Hongze Guo, Ke Gao, Yue Yu, Yingchang Liu · 5 authors
This paper introduces a diluted prediction method for bitcoin and gold based on cycle prediction. This method does not need to quantify the external parameters like robot learning and neural network autoregressive model, but mainly uses ARIMA to feedback the parameter values into risk coefficients under the condition of obtaining the optimal solution circularly, and the price prediction of a single period in the future is carried out with a fixed number of samples, thus realizing the high-precision prediction of bitcoin and gold prices. In the application simulation, the real data of bitcoin and gold from 2016 to 2021 are selected. After 1000 times of Monte Carlo simulations, 919 times of the yield is more than 3 times, 157 times of the yield is more than 8 times, and the minimum yield is about 2 times. At the same time, this paper puts forward an investment strategy for this prediction method, which realizes a very safe profit with a final return rate of 6.2 times under the condition of making full use of the prediction risk coefficient. The prediction method and investment scheme bring a brand-new high-precision prediction method and targeted investment strategy with high safety coefficient to all the investors, which has great economic value.
The paper employs a threshold regression framework conditioned by two COVID-19 related proxies, to investigate whether Bitcoin and Ether exhibit short-term safe haven or diversifier features for stock and bond markets. Both cryptocurrencies fulfil a diversifier role for the responsible investments represented by sustainable stock market indices, a safe haven role for major bond markets and a mixed role for a selection of representative stock market indices. Furthermore, in times characterized by an increasing number of COVID-19 daily cases or deaths the statistical relationship between both cryptocurrencies and the main financial market determinants weakens.
Xunfa Lu, Nan Huang, Zhitao Ye, Kin Keung Lai · 5 authors
The COVID-19 causes strong spillover effects between financial markets. This paper explores the dynamic spillover effects among cryptocurrency, clean energy and oil during the COVID-19 by employing TVP-VAR extended joint connectedness approach. The empirical results show that clean energy and oil markets appear to be the net receivers of spillovers, whereas cryptocurrency market appears to be a net transmitter of spillovers. The dynamic total connectedness experiences a rapid increase in March 2020 when the COVID-19 spreads around the world.
The price of cryptocurrency is easily affected by various economic, political and other factors, with huge fluctuation, which makes it difficult to predict, compared with stocks and other financial products. Therefore, the prediction of its short-term return in this paper can provide some valuable suggestions for investors. This paper uses XGBoost algorithm to predict 14 kinds of cryptocurrency markets, experiments based on the data applied by KAGGLE competition platform, and expands the data features combined with feature engineering. Experimental data express that our advanced model has significantly improved forecast performance compared with other traditional machine learning algorithms. Specifically, the prediction performance of XGBoost algorithm is 12.5%, 16.6% and 43.3% higher than that of Gradient Boosting model, SVM algorithm and Linear Regression algorithm respectively. In addition, we also rank the importance of all the features of the simulation, and give some constructive suggestions to guide the future work.
Bitcoin Pricing Kernels (PKs) are estimated using a novel data set from Deribit, the leading Bitcoin options exchange. The PKs, as the ratio between risk-neutral and physical density, dynamically reflect the change in investor preferences. Thus, the PKs improve the understanding of investor expectations and risk premiums in a new asset class. Bootstrap-based confidence bands are estimated in order to validate the results. Investors are heterogeneous in their risk profiles and preferences with respect to volatility and investment horizon. The empirical PKs turn out to be U-shaped for short-dated instruments and W-shaped for long-dated instruments. We find that investors are willing to pay a substantial risk premium to insure themselves against short-term price movements. The risk premium is smaller for longer-dated instruments and their traders are risk averse. The shape of the empirical PKs reveals the existence of a time-varying risk premium. The similarity between the shape of empirical PKs for Bitcoin and other markets that represent aggregate wealth shows that Bitcoin is becoming an established asset class.
Zubair Ahmad, Zahra Almaspoor, Faridoon Khan, Sharifah E. Alhazmi · 7 authors
<abstract><p>Cryptocurrency is a digital currency and also exists in the form of coins. It has turned out as a leading method for peer-to-peer online cash systems. Due to the importance and increasing influence of Bitcoin on business and other related sectors, it is very crucial to model or predict its behavior. Therefore, in recent, numerous researchers have attempted to understand and model the behaviors of cryptocurrency exchange rates. In the practice of actuarial and financial studies, heavy-tailed distributions play a fruitful role in modeling and describing the log returns of financial phenomena. In this paper, we propose a new family of distributions that possess heavy-tailed characteristics. Based on the proposed approach, a modified version of the logistic distribution, namely, a new modified exponential-logistic distribution is introduced. To illustrate the new modified exponential-logistic model, two financial data sets are analyzed. The first data set represents the log-returns of the Bitcoin exchange rates. Whereas, the second data set represents the log-returns of the Ethereum exchange rates. Furthermore, to forecast the high volatile behavior of the same datasets, we apply dual machine learning algorithms, namely Artificial neural network and support vector regression. The effectiveness of these models is evaluated against self exciting threshold autoregressive model.</p></abstract>
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.
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.
"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."
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.
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.
Francisco Jareño, MarÃa 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>
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.
<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>