What drives the Bitcoin price? A factor augmented error correction mechanism investigation

This article aims to determine what drives the price of Bitcoin. To achieve this aim, a large set of data is analysed using VEC models augmented by factors representing unobservable economic forces. They have been obtained by means of principal component analysis. This method enables us to contribute to the existing literature on Bitcoin in two ways. First, we employ the dimension reduction technique to combine variables from several papers. Second, we estimate several unobservable economic concepts instead of utilizing proxy variables as is usually done. We find that the main factor driving the Bitcoin price is its popularity. Hence, our result not only confirms some previous findings but reinforces them by providing a better definition of popularity. Finally, we conclude that the Bitcoin price is not affected by supply and demand factors in the way that is natural for conventional currencies.     

Bitcoin for energy commodities before and after the December 2013 crash: diversifier,hedge or safe haven?

We study the relationship between Bitcoin and commodities by assessing the ability of Bitcoin to act as a diversifier, hedge, or safe haven against daily movements in commodities in general, and energy commodities in particular. We focus on energy commodities because energy, in the form of electricity, is an essential input in the Bitcoin production. For the entire period, results show that Bitcoin is a strong hedge and a safe-haven against movements in both commodity indices. We further examine whether that ability is also present for non-energy commodities and our analysis show insignificant results when energy commodities are excluded from the general commodity index. We also account for the December 2013 Bitcoin price crash and our results reveal that Bitcoin hedge and safe-haven properties against commodities and energy commodities are only present in the pre-crash period, whereas in the post-crash period Bitcoin is no more than a diversifier. In addition to uncovering the time-varying role of Bitcoin, we highlight the dissimilarity in the dynamic correlations between the extreme downward and extreme upward movements.     

Price clustering on the Shanghai Stock Exchange

We investigate price clustering of intraday trades and negotiated block trades on the Shanghai Stock Exchange (SSE) from 2003 to 2009. Prices of traded assets tend to cluster on certain final digits, such as 0 and 5. In Chinese culture, 8 is associated with good luck and 4 with death so these numbers may be attractive or avoided. We find that price clustering on the final digit of 0 is significantly higher during the morning call auction and early in the trading day. We find no evidence of price clustering for the digit 8, but there is a significant dearth of prices ending in the inauspicious number 4. Price clustering is significantly higher for negotiated block trades, for which about 28% end with 0. Multivariate analysis shows that price clustering is lower for more liquid firms, but higher for firms with higher return volatility, a higher price level, or when the market is volatile. Our evidence supports the costly negotiation hypothesis. Our results also support the attraction hypothesis in that we document significant price clustering at round numbers and even numbers even after controlling for factors that are associated with price uncertainty.     

Can volume predict Bitcoin returns and volatility? A quantiles-based approach

Prior studies on the price formation in the Bitcoin market consider the role of Bitcoin transactions at the conditional mean of the returns distribution. This study employs in contrast a non-parametric causality-in-quantiles test to analyse the causal relation between trading volume and Bitcoin returns and volatility, over the whole of their respective conditional distributions. The nonparametric characteristics of our test control for misspecification due to nonlinearity and structural breaks, two features of our data that cover 19th December 2011 to 25th April 2016. The causality-in-quantiles test reveals that volume can predict returns – except in Bitcoin bear and bull market regimes. This result highlights the importance of modelling nonlinearity and accounting for the tail behaviour when analysing causal relationships between Bitcoin returns and trading volume. We show, however, that volume cannot help predict the volatility of Bitcoin returns at any point of the conditional distribution.     

Spillovers between Bitcoin and other assets during bear and bull markets

This article contributes to the embryonic literature on the relations between Bitcoin and conventional investments by studying return and volatility spillovers between this largest cryptocurrency and four asset classes (equities, stocks, commodities, currencies and bonds) in bear and bull market conditions. We conducted empirical analyses based on a smooth transition VAR GARCH-in-mean model covering daily data from 19 July 2010 to 31 October 2017. We found significant evidence that Bitcoin returns are related quite closely to those of most of the other assets studies, particularly commodities, and therefore, the Bitcoin market is not isolated completely. The significance and sign of the spillovers exhibited some differences in the two market conditions and in the direction of the spillovers, with greater evidence that Bitcoin receives more volatility than it transmits. Our findings have implications for investors and fund managers who are considering Bitcoin as part of their investment strategies and for policymakers concerned about the vulnerability that Bitcoin represents to the stability of the global financial system.     

Bitcoin—A hype or digital gold? Global evidence

This study explores whether Bitcoin constitutes as a hedging instrument whilst seeking portfolio diversification opportunities among sustainable, conventional and Islamic asset classes since Bitcoin emerges as a distinct alternative investment and asset class across the world. We apply multivariate generalised autoregressive conditional heteroscedastic-dynamic conditional correlation and continuous wavelet transforms based on the recent data set ranging from August 18, 2011, to September 10, 2018. First, our findings show that Bitcoin returns are mean-reverting which implies that its value tends to come down to mean value in the long run and not completely crushed to zero irrespective of price changes suggesting Bitcoin as a sustainable asset class. Second, the time-invariant model shows that Bitcoin offers portfolio diversification opportunities with almost all equity indices, in particular, Dow Jones Islamic followed by FTSE 4 Good index. Finally, the time-variant analysis reconfirms that Bitcoin offers portfolio diversification benefits both in the short and long run. These findings carry meaningful policy considerations for fund managers and cross-country investors.     

The influence of Taiwan’s stock market on Bitcoin’s price under Taiwan’s monetary policy threshold

ABSTRACT

This study uses a smooth transition autoregressive model with exogenous variables (STARX) to investigate whether there is a nonlinear relationship between Bitcoin and Taiwan’s stock market taking into account Taiwan’s monetary policy threshold during 2 February 2012 to 31 August 2019. The statistical results show there is a threshold effect and confirm a nonlinear relationship between Taiwan’s stock market and Bitcoin, with variations over time and across Bitcoin and Taiwan’s stock market. Specifically, we find that Bitcoin responds asymmetrically to Taiwan’s stock market according to the threshold value. Furthermore, the return on the closing price of TAIEX with a lag of two periods under Taiwan’s monetary policy threshold has a nonlinear impact on the return on the closing price of Bitcoin.     

Can Bitcoin become money? The monetary rule problem

The growing literature on Bitcoin can be divided into two groups. One performs an economic analysis of Bitcoin focusing on its monetary characteristics. The other one takes a financial look at the price of Bitcoin. Interestingly, both of these groups have not given much more than passing comments to the problem of whether or not Bitcoin has the right monetary rule in order to become a well‐established currency. This paper argues that Bitcoin in particular, and cryptocurrencies in general, do not have a good monetary rule and that this shortcoming seriously limits its prospect of becoming widely used money.     

Modelling the dynamics of Bitcoin and Litecoin: GARCH versus stochastic volatility models

We examine and compare a large number of generalized autoregressive conditional heteroskedastic (GARCH) and stochastic volatility (SV) models using series of Bitcoin and Litecoin price returns to assess the model fit for dynamics of these cryptocurrency price returns series. The various models examined include the standard GARCH(1,1) and SV with an AR(1) log-volatility process, as well as more flexible models with jumps, volatility in mean, leverage effects, t-distributed and moving average innovations. We report that the best model for Bitcoin is SV-t while it is GARCH-t for Litecoin. Overall, the t-class of models performs better than other classes for both cryptocurrencies. For Bitcoin, the SV models consistently outperform the GARCH models and the same holds true for Litecoin in most cases. Finally, the comparison of GARCH models with GARCH-GJR models reveals that the leverage effect is not significant for cryptocurrencies, suggesting that these do not behave like stock prices.     

A high-frequency analysis of price resolution and pricing barriers in equities on the adoption of a new currency

We use ultra high frequency (trade by trade) data to demonstrate that equity price clustering and pricing predictability around psychologically important prices in Greece switches away from drachma-focused with the introduction of the euro, but does not immediately switch to euro-clustering. The change in trader price focus around the euro introduction addresses an open debate in the clustering literature on whether the presence of clustering is a bias related to the current prices or anchoring to past prices. Our findings of a decline in drachma clustering, but lack of switch to euro effects supports the case for clustering being a trading feature that is slow to transfer to new pricing regimes. A key advantage of the ultra high frequency dataset is we are also able to demonstrate the presence of psychological pricing barriers related to each currency that are not detectable in daily data.     

Bitcoin mining: converting computing power into cash flow

Bitcoin is the world’s leading cryptocurrency, with a market capitalization briefly exceeding $300 billion. This hints at Bitcoin’s amorphous nature: Is this a monetary or a corporate measure? Hard values become explicit in the processing of transactions and the digital mining of Bitcoins. Electricity is a primary input cost. Bitcoins earned are often used to circumvent local currency controls and acquire US dollars. For the period August 2010 to February 2018, we examine the financial components of Bitcoin mining revenues, their statistical contribution to daily changes, and to its variance. We provide empirical evidence that Bitcoin transaction processing is capacity constrained.     

On the predictive power of ARJI volatility forecasts for Bitcoin

Motivated by the recent literature on cryptocurrency volatility dynamics, this paper adopts the ARJI, GARCH, EGARCH, and CGARCH models to explore their capabilities to make out-of-sample volatility forecasts for Bitcoin returns over a daily horizon from 2013 to 2018. The empirical results indicate that the ARJI jump model can cope with the extreme price movements of Bitcoin, showing comparatively superior in-sample goodness-of-fit, as well as out-of-sample predictive performance. However, due to the excessive volatility swings on the cryptocurrency market, the realized volatility of Bitcoin prices is only marginally explained by the GARCH genre of employed models.     

Bitcoin price and its marginal cost of production: support for a fundamental value

This study back-tests a marginal cost of production model proposed to value the digital currency Bitcoin. Results from both conventional regression and vector autoregression (VAR) models show that the marginal cost of production plays an important role in explaining Bitcoin prices, challenging recent allegations that Bitcoins are essentially worthless. Even with markets pricing Bitcoin in the thousands of dollars each, the valuation model seems robust. The data show that a price bubble that began in the Fall of 2017 resolved itself in early 2018, converging with the marginal cost model. This suggests that while bubbles may appear in the Bitcoin market, prices will tend to this bound and not collapse to zero.     

Price Clustering and Investor Sentiment

Among the anomalous findings in the finance literature, perhaps the most persistent is the finding that security prices tend to cluster on round pricing increments. The author examines how investor sentiment influences the degree of price clustering. Both univariate and multivariate tests show a contemporaneous correlation between price clustering and investor sentiment. Recognizing the need to make stronger causal inferences, the author conducts 2 additional sets of tests. First, the author uses the technology bubble period as natural experiment and examine the price clustering of technology vis-à-vis nontechnology stocks. Results show that price clustering is markedly higher in tech stocks than in nontech stocks during this period of rising, sector-specific, investor sentiment. Second, the author estimates a vector autoregression process and examines the impulse responses of price clustering to exogenous shocks in investor sentiment. The results from these tests indicate that causation flows from sentiment to clustering instead of the other way around.     

Quantile spillovers and dependence between Bitcoin,equities and strategic commodities

This paper has two aims. We first examine the dynamic spillovers between Bitcoin and 12 developed equities, gold, and crude oil for different market conditions using a Bayesian Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with daily spot prices. Our econometric approach enables us to capture the left and right tails as well as the shoulders of the return distribution corresponding to volatility spillovers under the bear, normal, and bull market states among these financial assets. We quantify and trace the dependence and directional predictability from Bitcoin to other assets using the sample cross-quantilogram. Our key findings offer convincing evidence of time variation in the level of volatility. Spillovers between Bitcoin and other financial assets intensify during extreme global market conditions. Secondly, results from the cross-quantilogram indicate strong dependence and positive directional predictability between Bitcoin and most equities and crude oil when market returns are bullish. However, during the bearish market period, there is negative dependence and predictability from Bitcoin to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market only. This implies that Bitcoin can act as a hedge to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market. However, insignificant dependence and directional predictability from Bitcoin to the remaining assets indicate that Bitcoin may act as a safe-haven to these assets during bearish markets. Our findings hold important implications for both international investors and portfolio managers who consider Bitcoin as part of their portfolio diversification and other investment strategies.     

Cryptocurrencies and Cagan’s model of hyperinflation

The drivers of the prices of Bitcoin and Ethereum are studied within a framework based on Cagan’s model of hyperinflation. In the model, the prices of the cryptocurrencies are driven by stochastic adoption and velocity shocks as well as endogenous expectations of future prices. The model is estimated with data for prices, transaction volumes, and money supplies. A majority of price fluctuations in both currencies can be attributed to shocks in adoption, velocity shocks are much less important. The money demand sensitivity to expected price changes is estimated to be larger for Bitcoin than for Ethereum, and both have higher sensitivity than fiat currencies during episodes of hyperinflation.     

Evaluation by the Aumann and Serrano performance index and Sharpe ratio: Bitcoin performance

We compare Bitcoin performance based on the Aumann and Serrano performance index and Sharpe ratio assuming that asset returns follow the class of discrete normal mixture distributions. The Aumann and Serrano performance index can take into account higher moments of the underlying distribution of assets and is relevant for risk-averse investors. We evaluate Bitcoin performance based on the Aumann and Serrano index relative to the performance of other assets. Our evaluation shows that Bitcoin is rated highly by the Sharpe ratio but rated very poorly by the Aumann and Serrano index. We also find some stock assets can beat Bitcoin by the Sharpe ratio when an investment horizon is monthly.     

Cyclicality of commodity markets with respect to the U.S. economic policy uncertainty based on granger causality in quantiles

Given the importance of the U.S. in global commodity markets, the goal is to explore whether U.S. economic policy uncertainty impacts the price performance of certain commodities. The analysis uses the Granger causality in quantiles method that allows us to test whether there are different effects under different market conditions. The results document that economic uncertainty impacts the returns on the commodities considered, with the effects clustering around the tail of their conditional distribution. Robust evidence was obtained under alternative definitions of uncertainty.     

Fancy Bitcoin and conventional financial assets: Measuring market integration based on connectedness networks

We investigate the relationship between Bitcoin and conventional financial assets from a perspective on the connectedness of asset networks. We adopt the method of measuring connectedness proposed by Diebold and Yilmaz (2009, 2012, and 2014) in a VAR system to study the dynamic interdependence between returns in Bitcoin, stocks, oil, and gold. We find that the connectedness between bitcoin and conventional assets is weak. The separation of positive and negative returns in the Bitcoin market shows the existence of an asymmetric pattern of the spillover effects between Bitcoin and conventional assets. A rolling window analysis finds that although Bitcoin prices experience a rising link to other financial assets, the magnitude is proven to be moderate. However, connectedness via negative returns is much stronger than via positive ones and exhibits a clearly increasing trend in recent periods. Our results in application are generally robust to other popular cryptocurrencies, such as ETH and Ripple. The findings presented in this paper have important implications for financial market participants, policymakers, and researchers in light of projected increases in the adoption of Bitcoin, as well as the rapid development of cryptocurrency.     

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.