AleÅ¡ KozubÃk University of Žilina – Faculty of Management Science and Informatics – Department of the Mathematical Methods and Operations Research, Univerzitná 8215/1, 010 26 Žilina, Slovak Republic DOI: https://doi.org/10.31410/ITEMA.2018.507 ​ 2nd International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2018 – Graz, Austria, November 8, […]
OlaOluwa S. Yaya, Ephraim A Ogbonna, Olusanya E. Olubusoye
The present paper investigates persistence and dependence of Bitcoin on other popular alternative coins. We employ fractional integration approach in our analysis of persistence while a more recent fractional cointegration technique in VAR set-up, proposed by Johansen and co-authors is used to investigate dependency of the paired variables. Having segregated the series into periods before crash and those after the crash as determined by Bitcoin pricing, we obtain results of interests. Higher persistence of shocks is expected after the crash due to speculations in the mind of cryptocurrency traders, and more evidences of non-mean reversions, implying chances of further price fall in cryptocurrencies. Cointegration analysis between Bitcoin and alternative coin exists during both periods, with weak correlation observed mostly in the post-crash period. We hope the findings will serve as guide to investors in cryptocurrency.
With the popularity of cryptocurrency like bitcoins in recent years, the social circles have been confusing whether cryptocurrency is real money essentially. Lots of voices have clarified the question from the traditional view that regards the nature of money as commodity. However, historical evidences have proved that the traditional theory deviates from the real nature of money originating from debt and is not exactly true. State Theory of Money holds the debt-based opinion on the nature of money and regards the nature of money as the debt of state, which is allowed to be the payment of tax. Therefore based on this, the paper analyzes the debt nature of money and the characteristics of cryptocurrency like bitcoins, and draws the conclusion that cryptocurrency like bitcoins is not accepted by the state as the payment of tax, not the national debt, so not the currency.
This paper attempts to establish that some inherent features of the Bitcoin price can be exploited to produce better forecast results for stock prices. It does so by constructing predictive models for stock prices of G7 countries with symmetric and asymmetric prices of Bitcoin. The underlying statistical properties of Bitcoin prices such as persistence and conditional heteroscedasticity are captured in the estimation process using the Westerlund and Narayan (2015) estimator that allows for such effects in forecasting. There are two striking findings from the analysis. First, the results suggest that accounting for asymmetries is more likely to enhance the predictive power of Bitcoin in forecasting stock prices regardless of the data sample and forecast horizon. Secondly, the Bitcoin-based predictive model for stock prices, particularly the asymmetric variant, outperforms the Fractionally Integrated Autoregressive Moving Average (ARFIMA) model. While there are concerns as to whether the cryptocurrencies are veritable substitutes to the conventional financial assets, their close link with the developed stock exchanges such as those in the G7 countries suggests that they share some common characteristics such as news effects [asymmetries] which can be exploited when forecasting the behaviour of stock prices.
The aim of this paper is to analyze the demand of both traditional and new media of exchange – as cryptocurrencies and central bank digital currencies – proposing a novel specification of the demand for money. In this specification, the medium of payment (MOP) has three properties: the first two are the MOP’s standard functions as a medium of exchange and as a store of value, while the third is a novel function as a store of privacy (anonymity value). The proposed framework is tested using a laboratory experiment. Our results show that anonymity matters, but less of the other two properties; at the same time, the presence of anonymity increases the overall appeal of a MOP, particularly if the individuals are risk prone; given anonymity, the sacrifice ratio between liquidity risk and opportunity cost are relatively high.
The purpose of this research is to identify how effective the determinants of the price changes in cryptocurrencies are and if they are predictable. The study addresses several independent variables that are in our consideration which may impact the prices the most. To obtain the results, panel data has been used to run fixed effects models. Then I treated them as time series data to run dynamic, distributed lags, and first-differencing regression models. Important political shocks and instabilities have been analyzed and interpreted in this paper. In the light of our findings we were able to comment on the complex relation between cryptocurrency prices and socio-political situations throughout the time range. The results address that cryptocurrency price changes are not predictable. It is hard to say what does affect the most prices. Internet search trends seem to have an impact but at the end it has been found that the correlation is not strong. From an economist's viewpoint, investing in cryptocurrencies without analyzing price changes and news might be disastrous and we can call it basically gambling. Cryptocurrencies shouldn't be seen as a gambling medium and should be taken more seriously like an investment medium. In some specific occasions investing in cryptocurrencies may lead lucrative income.
The main aim of this paper is to examine interdependencies between prices of cryptocurrencies, with the special focus on Bitcoin. The analysis is conducted in two stages and results are compared between two consequent sub-periods. In order to analyze topological properties of cryptocurrency market, the Minimum-Spanning Tree technique is used. Results indicate that Bitcoin plays one of the most important roles in the cryptocurrency market, while other cryptocurrencies form clusters and such forming has a sensible economic interpretation. In the second stage, main cryptocurrencies from each of formed clusters are analyzed using the Vector Autoregression methodology. The results from VAR (1) indicate that demand shocks in Bitcoin price are not contagious to other cryptocurrencies, while some interdependencies within the formed clusters may be observed. Overall, results indicate that conclusions drawn from the analysis of Bitcoin shall not be generalized to the entire cryptocurrency market.
Julia Reynolds, Leopold SSgner, Martin Wagner, Dominik Wied
This paper applies recently developed procedures to monitor and date so-called "financial marketdislocations", defined as periods in which substantial deviations from arbitrage parities take place. In particular, we focus on deviations from the triangular arbitrage parity for exchange rate triplets from a cointegration perspective. Due to increasing attention on and importance of mispricing in the market for cryptocurrencies, we include the cryptocurrency Bitcoin in addition to fiat currencies. We do not find evidence for substantial deviations from the triangular arbitrage parity when only traditional fiat currencies are concerned, but document significant deviations from triangular arbitrage parities in the newer markets for Bitcoin. We confirm the importance of our results for portfolio strategies by showing that a currency portfolio that trades based on our detected break-points outperforms a simple buy-and-hold strategy.
Distributed ledger technologies replace central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows down cross-market trading and exposes arbitrageurs to price risk. We theoretically derive arbitrage bounds induced by settlement latency. Using Bitcoin orderbook and network data, we estimate average arbitrage bounds of 121 basis points, explaining 91% of the cross-market price differences, and demonstrate that asset flows chase arbitrage opportunities. Controlling for inventory holdings as a measure of trust in exchanges does not affect our main results. Blockchain-based settlement without trusted intermediation thus introduces a non-trivial friction that impedes arbitrage activity.
Usman Amjad, Tahseen Ahmed, Humera Tariq, Amir Hussain
Quantum computing has emerged as a new dimension with various applications in different fields like robotic, cryptography, uncertainty modeling etc. On the other hand, nature inspired techniques are playing vital role in solving complex problems through evolutionary approach. While evolutionary approaches are good to solve stochastic problems in unbounded search space, predicting uncertain and ambiguous problems in real life is of immense importance. With improved forecasting accuracy many unforeseen events can be managed well. In this paper a novel algorithm for Fuzzy Time Series (FTS) prediction by using Quantum concepts is proposed in this paper. Quantum Evolutionary Algorithm (QEA) is used along with fuzzy logic for prediction of time series data. QEA is applied on interval lengths for finding out optimized lengths of intervals producing best forecasting accuracy. The algorithm is applied for forecasting Taiwan Futures Exchange (TIAFEX) index as well as for Bitcoin crypto currency time series data as a new approach. Model results were compared with many preceding algorithms.