This paper examines pricing efficiency of cryptocurrencies and some traditional assets measuring the level of market efficiency with Adjusted Market Inefficiency Measure. The patterns of several cryptocurrenciesâ price dynamics over the last 4 years are compared with those of traditional assets. Correlation and mutual information matrices for AMIM are obtained using different estimation intervals. The results across different time scales are tested for noise using permutation entropy technique, empirical estimations are represented in statistical complexity plane to show the structure of efficiency links. Usage of AMIM in short window estimation is justified. Efficiency levels seem to be closely connected if judged from the standpoint of information theory at all time frames. Efficiency linkages become more linear at larger analysis periods. Cryptocurrencies seem to be more closely connected to equities, especially S&P500. Bursts of inefficiency on cryptocurrencies markets spread to equity markets and are possibly mediated in bank system. Commodities seem to be more independently priced.
Murat Tiniç, Ahmet Åensoy, Erdinç AkyÄąldÄąrÄąm, Shaen Corbet
Abstract In this article we investigate the influence that information asymmetry may have on future volatility, liquidity, market toxicity, and returns within cryptocurrency markets. We use the adverseâselection component of the effective spread as a proxy for overall information asymmetry. Using order and trade data from the Bitfinex exchange, we first document statistically significant adverseâselection costs for major cryptocurrencies. Also, our results suggest that adverseâselection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverseâselection risk on transaction costs in cryptocurrency markets. Finally, we document that adverseâselection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.
This paper aims to analyze and compare the ability of bitcoin, gold, and dollar to diversify the risk of traditional market such as crude oil and stock markets. Specifically, we model the linkages between bitcoin, gold, dollar, crude oil, and stock markets using the GARCHâEVTâcopula approach. The results show that the gold market is in the central position among these markets, which is consistent with the status of gold as a major safe asset. Before the outbreak of COVIDâ19, bitcoin and the dollar also had the ability to diversify risks, although less effective than gold. However, during the COVIDâ19 period, gold loses its dominant position and gold, bitcoin, and dollar can no longer act as a hedge. We measure the value at risk (VaR) and expected shortfall (ES) of simulated portfolios constructed based on these five markets and use several backtesting methods to check the validity of the risk measures. The backtesting results show that our model can provide accurate risk measures before and within the COVIDâ19 period, which may help investors and risk managers construct the optimal portfolios.
This research investigates and tests for the presence of time-of-the-day effects on the Bitcoin network. Results indicate that NYSE trading sessions lead Bitcoin trading activity, both on the blockchain and centralised exchanges. Effects are found to have strengthened over time, however, simultaneously diminished at the weekend indicating significant exchange interactions, and that Bitcoin has developed somewhat outside its intended design parameters and is influenced by other forces such as those originating from NYSE trading. While proponents consider Bitcoin trading to be â24/7â, our findings suggest that both transaction and on-chain network activity are best described to be, at best, â12/5â, presenting significant implications for traders, with regards to centralised exchange liquidity and the speed of their transaction inclusion on the blockchain. Finally, the role and influence of both algorithm and volatility traders cannot be eliminated.
Matthew T. McDonald, Koami Soulemane Hayibo, Finn K. Hafting, Joshua M. Pearce
Solar photovoltaic (PV) technology offers a promising means to alleviate environmental and electricity costs challenges for cryptocurrency miners. To analyze this promise, this study investigated the feasibility of using electricity from individually optimized PV systems to power: 1) an individual Bitcoin miner, 2) a DIY intermodal shipping container holding 50 miners, and 3) a commercial mining farm container holding 408 miners. In a controlled lab environment, miners were monitored for electricity use. Then using these values, numerical simulations of both the PV system yield and sensitivity ranges based on the Bitcoin price, Bitcoin halving events, and miner hardware were investigated for informed financial planning. In addition, sensitivity for geographic locations in North America, utility electric rates and PV capital costs were analyzed. The profitability and return on investment (ROI) varied by location primarily because of the geographic distribution of solar flux and utility rates. The ROI for using PV with Bitcoin mining was found to be negative for Toronto and Montreal because of low-cost electricity, while it was 8% for Calgary. In the U.S. cities evaluated, the ROIs were substantial and ranged from 34% in New York, to 64% in Boulder, and up to 104% in Los Angeles. Although the study is based in North America regarding energy rates, climate, and energy laws, the analysis methodology is generalizable globally and grants the average cryptocurrency business the knowledge to make an informed decision on whether to pursue this venture from a financial and environmental perspective. This study contributes to the body of knowledge in cryptocurrency mining by providing an economic means of environmental preservation by powering cryptocurrency miners with renewable solar energy.
The impacts of COVIDâ19 have spread rapidly to global financial markets. In this context, combining the spillover index method introduced by Diebold and Yilmaz (2012) and the complex network analysis framework, we examined the volatility connectedness and the topological structure among the top ten cryptocurrencies before and during the COVIDâ19 crisis. The results revealed that the total volatility connectedness of the cryptocurrency market markedly increased following the outbreak of COVIDâ19; statically, Bitcoin, Ethereum, Cardano, and Bitcoin Cash were the net transmitters before COVIDâ19, while Bitcoin, Ethereum, Ripple, Litecoin, Cardano, and Stellar became the major net transmitters in the market after COVIDâ19. Dynamically, the dynamic performance of different cryptocurrencies during the COVIDâ19 pandemic was heterogeneous, and the possible driving factors are diverse. Moreover, from network analysis, we further found that the COVIDâ19 crisis has significantly changed the topological structure of the cryptocurrency market. Our findings may help understand the typical dynamics in the cryptocurrency market and provide significant implications for portfolio managers, investors, and government agencies in times of highly stressful events like the COVIDâ19 crisis.
Research background: The Russian invasion on Ukraine of February 24, 2022 sharply raised the volatility in commodity and financial markets. This had the adverse effect on the accuracy of volatility forecasts. The scale of negative effects of war was, however, market-specific and some markets exhibited a strong tendency to return to usual levels in a short time. Purpose of the article: We study the volatility shocks caused by the war. Our focus is on the markets highly exposed to the effects of this conflict: the stock, currency, cryptocurrency, gold, wheat and crude oil markets. We evaluate the forecasting accuracy of volatility models during the first stage of the war and compare the scale of forecast deterioration among the examined markets. Our long-term purpose is to analyze the methods that have the potential to mitigate the effect of forecast deterioration under such circumstances. We concentrate on the methods designed to deal with outliers and periods of extreme volatility, but, so far, have not been investigated empirically under the conditions of war. Methods: We use the robust methods of estimation and a modified Range-GARCH model which is based on opening, low, high and closing prices. We compare them with the standard maximum likelihood method of the classic GARCH model. Moreover, we employ the MCS (Model Confidence Set) procedure to create the set of superior models. Findings & value added: Analyzing the market specificity, we identify both some common patterns and substantial differences among the markets, which is the first comparison of this type relating to the ongoing conflict. In particular, we discover the individual nature of the cryptocurrency markets, where the reaction to the outbreak of the war was very limited and the accuracy of forecasts remained at the similar level before and after the beginning of the war. Our long-term contribution are the findings about suitability of methods that have the potential to handle the extreme volatility but have not been examined empirically under the conditions of war. We reveal that the Range-GARCH model compares favorably with the standard volatility models, even when the latter are evaluated in a robust way. It gives valuable implication for the future research connected with military conflicts, showing that in such period gains from using more market information outweigh the benefits of using robust estimators.
This paper aimed to assess the effect of the cryptocurrency market on firmsâ market value, especially on the sectoral level, in Africa. To reach the studyâs main goal, the authors adopted the Panel-Corrected Standard Errors (PCSEs) and Panel Double-Clustered Standard Errors (PDCSEs). Using firm-level data, the results of this study can be summarized as follows: (a) The cryptocurrency market hurts the firm market value in Africa. (b) The firms operating across different sectors respond disproportionally to the cryptocurrency market. For instance, the sectors that offer low returns in Africa (industrial, energy, financial) negatively respond to the cryptocurrency market, while the sectors that offer high returns (real estate and information technology) are not significantly affected. (c) The cryptocurrency market has a perverse effect on less experienced and highly indebted firms. (d) The consistent policies of governments to ban cryptocurrency do not work efficiently.
Stock market is susceptible to various external shocks for its tight dependence on economic fundamentals, financial speculation, and fragile emotions in massive traders, making it a very risky market for investors. In this paper, we aim to identify whether commonly recognized safeâhaven assets, that is, bitcoin, gold, and commodities, can provide investors with effective hedging utility in international stock markets, especially during periods of extreme market turbulence. By using the spillover index method based on the TVPâVAR model, we find that firstly, bitcoin, gold, and commodities can only offer weak hedging effects on stock markets. Furthermore, their abilities to act as a safe haven are ranked as: commodities > gold > bitcoin. Secondly, in general, we have observed the increasing hedging ability of these safeâhaven assets in times of extreme market turmoil. Thirdly, among international stock and safeâhaven asset markets, the world and the developed stock markets act as the net spillover transmitters, while bitcoin, gold, and commodities are the net recipients. Lastly, the total spillover effects are timeâvarying and increase significantly after the outbreak of extreme events.
Crypto-coins (also known as cryptocurrencies) are tradable digital assets. Notable examples include Bitcoin, Ether and Litecoin. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins across owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices across all different sets of crypto-assets, it remains unclear what and if there are tight relations between the trading prices of different cryptocoins. Major coin exchanges (i.e., Coinbase) provide trend correlation indicators to coin owners, suggesting possible acquisitions or sells. However, these correlations remain largely unvalidated. In this paper, we shed lights on the trend correlations across a large variety of cryptocoins, by investigating their coin-price correlation trends over a period of two years. Our experimental results suggest strong correlation patterns between main coins (Ethereum, Bitcoin) and alt-coins. We believe our study can support forecasting techniques for time-series modeling in the context of crypto-coins. We release our dataset and code to reproduce our analysis to the research community.
Cryptocurrencies are widely known for their limited publicly available information, making it challenging to predict market returns. Technical analysis has emerged as an essential tool in this context, but its effectiveness in the cryptocurrency market remains an open question. Using data from nearly 3,000 cryptocurrencies at daily, weekly, and monthly horizons from 2013 to 2022, we systematically re-examine the efficacy of trend-based technical indicators in predicting cryptocurrency market returns and find that price-based signals are more effective in predicting short-term horizons, while volume-based signals are more powerful in predicting long-term horizons. Further analysis shows that machine learning techniques can significantly improve the performance of technical indicators, and technical indicators based on different information respond differently to the COVID-19 outbreak. These results provide direct evidence that volume imparts information to technical analysis independently of price.