Blockchain Papers

Mykola Babiak, Mustafa Berke Erdis

No abstract is available for this record.

Anh H. Le

In this paper, I introduce a New Keynesian - Dynamic Stochastic General Equilibrium (NK-DSGE) model to examine the implications of CBDCs and cryptocurrency in an open economy for emerging markets. In our model, cryptocurrency is implemented as a form of deposit in banks where bankers can also receive deposits from abroad. Lastly, CBDCs are introduced as a payment and saving instrument. I find that cryptocurrency has a crucial role in banking sectors and a significant effect on the dynamic of foreign debt which is highly important for emerging markets. Moreover, I uncover that CBDCs can generate welfare gains but the gain varies with their designs.

Cen Cai, Wencheng Li, Hui Han, Mingxi Liu

The wild swings in Bitcoin’s valuation keep attracting authorities’ and policy-makers interest. Thus at present, many researchers are focus on analyzing and forecasting. The existing studies on Bitcoin price prediction are mainly in two ways: (1) study how economic factors, market and investor sentiment indicators influence Bitcoin price; (2) apply machine learning and artificial neural networks to predict the value of Bitcoin. This paper aims to implement a scenario analysis method to generate various hypothetical events and then determine their effects on the value of Bitcoin price. Scenario analysis is normally used to measure financial risk. In this paper, we propose a method that combines scenario analysis with historical data. We further aim to find the correlations among scenarios and examine the relationship between the significant shocks and Bitcoin prices. Our findings suggest that what-if analysis is a good way to measure the risk exposure of Bitcoin. The method can also be used for worse-scenario analysis to check how Bitcoin performs during crisis periods.

Miloš Zrnić, Angelina Njeguš, Ivana Brdar, Jelena Košutić

The subject of this research are challenges in the application of cryptocurrencies in tourism, with focus on the Republic of Serbia. The paper will present examination of the attitudes and opinions of the respondents in the tourist market of the Republic of Serbia about cryptocurrencies and their willingness to accept them in their work, business and travel. The methods used in the research are: hypothetical-deductive, structured (closed type) questions, dichotomic single choice questions; both nominal and interval measurement scales. Research should contribute to a better understanding of the functioning of digital currencies in the tourism sector and a closer understanding of the challenges and prospects for their application.

David L. John, Bela Stantić

No abstract is available for this record.

Collin Brown, Jonathan Chiu, Thorsten V. Koeppl

"Bitcoin is a decentralized payment system: a central institution to verify and settle transactions does not exist. What drives the transaction fees in this system? Can it remain tamper proof in the long run? We develop an empirical model to study how the demand for bitcoin influences transaction fees. To identify supply and demand effects, we analyze two types of events—two rapid price increases since late 2017 and the introduction of a new feature called Segwit. We find that Segwit reduced fee revenue by about 70%. Fee revenue is maximized at a block size of about 0.6 megabytes when Segwit adoption remains at current levels. In addition, large sustained price increases are required to keep mining rewards constant in the long run."

Dorien Herremans, Kah Wee Low

The cryptocurrency market is highly volatile compared to traditional financial markets. Hence, forecasting its volatility is crucial for risk management. In this paper, we investigate CryptoQuant data (e.g. on-chain analytics, exchange and miner data) and whale-alert tweets, and explore their relationship to Bitcoin's next-day volatility, with a focus on extreme volatility spikes. We propose a deep learning Synthesizer Transformer model for forecasting volatility. Our results show that the model outperforms existing state-of-the-art models when forecasting extreme volatility spikes for Bitcoin using CryptoQuant data as well as whale-alert tweets. We analysed our model with the Captum XAI library to investigate which features are most important. We also backtested our prediction results with different baseline trading strategies and the results show that we are able to minimize drawdown while keeping steady profits. Our findings underscore that the proposed method is a useful tool for forecasting extreme volatility movements in the Bitcoin market.

Yanzhao Zou, Dorien Herremans

No abstract is available for this record.

Simon Trimborn, Hanqiu Peng, Ying Chen

Known as an active global virtual money network, the Bitcoin blockchain, with millions of accounts, has played a continually increasingly important role in fund transition, digital payment, and hedging. We propose a method to Detect Influencers in Network AutoRegressive models (DINAR) via sparse-group regularization to detect regions influencing others across borders. For a granular analysis, we analyse whether the transaction size plays a role in the dynamics of the cross-border transactions in the network. With two-layer sparsity, DINAR enables discovering (1) the active regions with influential impact on the global digital money network and (2) whether changes in the size of the transaction affect the dynamic evolution of Bitcoin transactions. In the analysis of real data of the Bitcoin blockchain from Feb 2012 to December 2021, we find that influence from certain regions is linked to the economic need to use BTC, such as to circumvent sanctions, avoid high inflation, and to carry out transactions through off-shore markets. The effects are robust to different groupings, evaluation periods, and choices of regularization parameters. • Development of a method to Detect Influencers in Network AutoRegressive models (DINAR). • Investigation of Bitcoin users impact across regions on the digital money network. • Influence is linked to economic need of using Bitcoin such as to circumvent sanctions and to avoid high inflation. • The ban of cryptos in China caused a change in regions influence on the blockchain, moving from Asia to North America. • Increase in BTC transaction fees shifted impact from less affluent regions and towards wealthier ones.

Jeremy Bertomeu, Xiumin Martin, Ibrahima Sall

No abstract is available for this record.

Cindy Lu

No abstract is available for this record.

Francisco Jareño, María de la O González, Pascual Belmonte

<abstract><p>Using NARDL methodology, this research investigates some asymmetric and non-linear interconnections between leading cryptocurrency and commodity returns. Thus, this study explores potential interconnections between these cryptocurrencies and commodity markets in the period between March 07, 2018, and March 26, 2021. This paper splits the entire sample period into two independent sub-periods in order to enhance robustness: pre-COVID and COVID, to examine the impact of the pandemic on these markets. Our results confirm that the most relevant interconnection (in terms of cointegration, short- and long- asymmetry, and the persistence of the lags) between cryptos and commodities is focused on COVID-19, the pandemic sub-period, in line with previous literature. Finally, the study reveals that some cryptocurrencies such as Tether could serve as a diversifying asset or even a safe haven, in certain scenarios, in investment strategies.</p></abstract>

Srinidhi Kanuri

Bitcoin was the first cryptocurrency in the world launched in 2009 while Ethereum was the second one launched in 2015. This paper evaluates the risk and return for both the cryptocurrencies using CMBI Bitcoin and CMBI Ethereum indices as their proxies since their inception and compares them to 10 traditional asset classes -- U.S. stocks, developed country stocks, emerging market stocks, gold, commodities, energy, U.S. bonds, global bonds, U.S. real estate and global real estate. Both the cryptocurrencies had very high risk, absolute returns and risk-adjusted performance (Sharpe, Sortino and Omega Ratios) compared to other traditional asset classes. Ethereum and Bitcoin also had very high cumulative returns and generated much more wealth compared to other asset classes. However, both the cryptocurrencies also had very high maximum drawdown and monthly downside deviation compared to traditional asset classes. Overall, Bitcoin and Ethereum are extremely high risk, high reward plays for investors.

Alessio Brini, Jimmie Lenz

No abstract is available for this record.

Bharti Bharti, Ashish Kumar

<abstract> <p>This paper examines the evidence of herding in the revolutionary cryptocurrency market for the period from January 2017 to December 2020. The study employs quantile regression technique for investigating herd behaviour during market asymmetries of rising and falling returns, extreme market returns, high volatility, and the exogenous event of the COVID-19 pandemic. The results provide evidence of pronounced herding during the bull phase, extreme down-markets, and high volatility. These results indicate that herd hunch is prevalent in the cryptocurrency market as investors exhibit imitation while ignoring their own knowledge and beliefs. Also, the phenomenon is more vividly observed during the panic period of COVID-19.</p> </abstract>

Daniil Parfenov

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 cryptocurrencies’ 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.

Dun Jia, Yifan Li

No abstract is available for this record.

Murat Tiniç, Ahmet Şensoy, Erdinç Akyıldırım, 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 adverse‐selection 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 adverse‐selection costs for major cryptocurrencies. Also, our results suggest that adverse‐selection costs, on average, correspond to 10% of the estimated effective spread, indicating an economically significant impact of adverse‐selection risk on transaction costs in cryptocurrency markets. Finally, we document that adverse‐selection costs are important predictors of intraday volatility, liquidity, market toxicity, and returns.

Azhar Mohamad

No abstract is available for this record.

Feng Jin, Jingwei Li, Guangchen Li

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 GARCH‐EVT‐copula 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 COVID‐19, bitcoin and the dollar also had the ability to diversify risks, although less effective than gold. However, during the COVID‐19 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 COVID‐19 period, which may help investors and risk managers construct the optimal portfolios.

Hossein Jahanshahloo, Shaen Corbet, Les Oxley

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 ‘24/7’, our findings suggest that both transaction and on-chain network activity are best described to be, at best, ‘12/5’, 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.

Xin Sui, Guifen Shi, Guanchong Hou, Shaohan Huang · 5 authors

The impacts of COVID‐19 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 COVID‐19 crisis. The results revealed that the total volatility connectedness of the cryptocurrency market markedly increased following the outbreak of COVID‐19; statically, Bitcoin, Ethereum, Cardano, and Bitcoin Cash were the net transmitters before COVID‐19, while Bitcoin, Ethereum, Ripple, Litecoin, Cardano, and Stellar became the major net transmitters in the market after COVID‐19. Dynamically, the dynamic performance of different cryptocurrencies during the COVID‐19 pandemic was heterogeneous, and the possible driving factors are diverse. Moreover, from network analysis, we further found that the COVID‐19 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 COVID‐19 crisis.

Piotr Fiszeder, Marta Małecka

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.