The development of Bitcoin futures : exploring the interactions between cryptocurrency derivatives

We utilise a high-frequency analysis to investigate the period surrounding the establishment of two new futures contracts based on the performance of Bitcoin. Our analysis shows that there have been significant pricing effects sourced from both fraudulent and regulatory unease within the industry. While analysing breakpoints in efficiency, we verify the view that Bitcoin futures dominate price discovery relative to spot markets. However, we add to this research by finding that CBOE futures are found to be the leading source of informational flow when compared directly to their CME equivalent

Information demand and cryptocurrency market activity

This paper studies the relationship between information demand measured by Google search volume index, price returns, and trading volume for five major cryptocurrencies. We find that past information demand flows significantly influence the volume of all cryptocurrencies except for Litecoin. Moreover, trading volumes are found to Granger cause the information demand flows of Bitcoin, Ripple, and Litecoin, while previous day’s returns significantly influence the information demand flows of all the altcoins

Modelling UK house prices with structural breaks and conditional variance analysis

This paper differs from previous research by examining the existence of structural breaks in the UK regional house prices as well as in the prices of the different property types (flats, terraced, detached and semi-detached houses) in the UK as a whole, motivated by the uncertainty in the UK housing market and various financial events that may lead to structural changes within the housing market. Our paper enhances the conventional unit root tests by allowing for structural breaks, while including structural break tests strengthens our analysis. Our empirical results support the existence of structural breaks in the mean equation in seven out of thirteen regions of the UK as well as in three out of four property types, and in the variance equation in six regions and three property types. In addition, using a multivariate GARCH approach we examine both the behaviour of variances and covariances of the house price returns over time. Our results have significant implications for appropriate economic policy selection and investment management

Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era

This chapter surveys the state-of-the-art in forecasting cryptocurrency value by Sentiment Analysis. Key compounding perspectives of current challenges are addressed, including blockchains, data collection, annotation, and filtering, and sentiment analysis metrics using data streams and cloud platforms. We have explored the domain based on this problem-solving metric perspective, i.e., as technical analysis, forecasting, and estimation using a standardized ledger-based technology. The envisioned tools based on forecasting are then suggested, i.e., ranking Initial Coin Offering (ICO) values for incoming cryptocurrencies, trading strategies employing the new Sentiment Analysis metrics, and risk aversion in cryptocurrencies trading through a multi-objective portfolio selection. Our perspective is rationalized on the perspective on elastic demand of computational resources for cloud infrastructures

An empirical analysis of the Scottish housing market by property type

This paper studies house price dynamics of the different property types in Scotland. We find evidence of i) breakpoints around the recent financial crisis in three property types (flats, terraced, semi-detached) and in the average house prices, ii) volatility clustering in the detached house prices, with the CGARCH being the optimal volatility model, iii) negative impact of the unemployment and interest rates on house prices irrespective of the property type and positive effect of the CPI in the prices of the detached, terraced and average houses. Our results have significant implications for appropriate economic policy selection and investment management

High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis

In this paper, we analyse co-movements and correlations between Bitcoin and thirty-one of the most-tradable crypto assets using high-frequency data for the period from January 2019 to December 2020. We apply the Diagonal-BEKK model to data from the pre-COVID and COVID-19 periods, and identify significant changes in patterns of co-movements and correlations during the pandemic period. We also employ the Minimum Spanning Tree (MST) and Planar Maximally Filtered Graph (PMFG) methods to study the changes of the crypto asset network structure after the COVID-19 outbreak. While the influential role of Bitcoin in the digital asset ecosystem has been confirmed, our novel findings reveal that due to recent developments in the blockchain ecosystem, crypto assets that can be categorised as dApps and protocols have become more attractive to investors than pure cryptocurrencies