Multifractal analysis is a forecasting technique used to study the scaling regularity properties of financial returns, to analyze the long-term memory and predictability of financial markets. In this paper, we propose a novel structural detrended multifractal fluctuation analysis (S-MF-DFA) to investigate the efficiency of the main cryptocurrencies. The new methodology generalizes the conventional approach by allowing it to proceed on the different fluctuation regimes previously determined using a change-points detection test. In this framework, the characterization of the various exogenous factors influencing the scaling behavior is performed on the basis of a single-factor model, thus creating a kind of self-explainable machine learning for price forecasting. The proposal is tested on the daily data of the three among the main cryptocurrencies in order to examine whether the digital market has experienced upheavals in recent years and whether this has in some ways led to a structured multifractal behavior. The sampled period ranges from April 2017 to December 2022. We especially detect common periods of local scaling for the three prices with a decreasing multifractality after 2018. Complementary tests on shuffled and surrogate data prove that the distribution, linear correlation, and nonlinear structure also explain at some level the structural multifractality. Finally, prediction experiments based on neural networks fed with multi-fractionally differentiated data show the interest of this new self-explained algorithm, thus giving decision-makers and investors the ability to use it for more accurate and interpretable forecasts.
Günümüzde ekonomilerin, işletmelerin başarılı ve sürdürülebilir bir şekilde büyümesi için sermaye piyasaları önem arz etmektedir. Varlık fiyatları alternatif yatırım araçları olmaları yönüyle hisse senedi piyasaları ile etkileşim içindedir. Dolayısıyla varlık fiyatlarında oluşan balonların hisse senedi piyasaları ile ilişki içinde olması beklenmektedir. Bu çalışmada 08:2010 ile 10:2022 arası aylık verilerle Dolar, Euro, Bitcoin, CDS ve mevduat faizi değişkenlerinde balon varlığı incelenmiştir. Ele alınan değişkenlerde balon oluşumunun varlığı durumunda bu balonların BIST 100 endeksi oynaklığına etkilerinin incelenmesi amaçlanmıştır. Balonların varlığı SADF ve GSADF testleri ile analiz edilirken, TARCH ve ARCH-GARCH modelleri yardımıyla oynaklık belirlenmeye çalışılmıştır. USD, Euro, Bitcoin değişkeni için ele alınan dönem boyunca istatistiksel olarak önemli balon oluşumları söz konusu iken, CDS ve mevduat değişkeni için söz konusu dönemde istatistiksel olarak önemli bir balon oluşumu gözlemlenmemiştir. USD ve Euro değişkenlerinde meydana gelen balonların BIST 100 endeks getirisinde oynaklığı artırdığı söylenebilir. Ancak BITCOIN de yaşanan balonların istatistiksel olarak anlamlı bir etkisinin olmadığı görülmüştür.
Men Qin, Chi‐Wei Su, Yunxu Wang, Nicoleta Mihaela Doran
Exploring the safe-haven characteristics of bitcoin from novel perspectives is crucial to diversify the investment and reap the benefits. This investigation employs bootstrap full-and sub-sample techniques to probe time-varying interrelation between global supply chain pressure (GSCP) and bitcoin price (BP), and further answer if “digital gold” could resist the strains of global supply chain. The empirical outcomes suggest that GSCP positively and negatively affects BP. The positive influence points out that high GSCP might boost the international bitcoin market, driving BP to rise, which indicates that “digital gold” could resist the pressures of global supply chain. But the negative effect of GSCP on BP could not support the above view, mainly affected by the weak purchasing power and more valuable assets, which is not consistent with the assumption of the inter-temporal capital asset pricing model (ICAPM). In turn, GSCP is adversely affected by BP, highlighting that the international bitcoin market may be viewed as a stress reliever for the global supply chain. Against a backdrop of the deteriorative Russia-Ukraine war and the intensifying global supply chain crisis, the above conclusions could bring significative lessons to the public, enterprises and related economies.
This research paper provides an in-depth analysis of cryptocurrency exchanges by examining their types, regulatory environment, challenges, and user behavior. We conducted a comparative study of ten popular cryptocurrency exchanges and collected data on user behavior and preferences through surveys, interviews, and website analysis. Our findings reveal that crypto exchanges face numerous challenges such as security, liquidity, and regulatory compliance. We also found that users prefer exchanges that offer a wide range of cryptocurrencies, high liquidity, low fees, and strong security measures. This research contributes to the understanding of the cryptocurrency industry and provides insights for policymakers, investors, and users. Keywords : Cryptocurrency Exchanges, Bitcoin, Trading, Portability and Vulnerability
Cryptocurrencies have become a world-class phenomenon, with governments, companies and investors facing huge challenges and opportunities. Bitcoin is the one of most widely known cryptocurrencies. People prefer to use Bitcoin as an asset to invest for profit and hedge risk than for its payment function. This is the reason for the study of bitcoin market is so important. Many scholars had used daily data on bitcoin to construct different GARCH models to analyse the volatility of its returns. This research builds a GARCH model for the logarithmic return series of the bitcoin price to understand the volatility of the bitcoin price over the experimental time horizon. The experimental results show that the price of bitcoin is more volatile and vulnerable to external shocks. The analysis suggests that Bitcoin is more clearly a speculative financial instrument and that the price of Bitcoin is highly frothy.
The response of the Bitcoin market to the novel coronavirus (COVID-19) pandemic is an example of how a global public health crisis can cause drastic market adjustments or even a market crash. Investor attention on the COVID-19 pandemic is likely to play an important role in this response. Focusing on the Bitcoin futures market, this paper aims to investigate whether pandemic attention can explain and forecast the returns and volatility of Bitcoin futures. Using the daily Google search volume index for the "coronavirus" keyword from January 2020 to February 2022 to represent pandemic attention, this paper implements the Granger causality test, Vector Autoregression (VAR) analysis, and several linear effects analyses. The findings suggest that pandemic attention is a granger cause of Bitcoin returns and volatility. It appears that an increase in pandemic attention results in lower returns and excessive volatility in the Bitcoin futures market, even after taking into account the interactive effects and the influence of controlling other financial markets. In addition, this paper carries out the out-of-sample forecasts and finds that the predictive models with pandemic attention do improve the out-of-sample forecast performance, which is enhanced in the prediction of Bitcoin returns while diminished in the prediction of Bitcoin volatility as the forecast horizon is extended. Finally, the predictive models including pandemic attention can generate significant economic benefits by constructing portfolios among Bitcoin futures and risk-free assets. All the results demonstrate that pandemic attention plays an important and non-negligible role in the Bitcoin futures market. This paper can provide enlightens for subsequent research on Bitcoin based on investor attention sparked by public emergencies.
As the price of virtual currency fluctuates greatly, precise prediction and appropriate trading strategies can bring investors best returns. This paper predicted the price of Ethereum and Bitcoin in the light of autoregressive integrated moving average model (ARIMA) and get a R2 of 0.995 and 0.993 respectively, which indicates the model can yield reasonable predictions. Then their investment ratios are set to 0.88 and 1.12 respectively by analytic hierarchy process (AHP). Particle swarm optimization (PSO) is used to solve the daily revenue function formed by the predicted price and the current price. Finally, the paper compared the returns yielded by the PSO trading strategy optimized by AHP and the strategy without optimization. It can be concluded that the AHP has a possibility of 64.66 per cent to yield more returns when used.
Chekwube V. Madichie, Franklin N. Ngwu, Eze A. Eze, Olisaemeka D. Maduka
Cryptocurrencies have, over the years, gained an unprecedented prominence in financial discourse, with the market fielding over 5,300 digital currencies and reaching over $2 trillion in market capitalisation in 2022. The surge in market values of digital currencies and their popularity in the world of e-commerce have remained unabated and equally received special attention from researchers focusing on identifying the underlying factors that drive changes in their market values. Thus, this study models the dynamics of the prices of cryptocurrencies alongside their interconnectedness, focusing on Bitcoin, Ethereum, and Litecoin along the time and frequency dimensions of monthly data from 1 March 2016 to 05/31/2022. Based on the ARDL model, results show that the volume of transactions of Bitcoin, Ethereum, and Litecoin, oil prices, and gold prices exert a more significant positive influence on their prices in the longrun than in the shortrun. However, the publicity of the selected cryptocurrencies (google search rates) does not significantly influence their prices. Interestingly, results from the Wavelet Granger causality tests show no causality between the raw series of Bitcoin, Ethereum, and Litecoin prices. However, a bi-directional causality exists between Bitcoin and Ethereum prices during the longrun in their low frequencies, a unidirectional causality running from Bitcoin to Litecoin prices during the longrun in their low frequencies, and a unidirectional causality running from Litecoin to Ethereum prices during the shortrun, medium run and longrun in their high, medium, and low frequencies. These findings have profound implications for the global financial market and investor decisions.
The combination of Deep Learning and GARCH-type models has been proved to be superior to the single models in forecasting of volatility in various markets such as energy, main metals, and especially stock markets. To verify this hypothesis for cryptocurrencies market, we constructed various Deep Learning models based on Feed Forward Neural Networks (DFFNNs) and Long Short-Term Memory (LSTM) networks and evaluated their performance in forecasting the volatility of 27 cryptocurrencies. Then, different hybrid models were built in which the outputs of three GARCH-type models, namely GARCH, EGARCH, and APGARCH, with three different assumptions for the residuals’ distribution were fed into the DFFNN and LSTM networks. In other words, GARCH-type models were utilized as feature extractors and the deep learning models leveraged a sequence of extracted features as their inputs to produce the volatility of the next day. Our findings revealed that not only the deep learning models improve the forecasts of GARCH-type models with any distribution assumption, the forecasts of GARCH-type models as informative features can significantly increase the predictive power of the studied deep learning models; namely, the DFFNN and LSTM models.
Purpose In this paper, the authors examine the short-term and long-term impact of general economic policy uncertainty (EPU) and crypto-specific policy uncertainty on Bitcoin’s (BTC) exchange inflows – a form of crypto investor behaviors that the authors expect to drive the cryptocurrency volatility. Design/methodology/approach The authors use an autoregressive distributed lag (ARDL), coupled with the bounds testing approach by Pesaran et al. (2001), to analyze a weekly dataset of BTC’s exchange inflows and relevant policy uncertainty indices. Findings The authors observe both short-term and long-term impacts of the crypto-specific policy uncertainty on BTC’s exchange inflows, whereas the general EPU only explains these inflows in a short-term manner. In addition, the authors find exchange inflows of BTC “Granger” cause its price volatility. Furthermore, the authors document a significant and relatively persistent response of BTC volatility to shocks to its exchange inflows. Originality/value This study’s findings offer significant contributions to research in policy uncertainty and investor behaviors.
Maria Chiara Pocelli, Manuel L. Esquível, Nadezhda P. Krasii
We present an analysis on variability Bitcoin characteristics that help to quantitatively differentiate Bitcoin from the state-owned traditional currencies and the asset Gold. We provide a detailed study on returns of exchange rates—against the Swiss Franc—of several traditional currencies together with Bitcoin and Gold; for that purpose, we define a distance between currencies by means of the spectral densities of the ARMA models of the returns of the exchange rates, and we present the computed matrix of the distances between the chosen currencies. A statistical analysis of these matrix distances is further proposed, which shows that the distance between Bitcoin and any other currency or Gold is not comparable to any of the distances between currencies or between currencies and Gold and not involving Bitcoin. This result shows that Bitcoin is essentially different from the traditional currencies and from Gold, at least in what concerns the structure of its variance and auto-covariances.
Bogdan Andrei Dumitrescu, Carmen Obreja, Ionel Leonida, Dănuț Georgian Mihai · 5 authors
This paper contributes to the literature dedicated to the interlinkages between cryptocurrencies and currencies by investigating whether Bitcoin price movements affect the exchange rates of a sample of nine European countries with non-euro currencies. By resorting to the novel unconditional quantile regression, we show that there is a statistically significant link between Bitcoin price movements and changes in nominal exchange rates. In normal market conditions, an increase in the price of Bitcoin can be associated with an appreciation of the currencies from our sample, while during the COVID-19 pandemic, the relationship inversed. In addition, we find heterogeneities in this relationship, depending on the level of change in the nominal exchange rate. The results emphasize the relevance of Bitcoin price movements to the conduct of monetary policy through the exchange rate channel and that investors in cryptocurrencies and various financial assets denominated in the currencies from our sample can benefit from diversification by including both types of assets in their portfolios.
Bitcoin, as a virtual cryptocurrency with both property of investment and currency, is widely investigated for its potential as a safe haven in world volatility. This paper, using classic time series model VAR and ARMA-GARCH, aims to study whether Bitcoin has safe-haven value in geopolitical events, which is based on the Russia-Ukraine conflict. By quantifying the impact of Russia-Ukraine conflict with crude oil prices and considered logarithmic yield, this study finds out both the positive and negative effects to Bitcoin yield from temporary shocks and long-term fluctuations of geopolitical. The VAR accumulation shows that geopolitics will have cumulative net positive impacts on Bitcoin's yield in the short term, that is, Bitcoin can be seen as a short-term safe haven for investors with brief profit needs. However, more results show that the impact of geopolitics on Bitcoin is difficult to determine, and the long-term impact is close to zero. The inadequate evidence of safe-haven value means that long-term investors need to consider Bitcoin cautiously. Based on the current background of Russia-Ukraine conflict, the study can both promote the academic understanding of Bitcoin’s value in geopolitical conflict, and help the investors make the right choice in world volatility.
<p>We extend the Shariah-compliant digital assets and Islamic Fintech literature through exploring the time-frequency associations between the volatility index (VIX) and cryptocurrencies (both Islamic and traditional). Employing wavelet-based technique, we find that Islamic cryptocurrencies demonstrate low or no coherency with stock market volatility compared to traditional cryptocurrencies (except Tether) during the whole time and frequency bands, highlighting the hedging capabilities of Islamic cryptocurrencies. Tether also serves the same against VIX, as there is a low or favorable link between these variables. Finally, our findings would be prolific to digital currency traders and investors in designing the portfolio strategies.</p>
Cryptocurrencies and tourism have gained traction worldwide in the last few years. However, no research has been conducted to understand the relationship between the two. This paper examines the impact of the volatility spillover effects (VSE) of cryptocurrencies on the tourism sector in India. Using monthly time-series data (from August 2015 to January 2021) of the selected cryptocurrencies and foreign tourist arrivals (FTA) and foreign exchange earnings (FEE) from foreign tourism, we assess the volatility (short and long-term) impacts of cryptocurrencies on tourism (through changes in the monthly number of FTAs in India and FEEs of India through foreign tourism). The study applies Multivariate GARCH models (BEKK-GARCH and mGJR-GARCH). The findings suggest that there is an existence of volatility connections between cryptocurrencies and foreign tourism in India. These findings have noticeable implications for policymakers to understand the importance of cryptocurrency and blockchain for tourism sector policies in India.
Our investigation strives to unearth the best portfolio hedging strategy for the G7 stock indices through Bitcoin and gold using daily data relevant to the period 2 January 2016 to 5 January 2023. This study uses the DVECH-GARCH model to model dynamic correlation and then compute optimal hedge ratios and hedging effectiveness. The empirical findings show that Bitcoin and gold were rather effective hedge assets before COVID-19 and diversifiers during the pandemic and Russia–Ukraine war. From hedging effectiveness perspectives, gold and Bitcoin are safe-haven assets, and the investment risk of G7 stock indices could be hedged by taking a short position during thepandemic period and war except for the pair Nikkei/Gold. Additionally, gold beats Bitcoin in terms of hedging efficiency. We thus demonstrate the central role of Bitcoin and gold as financial market participants, particularly during market turmoil and downward movements. Our findings can be of interest to investors, regulators, and governments to take into consideration the role of Bitcoin in financial markets.
Mohammad Ashraful Ferdous Chowdhury, Mohammad Abdullah, Masud Alam, Mohammad Zoynul Abedin · 5 authors
This paper examines the efficiency and asymmetric multifractal features of NFTs, DeFi, cryptocurrencies, and traditional assets using Asymmetric Multifractal Cross-Correlations Analysis covering the period from November 2017 to February 2022. Considering the full sample with a significant variation among asset classes, the study reveals DeFi-DigiByte is the most efficient while the cryptocurrency-Tether is the least efficient. However, S&P 500 showed high efficiency before COVID-19, and DeFi-Enjin Coin advanced as the most efficient asset during COVID-19. The volatility dynamics of NFTs, DeFi, and cryptocurrencies follow strong nonlinear cross-correlations, but evidence of weaker nonlinearity exists in traditional assets. Additionally, the sensitivity to smaller events in bull markets is high for NFTs and DeFi. The findings have significant implications for portfolio diversification when an investor's portfolio set includes traditional assets and cryptocurrency and relatively new blockchain-based assets like NFTs and DeFi.