 5.8 million privates as well as institutional investors are in the numerous investment networks and access to markets has become easier over time. In a number of online markets, major crypto currencies can be purchased using fiat currency, and then used in order to purchase less known crypto currencies. The average trading amount is globally exceeding $15bn. About 170 money market funds had been invested in cryptocurrencies since 2017, and Bitcoin futures are launched in order to satisfy the Bitcoin trading and hedging demand for the market. The main objective of the work is to predict the Bitcoin prices, one of the most popular and widely used cryptocurrency which is a source of attraction for many investors as a source of profit or investment. But the market for the cryptocurrencies been volatile since the day it was first introduced. So, the approach towards the survey is to use LSTM RNN and use the available dataset and train the model to give the highest possible accuracy and to provide a real-time price of the Bitcoin for the following days.
In this paper, it has been aimed to reveal the possible effects of Covid-19 Coronavirus epidemic on stock markets. In the analysis using daily data between 23 January 2020 and 13 March 2020, possible effects on stock markets has been investigated with Maki (2012) cointegration test using both Covid-19 daily total death and Covid-19 daily total case. According to the results obtained, all stock markets examined with total death act together in the long run. It has been understood that total cases have cointegration relationship of SSE, KOSPI and IBEX35 and do not have cointegration relationship with FTSE MIB, CAC40, DAX30. In this regard, it is considered as one of the optimal option for investors to avoid investments in stock markets, turn to investment in gold markets, which is the safe investment port of each crisis period in long run. Also, considering the possibility of turning all life into an internet environment, turning to cryptocurrencies is seen as another alternative option for investors. In this direction, it will be the preference of investors to turn to derivative markets and to the stock markets of countries where Covid-19 is relatively rare to avoid risk.