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2,335 results · page 90 of 98
Paraskevi Katsiampa
No abstract is available for this record.
Eduard Baumöhl
This paper aims to elucidate the connectedness between major forex currencies and cryptocurrencies using the quantile cross-spectral approach recently proposed by Baruník and Kley (2015). The sample covers six forex currencies and six cryptocurrencies over the period of 1 September 2015 to 29 December 2017. Compared with the results obtained from standard correlations and detrended moving-average cross-correlation analysis (DMCA), the quantile cross-spectral approach provides richer information on the dependence structure across different quantiles and frequencies. The most interesting result is that the intra-group dependencies are positive in the lower extreme quantiles, while inter-group dependencies are negative. This result holds in both the short- and long-term perspectives. Thus, it is worth diversifying between these two currency groups.
Christian Conrad, Anessa Custovic, Éric Ghysels
We use the GARCH-MIDAS model to extract the long- and short-term volatility components of cryptocurrencies. As potential drivers of Bitcoin volatility, we consider measures of volatility and risk in the US stock market as well as a measure of global economic activity. We find that S&P 500 realized volatility has a negative and highly significant effect on long-term Bitcoin volatility. The finding is atypical for volatility co-movements across financial markets. Moreover, we find that the S&P 500 volatility risk premium has a significantly positive effect on long-term Bitcoin volatility. Finally, we find a strong positive association between the Baltic dry index and long-term Bitcoin volatility. This result shows that Bitcoin volatility is closely linked to global economic activity. Overall, our findings can be used to construct improved forecasts of long-term Bitcoin volatility.
Alexander Brauneis, Roland Mestel
No abstract is available for this record.
Tao Li, Donghwa Shin, Baolian Wang
Abstract We document numerous occurrences of pump-and-dump schemes (P&Ds) targeting cryptocurrencies, which tend to trigger short-term episodes that feature dramatic increases in prices, volume, and volatility, followed by quick reversals. The evidence we document, including price run-ups before P&Ds start, suggests wealth transfers from outsiders to insiders. Our findings based on wallet-level data are consistent with the reasoning that gambling preferences, overconfidence, and naïve reinforcement learning help explain P&D participation. Finally, exploiting two natural experiments in which exchanges altered P&D policies, we find evidence consistent with the idea that P&Ds contribute to reduced cryptocurrency liquidity and lower prices.
David Vidal-Tomás, Ana M. Ibáñez, José Emilio Farinós Viñas
No abstract is available for this record.
Laura Alessandretti, Abeer ElBahrawy, Luca Maria Aiello, Andrea Baronchelli
Machine learning and AI-assisted trading have attracted growing interest for the past few years. Here, we use this approach to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. We analyse daily data for $1,681$ cryptocurrencies for the period between Nov. 2015 and Apr. 2018. We show that simple trading strategies assisted by state-of-the-art machine learning algorithms outperform standard benchmarks. Our results show that nontrivial, but ultimately simple, algorithmic mechanisms can help anticipate the short-term evolution of the cryptocurrency market.
Paraskevi Katsiampa, Shaen Corbet, Brian M. Lucey
No abstract is available for this record.
Yukun Liu, Aleh Tsyvinski
Abstract We establish that cryptocurrency returns are driven and can be predicted by factors that are specific to cryptocurrency markets. Cryptocurrency returns are exposed to cryptocurrency network factors but not cryptocurrency production factors. We construct the network factors to capture the user adoption of cryptocurrencies and the production factors to proxy for the costs of cryptocurrency production. Moreover, there is a strong time-series momentum effect, and proxies for investor attention strongly forecast future cryptocurrency returns.
Nicola Borri
No abstract is available for this record.
Dirk G. Baur, Thomas Dimpfl
No abstract is available for this record.
Igor Makarov, Antoinette Schoar
No abstract is available for this record.
Sven Thies, Péter Molnár
No abstract is available for this record.
Carlos Trucíos
No abstract is available for this record.
Thomas Walther, Tony Klein, Elie Bouri
We apply the GARCH-MIDAS framework to forecast the daily, weekly, and monthly volatility of five highly capitalized Cryptocurrencies (Bitcoin, Etherium, Litecoin, Ripple, and Stellar) as well as the Cryptocurrency index CRIX. Based on the prediction quality, we determine the most important exogenous drivers of volatility in Cryptocurrency markets. We find that the Global Real Economic Activity outperforms all other economic and financial drivers under investigation. We also show that the Global Real Economic Activity provides superior volatility predictions for both, bull and bear markets. In addition, the average forecast combination results in low loss functions. This indicates that the information content of exogenous factors is time-varying and the model averaging approach diversifies the impact of single drivers.
Wang Chun Wei
In recent years, Tether issuances (or 'grants') have increased significantly, which correlated broadly with a significant rise in Bitcoin valuation. This paper examines the impact of cryptocurrency issuances on subsequent cryptocurrency returns. It is argued that as Tether is the undisputed 'stable coin', the minting of new Tether acts similarly to monetary expansion in cryptocurrency markets, inflating the prices of Bitcoin. We construct a VAR model and show contrary to investor expectations, Tether issuances do not impact subsequent Bitcoin returns, however, they do impact traded volumes. We also document an increase in Tether trading following a subsequent decrease in Bitcoin returns. This illustrates investor preferences for lower volatility crypto-assets in periods following negative Bitcoin returns.
Efthymia Symitsi, Konstantinos Chalvatzis
No abstract is available for this record.
Emiliano Pagnotta, Andrea Buraschi
We address the valuation of bitcoins and other blockchain tokens in a new type of production economy: a decentralized financial network (DN). An identifying property of these assets is that contributors to the DN trust (miners) receive units of the same asset used by consumers of DN services. Therefore, the overall production (hashrate) and the bitcoin price are jointly determined. We characterize the demand for bitcoins and the supply of hashrate and show that the equilibrium price is obtained by solving a fixed-point problem and study its determinants. Price-hashrate “spirals” amplify demand and supply shocks.
Halvor Aarhus Aalborg, Péter Molnár, Jon Erik de Vries
No abstract is available for this record.
Bruno Biais, Christophe Bisière, Matthieu Bouvard, Catherine Casamatta · 5 authors
ABSTRACT We offer a general equilibrium analysis of cryptocurrency pricing. The fundamental value of the cryptocurrency is its stream of net transactional benefits, which depend on its future prices. This implies that, in addition to fundamentals, equilibrium prices reflect sunspots. This in turn implies multiple equilibria and extrinsic volatility, that is, cryptocurrency prices fluctuate even when fundamentals are constant. To match our model to the data, we construct indices measuring the net transactional benefits of Bitcoin. In our calibration, part of the variations in Bitcoin returns reflects changes in net transactional benefits, but a larger share reflects extrinsic volatility.
Shaen Corbet, Brian M. Lucey, Maurice Peat, Samuel A. Vigne
No abstract is available for this record.
Lee A. Smales, Dirk G. Baur
No abstract is available for this record.
Andrew Urquhart
No abstract is available for this record.
Andrew Urquhart, Hanxiong Zhang
No abstract is available for this record.