This systematic literature review summarizes the extant research in the Behavioral Finance (BeFi) and digital asset spaces to understand better the interactions of behavioral effects on the pricing of assets constructed, enabled, and exchanged in Decentralized Finance (DeFi) markets. We find that asset pricing in these rapidly evolving markets is better explained through BeFi than through traditional finance (TradFi) theory. Investor attention, sentiment, heuristics and biases, and network effects interact to form a highly volatile and dynamic market. We offer a deterministic research framework with propositions for future research. We further provide investors with a theoretically and empirically supported structure to better inform their decisions through an understanding of BeFi applications to DeFi.
This article explores the complexities of cryptocurrency price volatility during times of crisis. We analyze time series data with long-term memory or long-range dependence to understand the impacts of crises on cryptocurrency prices. Specifically, we examine the effects of the Covid-19 pandemic and the Russo-Ukrainian war on cryptocurrency markets, as well as the role of investor sentiment in price fluctuations during periods of uncertainty. To do so, we use fractionally integrated models to analyze the short- and long-term effects of these external factors on cryptocurrency prices. Our study mainly focuses on Bitcoin returns volatility using specific fractionally integrated models during four sub-period of historical crises from 2014. It assesses and compares the fractionally integrated models of the GARCH, the FIGARCH-BBM, the FIGARCH-CHUNG, FIEGARCH, and the FIAPARCH-BBM during the sub-periods of the pre-Covid-19, of the Covid-19 situation, between the Covid-19 and the Russo-Ukrainian War, and of the Russo-Ukrainian War. Conditional volatility models' parameters are first estimated from the four sub-sample data series BTC/USD exchange rate returns and it is calculated. Estimated conditional volatilities are then compared to specific volatilities relying on information criteria, after which the models are ranked. Finally, we test the specifics fractionally integrated volatility models with the normality test, the Q-Statistics on Standardized Residuals Test, the ARCH Test, and the graphic analysis. The specific volatility model of the first sub-period pre-Covid-19 is FIAPARCH-BBM (2,1). BTC/USD returns evolution during the Covid-19 crisis indicates that the FIEGARCH (2,2) is the appropriate volatility model. In addition, our results find that the FIEGARCH (2,1) is the appropriate model of volatility over the third sub-period and during the Russo-Ukrainian War period. By extrapolating the results of the four events, the study showed that the series of BTC/USD returns sampled over the four sub-periods were not immune to risk leading to historical crisis situations. The fluctuations of Bitcoin data during a political or economic event influence the choice of volatility models and their coefficients. More specifically, the parameters of the determined models of conditional volatility show that a war will make cryptocurrency more important on the exchange market even than an epidemic in the example of Covid-19. Our results suggest that the pandemic and geopolitical tensions have had a significant impact on cryptocurrency prices, but investor sentiment has played a crucial role in exacerbating price volatility. Additionally, we demonstrate the effectiveness of fractionally integrated models in predicting cryptocurrency prices during times of crisis. In summary, this study provides important insights into the dynamics of cryptocurrency markets during global crises, highlighting the need for sophisticated modeling techniques to effectively capture the complexities of these markets.
Anurag Dutta, Liton Chandra Voumik, A. Ramamoorthy, Samrat Ray · 5 authors
Cryptocurrencies are in high demand now due to their volatile and untraceable nature. Bitcoin, Ethereum, and Dogecoin are just a few examples. This research seeks to identify deception and probable fraud in Ethereum transactional processes. We have developed this capability via ChaosNet, an Artificial Neural Network constructed using Generalized Luröth Series maps. Chaos has been objectively discovered in the brain at many spatiotemporal scales. Several synthetic neuronal simulations, including the Hindmarsh–Rose model, possess chaos, and individual brain neurons are known to display chaotic bursting phenomena. Although chaos is included in several Artificial Neural Networks (ANNs), for instance, in Recursively Generating Neural Networks, no ANNs exist for classical tasks entirely made up of chaoticity. ChaosNet uses the chaotic GLS neurons’ property of topological transitivity to perform classification problems on pools of data with cutting-edge performance, lowering the necessary training sample count. This synthetic neural network can perform categorization tasks by gathering a definite amount of training data. ChaosNet utilizes some of the best traits of networks composed of biological neurons, which derive from the strong chaotic activity of individual neurons, to solve complex classification tasks on par with or better than standard Artificial Neural Networks. It has been shown to require much fewer training samples. This ability of ChaosNet has been well exploited for the objective of our research. Further, in this article, ChaosNet has been integrated with several well-known ML algorithms to cater to the purposes of this study. The results obtained are better than the generic results.
This study examines the time-varying connectedness among the realized volatilities of seven major cryptocurrencies between January 2020 and May 2022. To this end, we implement the time and frequency connectedness time-varying parameter vector autoregression (TVP-VAR) approaches. Our findings propose that (i) the COVID-19 pandemic significantly affected the dynamic connectedness; (ii) the total connectedness index hits its apex around the official announcement of the pandemic; (iii) in line with previous studies Ethereum, Bitcoin, and Link are the largest propagators/recipients of shocks; (iv) the tightest volatility interdependencies are related to the short-run.
Rasoul Amirzadeh, Asef Nazari, Dhananjay Thiruvady, Mong Shan Ee
This study identifies the key factors influencing the price movements of major cryptocurrencies, Bitcoin, Binance Coin, Ethereum, Litecoin, Ripple, and Tether, using Bayesian networks (BNs). This study addresses two key challenges: modelling price movements in highly volatile cryptocurrency markets and enhancing predictive performance through discretisation-aware Bayesian Networks. It analyses both macro-financial indicators (gold, oil, MSCI, S and P 500, USDX) and social media signals (tweet volume) as potential price drivers. Moreover, since discretisation is a critical step in the effectiveness of BNs, we implement a structured procedure to build 54 BNs models by combining three discretisation methods (equal interval, equal quantile, and k-means) with several bin counts. These models are evaluated using four metrics, including balanced accuracy, F1 score, area under the ROC curve and a composite score. Results show that equal interval with two bins consistently yields the best predictive performance. We also provide deeper insights into each network's structure through inference, sensitivity, and influence strength analyses. These analyses reveal distinct price-driving patterns for each cryptocurrency, underscore the importance of coin-specific analysis, and demonstrate the value of BNs for interpretable causal modelling in volatile cryptocurrency markets.
This study investigates the diversifier, hedge and safe haven properties of stablecoins against various financial assets including cryptocurrencies such as Bitcoin, Ether, XRP and stock market indices. Using quantile coherency we show that stablecoins included in the study act as weak hedges in normal conditions and weak safe havens when considering moments of market turmoil and there is little evidence to support the existence of any contagion effects between the cryptocurrency and stablecoin markets. Aforementioned results are not significantly influenced by the choice of investment horizon. We further evaluate the implications of those results for the question of whether stablecoins are in fact stable.
This study examines whether the Bitcoin market satisfies the (weak-form) efficient market hypothesis using a quantum harmonic oscillator, which provides the state-specific probability density functions that capture the superimposed Gaussian and non-Gaussian states of the log return distribution. Contrasting the mixed evidence from a variance ratio test, the high probability allocated to the ground state suggests a near-efficient Bitcoin market. Findings imply that as Bitcoin evolves into an efficient market, speculators might encounter difficulty in exploiting profitable trading strategies. Furthermore, when policymakers initiate tight regulations to control the market, they should closely monitor market efficiency as an index of price distortion.
This paper examines whether Bitcoin is an excellent hedge asset by applying the GARCH model. First, the correlation test finds that bitcoin's returns against the US dollar and gold are not significantly correlated. Also, based on comparisons across events, bitcoin returns perform more neutrally, unlike traditional hedges such as gold, which exhibit a significant negative correlation between performance and hedge in an emergency. In addition, the high volatility of Bitcoin compared to other varieties suggests that investors choosing to invest in Bitcoin will expose to high-risk return volatility. Therefore, bitcoin is more of a speculative asset than a safe haven.
In this paper, we examine whether bitcoin has the potential to become safe-haven asset that can rival gold in the future. We observed, compared and analyzed and the performance of bitcoin and gold in face of a falling market and inflation pressure. We can see if investors can rely on bitcoin to reduce risk exposure significantly through empirical tests. At the end of our research, we found that bitcoin did not perform as well as gold did when faced with market crash and inflation. Therefore, we conclude that bitcoin does not yet show the potential to possess risk-proof merits as gold, the traditional high-quality hedge asset. Gold would probably remain the preferred hedge asset against cryptocurrency for now.
According to the monetary theory, this paper believes that the demand for Bitcoin mainly includes two aspects: transaction demand and investment demand. This paper further discusses the impact of different demands on the price of Bitcoin based on two aspects of demand. Transaction demand and investment demand together affect the supply and demand relationship of the Bitcoin market. The empirical results show that the volatility of Bitcoin price is higher than that of international currencies and stocks as investment tools. This article emphasizes that the price of Bitcoin is primarily affected by supply and demand.
Researchers put efforts into explanations of the momentum phenomenon and improvements of the momentum strategy since the emergence of momentum in 1993. Interested in anomalies appearing as exhibited in traditional asset markets, adequate studies are launched on the nascent phenomenon emergers in the last decade, the cryptocurrency market. Recent studies have shown that there is hardly any cross-sectional momentum in the cryptocurrency market. To explore the momentum anomaly additionally in the cryptocurrency market, this paper implemented a time-series momentum on cross-sectional winners for improvement. Previous studies have introduced detecting the turning point between long-term slow time-series factor and short-term fast time-series factor contributes to predicting the trend well. Furthermore, a threshold decided by a certain machine learning model suggests better performance. In this paper. A multilayer perceptron (MLP) is utilized to learn the weights of time-series factors. The combination of cross-sectional momentum and time-series momentum shows advantages and the MLP learned weighted strategy is preferable.
Bubbles in asset prices have attracted the attention of economists for centuries. Extreme increases in asset prices, followed by their sudden decline, create a turbulent effect on the economy and even invite crises in time. For this reason, some measurement techniques have been employed to investigate the price bubbles that may occur. This study explores the possible speculative price bubbles of Bitcoin, Ethereum, and Binance Coin cryptocurrencies, compares them with the pre-and post-COVID-19 period, and examines asymmetric causality relationships between variables. Therefore, we analyzed the price bubbles of these cryptocurrencies using the closing price for daily data between 16.01.2018 and 31.12.2021 by the Supremum Augmented Dickey-Fuller (SADF) and the Hatemi-J (2012) asymmetric causality test. In this context, 1446 observations, 723 of which were before COVID-19 and 723 after COVID-19, were employed in the study. Looking at the SADF analysis results, we detected 103 price bubbles before COVID-19 for the three cryptocurrencies, while we determined 599 price bubbles after COVID-19. The common finding in the asymmetric causality test results is that there is a causality relationship between the negative shocks faced by one cryptocurrency and the positive shocks faced by the other cryptocurrencies.
Non-fungible token (NFT) bubbles are a problematic issue, and this study aims to predict NFT bubbles using an extended log-periodic power law singularity (LPPLS) model. The classic LPPLS model targets the endogenous nature of bubbles caused by the mimetic behavior of investors without external influences; however, the extended model attempts to incorporate exogenous influences. First, we compare the performance of the two models for NFT price prediction. The exogeneous variable in the extended model is cryptocurrency volatility. Then, we calculate the bubble confidence using both models. The results show that the explanatory power and forecasting accuracy of the extended model are superior in all projects. We also find that the bubble confidence indicator reinforces the results of bubble prediction.
Blockchain research is now heavily centred on cryptocurrency, which has drawn the attention of financial academics. The availability of numerous types of data and a wealth of resources in bitcoin research facilitates the application of machine learning algorithms. However, a through analysis and a complete study of machine learning-based cryptocurrencies need further research. Cryptocurrency price prediction is the most pertinent research topic and the algorithms being used in cryptocurrency research are not unique. Various researcher are using combination of multiple machine learning algorithms. Therefore, in this study, the research related to cryptocurrency price prediction using machine learning is summarized along with prominent research challenges related to application of machine learning algorithms in cryptocurrency.
The aim of the paper is twofold: first, to examine the hedging effectiveness of cryptocurrencies and cryptocurrency portfolios for European equities in bearish and bullish market conditions, and second, to contrast cryptocurrencies with gold as a safe haven asset. To this end, daily data from 2018 to 2022 were employed in a linear and nonlinear Autoregressive Distributed Lag (ARDL) framework. The findings have significant implications for investors, financial intermediaries and regulators.
This paper proposes a nonparametric directional dependence by using the local polynomial regression technique. With data generated from a bivariate copula having a nonmonotone regression structure, we show that our nonparametric directional dependence is superior to the copula directional dependence method in terms of the root-mean-square error. To validate the directional dependence with real data, we use the log returns of daily prices of Bitcoin, Ethereum, Ripple, and Stellar. We conclude that our nonparametric directional dependence, by using the local polynomial regression technique with asymmetric-threshold GARCH models for marginal distributions, detects the directional dependence better than the copula directional dependence method by an asymmetric GARCH model.
Join the age-old debate over which asset is the superior store of value: Bitcoin or gold? These two heavyweight candidates have captured the interest of investors all across the world. Bitcoin's decentralized characteristics and limited supply make it an appealing alternative to existing fiat currencies, whereas gold has long been seen as a reliable hedge against inflation and currency depreciation. This paper goes into the benefits and cons of each asset, examining its ability to hold value over time. So, join us as we assess if Bitcoin or gold is the ultimate heavyweight champion of the global economy's store of value.
Abstract This paper is motivated by Bitcoin’s rapid ascension into mainstream finance and recent evidence of a strong relationship between Bitcoin and US stock markets. It is also motivated by a lack of empirical studies on whether Bitcoin prices contain useful information for the volatility of US stock returns, particularly at the sectoral level of data. We specifically assess Bitcoin prices’ ability to predict the volatility of US composite and sectoral stock indices using both in-sample and out-of-sample analyses over multiple forecast horizons, based on daily data from November 22, 2017, to December, 30, 2021. The findings show that Bitcoin prices have significant predictive power for US stock volatility, with an inverse relationship between Bitcoin prices and stock sector volatility. Regardless of the stock sectors or number of forecast horizons, the model that includes Bitcoin prices consistently outperforms the benchmark historical average model. These findings are independent of the volatility measure used. Using Bitcoin prices as a predictor yields higher economic gains. These findings emphasize the importance and utility of tracking Bitcoin prices when forecasting the volatility of US stock sectors, which is important for practitioners and policymakers.
Chiang-Ching Tan, Pick-Soon Ling, Siew-Ling Sim, Kelvin Lee Yong Ming
This study examined the capabilities of six cryptocurrencies as a hedge and safe haven against the stock indices and foreign exchange rate in the East Asia-5 markets. According, MGARCH-DCC was adopted and implemented in data collection processes together with Rathner and Chiu regression method, which spanned from April 2013 to December 2019. The results revealed that these cryptocurrencies had dissimilar hedging and safe haven capabilities across various stock indices and exchange rates in the East Asia-5 markets. In particular, Bitcoin, Litecoin, and Ethereum offered strong hedge properties on most of the East Asia-5 equity indices. Moreover, Bitcoin and Litecoin only provided a safe haven for Japanese Yen currency, while Taiwanese equity indices and Chinese Yuan currency can be safely protected via an investment into Stellar.