Alla Petukhina, Simon Trimborn, Wolfgang Karl HĂ€rdle, Hermann Elendner
Cryptocurrencies (CCs) have risen rapidly in market capitalization over the past years. Despite striking volatility, their high average returns and low correlations have established CCs as alternative investment assets for portfolio and risk management. We investigate the benefits of adding CCs to well-diversified portfolios of conventional financial assets for different types of investors, including risk-averse, return-maximizing and diversification-seeking investors who may trade at different frequencies, namely, daily, weekly or monthly. We calculate out-of-sample performance and diversification benefits for the most popular portfolio-construction rules, including mean-variance optimization, risk-parity, and maximum-diversification strategies, as well as combined strategies. Our results demonstrate that CCs can improve the risk-return profile of portfolios, but their benefit depends on investor objectives. In particular, diversification strategies (maximizing the portfolio diversification index or equating risk contributions) draw appreciably on CCs and show, in line with spanning tests, CCs to be non-redundant extensions of the investment universe. However, when we introduce liquidity constraints via the LIBRO method to account for illiquidity of many CCs, out-of-sample performance drops considerably, while the diversification benefits persist. We conclude that the utility of CC investments strongly depends on investor characteristics.
This paper investigates volatility spillovers among six competitor Cryptocurrencies from August 8, 2015 to September 01, 2019. A Generalized VAR framework is used to measure time varying spillovers index. Results provide evidence of (i) a rise in volatility spillovers transmitted among monitored Cryptocurrencies since the second quarter of 2017. (ii) Ethereum acts as the major contributor on spillovers index, contrary to Ripple that presents the main recipient of spillovers. (iii) the pairwise (Monero-Ripple) and (Bitcoin-Ethereum) present a low connectedness level driving consequently beneficial diversification opportunities for cryptocurrency investors.
Abstract This study investigates how twelve cryptocurrencies with large capitalization get influenced by the three cryptocurrencies with the largest market capitalization (Bitcoin, Ethereum, and Ripple). Twenty alternative specifications of ARCH, GARCH as well as DCC-GARCH are employed. Daily data covers the period from 1 January 1 2018 to 16 September 2018, representing the intense bearish cryptocurrency market. Empirical outcomes reveal that volatility among digital currencies is not best described by the same specification but varies according to the currency. It is evident that most cryptocurrencies have a positive relationship with Bitcoin, Ethereum and Ripple, therefore, there is no great possibility of hedging for crypto-currency portfolio managers and investors in distressed times.
We empirically examine the initial returns of Initial Coin Offerings (ICOs) and show that ICO underpricing is enormous, which implies that cryptocurrency markets are inefficient. Moreover, we find that having a short offering phase, not holding a presale, a precisely written whitepaper, and the creation of an independent blockchain all have a positive impact on ICOsâ initial returns. Our results also suggest that the driving factor behind initial returns is the movement of the cryptocurrency markets, measured by both Bitcoin and Ethereum returns. In addition, whether or not the jurisdiction has cryptocurrency regulations is an influential indicator. ICOs that belong to the high-tech services and platform industries have higher initial returns. Conventional financial assets, such as the stock market and gold, have a positive influence on ICOsâ initial returns.
Education on cryptocurrency is essential for individuals to make informed decisions regarding foreign investment in digital assets. In recent years there is an exponential increase in the price of cryptocurrency due to its easy trading especially in developing countries so the trend of financial institutions buying cryptocurrency into their portfolios has grown in the past decade which results in economic growth. The first completely digital assets that asset managers have included are cryptocurrencies. Traders have a unique opportunity to forecast price swings due to social mediaâs impact on cryptocurrency prices. Trading using Al and Machine Learning has drawn more attention in recent years. One could investigate the above hypothesis to determine if it is feasible to capitalize on the Bitcoin marketâs inefficiency for the purpose of generating unusually high profits. The advanced machine-learning techniques enable straightforward trading strategies to exceed conventional benchmarks. The findings demonstrate how basic computational processes might assist predict the near-term development of the bitcoin market. Further, there are prediction and comparison prices using SVM and Random Forest algorithms on the basics of efficiency while changing the number of days.
Mubbashar Altaf Khan, Mohsin M. Jamali, Taras Maksymyuk, Juraj Gazda
Cognitive radio (CR) technology offers the possibility of an increase in spectrum utilization efficiency to resolve the prevalent spectrum scarcity problem. The economic survival of secondary spectrum markets (SSMs) is heavily dependent on the sharing of both the licensed spectrum and spectrum infrastructure by primary licensed operators (PLOs). In this research, an automated pricing model using a blockchain token called the spectrum dollar has been implemented for secondary radio spectrum trade. The use of spectrum dollars enables noncash-based secondary spectrum trade among PLOs based on a floor-and-trade rule. The pricing of spectrum dollars and the associated revenue shares are based on the underlying secondary spectrum trading behaviours of PLOs. PLOs that do not contribute enough secondary spectra to the SSM (to satisfy demand) suffer a loss proportional to the difference between their earned revenues and the specified floor value in the SSM. The secondary spectrum trade is assumed to be centrally managed by a spectrum broker, which announces the floor value for each bidding period while ensuring nonnegative revenue for the market itself. The use of the spectrum dollar along with the floor-and-trade methodology eliminates the possibilities for economic malpractice by PLOs that could increase spectrum reuse costs. In addition, the floor value provides automatic regulatory control to ensure the economic viability and prevent the technological hijacking of future SSMs.
This paper reviews the empirical literature on the highly popular phenomenon of herding behaviour in the markets of digital currencies. Furthermore, a comparison takes place with outcomes from earlier studies about traditional financial assets. Moreover, we empirically investigate herding behaviour of 240 cryptocurrencies during bull and bear markets. The present survey suggests that empirical findings about whether herding phenomena have made a significant appearance or not in cryptocurrency markets are split. The Cross-sectional absolute deviations (CSAD) and Cross-sectional standard deviations (CSSD) approaches for measuring herding tendencies are found to be the most popular. Different behaviour is detected in bull periods compared to bear markets. Nevertheless, evidence from primary studies indicates that herding is stronger during extreme situations rather than in normal conditions. However, our empirical estimations reveal that herding behaviour is evident only in bull markets. These findings cast light on and provide a roadmap for investment decisions with modern forms of liquidity.
Alla A. Petukhina, Raphael C. G. Reule, Wolfgang Karl HĂ€rdle
This research analyses high-frequency data of the cryptocurrency market in regards to intraday trading patterns related to algorithmic trading and its impact on the European cryptocurrency market. We study trading quantitatives such as returns, traded volumes, volatility periodicity, and provide summary statistics of return correlations to CRIX (CRyptocurrency IndeX), as well as respective overall high-frequency based market statistics with respect to temporal aspects. Our results provide mandatory insight into a market, where the grand scale employment of automated trading algorithms and the extremely rapid execution of trades might seem to be a standard based on media reports. Our findings on intraday momentum of trading patterns lead to a new quantitative view on approaching the predictability of economic value in this new digital market.
ABSTRACT: We study the ability of hedge funds to restructure target firms. A purchase of at least 3% of a target firmâs stake is subject to a 13D SEC Filing in the US. We use these filings to investigate the impact of such transactions in the period 2009â2020. Our method of choice is the event study approach. We set the event on the date of the transaction and compute cumulative abnormal returns (CARs) within a specified event window. Based on accounting metrics, such as return on equity and return on assets, we study how restructuring impacts target companyâs capital structure. Based on SEC Section 13G filings, we are further able to distinguish between acquisitions with active and passive aims. We find that firms targeted for active purposes achieve higher abnormal returns and overall higher performance. We further look on the impact of the overall stock-market cycle on abnormal returns. We find that the level of abnormal returns for actively targeted companies remains higher with no regard to the market cycle. Based on these findings, we draw conclusions on the overall impact of hedge fund activism. KEY WORDS: Hedge funds, Shareholder Activism, Abnormal Returns, Event study, Restructuring
We examine diversification when cryptocurrencies are included in investment portfolios, around China prohibiting initial coin offerings on 4 September 2017. We discover, once we account for liquidity, that all portfolio diversification benefits of cryptocurrencies are eliminated.
Raja NabeelâUdâDin Jalal, Massimo SARGIACOMO, Najam Us Sahar, UmâEâRoman Fayyaz
The study investigates herding behavior in cryptocurrencies in different situations. This study employs daily returns of major cryptocurrencies listed in CCI30 index and sub-major cryptocurrencies and major stock returns listed in Dow-Jones Industrial Average Index, from 2015 to 2018. Quantile regression method is employed to test the herding effect in market asymmetries, inter-dependency and intra-dependency cases. Findings confirm the presence of herding in cryptocurrency in upper quantiles in bullish and high volatility periods because of overexcitement among investors, which lead to high volume trading. Major cryptocurrencies cause herding in sub-major cryptocurrencies, but it is a unidirectional relation. However, no intra-dependency effect among cryptocurrencies and equity market is observed. Results indicate that in the CKK model herding exists at upper quantile in market that may be due when the market is moving fast, continuously trading, and bullish trend are prevailing. Further analysis confirms this narrative as, at upper quantile, the beta of bullish regime is negative and significant, meaning the main source of market herding is a bullish trend in investment, which increases market turbulence and gives investors opportunity to herd. Also, we found that herding in cryptocurrencies exits in high volatility periods, but this herding mostly depends on market activity, not market movement.
Huthaifa Alqaralleh, Alaâa Adden Abuhommous, Ahmad Alsaraireh
This study is set out to model and forecast the cryptocurrency market by concentrating on several stylized features of cryptocurrencies. The results of this study assert the presence of an inherently nonlinear mean-reverting process, leading to the presence of asymmetry in the considered return series. Consequently, nonlinear GARCH-type models taking into account distributions of innovations that capture skewness, kurtosis and heavy tails constitute excellent tools for modelling returns in cryptocurrencies. Finally, it is found that, given the high volatility dynamics present in all cryptocurrencies, correct forecasting could help investors to assess the unique risk-return characteristics of a cryptocurrency, thus helping them to allocate their capital.
Ziyang Ji, Victor Chang, Hao Lan, Ching-Hsien Robert Hsu · 5 authors
As one of the most significant components of financial technology (FinTech), blockchain technology arouses the interests of numerous investors in China, and the number of companies engaged in this field rises rapidly. The emotion of investors has an effect on stock returns, which is a hot topic in behavioral finance. Blockchain is an essential part of FinTech, and with the fast development of this technology, investorsâ sentiment varies as well. The online information that directly reflects investorsâ mood could be utilized for mining and quantifying to construct a sentiment index. For a better understanding of how well some factors adequately explain the return of stocks related to blockchain companies in the Chinese stock market, the Fama-French three-factor model (FFTFM) will be introduced in this paper. Furthermore, sentiment could be a new independent variable to enhance the explanatory power of the FFTFM. A comparison between those two models reveals that the sentiment factor could raise the explanatory power. The results also indicate that the Chinses blockchain industry does not own the size effect and book-to-market effect.
Analysing a set of 200 cryptocurrencies over the period from 2015 to 2019, we document a significant return reversal effect that holds at the daily, weekly, and monthly rebalancing frequencies and is robust to controls for differences in size, turnover, and illiquidity. Moreover, the reversal effect persists during both halves of our sample period and following periods of both high and low market implied volatility. Consistent with the effect being driven by a combination of market inefficiency and compensation for liquidity provision, we find reversals are most pronounced among smaller capitalization and less liquid cryptocurrencies.
We coin the term *Protocols for Loanable Funds (PLFs)* to refer to protocols\nwhich establish distributed ledger-based markets for loanable funds. PLFs are\nemerging as one of the main applications within Decentralized Finance (DeFi),\nand use smart contract code to facilitate the intermediation of loanable funds.\nIn doing so, these protocols allow agents to borrow and save programmatically.\nWithin these protocols, interest rate mechanisms seek to equilibrate the supply\nand demand for funds. In this paper, we review the methodologies used to set\ninterest rates on three prominent DeFi PLFs, namely Compound, Aave and dYdX. We\nprovide an empirical examination of how these interest rate rules have behaved\nsince their inception in response to differing degrees of liquidity. We then\ninvestigate the market efficiency and inter-connectedness between multiple\nprotocols, examining first whether Uncovered Interest Parity holds within a\nparticular protocol and second whether the interest rates for a particular\ntoken market show dependence across protocols, developing a Vector Error\nCorrection Model for the dynamics.\n
As cryptocurrencies emerged only recently, they are subject to only very limited financial regulations. In this paper we study which variables can predict bubbles in the prices of eight major cryptocurrencies, focusing on uncertainty measures as predictors. We detect multiple bubble periods for all eight cryptocurrencies, particularly in 2017 and early 2018. We find that higher volatility, trading volume and transactions are positively associated with the presence of bubbles across cryptocurrencies. Regarding the uncertainty variables, the VIX-index consistently demonstrates negative relationships with bubble occurrence, while the EPU-index mostly exhibits positive associations with bubbles. These results may assist authorities in designing appropriate regulations.