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1,505 results · page 26 of 63
Junyi Hu, Anthony Lee Zhang
No abstract is available for this record.
Almir Alihodžić
The main goal of this research is to evaluate the returns and risks of the following types of assets: Bitcoin, EUR Stoxx 50, gold, bonds: government bonds ICE Bof A 1-10 Year excluding Italy and Greece and the corporate bond index ICEB of A 1-10 Year AA. The paper tested a total of ten portfolios according to different scenarios for digital and financial assets. Also, in the paper, greater measures of risk and return were calculated with the aim of forming an optimal portfolio with minimal risk. The results of this research revealed that the correlation between Bitcoin and other forms of financial assets is generally low and negative, which can be a good instrument for portfolio diversification, and positively affect portfolio performance. Also, the results of this study showed that in terms of volatility and return measure of a total of ten portfolios, the second portfolio (whose structure consists of Bitcoin, Euro Stoxx 50, gold, government bonds ICE Bof A 1-10 Year - excluding Italy and Greece and the corporate index bond ICEBof A 1-10 Year AA) is the most optimal portfolio. The findings of this research can serve in risk and loss assessments of portfolio managers, investors, and regulators.
Mohammadhossein Lashkaripour
No abstract is available for this record.
Kassi Assamoi, Adelphe Ekponon, Zihan Guo
No abstract is available for this record.
Viviane de Senna, Adriano Mendonça Souza
ABSTRACT Cryptocurrencies are assets with transactions managed by new methods compared to traditional transactions mediated by Stock Exchanges. The insertion of these assets can change the economic system. The objective of the study is to analyze a set of articles published in international databases of scientific content on cryptocurrencies and the relations with the Stock Exchanges to understand the evolution of the theme over time. The consultation was carried out in the Scopus and Web of Science databases, where 196 articles were analyzed, these indicated learning algorithms, electronic trading, financial and digital markets thematic evolution. The main studies focused on investigating the behavior of cryptocurrencies in the face of market variables, cryptocurrencies as a safe haven or diversification, analysis of prices and the impact of emotional value on cryptocurrencies. The most relevant articles, the citations and co-citations network of these, provided insights into not yet known literature, such authors are Baur et al., 2018; Ji et al., 2020; Peng et al., 2018; Symitsi & Chalvatzis, 2019; Urquhart, 2017.
Niek Deprez, Michael Frömmel
No abstract is available for this record.
G H Gouri, Vineetha Das
No abstract is available for this record.
Sita Kedvarin, Kanis Saengchote
No abstract is available for this record.
Ilias Filippou, My T. Nguyen, Ganesh Viswanath-Natraj
No abstract is available for this record.
Kristof Lommers, Jack Kim, Mohamed Baioumy
No abstract is available for this record.
Erdinç Akyıldırım, Ahmet Faruk Aysan, Oğuzhan Çepni, Shaen Corbet
No abstract is available for this record.
Peter J. Phillips, Gabriela Pohl
No abstract is available for this record.
Sean Grover
Exchange-traded funds (ETFs) investing in bitcoin futures contracts first listed for trading in the fall of 2021. This research evaluates the extent to which the returns of bitcoin futures and bitcoin correspond to determine if bitcoin futures provide an effective proxy for a direct bitcoin investment. A no-arbitrage framework for bitcoin futures is established, which provides the basis for the empirical analyses that follow. The empirical analyses of returns correspondence between bitcoin futures and bitcoin use daily and monthly returns to estimate single-factor asset pricing regressions, finding coefficients of expected magnitude and that bitcoin returns explain over 97% of the variation in bitcoin futures returns. This research also estimates two-factor asset pricing regressions that include a novel excess carry term. The two-factor regressions find statistically significant excess carry term coefficients and over 99% explained variation. Finding strong evidence that the returns of bitcoin futures and bitcoin closely correspond, this research concludes that bitcoin futures provide an effective proxy for a direct bitcoin investment.
Olga Klein, Roman Kozhan, Ganesh Viswanath-Natraj, Junxuan Wang
No abstract is available for this record.
Sinda Hadhri
No abstract is available for this record.
David Ardia, Keven Bluteau
We examine the influence of Twitter promotion on cryptocurrency pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. These findings shed light on the pivotal role of Twitter promotion in cryptocurrency manipulation, offering valuable insights into participant behavior and market dynamics.
Basile Caparros, Amit Chaudhary, Olga Klein
Liquidity providers (LPs) on decentralized exchanges (DEXs) can protect themselves from adverse selection risk by updating their positions more frequently. However, repositioning is costly, because LPs have to pay gas fees for each update. We analyze the causal relation between repositioning and liquidity concentration around the market price, using the entry of blockchain scaling solutions, Arbitrum and Polygon, as our instruments. Lower gas fees on scaling solutions allow LPs to update more frequently than on Ethereum. Our results demonstrate that higher repositioning intensity and precision lead to greater liquidity concentration, which benefits small trades by reducing their slippage.
Mykola Pinchuk
This paper examines the response of major cryptocurrencies to macroeconomic news announcements (MNA). While other cryptocurrencies exhibit no reaction to major MNA, Bitcoin responds negatively to inflation surprise. Price of Bitcoin decreases by 24 bps in response to a 1 standard deviation inflationary surprise. This reaction is inconsistent with widely-held beliefs of practitioners that Bitcoin can hedge inflation. I do not find support for the hypothesis that the negative response of Bitcoin to inflation is due to its negative exposure to interest rates. Instead, I find support for the hypothesis that Bitcoin is strongly affected by the shift in consumption-savings decisions, driven by the rise in inflation. Consistent with this view, Bitcoin has negative exposure to a proxy for the consumption-savings ratio.
Antzelos Kyriazis, Iason Ofeidis, Georgios Palaiokrassas, Leandros Tassiulas
This paper studies the effects of unexpected changes in US monetary policy on digital asset returns. We use event study regressions and find that monetary policy surprises negatively affect BTC and ETH, the two largest digital assets, but do not significantly affect the rest of the market. Second, we use high-frequency price data to examine the effect of the FOMC statements release and Minutes release on the prices of the assets with the higher collateral usage on the Ethereum Blockchain Decentralized Finance (DeFi) ecosystem. The FOMC statement release strongly affects the volatility of digital asset returns, while the effect of the Minutes release is weaker. The volatility effect strengthened after December 2021, when the Federal Reserve changed its policy to fight inflation. We also show that some borrowing interest rates in the Ethereum DeFi ecosystem are affected positively by unexpected changes in monetary policy. In contrast, the debt outstanding and the total value locked are negatively affected. Finally, we utilize a local Ethereum Blockchain node to record the activity history of primary DeFi functions, such as depositing, borrowing, and liquidating, and study how these are influenced by the FOMC announcements over time.
Zhenzhen Fan, Feng Jiao, Lei Lü, Xin Tong
No abstract is available for this record.
Paola Di Casola, Maurizio Michael Habib, David Tercero‐Lucas
No abstract is available for this record.
Ralitsa Petkova
No abstract is available for this record.
Chiara Lesa, Ronald Hochreiter
Pair trading is a strategy which relies on betting on the relative mispricing of the spread between two securities which share a long-term relationship. These strategies have shown to perform well with equities, however not much research has been conducted in the field of cryptocurrencies, even though this asset class has shown characteristics suggesting suitability for pair trading. The Distance Methods and Cointegration Method are applied to a set of cryptocurrencies at formation and trading periods of daily and hourly data. It is shown that the frequency of the selection period does not influence the pairs selected. Cointegration-selected pairs generally outperforms Distance selected pairs. When trading frequency is analysed, intraday trading is more profitable, but not when using a stop-loss. Cointegration over-performs Distance, as the cost of the latter selection are increased by the higher number of trades.
Federico P. Cortese, Petter N. Kolm, Erik Lindström
Abstract We apply the statistical sparse jump model, a recently developed, interpretable and robust regime-switching model, to infer key features that drive the return dynamics of the largest cryptocurrencies. The algorithm jointly performs feature selection, parameter estimation, and state classification. Our large set of candidate features are based on cryptocurrency, sentiment and financial market-based time series that have been identified in the emerging literature to affect cryptocurrency returns, while others are new. In our empirical work, we demonstrate that a three-state model best describes the dynamics of cryptocurrency returns. The states have natural market-based interpretations as they correspond to bull, neutral, and bear market regimes, respectively. Using the data-driven feature selection methodology, we are able to determine which features are important and which ones are not. In particular, out of the set of candidate features, we show that first moments of returns, features representing trends and reversal signals, market activity and public attention are key drivers of crypto market dynamics.