Blockchain Papers

Ladislav Krištoufek

No abstract is available for this record.

Bikramaditya Ghosh, Spyros Papathanasiou, Georgios Pergeris

Abstract Many studies have associated cryptocurrencies with bubbles, especially during stressed market conditions such as the recent outbreak of the second wave of COVID‐19. Although the majority of studies have focused on Bitcoin, we investigate the predictability of bubble formation in the cryptocurrency market by using the log‐periodic power law and we uncover some important stylized facts of this market. Our sample consists of data for a selection of 15 cryptocurrencies for the period between 1 January 2021 and 1 September 2021 which coincides with the second wave of COVID‐19. We analyse 86 speculative bubbles, and we find that the cryptocurrency market has three times higher drawdown over equities during stressed market conditions.

Joel J Benjamin, R Surendran, Tharindu Sampath

Forecasting economic goods market recoveries is challenging due to the volatility and uncertainty of the market's structure. Since the introduction of machine learning and increased computer power, programmable forecasting approaches have been found to be particularly effective in predicting stock values. Random Decision Forest, K - Nearest Neighbors, and Linear Regression were among the natural processing techniques employed in this study to forecast the costs of popular crypto currencies like Bit coin and Ethereum over the next few years. The model is given financial data on stock prices from the start of the day to the end of the day. Bitcoin's value may or may not improve in the future because it has been on the market for a decade. Ethereum was created in 2015 and is now the second most popular crypto currency on the market. The value of Ethereum is nearly comparable to that of Bit coin. After a few years, it is doubtful whether bit coin will be available on the market. As a result, buyers seek forecasts in order to invest in the right crypto currency and profit from it. The user can use this forecast to anticipate the future of both crypto currency prices. To assess the value of crypto currencies, we used three forecasted analytic techniques, and we can compare the accuracy of the three algorithms to see which one is the best.

Marco Alberto Javarone, Gabriele Di Antonio, Gianni Valerio Vinci, Raffaele Cristodaro · 6 authors

Abstract The behaviour of Bitcoin owners is reflected in the structure and the number of bitcoin transactions encoded in the Blockchain. Likewise, the behaviour of Bitcoin traders is reflected in the formation of bullish and bearish trends in the crypto market. In light of these observations, we wonder if human behaviour underlies some relationship between the Blockchain and the crypto market. To address this question, we map the Blockchain to a spin-lattice problem, whose configurations form ordered and disordered patterns, representing the behaviour of Bitcoin owners. This novel approach allows us to obtain time series suitable to detect a causal relationship between the dynamics of the Blockchain and market trends of the Bitcoin and to find that disordered patterns in the Blockchain precede Bitcoin panic selling. Our results suggest that human behaviour underlying Blockchain evolution and the crypto market brings out a fascinating connection between disorder and panic in Bitcoin dynamics.

Serhii Kozlovskyi, Ярослав Петруненко, Hennadii Mazur, Віра Бутенко · 5 authors

The cryptocurrency market is not regulated, people and companies wishing to invest in cryptocurrency do not have the same protection as when investing in other assets. In the absence of information and regulatory laws, investors should decide if cryptocurrencies make sense for their financial goals and what kind of investment strategy to choose not to go bankrupt. The aim of the study is to determine the probability of “tail events” and to assess in this way the probability of bankruptcy when investing in cryptocurrency using the Monte Carlo method. The analysis is carried out on the period from September 1, 2014 up to July 1, 2022. Despite the fact that today there are more than 10,000 types of cryptocurrencies, Bitcoin was chosen to assess the probability of bankruptcy. The reason is that Bitcoin is the world’s first decentralized cryptocurrency and its data is stored in a long-term history, which allows testing a long-term investment strategy. Besides, Bitcoin has not gone through a period of persistent inflation that makes the result of testing a short-term investment strategy more reliable. To date, there are around 25 million Bitcoin holders, representing 42.2% of the crypto market. Almost all cryptocurrencies have been proven to follow Bitcoin. The probability of bankruptcy for a short-term cryptocurrency investment strategy is about 17%-23%. For a long-term cryptocurrency investment strategy, the probability of bankruptcy fluctuates from 13% to 16%. Contrary to popular belief, investors looking to avoid bankruptcy should prefer a long-term strategy. The best way for cryptocurrency investors to protect themselves from bankruptcy is to alternate long and short investment periods.

Ladislav Krištoufek, Elie Bouri

No abstract is available for this record.

Yiwen Gong, Mingtao Zhang, Xiaoyuan Zhang

As cryptocurrencies become the target of many investors, it is speculated that there may be a correlation between the trading prices of cryptocurrencies and other assets (e.g., TESLA and BITCOIN). On this basis, we try to build an arbitrage model among the TESLA, BITCOIN, and DOGECOIN to validate the feasibility by simulations using their trading data for 5 years. After conducting the Augmented Dickey-Fuller test, Co-integration test, etc., TESLA and BITCOIN are best correlated that co-integrated over a relatively long period. Within the range of co-integration, we construct the arbitrage model and design the transaction signals by setting a certain threshold. Subsequently, backtestings are carried out accordingly, where different spreads as trading thresholds lead to different results with large differences in returns. These results shed light on the decision on arbitrage investments for cryptocurrencies and other assets.

Fengyang Guo, Xun Xiao, Artur Hecker, Schahram Dustdar

IOTA blockchain system is lightweight without heavy proof-of-work mining phases, which is considered a promising service platform of Internet of Things applications. IOTA organizes ledger data in a directed acyclic graph (DAG), called Tangle, rather a chain structure as in traditional blockchains. With arriving messages, IOTA tangle grows in a special way, as multiple messages can be attached to the tangle at different locations in parallel. Hence, the network dynamics of an operational IOTA system would justify a thorough study, which is currently unexplored in the literature. In this article, we present the first theoretical modeling for the evolving IOTA tangle based on stochastic analysis. After analyzing snapshots of the real-world IOTA ledger data, our key finding suggests that IOTA tangle follows a rather atypical double Pareto Lognormal (dPLN) degree distribution. In contrast, typical power-law and exponential distributions do not accurately reflect the fact. For model parameter estimation, we further realize that using generic optimization solvers cannot yield quality fitting results. Thus, we design an alternative algorithm based on expectation-maximization (EM) framework. We evaluate the proposed model and fitting algorithm with official data provided by the IOTA Foundation. Quantitative comparisons confirm the fitting quality of our proposed model and algorithm. The whole analysis reveals a deeper understanding of the internal mechanism of the IOTA network.

Yuye Zhou

On February 24, 2022, Russia’s invasion of Ukraine marked a full-scale escalation of the Russian-Ukrainian conflict into war. The global economy and finance were affected by the Russian-Ukrainian conflict, which most obvious is that crude oil prices continued to rise rapidly. With the development of the times, cryptocurrencies are becoming more and more important and cannot be ignored. Cryptocurrency may serve as an effective alternative or balancing asset to cash, which may depreciate over time due to inflation. In addition to the real commodity market, the Russian-Ukrainian conflict would certainly have a certain impact on the cryptocurrency market. Bitcoin is the largest cryptocurrency and can represent the changes in the entire cryptocurrency market to a certain extent. This paper examines the dynamic impact of the Russian-Ukrainian conflict on Bitcoin returns and volatility. There are two main results in this paper: the increase in the futures crude oil price has a significant dynamic correlation with the Bitcoin yield, but this relationship is short-term and will disappear over time; the increase in futures crude oil prices will not lead to greater fluctuations in Bitcoin yields. This result can be generalized to the entire cryptocurrency market, which means the Russian-Ukrainian conflict would have a short-term impact on the entire cryptocurrency, but this effect won’t continue for the long-term. Also, this research shows that the cryptocurrency market is independent to some extent.

Alessandro Cremaschini, Antonio Punzo, Eliano Martellucci, Antonello Maruotti

This study provides an empirical analysis on the main univariate and multivariate stylized facts iin return series of the two of the largest cryptocurrencies, namely Ethereum and Bitcoin. A Markov-Switching Vector AutoRegression model is considered to further explore the dynamic relationships between cryptocurrencies and other financial assets. We estimate the presence of volatility clustering, a rapid decay of the autocorrelation function, an excess of kurtosis and multivariate little cross-correlation across the series, except for contemporaneous returns. The analysis covers the pandemic period and sheds lights on the behaviour of cryptocurrencies under unexpected extreme events.

An Pham Ngoc Nguyen, Tai Tan, Marija Bezbradica, Martin Crane

We analyze the correlation between different assets in the cryptocurrency market throughout different phases, specifically bearish and bullish periods. Taking advantage of a fine-grained dataset comprising 34 historical cryptocurrency price time series collected tick-by-tick on the HitBTC exchange, we observe the changes in interactions among these cryptocurrencies from two aspects: time and level of granularity. Moreover, the investment decisions of investors during turbulent times caused by the COVID-19 pandemic are assessed by looking at the cryptocurrency community structure using various community detection algorithms. We found that finer-grain time series describes clearer the correlations between cryptocurrencies. Notably, a noise and trend removal scheme is applied to the original correlations thanks to the theory of random matrices and the concept of Market Component, which has never been considered in existing studies in quantitative finance. To this end, we recognized that investment decisions of cryptocurrency traders vary between bearish and bullish markets. The results of our work can help scholars, especially investors, better understand the operation of the cryptocurrency market, thereby building up an appropriate investment strategy suitable to the prevailing certain economic situation.

Jan Kubal, Ladislav Krištoufek

No abstract is available for this record.

Samaira Tomer

The growth in information and communication technology has led to phenomenons in the financial sector as well. This primarily alludes to the introduction of cryptocurrencies, a decentralised medium of exchange, which provides an alternative to the centuries-old idea of physical money. There is a visible relationship between the principles of behavioural finance and the value/returns that these cryptocurrencies have. These currencies are not dependent on the behaviour of the financial markets and economy but instead on the supply and demand of the currency along with its popularity which is dependent purely on the individuals.

P. Chandra Sekhar, M Padmaja, Biswajit Sarangi, Aditya Aditya

Digital currency arose because of progress in financial technology and created opportunities for profitable cryptocurrency investment. The high instability of Bitcoin made cryptocurrency trading so worthwhile in the last few years. Investors are looking for a secure mechanism to forecast the cryptocurrency price fluctuations in the market that will fuel their investment strategies. The algorithms such as random forest, Bayesian neural network, or long-short-term-memory (LSTM) neural network analyze the price fluctuations of the cryptocurrencies through the historical data and attain high precision. This paper analyzed a few shortcomings of the LSTM network and explored the vital parameters to overcome them. This paper employed the XGBoost algorithm to predict cryptocurrency prices better and found a better mean value deviation error than LSTM.

Zezheng Tong, John W. Goodell, Dehua Shen

No abstract is available for this record.

Boxiang Jia, Dehua Shen, Wei Zhang

No abstract is available for this record.

Kubra Yildiz, Sefa Dedebek, Feyza Yıldırım Okay, Mehmet Ulvi Şimşek

Anomaly detection in the financial sector has a critical importance for financial markets, investors, and regulatory authorities. As financial environments change, real-time detection of anomalies becomes more difficult due to the increase in data speed and volume with increasing digitization. Recently, deep learning (DL) algorithms have been used as a promising approach to solving the anomaly detection problem. In this study, DL-based anomaly detection model in the financial sector is presented using various DL algorithms including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and one-dimensional Convolutional Neural Network (1dCNN). In addition, hyperparameter optimization is performed with the grid search method. These methods are applied to two popular financial data, Tesla's stock market and Ethereum cryptocurrency data sets. Accordingly, a comparative analysis is conducted on these data sets with DL algorithms. Experimental results clearly show that GRU algorithm has the highest prediction score in both data sets, while 1dCNN algorithm has the lowest prediction score. In addition, anomaly values are demonstrated graphically with GRU for both data sets.

Pavlos I. Zitis, Yiannis Contoyiannis, Stelios M. Potirakis

No abstract is available for this record.

Seong-Wan Park, Seungju Lee, Yunyoung Lee, Hyungjin Ko · 7 authors

In decentralized finance (Defi), market participants are allowed to have the right to manage their own funds as opposed to centralized finance (Cefi) with a central custodian, centralized exchanges (CEX). Most Defi projects provide their own service and simultaneously issue a unique token that can be traded in decentralized exchanges (DEX). However, the values of these tokens have rarely been studied. We confirm that the prices of tokens in the Defi market have a persistent tendency to move together. We also demonstrate that the correlation gradually increased from the Defi market’s inception and the price co-movement increased in a bear market, and conversely, decreased in a bull market. Specifically, we find a notable difference in the level of price co-movement between CEX cryptocurrencies and DEX tokens.

Jimmy E. Hilliard, Julie T.D. Ngo

We investigate Bitcoin pricing characteristics and find evidence of jumps and positive convenience yield. We develop a theoretical jump diffusion model for options on spots and use simulations to evaluate non-linear parameter estimates. Data from the Deribit exchange is used to compare the performance of the jump diffusion models with Practitioner Black–Scholes models. Using Diebold–Marino statistics and standard error metrics, we find that the jump diffusion models significantly outperform Practitioner Black–Scholes models. We conclude that Bitcoin behaves more like a commodity than a currency.

Z. John Zhang

No abstract is available for this record.

Müge SAĞLAM BEZGİN

Bu çalışmada, piyasa istikrarı ve yatırımcı ufkunu açıklayan, finansal zaman serilerinin normal dağılmadığını ve finansal zaman serilerinde kendine benzerlik özelliği olduğunu ifade eden fraktal piyasa hipotezinin iki gelişmekte olan, iki gelişmiş piyasada ve iki kripto varlıkta geçerliliğinin Hurst Üsteli- Yeniden ölçeklendirilmiş aralık (R/S) Analizi yöntemi aracılığıyla araştırılması amaçlanmıştır. MSCI sınıflamasına göre gelişmiş piyasalar olarak SP500 ve FTSE, gelişmekte olan piyasalar olarak Borsa İstanbul 100 ve Shanghai Endeksi incelemeye dahil edilmiştir. Kripto varlıklarda ise işlem hacmi en yüksek olan Bitcoin ve Ethereum değişkenleri incelemeye dahil edilmiştir. Çalışma bulgularına göre incelenen tüm endekslerde fraktal piyasa hipotezinin varlığı kabul edilirken, uzun hafızanın rolü ise değişmektedir. Tüm değişkenlerde Hurst üsteli değeri 0.5 değerinden yüksektir. Hurst üsteli sonuçlarına göre tüm değişkenlerde zaman serisinin kalıcı davranış gösterdiğine ilişkin hipotez kabul edilmiştir. Uzun hafızanın kalıcılığın en düşük olduğu değişken FTSE’dir. Gelişmekte olan borsalarda uzun hafıza ve kalıcılık gelişmiş borsalara göre daha yüksekken tüm değişkenler içerisinde uzun hafızanın en güçlü olduğu ve kalıcılığın en yüksek olduğu değişken ise Bitcoin’dir.

Carol Alexander

The implications for exchange-traded products and delta-hedging options

Noureddine Benlagha, Wael Hemrit

Purpose The present work endeavors to explore the potential nonlinear and asymmetric effects of supply fundamental properties of Bitcoin mining process (velocity, size and stock of Bitcoins, cost of production and mining revenue), DJIA, VIX, economic policy uncertainty and Google Trend on the price of Bitcoin (PB). Design/methodology/approach The authors apply the Nonlinear Autoregressive Distributed lag (NARDL) approach for the period from November 31, 2013 to December 30, 2020. Findings The asymmetric effects of inflation, the size of Bitcoin economy, reveal a positive impact on the PB in the short and long run. In the short run, Bitcoin price shows negative statistically significant sensitivity to positive (negative) changes in DJIA (VIX) index. In addition, Google Trends have an impact on Bitcoin prices indicating that the Bitcoin market is also driven by investors' sentiments. In the long run, negative policy uncertainty shocks increase the PB while in the short run, negative shocks decrease it. Originality/value The authors give credence to the best ways of understanding the existence of asymmetries in the link between the PB and a number of influential macro-finance variables to improve the appropriate asset allocation and portfolio management.