Public health harms from gambling are now well recognised. Public Health England estimates societal gambling-related harm in England exceeds £1·27 billion. Australian research estimates that the years lost to disability from gambling exceeds that of diabetes.1 Policy makers have begun to take action; gambling advertising and sponsorship is banned in Italy, and English football clubs and the UK government are currently considering sponsorship bans for the Premier League.2
Alfonso Guarino, Luca Grilli, Domenico Santoro, Francesco Messina · 5 authors
Abstract Financial bubbles represent a severe problem for investors. In particular, the cryptocurrency market has witnessed the bursting of different bubbles in the last decade, which in turn have had spillovers on all the markets and real economies of countries. These kinds of markets and their unique characteristics are of great interest to researchers. Generally, investors and financial operators study market trends to understand when bubbles might occur using technical analysis tools. Such tools, which have been historically used, resulted in being precious allies at the basis of more advanced systems. In this regard, different autonomous, adaptive and automated trading agents have been introduced in the literature to study several kinds of markets. Among these, we can distinguish between agents with Zero/Minimal Intelligence (ZI/MI) and Computational Intelligence (CI) -based agents. The first ones typically trade on the market without resorting to complex learning strategies; the second ones usually use (deep) reinforcement learning mechanisms. However, these trading agents have never been tested on the cryptocurrencies market and related financial bubbles, which are still mostly overlooked in the literature. It is unclear how these agents can make profits/losses before, during, and after a bubble to adjust their strategy and avoid critical situations. This paper compares a broad set of trading agents (between ZI/MI and CI ones) and evaluates them with well-known financial indicators (e.g., volatility, returns Sharpe ratio , drawdown, Sortino and Omega ratio ). Among the experiment’s outcomes, ZI/MI agents were more explainable than CI ones. Based on the results obtained above, we introduce GGSMZ , a trading agent relying on a neuro-fuzzy mechanism. The neuro-fuzzy system is able to learn from the trades performed by the agents adopted in the previous stage. GGSMZ ’s performances overcome those of other tested agents. We argue that GGSMZ could be used by investors as a decision support tool.
This study explores whether and to what extent cryptocurrency ecosystem network connectivity predicts Bitcoin returns across quantiles of the return distribution. The facets of cryptocurrency ecosystem network connectivity we consider include connectivity between the on- and off-chain segments of the Bitcoin market, the intensity and synchronization of social and traditional crypto-focused media activity, the intensity of network correlations between cryptocurrencies. We identify tail behaviour predictors employing a quantile regression approach. The results demonstrate the effectiveness of several connectivity measures in predicting both price spikes and downfalls, but in a different way before and during the COVID-19 outbreak.
The cryptocurrency market is generally accepted in the world, and its price has soared and plummeted sharply. Meanwhile, skewness is an index reflecting the rapid rise and fall of asset prices in a short period. Studying the relationship between the skewness of cryptocurrency and its returns can help risk evaders expect bad news and provide a reference for risk enthusiasts to make investment decisions. Therefore, through univariate combination analysis, this paper groups cryptocurrency according to the skewness of the previous month before buying and holding it in the next week. Moreover, the excess return series are calculated to make statistical tests on it. Then we construct a three-factor model of cryptocurrency and adjust the return series. To enhance the robustness of the conclusion, we also use other measures of skewness such as idiosyncratic skewness to conduct a univariate combination analysis. The results show that a positive correlation between cryptocurrency skewness and its returns exists, which can be used as a reference index of the returns.
Distributed ledger technology (DLT) is a disruptive technology with the potential to reengineer the entire trading cycle by alleviating inefficiencies such as time lags, multiple record keeping, human errors, and transparency common with the traditional trade cycle. This study evaluates the potential benefits of DLT in mitigating information asymmetry in trading relationships and how a DLT model can be deployed to revamp the trading process. We find that information friction results from differences in stakeholder preferences by identifying and categorizing information friction into 4 groups through a review of key studies in leading management journals. This finding aligns with conclusions reached in scientific research that the benefits of DLT prevail in markets with imperfect information. In addition, we illustrate the potential benefits of DLT in mitigating inefficiencies in trading relationships resulting from information asymmetry. The article concludes with a word of caution for potential users to take gradual steps of adoption to keep pace with changing technology so as not to become laggards.
This is the first study to examine the quantile connectedness for returns-volume and volatility-volume pairs for the three non-fungible tokens (THETA, Tezos, and Enjin Coin) using the quantile VAR approach. The results report the highest connectedness of volume with returns and volatility in the extreme upper quantile compared to other quantiles, implying the asymmetric connectedness. The spillover effect is observed from volume to returns and volatilities in extreme upper and lower market conditions, whereas opposite direction of spillovers is evident for the selected non-fungible tokens at median quantile. Our findings are useful for investors in predicting the returns and risk of NFTs using trading volume in the extreme market conditions.
Over the past years, cryptocurrencies have drawn substantial attention from the media while attracting many investors. Since then, cryptocurrency prices have experienced high fluctuations. In this paper, we forecast the high-frequency 1 min volatility of four widely traded cryptocurrencies, i.e., Bitcoin, Ethereum, Litecoin, and Ripple, by modeling volatility to select the best model. We propose various generalized autoregressive conditional heteroscedasticity (GARCH) family models, including an sGARCH(1,1), GJR-GARCH(1,1), TGARCH(1,1), EGARCH(1,1), which we compare to a multivariate DCC-GARCH(1,1) model to forecast the intraday price volatility. We evaluate the results under the MSE and MAE loss functions. Statistical analyses demonstrate that the univariate GJR-GARCH model (1,1) shows a superior predictive accuracy at all horizons, followed closely by the TGARCH(1,1), which are the best models for modeling the volatility process on out-of-sample data and have more accurately indicated the asymmetric incidence of shocks in the cryptocurrency market. The study determines evidence of bidirectional shock transmission effects between the cryptocurrency pairs. Hence, the multivariate DCC-GARCH model can identify the cryptocurrency market’s cross-market volatility shocks and volatility transmissions. In addition, we introduce a comparison of the models using the improvement rate (IR) metric for comparing models. As a result, we compare the different forecasting models to the chosen benchmarking model to confirm the improvement trends for the model’s predictions.
Mnacho Echenim, Emmanuel Gobet, Anne-Claire Maurice
We design a novel calibration procedure that is designed to handle the specific characteristics of options on cryptocurrency markets, namely large bid-ask spreads and the possibility of missing or incoherent prices in the considered data sets. We show that this calibration procedure is significantly more robust and accurate than the standard one based on trade and mid-prices.
This paper tests the random walk hypothesis in the cryptocurrency market. Based on the well-known Meese–Rogoff puzzle, we evaluate whether cryptocurrency returns are predictable or not. For this purpose, we conduct in-sample and out-of-sample analyses to examine the forecasting power of our model built with autoregressive components and lagged returns of BITCOIN, compared with the random walk benchmark. To this end, we considered the 13 major cryptocurrencies between 2018 and 2022. Our results indicate that our models significantly outperform the random walk benchmark. In particular, cryptocurrencies tend to be far more persistent than regular exchange rates, and BITCOIN (BTC) seems to improve the predictive accuracy of our models for some cryptocurrencies. Furthermore, while the predictive performance is time varying, we find predictive ability in different regimes before and during the pandemic crisis. We think that these results are helpful to policymakers and investors because they open a new perspective on cryptocurrency investing strategies and regulations to improve financial stability.
H. M. Tenkam, Jules C. Mba, Sutene Mwambetania Mwambi
This paper focuses on the selection and optimisation of a cryptoasset portfolio, using the K-means clustering algorithm and GARCH C-Vine copula model combined with the differential evolution algorithm. This integrated approach allows the construction of a diversified portfolio of eight cryptocurrencies and determines an optimal allocation strategy making it possible to minimize the conditional value-at-risk of the portfolio and maximise the return. Our results show that stablecoins such as True-USD are negatively correlated to the other cryptoassets in the portfolio and could therefore be a safe haven for crypto-investors during market turmoil. Our findings are in line with previous studies exhibiting stablecoins as potential diversifiers.
In this research, we provided an answer to a very important trading question, what is the optimal number of technical tools in order to achieve the best trading results for both swing trade that uses daily bars and intraday trade that uses minutes bars? We designed Machine Learning (ML) systems that can trade four major cryptocurrencies: Bitcoin, Ethereum, BNB, and Solana. We found that more indicators do not necessarily mean better trading performance. Swing traders that use daily bars should trade Bitcoin and Solana using Ichimoku Cloud (IC) plus Moving Average Convergence Divergence (MACD), Ethereum with IC plus Chaikin Money Flow (CMF), and BNB with IC alone. With regard to intraday trading, we documented that different cryptocurrencies should be trading using different time frames. These results emphasize that the optimal number of indicators that are used to trade daily bars is one or, at maximum, two. The Multi-Layer (MUL) system that consists of all three examined technical indicators failed to improve the trading results for both days (swing) and intraday trades. The main implication of this study for traders is that more indicators does not necessarily improve trades performances.
John W. Goodell, Ilan Alon, Laura Chiaramonte, Alberto Dreassi · 6 authors
We investigate the impact of BRICS regulatory announcements on cryptocurrency volatilities and returns. Results evidence risk substitutions after announcements moving from ETH, XRP and LTC to BTC and vice versa, with BTC having volatility reactions to regulatory announcements that differ from those of other cryptocurrencies. Bootstrap quantile regression indicates a stronger detrimental impact of announcements when BTC is currently manifesting lower volatility and higher daily returns. Robustness checks confirm our findings, as well as evidence that the cryptocurrencies in our sample are considerably more reactive to BRICS announcements than US Fed announcements, suggesting important linkages between emerging markets and cryptocurrencies.
In this paper, we take a forecasting perspective and compare the information content of a set of market risk factors, cryptocurrency-specific predictors, and sentiment variables for the returns of cryptocurrencies vs traditional asset classes. To this aim, we rely on a flexible dynamic econometric model that not only features time-varying coefficients, but also allows for the entire forecasting model to change over time to capture the time variation in the exposures of major digital currencies to the predictive variables. Besides, we investigate whether the inclusion of cryptocurrencies in an already diversified portfolio leads to additional economic gains. The main empirical results suggest that cryptocurrencies are not systematically predicted by stock market factors, precious metal commodities or supply factors. On the contrary, they display a time-varying but significant exposure to investors' attention. In addition, also because of a lack of predictability compared to traditional asset classes, cryptocurrencies lead to realized expected utility gains for a power utility investor.
In this paper, we give a simple but very general definition of 'price stability' for a class of markets. This class of markets includes the popular constant function market makers (CFMMs) such as Uniswap, Curve, and Balancer, used extensively in decentralized finance (DeFi), which now have daily trading volumes in the billions of dollars. We show that our definition of price stability is deeply connected to the curvature of the trading function used in the CFMM, making the folk intuition that "flatter CFMMs are more price stable" more concrete. We also show that this definition gives sufficient conditions for the profitability of liquidity providers, and, similar to the classical market microstructure literature, gives bounds on the edge of informed traders and bounds on the losses of liquidity providers. We also show how these bounds help explain some of the behaviors observed in decentralized finance in the second half of 2020, including the rise of 'yield farming ' and 'vampire attacks.'
We analyze the predictability of the bitcoin market across prediction horizons ranging from 1 to 60 min. In doing so, we test various machine learning models and find that, while all models outperform a random classifier, recurrent neural networks and gradient boosting classifiers are especially well-suited for the examined prediction tasks. We use a comprehensive feature set, including technical, blockchain-based, sentiment-/interest-based, and asset-based features. Our results show that technical features remain most relevant for most methods, followed by selected blockchain- based and sentiment-/interest-based features. Additionally, we find that predictability increases for longer prediction horizons. Although a quantile-based long-short trading strategy generates monthly returns of up to 39% before transaction costs, it leads to negative returns after taking transaction costs into account due to the particularly short holding periods. Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is.
Mustafa Disli, Fatima Abd Rabbo, Thibault Leneeuw, Ruslan Nagayev
Binance, the largest cryptocurrency exchange by traded value, relocated from Hong Kong (origin market) to Malta (destination market). This study exploits this relocation event by examining the comovement of Binance's native token with the native tokens of other cryptocurrency exchanges in the origin and destination markets. Using multivariate regression analysis, our results show that Binance experienced a significant decline in comovement with its origin market after moving to Malta. The results are less evident for the destination market; however, an increase in comovement immediately after the relocation of Binance is notable.
In this study, we explore the research published from 2009 to 2021 and summarize what extant literature has contributed in the last decade to the analysis of volatility and risk management in cryptocurrency investment. Our samples include papers published in journals ranked across different fields in ABS ranked journals. We conduct a bibliometric analysis using VOSviewer software and perform a literature review. Our findings are presented in terms of methodologies used to model cryptocurrencies’ volatility and also according to their main findings pertaining to volatility and risk management in those assets and using them in portfolio management. Our research indicates that the models that consider the Markov-switching regime seem to be more consensual among the authors, and that the best machine learning technique performances are hybrid models that consider the support vector machines (SVM). We also argue that the predictability of volatility, risk reduction, and level of speculation in the cryptocurrency market are improved by the leverage effects and the volatility persistence.
Lioba Heimbach, Eric Schertenleib, Roger Wattenhofer
Trade execution on Decentralized Exchanges (DEXes) is automatic and does not require individual buy and sell orders to be matched. Instead, liquidity aggregated in pools from individual liquidity providers enables trading between cryptocurrencies. The largest DEX measured by trading volume, Uniswap V3, promises a DEX design optimized for capital efficiency. However, Uniswap V3 requires far more decisions from liquidity providers than previous DEX designs. In this work, we develop a theoretical model to illustrate the choices faced by Uniswap V3 liquidity providers and their implications. Our model suggests that providing liquidity on Uniswap V3 is highly complex and requires many considerations from a user. Our supporting data analysis of the risks and returns of real Uniswap V3 liquidity providers underlines that liquidity providing in Uniswap V3 is incredibly complicated, and performances can vary wildly. While there are simple and profitable strategies for liquidity providers in liquidity pools characterized by negligible price volatilities, these strategies only yield modest returns. Instead, significant returns can only be obtained by accepting increased financial risks and at the cost of active management. Thus, providing liquidity has become a game reserved for sophisticated players with the introduction of Uniswap V3, where retail traders do not stand a chance.