Raja Nabeel‐Ud‐Din Jalal, Massimo SARGIACOMO, Najam Us Sahar, Um‐E‐Roman 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, Ala’a 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.
Cryptocurrencies have emerged in the last decade as a new asset class unlikely to disappear despite its extraordinary volatility. Futures contracts on Bitcoins were introduced in December 2017 by the Chicago Mercantile Exchange followed by options in January 2020. Our goal in this paper is threefold: (i) present the main features of cryptocurrency spot and derivative markets; (ii) argue that the custody recently granted by large financial institutions to their large customers for their bitcoins shows that Bitcoins are very similar to commodities, allowing the extension to bitcoins of the convenience yield introduced by Working (American Economic Review, vol. 39, 1949, pp. 1254–1262) in the Theory of Storage; (iii) use the prices of options traded on the Deribit Exchange to build the volatility smiles and skews observed at different dates of 2019 for short and long dated maturities and compare them to smiles/skews of equity options.
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, investors’ sentiment varies as well. The online information that directly reflects investors’ 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.
Lewis Gudgeon, Sam M. Werner, Daniel Pérez, William J. Knottenbelt
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
Fredrik Aurbakken Enoksen, Ch.J. Landsnes, Katarína Lučivjanská, Péter Molnár
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.
Purpose Our study focuses on analyzing the trading behaviour of the investors who invest in these currencies to review their trading patterns which may help us to understand the price formation of cryptocurrencies in this market. Design/methodology/approach We used Chang et al. (2000) measure to calculate herding that is based on cross-section absolute dispersion of stock returns (CSAD). We further analyse the nature of the same in different market regimes, that is up market, down market, high volatile market, low volatile market etc. Findings Applying different methodologies both static and time varying, we find that herding is pronounced when the market is either passing through stress or has become highly volatile. Anti-herding is found in a less volatile market or in a bullish market. Practical implications Our results are also helpful for the policy makers in designing stricter regulations to provide safe investment environment to the investors. Originality/value Our study in an extension of the literature in same direction and contribute in numerous ways. As the number of digital currencies is growing day by day and we have around 2,200 digital currencies trading across the world, we increased our sample size up to 100 most traded currencies. While majority of the studies cover the period 2015–2018, our study comprises the largest sample size starting from August 2013 to April 2019. We use the static model to find herding and simultaneously try to detect herding under different market regimes: up market and down market.
The global usage and acceptability of bitcoin and other forms of cryptocurrencies as another \nmeans of payment have attracted the attention of financial and economic experts in recent times, but \nresearch on these means of payment and their relationship with economic and financial variables are \nscanty in Nigeria. This study, therefore, examined the nexus between the two key economic and \nfinancial variables (exchange rate and stock market price) and the most traded cryptocurrency (Bitcoin \nand Etherum) in Nigeria. The study used monthly data between August 2015 and December 2019 and \nemployed the Generalized Autoregressive Conditional Heteroscedasticity (GARCH 1,1), Exponential \nGeneralized Autoregressive Conditional Heteroscedasticity (EGARCH 1,1), and Granger causality \ntechnique to estimate the reaction of the volatility of exchange rates and stock market prices to volatility \nin cryptocurrency prices. The result shows that the stock market price is more influenced by the \ninstability of bitcoin and ethereum prices than the exchange rate in Nigeria. Further, there is evidence \nof a one-way causality from bitcoin and ethereum to all share index. Given these findings, there is a \nneed for the stock market investors in Nigeria to pay rapped attention to the movement of \ncryptocurrency prices.