The uncertainty due to the COVID-19 outbreak has encouraged investors to look for value hedging instruments to minimize risk, which can be in the form of hedging assets or safe-haven assets. In response to it, this study aims to find out whether Bitcoin, Ethereum, and gold can behave as hedging and safe-haven assets before and amid the pandemic in Indonesia. The strategy is by observing the effects of volatility and return of Bitcoin, Ethereum, and gold on the Indonesian stock market. This study employed both quantile regression and simple linear regression models on data of daily closing price taken before and during COVID-19. This study finds that they can be hedge and safe-haven assets during the COVID-19 pandemic in Indonesia. The findings show some significant correlations between assets that can help investors determine which assets can be hedging instruments.
This paper investigates the causality and cointegration relationships between seven major cryptocurrencies, namely Bitcoin (BTC), Binance Coin (BNB), Cardano (ADA), Dogecoin (DOGE), Ethereum (ETH), Polkadot (DOT) and Ripple (XRP), using Johansen Cointegration and Granger Causality tests over the period from August 21, 2020 to April 19, 2021. Results indicate that there exists cointegration among cryptocurrencies in the long run. Findings also show that there is a bi-directional causal relationship between BNB and ETH. Additionally, BNB appears to be Granger cause of ADA, DOGE and DOT. On the other hand, analyses provide evidence of one-way causality running from XRP to both DOGE and DOT. These results might have some important implications for investors in terms of portfolio management.
Purpose The study considers time-varying risk premium in investigating the capability of technical analysis (TA) to predict and outperform a buy–hold strategy in Bitcoin exchange rate returns. Design/methodology/approach The study tests the technical trading rule of fixed moving average (FMA) on daily actual and equilibrium returns of Bitcoin exchange rates. The equilibrium returns are computed using dynamic CAPM in conjunction with a VAR-MGARCH (1, 1) system. The empirical evaluation of the study uses a case study of four Bitcoin exchange rates (BTC/AUD, BTC/EUR, BTC/JPY and BTC/ZAR) for the period 19 June 2010 to 30 October 2020. Findings The findings are consistent with related studies in conventional foreign exchange markets that find TA to be profitable, especially in emerging markets. Nevertheless, the consideration of risk premium has the effect of reducing the abnormal returns. Also, further robust tests reveal that Bitcoin returns possess a momentum effect which prompts further study in efficient market hypothesis research. Practical implications The empirical findings of this study should benefit portfolio managers and active investors on the strength of TA to predict returns in a speculative market like the Bitcoin exchange rate market. Originality/value The study takes cognisance that cryptocurrency trading is speculative in nature which renders it a good candidate for TA methods. While there are studies that have explored the value of TA in Bitcoin exchange rates, these studies fail to incorporate the effects of time-varying risk premiums, the strength and focus of the current paper.
In the decade following the 2008 financial crisis, the coronavirus viral disease 2019 (COVID-19) pandemic and United States (US) President Trump’s Twitter account became representations of market uncertainty, attracting the financial research of (Goodell, 2020; Benton and Philips, 2020). Due to the popularity of these events and their impact on financial markets, many unanswered questions still persist, particularly, how the financial structure has changed during this unique time. The popularity of Bitcoin, one of the main cryptocurrencies, has caused a controversial topic to arise in recent academic research, namely, whether its function compares to that of conventional precious metals such as gold and platinum. This doctoral thesis aims to fill this research gap in two ways: (i) by addressing market reactions to the COVID-19 pandemic and political news by answering the question of how US legislators traded at an industry level during the ongoing COVID-19 pandemic, and how Trump’s Twitter account could shake the equity market during a trade war, and (ii) by examining the power of the gold and platinum ratio, which was first studied by (Huang and Kilic, 2019) ), in predicting Bitcoin as well as how political sentiment could drive the returns, volatility, and volume of this cryptocurrency. This thesis contributes to the empirical evidence in the areas mentioned above due to the growing attention on the financial function of cryptocurrency, the debatable effects of political news regarding the use of social media, and the eventual and unprecedented scale of the COVID-19 pandemic.
The goals of this paper are twofold: (1) to present a new method that is able to find linear laws governing the time evolution of Markov chains and (2) to apply this method for anomaly detection in Bitcoin prices. To accomplish these goals, first, the linear laws of Markov chains are derived by using the time embedding of their (categorical) autocorrelation function. Then, a binary series is generated from the first difference of Bitcoin exchange rate (against the United States Dollar). Finally, the minimum number of parameters describing the linear laws of this series is identified through stepped time windows. Based on the results, linear laws typically became more complex (containing an additional third parameter that indicates hidden Markov property) in two periods: before the crash of cryptocurrency markets inducted by the COVID-19 pandemic (12 March 2020), and before the record-breaking surge in the price of Bitcoin (Q4 2020 - Q1 2021). In addition, the locally high values of this third parameter are often related to short-term price peaks, which suggests price manipulation.
This paper explores how fear sentiment affects the price of Bitcoin by employing the rolling-window Granger causality tests. The analysis reveals negative influences from the volatility index (VIX) to Bitcoin price (BTC), which ascertains that Bitcoin can not be considered a haven in fear sentiment. Due to the liquidity in economic downside risks, BTC may decrease with high VIX to hedge losses, increasing during low VIX periods. The empirical results conflict with the intertemporal capital asset pricing model, which underlines that the increasing VIX can promote the price of Bitcoin. In turn, BTC positively impacts VIX, which shows that Bitcoin price can be treated as the main indicator for a more comprehensive analysis of the fear index. Under severe global uncertainty and changeable fluctuation of market sentiment, investors can optimize investment decisions based on market fear sentiment. The government can also consider VIX to grasp the trend of BTC to participate in cryptocurrency speculation effectively.
Abstract This paper analyses the return and realized volatility spillovers among Bitcoin, wilder hill clean energy index (ECO), S&P 500 as conventional stocks and West Texas Intermediate (WTI) from 11/11/2013 to 30/09/2021. We investigate the transmission mechanism with Time-Varying Parameter Vector Auto regression (TVP-VAR). Our findings indicate that stock markets such as clean energy and conventional transmit return shocks to Bitcoin and oil and receive volatility shocks from Bitcoin and oil. In addition, during non-crisis periods, Bitcoin and other financial markets are weakly related; but, during crisis periods, such as the great cryptocurrency crash in 2018 and the coronavirus pandemic in 2020, their connection increases significantly.
We present a textual analysis that explains how Elon Musk's sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world's wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla's CEO. However, we did not find evidence to support linkage between Musk's sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market.
Sitara Karim, Muhammad Abubakr Naeem, Nawazish Mirza, Jéssica Paule-Vianez
Purpose This study quantified the hedge and safe haven features of bond markets for multiple cryptocurrency indices from June 2014 to April 2021 to highlight whether bond markets offer hedging facilities to uncertainty indices of cryptocurrencies. Design/methodology/approach The authors employed the methodology of Baur and McDermott (2010) and AGDCC-GARCH model to measure the hedge and safe-haven characteristics of three bond markets (BBGT, SPGB and SKUK) for three uncertainty indexes of cryptocurrencies (UCRPR, UCRPO and ICEA). Findings The authors find that bond markets are neither hedge nor safe havens except for SKUK which is a safe haven investment for cryptocurrency indices and offers substantial diversification during the periods of economic fragility. In addition, the hedge effectiveness of SPGB outperforms other bonds during crisis periods and provides sufficient diversification potential for cryptocurrency indices. Practical implications The findings are important for policymakers, regulatory bodies, financial firms and investors in assessing hedge and safe haven characteristics of bond markets against cryptocurrency indices. Originality/value Employing the novel methodology of AGDCC-GARCH with three different bond markets and three uncertainty indices of cryptocurrencies, the current study adds to the existing strand of literature in terms of quantifying hedge and safe-haven attributes of bond markets for cryptocurrency uncertainty indexes.
The aim of the study investigates the return and volatility spillovers and conditional correlations between Borsa Istanbul Stock Exchange 100 Index (BIST100) and Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTH) using daily data for the period between August 07, 2015 and May 20, 2021 with VAR-DCC-GARCH model. We find no bidirectional return spillovers between BIST100 and cryptocurrencies. In line with the volatility spillover results of the study, it has been determined that there is a unidirectional shock transmission from BIST100 to BTC, XRP and LTH, and a unidirectional volatility spillover from BIST100 to BTC and ETH. Also, in the study, it has been determined that the dynamic conditional correlations between BIST100 and four cryptocurrencies have a highly variable over time and their average is very close to zero. However, in possible panic periods, the situation is reversed
Financial data are expensive and highly sensitive with limited access. We aim to generate abundant datasets given the original prices while preserving the original statistical features. We introduce the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) into the field of the stock market, futures market and cryptocurrency market. We train our model on various datasets, including the Hong Kong stock market, Hang Seng Index Composite stocks, precious metal futures contracts listed on the Chicago Mercantile Exchange and Japan Exchange Group, and cryptocurrency spots and perpetual contracts on Binance at various minute-level intervals. We quantify the difference of generated results (836,280 data points) and original data by MAE, MSE, RMSE and K-S distances. Results show that WGAN-GP can simulate assets prices and show the potential of a market simulator for trading analysis. We might be the first to look into multi-asset classes in a systematic approach with minute intervals across stocks, futures and cryptocurrency markets. We also contribute to quantitative analysis methodology for generated and original price data quality.
Mudassar Hasan, Muhammad Abubakr Naeem, Muhammad Arif, Syed Jawad Hussain Shahzad · 5 authors
We examine the dynamics of liquidity connectedness in the cryptocurrency market. We use the connectedness models of Diebold and Yilmaz (Int J Forecast 28(1):57-66, 2012) and Baruník and Křehlík (J Financ Econom 16(2):271-296, 2018) on a sample of six major cryptocurrencies, namely, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Monero (XMR), and Dash. Our static analysis reveals a moderate liquidity connectedness among our sample cryptocurrencies, whereas BTC and LTC play a significant role in connectedness magnitude. A distinct liquidity cluster is observed for BTC, LTC, and XRP, and ETH, XMR, and Dash also form another distinct liquidity cluster. The frequency domain analysis reveals that liquidity connectedness is more pronounced in the short-run time horizon than the medium- and long-run time horizons. In the short run, BTC, LTC, and XRP are the leading contributor to liquidity shocks, whereas, in the long run, ETH assumes this role. Compared with the medium term, a tight liquidity clustering is found in the short and long terms. The time-varying analysis indicates that liquidity connectedness in the cryptocurrency market increases over time, pointing to the possible effect of rising demand and higher acceptability for this unique asset. Furthermore, more pronounced liquidity connectedness patterns are observed over the short and long run, reinforcing that liquidity connectedness in the cryptocurrency market is a phenomenon dependent on the time-frequency connectedness.
We consider the performance of cryptocurrencies in the light of fundamental asset pricing and portfolio theory. We observe how a traditional focus on reducing asset return volatility with Markowitz diversification misses the significance of such volatility for growth. The recognition that asset growth is more likely subject to exponential or continuously compounding growth characteristics reveals that asset volatility can be exploited both across assets and across investment periods to deliver superior returns.
Bitcoin is currently the most widely used encryption currency in the world, and the Nasdaq Index, as the world's first stock market to use electronic trading, has a certain impact on the price of Bitcoin.Based on the Bitcoin closing price and Nasdaq index data from January 2020 to May 2022, this paper predicts the price of Bitcoin by using ARIMA and ARIMAX models respectively.The linkage was confirmed by the correlation test, and the fitting and prediction effect of the ARIMAX model with the Nasdaq index as the input variable were better than the ARIMA model.
The paper endeavour to explore the nexus between Bitcoin Rouble exchange rate and the Russian capital market using cointegration and vector error correction analysis taking the capital market indicators namely US Dollar Rouble exchange rate, MOEX index, RTX index, Moscow exchange trade turn over and RUONIA of Russia using daily data from 1/11/2021 to 18/4/2022 as a consequence of post pandemic recovery and sets back from war between Russia and Ukraine. The paper found that the trend line of Bitcoin Rouble rate is cyclical with four phases whose Wavelet threshold signal curve is explosive oscillatory. There are no short run causalities from the indicators of capital market to the Bitcoin Rouble price but there is insignificant and converging cointegrating long run causalities from those indicators where the relation between Bitcoin Rouble and US Dollar Rouble rate and MOEX index are significantly negative and the relation with RTX index is significantly positive. It was evident that there is little significant influence of Bitcoin Rouble pricing on the Russian capital market in the long run.
Blockchain is used by different industries like banking, healthcare, law enforcement, IOT, online music, digital transfer, and real estate for transaction security purposes. Blockchain is becoming more sustainable day by day. The objective of this study is to determine the interdependence of major stock market indices and cryptocurrencies, offering investors a potential path for diversification. A quantitative study will investigate the interdependency of cryptocurrencies on different stock market indices. These are selected on the basis of high market capitalization. The research will be based on secondary data collection. Strong correlation between crypto and stocks has been seen in developing or emerging market nations, which have been at the forefront of crypto development and adoption. In 2020–21, for example, the correlation between returns of the MSCI emerging markets index and Bitcoin was 0.34, increased 17-fold from the previous years. Stronger correlation indicates that Bitcoin is becoming a risky investment. Its correlation with stocks has risen above than that with other assets such as gold, investment grade bonds, and major currencies, indicating that risk diversification benefits are limited, contrary to prior beliefs. Increased crypto-stock interconnectedness increases the risks of spillover of investor sentiment spillovers between asset classes. As a result, a severe drop in Bitcoin prices may encourage investor risk aversion, resulting in a drop in stock market investment. Spillovers from the S&P 500 to Bitcoin are on average of equal magnitude, implying that sentiment in one market is passed.
When Bitcoin became one of the world's most popular investment options, the cryptocurrency industry has showed potential development, and it had a similar influence on the Indian financial sector too. Aside from Bitcoin, other altcoins are gaining popularity and dominating the cryptocurrency market. As a result, the goal of this research is to identify at the macroeconomic factors that influence Bitcoin prices, such as the USD/INR exchange rate, gold prices, crude oil prices, the New York Stock Exchange Dow Jones (NYSE) price, NIFTY price, and Sensex price, as well as the prices of nine alternative cryptocurrencies in the cryptocurrency market: Binance coin, Bitcoin Cash, Bitcoin SV, Ether, Ethereum, Litecoin, Monero, Tether, and ripple. Bitcoin volume and market capitalization are additional factors, undertaken in the study, that are potential influencers of cryptocurrency pricing. The time series data, which comprises of bi-weekly data for all variables, will be used from 2015 to 2020. The OLS (ordinary least square) regression model in EVIEWS will be used in this study to conduct an empirical analysis.
This study uses the DCC-GARCH model to compare the correlation between two types of cryptocurrencies in two different fields.In the context of the popularity of NFTs and the metaverse, new cryptocurrencies based on the metaverse have been favored by investors.Through empirical analysis of mana cryptocurrencies in the NFT market, we find that the new cryptocurrencies in the NFT market have high volatility to Bitcoin, Ethereum, and traditional cryptocurrencies in the past year.Therefore, we conclude that new cryptocurrencies are more likely to be one of the factors for portfolio diversification.
In recent years, the gold-bitcoin market and corresponding trading strategies have received more scholarly attentions. Predicting gold and bitcoin prices from historical data is a specific stream in this academic area. Many scholars have used financial methods or statistical methods to construct trading models. However, one of the limitations is that few previous studies combined both financial and statistical methods. Therefore, the present study aims to build a mathematical model that predicts price dynamics of gold and bitcoin and utilizes some connections between finance and statistics. To achieve this goal, some financial indicators were computed and Holt-Winters’ Model was applied. The research result shows that a trading strategy can be developed with the help of our proposed model and trading shrink ratio, which functions as the risk controller. The sensitivity test indicates that the proposed model has little sensitivity towards commission fees, which means that the model can be widely used in similar situations. In general, this study outlines an analytical approach to evaluate profits in gold-bitcoin market. Traders can generate considerable profits from the proposed trading strategy.
Cryptocurrencies show some properties that differ from typical financial instruments. For example, dynamic volatility, larger price jumps, and other market participants and their associated characteristics can be observed (Pardalos, Kotsireas, Guo, & Knottenbelt, 2020). Especially high tail risk (Sun, Dedahanov, Shin, & Li, 2021; Corbet, Meegan, Larkin, Lucey, & Yarovaya, 2018; Borri, 2019) leads to the question of whether the methods and procedures established in risk management are suitable for measuring the resulting market risks of cryptos appropriately. Therefore, we examine the risk measurement of Bitcoin, Ethereum, and Litecoin. In addition to the classic methods of market risk measurement, historical simulation, and the variance-covariance approach, we also use the extreme value theory to measure risk. Only the extreme value theory with the peaks-over-threshold method delivers satisfactory backtesting results at a confidence level of 99.9%. In the context of our analysis, the highly volatile market phase from January 2021 was crucial. In this, extreme deflections that have never been observed before in the time series have significantly influenced backtesting. Our paper underlines that critical market phases could not be sufficiently observed from the short time series, leading to adequate backtesting results under the standard market risk measurement. At the same time, the strength of the extreme value theory comes into play here and generates a preferable risk measurement.
Background: This paper analyses the influence of fluctuation in gold market on bitcoin prices. Based on previous studies, in present market conditions, volatility in gold prices have caused price changes in several other major assets in the market, such as crude oil. Gold fluctuations are likely to stimulate uncertainty in some other major assets. As bitcoin is becoming an alternative tool to hedge against inflation likewise to gold, the degree of uncertainty in bitcoin market is relatively high. Therefore, the study of causal relationship between gold and bitcoin markets has become appropriate since bitcoin has tremendous growth in its returns and shares many similarities with gold. Thereupon, this study reveals the evidence of Granger causality regression in different time spans to understand the relationship between gold and bitcoin. This relationship is beneficial to study since Granger causality hypothesis acknowledges whether gold’s historical prices are useful for forecasting the bitcoin market. Purpose: This study aims to analyze the relationship between gold and bitcoin market during an 8-year period from 2014 and 2022. Throughout this period, time spans which involves financial crises have been separated from the data set and tested separately to determine if there is a constant relationship between the variables. Through this, it has been intended to find the Granger causality link between gold and bitcoin market to see whether one is leading another one. Identifying the Granger causality correlation helps analyzing the patterns of correlation by using the empirical datasets, and to determine the strength of the Granger causal relationship’s nature between gold and bitcoin. Since the correlation itself does not explain why or how, but only if both markets move together, the Granger causality correlation between gold and bitcoin is the quantification of the impact that gold market performance has on bitcoin’s future price performance. Method: Since the collected data is time-series data, Augmented Dickey-Fuller tests have been conducted initially to the chosen tests. Following the results from ADF tests, Spearman’s Rho, iand Johansen’s Cointegration tests have been utilized to determine the long-term correlation between variables. Thereafter, Toda & Yamamoto and Dolado & Lütkepohl Granger Causality (TYDL-GC) method has been used to analyze the Granger causality link between the variables. Conclusion: The results of this study indicates that (i) no statistically significant correlation between gold and bitcoin market has been found according to the Spearman’s Rho test results, (ii) no long-term relationship has been found between gold and bitcoin according to cointegration test, (iii) gold does Granger Cause bitcoin prices. The evidence of causality link is unilateral from gold towards bitcoin market. Furthermore, it was observed that the Granger causality link weakens in short term and is not constant over time. The results fail to support the semi strong Efficient Market Hypothesis form. Thus, gold and bitcoin’s markets are efficient in the weak form but inefficient in the semi strong form. Since Granger causality has been found from gold towards bitcoin, one can construct a prediction model for bitcoin by using gold’s historical prices.