Blockchain Papers

Višnja Jurić, Vanja Šimičević, Domagoj Kajba

No abstract is available for this record.

Jun Deng, Huifeng Pan, Shuyu Zhang, Bin Zou

No abstract is available for this record.

Frida Gustafsson, Elias Bengtsson

No abstract is available for this record.

Andrejs Cekuls, Maximilian-Benedikt Koehn

The market of virtual currencies, called cryptocurrency, has grown immensely since 2008 in terms of market
\ncapitalisation and the numbers of new currencies. Bitcoin is one of the most famous cryptocurrency with an estimated
\nmarket capitalisation of nearly $ 69 billion. The fact that Bitcoin prices have fallen about 70% from their peak value and
\nmost indices were down double-digit year to date (2018) with a high daily volatility create the appearance that there has
\nto be a correlation.
\nThe purpose of this paper is to investigate the contagion effect between Bitcoin prices and the leading American,
\nEuropean and Asian equity markets using the dynamic conditional correlation (DCC) model proposed by Engle and
\nSheppard (2001).
\nContagion is defined in this context as the statistical break in the computed DCCs as measured by the shifts in their
\nmeans and medians. Even it is astonishing that the contagion is lower during price bubbles, the main finding indicates the
\npresence of contagion in the different indices among the three continents and proves the presence of structural changes
\nduring the Bitcoin bubble. Moreover, the analysis shows that specific market indices are more correlated with the Bitcoin
\nprice than others.

Jackie Johnson

No abstract is available for this record.

Ruozhou Liu, Shanfeng Wan

No abstract is available for this record.

Usman W. Chohan

No abstract is available for this record.

Carol Alexander, Arben Imeraj

No abstract is available for this record.

Kiril Desev, Stanimir Kabaivanov, Desislav Desevn, Kiril Desev · 6 authors

This paper analyses the efficiency of cryptocurrency markets by applying econometric models to different short-term investment horizons. A number of experiments are carried out to demonstrate that small training sets can still be used to build efficient and useful forecasts, which in turn can be transformed into straight-forward investment strategies. It also compares the application of selected models on cryptocurrency and mature stock markets. The forecasting accuracy of the models is explored using different error metrics and different horizons. The results suggest that the variation of the error estimates doesn’t appear to be tightly related to the maturity of the markets, but rather depends on the intrinsic characteristics of the analyzed time series.

Klaus Grobys, Niranjan Sapkota

No abstract is available for this record.

Aifan Ling, Zhikai Zhu

No abstract is available for this record.

Julian Barreiro‐Gomez, Hamidou Tembiné

This paper studies the blockchain cryptographic tokens by means of mean-field-type game theory. It introduces the variance-aware utility function per decision-maker to capture the risk of cryptographic tokens associated with the uncertainties of technology adoption, network security, regulatory legislation, and market volatility. We establish a relationship between the network characteristics, token price, number of token holders, and token supply. Both in-chain diversification and cross-chain diversification among tokens are examined by using a mean-variance approach. The results suggest that the number of tokens in circulation needs to be adjusted in order to capture risk-awareness and self-regulatory behavior in blockchain token economics. The Sharpe and Modigliani ratios for cryptographic tokens are revisited.

Gianna Figà‐Talamanca, Sergio M. Focardi, Marco Patacca

No abstract is available for this record.

Takahiro Hattori

After the seminal work of Liu et al. (2015) finds that the realized volatility (RV) using 5-minute intervals performs well, economists tend to use this simple measure in applications. Existing literature in the cryptocurrency already relies on 5-minute RV, but no paper has evaluated whether 5-minute RV performs well compared with other realized measures. Following Liu et al. (2015), we show that the 5-minute RV of Bitcoin performs well compared to other realized measures, meaning that this result justifies the existing literature that already uses this simple measure. This paper also indicates that realized measures with longer intervals such as 120-minute RV could provide inaccurate estimates.

Hui-Pei Cheng, Kuang‐Chieh Yen

No abstract is available for this record.

Rebecca Abraham, Zhi Tao

Cryptocurrencies are virtual currencies employed in blockchain transactions. They are particularly worthy of theoretical examination, given the limited academic literature on the subject. This paper constructs valuation models of bitcoin and altcoins, both as single investments and components of mutliple-asset portfolios. As single investments, cryptocurrencies are valued at the confluence of Legendre utility functions, with Esscher transformed Geometric Levy pricing processes. As part of portfolios, cryptocurrencies are contained in traditional Markowitz portfolios which are varied by increasing the proportion of the riskless asset, shorting the risky asset, or adding currency options. Theoretical formulations show that Markowitz models combined with bitcoin, located on the Capital Market Line (which we term CML portfolios), have low returns, mainly due to the presence of the riskless asset. Such portfolios are appropriately suited to the investment goals of risk-averse traders, while overlooking the preferences of risk-takers. To satisfy less risk-averse investors, we propose a high-return portfolio with 9 asset choices, consisting of risky assets, cryptocurrencies, US dollars, soybean futures, Treasury bond futures, oil futures, currency options on the US dollar, currency options on the Mexican peso, and technology, or biotechnology stocks. Laplace transforms are employed to suppress volatility, skewness, or kurtosis of returns, which empirical studies have found to contribute to tail risk contained in outliers in fat-tailed distributions.

Hans Gersbach

We suggest that flexible majority rules for currency issuance decisions foster the stability of a cryptocurrency. With flexible majority rules, the voteshare needed to approve a particular currency issuance growth is increasing with this growth rate. By choosing suitable parameters for these flexible majority rules, we show that optimal growth rates can be achieved in simple settings. Moreover, with flexible majority rules, changes in the composition of growth-friendly and growth-adverse agents only have a comparatively moderate impact on growth rates, and extreme growth rates are avoided. Finally, we show that optimal money growth rates are realized if agents entering financial contracts anticipate ensuing inflation rates determined by these flexible majority rules.

Rajendra Kulkarni

No abstract is available for this record.

Bernd Süßmuth

No abstract is available for this record.

Andy Cheng

One of the important tasks of every multi-asset portfolio managers is to assess how different asset classes interact with each other. Historical findings indicate that tradition risk asset classes exhibited various degrees of correlation, be they positive or negative, among each other. With the raise of crypto assets, such as bitcoin, it appears that crypto assets have gradually been considering as new investment class, at least from institutional aspect. This study reveals that the correlation of the digital currency with the longest price history, bitcoin, with other traditional assets is close to zero. Thus, from diversification point of view, this makes cryptocurrencies or bitcoin a perfectly uncorrelated asset which would benefit almost any portfolio. Further study is performed to investigate the cointegration relations among bitcoin and other asset classes. It is found that the spreads between bitcoin and some major tradition risk asset classes exist a mean reversion phenomenon. This enables asset managers to develop quantitative approaches for active management strategies. Models of cointegrated time series are common place in the literature and application in financial series. Correlation and cointegration are time series modelling techniques that have applied to financial markets. They are related but with different concepts. Correlation indicates co-movements in returns which is a short run measure requires frequent rebalancing to minimize losses, while cointegration measures long run tandem movements in prices to ensure long term performance for achieving returns. Two pairs of asset prices are found to have a common stochastic trend with stationary cointegrating vector, they are in theory considered for cointegration. This stochastic process displays a mean reversion in long run. If there exists a divergence in spread due to temporary shocks, one can expects to profit from performing pairs trading strategy by creating a short position on the outperforming one, at the same time with a long position on the underperforming one. In this study, trading signal would be generated for our pairs trading with bitcoin. Largely, our results empirically support over various asset classes during the period of estimation.

Shaen Corbet, Charles Larkin, Brian M. Lucey, Andrew Meegan · 5 authors

No abstract is available for this record.

OlaOluwa S. Yaya, Ephraim A Ogbonna

This present paper investigates day-of-the-week effect in some notable cryptocurrency in terms of pricing and market capitalizations. We applied fractional integration regression approach with dummies. We found non-significance of day-of-the-week effect in returns, while there is possible evidence of Monday and Friday effects in volatility of Bitcoin only. Non-significance of day-of-the-week effect in returns of Bitcoin and some other cryptocurrencies further support market efficiency of these markets.

Yu-Min Lian, Chi-Hung Cheng, Shih-Hsun Lin, Jui-Hsuan Lin

In this study, we make use of both the specific method of Monte Carlo simulation and the spot-futures parity with the cost of carry to establish a dynamic price model of Bitcoin futures and to conduct the appraisals and numerical analyses. More specifically, the electricity fees and equipment costs are taken into account and the proposed model is thereby built. Numerical results show that various cost factors have significant effects on the Bitcoin futures price. We employ Monte Carlo simulation to approximate the Bitcoin futures price and we use Python to program the computations.

Timothy Peterson

No abstract is available for this record.