The effect of the Russia–Ukraine war has fluctuated in Europe and Asia's economic conjuncture by virtue of constant shifting balances. The portfolios of investors who made decisions in uncertain conditions have been affected by these fluctuations that have caused volatility in the stock market's indexes. The aim of this study is to examine the impact of the Fear Index (FI), the Dollar Index, and Bitcoin on the volatility of the Borsa Istanbul 100 Index (BIST). Autoregressive distributed lag (ARDL) time series analysis was used for the study, which revealed that the Dollar Index has no effect on volatility, while the FI was found to have an effect on volatility both in the short and long runs. In addition, Bitcoin was determined to have an effect on volatility only in the long run. When the period of the data used is examined, the outbreak of the Russia–Ukraine war in February 2022 is thought to be the reason for the increase in the FI. It can be assumed that the decisions of investors to invest in the BIST were adversely affected by the war as a natural consequence of this, and investors who ceased investing in the BIST index opted to invest elsewhere.
Kokulo K. Lawuobahsumo, Bernardina Algieri, Arturo Leccadito
Abstract This study aims to jointly predict conditional quantiles and tail expectations for the returns of the most popular cryptocurrencies (Bitcoin, Ethereum, Ripple, Dogecoin and Litecoin) using financial and macroeconomic indicators as explanatory variables. We adopt a Monotone Composite Quantile Regression Neural Network (MCQRNN) model to make one- and five-steps-ahead predictions of Value-at-Risk (VaR) and Expected Shortfall (ES) based on a rolling window and compare the performance of our model against the Historical simulation and the standard ARMA(1,1)-GARCH(1,1) model used as benchmarks. The superior set of models is then chosen by backtesting VaR and ES using a Model Confidence Set procedure. Our results show that the MCQRNN performs better than both benchmark models for jointly predicting VaR and ES when considering daily data. Models with the implied volatility index, treasury yield spread and inflation expectations sharpen the extreme return predictions. The results are consistent for the two risk measures at the 1% and 5% level both, in the case of a long and short position and for all cryptocurrencies.
Currently, the financial landscape is evolving very quickly, new technologies and changes in customer wishes and fulfillment time make currencies take on different forms and functions, each presenting unique challenges and opportunities. This article explores the historical development and contemporary meaning of currencies, ranging from traditional units of account such as the ECU and the SDR to the emerging association of economic power, the BRICS and the disruptive force of cryptocurrencies. The article begins by tracing the historical evolution of these currencies, shedding light on their origins and roles in international finance. It examines the influence of the ECU, SDR and BRICS and their potential in reshaping the global financial order. The rise of cryptocurrencies, their underlying technology (blockchain), and their transformative impact on traditional financial systems are also explored in depth. Common challenges and issues facing these forms of currency are identified, including regulatory complexities, volatility, security concerns, and barriers to adoption. The article examines the integration of traditional coins, simple or composite, into the cryptocurrency ecosystem, offering insights into potential solutions to address these challenges. Regarding the future, in its dynamics, the article offers a forward-looking perspective on the evolving role of these currencies in a globalized economy, highlighting opportunities for adaptation, cooperation, and resettlement of geopolitical and financial grace. The paper concludes with a call to navigate the complexities of the modern financial landscape with flexibility, innovation and attention to socio-economic impact. This article serves as a comprehensive resource for economists, policymakers, investors, companies, and individuals seeking to understand the dynamic interplay of currencies in the ever-changing world of finance.
Kamer-Ainur Aivaz, Ionela Munteanu, Flavius Valentin Jakubowicz
Based on traditional market theory, this study aims to investigate whether conventional market investment slopes affect the unconventional Bitcoin market, considering both normal conditions and crises. This study examines three main characteristics of the economy-intensive blockchain system, namely reliability, investment slopes, financial and accounting aspects that ultimately determine the confidence in the choice to invest in cryptocurrency. The analysis focuses on the study of the Bitcoin (BTC) investment slopes during January 2014–April 2023, considering the specifics of blockchain technology and the inferences of ethics, reliability and real-world data on investment Tassets in the context of conventional regulated markets. Using an econometric model that incorporates reliability analysis techniques, factorial comparisons and multinomial regression using economic crisis periods as a dummy variable, this study reveals important findings for practical and academic purposes. The results of this study show that the investment slopes of Bitcoin (BTC) are mostly predictable for downward trends, when statistically significant correlations with the investment slopes of conventional stock markets are observable. The moderate or high increase in performance slopes pose several challenges for predictive analysis, as they are influenced by other factors than conventional regulated market performance inferences. The results of this study are of intense interest to researchers and investors alike, as they demonstrate that investment slopes analysis sheds light on the intricacies of investment decisions, allowing a comprehensive assessment of both conventional markets and Bitcoin transactions.
Since cryptocurrencies are becoming more widely used and accepted in the financial system, precise price forecasting is essential for optimizing bitcoin investments. In this research study, we evaluated various machine learning models, including linear regression (LR), decision tree regression (DT), random forest regression (RF), support vector regression (SVR), gradient boosting regression (GB), adaboost regression, extreme gradient boosting regression (XGR), light gradientboosting regression (LGBM), k-nearest neighbors regression (KNN), ridge, andlasso. Additionally, we incorporated two deep learning (DL) models, namely artificial neural networks (ANN) and convolutional neural networks (CNN), to forecast daily bitcoin prices (BP). The initial data was obtained from Kaggle, a well-known platform for data science projects, and we applied the min-max scaler technique for consistent scaling during preprocessing. To assess the predictive capabilities of the models, we utilized regression metrics such as root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R). Based on our findings, the CNN model demonstrated the highest effectiveness in predicting BPs among the DL models, with an RMSE of 0.0543, MAE of 0.0324, and an R value of 0.960. In the case of machine learning models, the RF model outperformed others, achieving an RMSE of 0.0246 and MAE of 0.0561. Investors, scholars, and decision-makers may all gain from these findings’ insightful revelations about BP forecasting. Developing these models further, investigating different preprocessing methods, and expanding the analysis to other cryptocurrencies might be the main goals of future research.
An Pham Ngoc Nguyen, Tai Tan, Marija Bezbradica, Martin Crane
We employ graph-based methods to examine the connectedness between cryptocurrencies of different market caps over time. By applying denoising and detrending techniques inherited from Random Matrix Theory and the concept of the so-called Market Component, we are able to extract new insights from historical return and volatility time series. Notably, our analysis reveals that changes in volatility-based network structure can be used to identify major events that have, in turn, impacted the cryptocurrency market. Additionally, we find that these structures reflect investors’ sentiments, including emotions like fear and greed. Using metrics such as PageRank, we discover that certain minor coins unexpectedly exert a disproportionate influence on the market, while the largest cryptocurrencies such as BTC and ETH seem less influential. We suggest that our findings have practical implications for investors in different ways: Firstly, helping them to avoid major market disruptions such as crashes, to safeguard their investments, and to capitalize on opportunities for high returns; Secondly, sharpening and optimizing the portfolios thanks to the understanding of cryptocurrencies’ connectedness.
Yang Junhua, Samuel Kwaku Agyei, Ahmed Bossman, Mariya Gubareva · 5 authors
To address ESG stock susceptibility to episodic shocks in financial markets, we use nonparametric quantile-based techniques applied to the 2014-2022 period. We (i) analyse the ability of traditional assets to predict ESG stocks returns, (ii) explore whether oil or gold serves as a safe haven for ESG stocks, and (iii) ascertain how ESG stocks respond to market sentiment, crypto-based uncertainty, and geopolitical risk (GPR). We find that gold, oil, market sentiment (tracked by the VIX), the implied volatility of crude oil (OVX) and GPR are significant predictors of ESG returns. None of gold or oil serves as a safe haven for ESG stocks, both acting just as diversifiers. In their turn, ESG could stocks hedge against the shocks from GPR and cryptocurrency-triggered market uncertainties in bearish states of the market. These findings are important for asset allocation and risk management, assisting investors in the already ongoing switch from ordinary to sustainable investments.
Abstract This study employs the Bayesian Networks (BN) and the wavelet coherence approaches to invest the relationship between Bitcoin volatility and financial asset classes (MSCI world equity index, S&P Goldman Sachs Commodity Index [GSCI], US index and Investment Grade Corporate Bond Index ETF [PIMCO]) using daily data for the period from August 2011 to October 2021. The results show that the causal relationship between Bitcoin and other financial assets varies depending on the market states. During the low volatility periods, Bitcoin has a stronger impact on the GSCI, while during the stability periods, it has a direct effect on the US index and the MSCI world index. In contrast, during high volatility periods, Bitcoin has a direct impact on both the GSCI and PIMCO indices. The key findings enabled us to provide implications for US investors to promote asset allocation and risk management covering both Bitcoin and traditional financial markets. The results suggest that policymakers should watch Botcoin closely to preserve financial stability.
In the realm of cryptocurrency forecasting, accurately predicting short-term Bitcoin log returns remains a challenging endeavor due to its inherent volatility and sensitivity to multifarious external factors. This study addresses this challenge by proposing an integrated approach that combines the capabilities of the TimesNet deep learning model with sentiment analysis techniques. TimesNet, specifically designed for time series data, has demonstrated proficiency in extracting salient patterns. When synergized with sentiment analysis, a more nuanced understanding of price determinants emerges. Preliminary results from our experiments indicate a significant enhancement in predictive accuracy within the Bitcoin market. Such advancements not only furnish investors and researchers with refined forecasting tools but also accentuate the burgeoning role of deep learning methodologies in the domain of financial forecasting.
Abstract This study contributes to the unconsolidated cryptocurrency literature, with a systematic literature review focused on cryptocurrency market microstructure. We searched Web of Science database and focused only on journals listed on 2021 ABS list. Our final sample comprises 138 research papers. We employed a quantitative and an integrative analysis, and revealed complex network associations, and a detailed research trending analysis. Our study provides a robust and systematic contribution to cryptocurrency literature by making use of a powerful and accurate methodology—the bibliographic coupling, also by only considering ABS academic journals, using a wider keyword scope, and not enforcing any restrictions regarding areas of knowledge, thus enhancing the contribution of extant literature by allowing the insights of more high-quality peripheral studies on the subject. The conclusions of this study are of extreme importance for researchers, investors, regulators, and the academic community in general. Our study provides high structured networking and clear information for research outlets and literature strands, for future studies on cryptocurrency investment, it also presents valuable insights to better understand the cryptocurrency market microstructure and deliver helpful information for regulators to effectively regulate cryptocurrencies.
This paper investigates the persistence in the cryptocurrency market, focusing on five distinct groups categorized by their market capitalization during the sample period from 2020 to 2023. The study aims to test two hypotheses: (H1) The degree of persistence in the cryptocurrency market is contingent on market capitalization, and (H2) The efficiency of the cryptocurrency market has increased in recent years. The methodology employed for this examination is R/S analysis. The results indicate that the cryptocurrency market maintains its inefficiency, and no significant variations in persistence are discerned among different cryptocurrency groups, leading to the rejection of H1. Outcomes related to H2 present a nuanced scenario. Specifically, Litecoin and Ripple exhibit supportive evidence for the Adaptive Market Hypothesis, suggesting an improvement in the efficiency of the cryptocurrency market in recent years. A noteworthy revelation pertains to the anomaly observed in Bitcoin. Despite being the most capitalized and liquid cryptocurrency, it demonstrates inefficiency akin to levels observed five years ago. The implications of this study contribute to the comprehension of cryptocurrency market efficiency. The findings challenge the assumptions of the Efficient Market Hypothesis, favoring instead the Adaptive Market Hypothesis. For practitioners, the results hold significance, providing evidence of price predictability, particularly in the case of Bitcoin. This suggests that trend trading strategies remain viable for generating abnormal profits in the cryptocurrency market. Acknowledgments Alex Plastun gratefully acknowledges financial support from the Ministry of Education and Science of Ukraine (0121U100473).
Abstract Since the onset of the COVID-19 pandemic, financial and commodity markets have exhibited significant volatility and displayed fat tail properties, deviating from the normal probability curve. The recent Russia-Ukraine war has further disrupted these markets, attracting considerable attention from both researchers and practitioners due to the occurrence of consecutive black swan events within a short timeframe. In this study, we utilized the Quantile-VAR technique to examine the interconnectedness and spillover effects between African equity markets and international financial/commodity assets. Daily data spanning from January 3, 2020, to September 6, 2022, was analyzed to capture tail risks. Our main findings can be summarized as follows. Firstly, the level of connectedness in returns is more pronounced in the lower and upper tails compared to the median. Secondly, during times of crisis, African equity markets primarily serve as recipients of systemic shocks. Lastly, assets such as Silver, Gold, and Natural Gas exhibit greater resilience to systemic shocks, validating their suitability as hedging instruments for African equities, in contrast to cryptocurrencies and international exchange rates. These findings carry significant implications for policymakers and investors in Africa equities.
This study investigates the influence of monetary policy and monetary policy uncertainties on Bitcoin returns, utilizing monthly data of BTC, and MPU from July 2010 to August 2023, and employing the Markov Switching Means VAR (MSM-VAR) method. The findings reveal that Bitcoin returns can be categorized into two distinct regimes: 1) regime 1 with low volatility, and 2) regime 2 with high volatility. In both regimes, an increase in MPU leads to a decline in Bitcoin returns: -0.028 in regime 1 and -0.44 in regime 2. This indicates that monetary policy uncertainty exerts a negative influence on Bitcoin returns during both downturns and upswings. Furthermore, the study explores Bitcoin's sensitivity to Federal Open Market Committee (FOMC) decisions.
This article explores the relationship between green energy and cryptocurrencies in the sustainable energy finance sector. The research findings contribute to our understanding of the application of green economy practice, enabling investors in financial markets, policymakers, and stakeholders to make informed decisions and develop specific strategies. Adopting the green economy paradigm makes it possible to promote collaboration and innovation by integrating ethical and responsible principles that can improve the overall quality of processes and boost sustainable growth. Cryptocurrencies have been widely used as financial instruments over the last decade. Given the development of the cryptocurrency market and the growing awareness of greener and more energy-efficient tokens, the green economy has become a popular topic for understanding economic and political issues. However, the literature still lacks clear evidence on how cryptocurrencies interact with green energies. Therefore, this study examines the long- and short-term relationships between dirty cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), clean cryptocurrencies such as Cardano (ADA), Ripple (XRP), Stellar (XLM), and green energies such as ISE Clean Edge Global Wind Energy, S&P Global Clean Energy, S&P TSX Renewable Energy and Clean Technology, Solactive China Clean Energy, in the period from January 2020 to September 2023. The results show that diversification is key, with clean cryptocurrencies such as ADA, XLM and XRP offering diversification opportunities alongside "dirty" cryptocurrencies such as BTC and ETH. Although sustainable energy indices show mixed evidence in the long and short term, they remain relevant for those who focus on clean energy investments. It is also becoming increasingly relevant for investors in sustainable portfolios to assess their environmental impact, especially for energy-intensive cryptocurrencies, and it is advisable to explore sustainable blockchain technologies.
Jinghua Wang, Geoffrey Ngene, Yan Shi, Ann Nduati Mungai
Policymakers and portfolio managers pay keen attention to sources of uncertainties that drive asset returns and volatility. The influence of uncertainty on Bitcoin has the potential to drive fluctuations in the entire cryptocurrency market. We investigate the predictability of thirteen economic policy uncertainty indices on Bitcoin returns. Using the Random Forest machine learning algorithm, we find that Singapore’s economic policy uncertainty (EPU) has the strongest predictive power on Bitcoin returns, followed by financial crisis (FC) uncertainty and world trade uncertainty (WTU). We further categorize these uncertainties into different groups. Interestingly, the predictability of uncertainty indices on Bitcoin returns within the international trade group is stronger compared to other uncertainty categories. Additionally, we observed that internet-based uncertainty measures have more predictive power of Bitcoin returns than newspaper- and report-based measures. These results are robust using various additional machine learning methods. We believe that these findings could be valuable for policymakers and portfolio managers when making decisions related to uncertainty drivers of cryptocurrency prices and returns.
José Antonio Núñez Mora, Mario Iván Contreras-Valdez, Roberto J. Santillán‐Salgado
This paper reports our findings on the return dynamics of Bitcoin and Ethereum using high-frequency data (minute-by-minute observations) from 2015 to 2022 for Bitcoin and from 2016 to 2022 for Ethereum. The main objective of modeling these two series was to obtain a dynamic estimation of risk premium with the intention of characterizing its behavior. To this end, we estimated the Generalized Autoregressive Conditional Heteroskedasticity in Mean with Normal-Inverse Gaussian distribution (GARCH-M-NIG) model for the residuals. We also estimated the other parameters of the model and discussed their evolution over time, including the skewness and kurtosis of the Normal-Inverse Gaussian distribution. Similarly, we determined the parameters that define the evolution of the estimated variance, i.e., the parameters related to the fitted past variance, square error and long-term average value. We found that, despite the market uncertainty during the COVID-19 emergency period (2020 and 2021), the selected cryptocurrencies’ return volatility and kurtosis were even greater for several other subperiods within our sample’s time frame. Our model represents an analytical tool that estimates the risk premium that should be delivered by Bitcoin and Ethereum and is therefore of interest to risk managers, traders and investors.
This paper aims to reveal the asymmetric co-integration relationship and asymmetric causality between Bitcoin and global financial assets, namely gold, crude oil and the US dollar, and make a comparison for their asymmetric relationship before and after the COVID-19 outbreak. Empirical results show that there is no linear co-integration relationship between Bitcoin and global financial assets, but there are nonlinear co-integration relationships. There is an asymmetric co-integration relationship between the rise in Bitcoin prices and the decline in the US Dollar Index (USDX), and there is a nonlinear co-integration relationship between the decline of Bitcoin and the rise and decline in the prices of the three financial assets. To be specific, there is a Granger causality between Bitcoin and crude oil, but not between Bitcoin and gold/US dollar. Before the outbreak of the COVID-19 pandemic, there was an Asymmetric Granger causality between the decline in gold prices and the rise in Bitcoin prices. After the outbreak of the pandemic, there is an asymmetric Granger causality between the decline in crude oil prices and the decline in Bitcoin prices. The COVID-19 epidemic has led to changes in the causality between Bitcoin and global financial assets. However, there is not a linear Granger causality between the US dollar and Bitcoin. Last, the practical implications of the findings are discussed here.
Nghiên cứu này sử dụng các mô hình GARCH, bao gồm EGARCH(1,1), GJR-GARCH(1,1), TGARCH(1,1) và APARCH(1,1) để khảo sát sự bất đối xứng trong biến động tỷ suất sinh lợi của các loại tiền điện tử như Bitcoin, Ethereum, Ripple (XRP), Binance Coin (BNB) và DigiByte (DGB) trong khoảng thời gian từ ngày 01 tháng 01 năm 2018 đến ngày 31 tháng 5 năm 2023. Kết quả cho thấy mô hình EGARCH(1,1) là mô hình tốt nhất để mô tả hiệu ứng bất đối xứng trong biến động tỷ suất sinh lợi của các chuỗi tiền điện tử. Sự biến động tăng nhiều hơn trong phản ứng với cú sốc tích cực hơn là cú sốc tiêu cực, hàm ý một hiệu ứng bất đối xứng khác với hiệu ứng thường thấy trên thị trường chứng khoán. Kết quả nghiên cứu giúp nhà đầu tư và nhà quản lý rủi ro trong thị trường tiền điện tử hiểu rõ hơn về sự biến động giá, nhận biết, đánh giá rủi ro một cách chính xác hơn và đưa ra các chiến lược đầu tư phù hợp.
In an age of rapidly changing technological revolutions, where cryptocurrencies and blockchain play key roles, studying the dynamics of cryptocurrency markets at the government level is becoming an urgent need, which is not just a step into the future, but also an opportunity for countries to act forward, based on data analysis and forecasting global economic trends. Every aspect of cryptocurrency - from financial stability to technological innovation - has the potential to transform the global landscape. Studying the interaction of cryptocurrencies with national interests will not only help to determine the positions of countries in this context, but also formulate effective strategies for managing this rapidly developing economic segment. It is important to realize that those states that integrate cryptocurrency market analysis into their strategies can best adapt to the challenges of the modern world and promote their economic prosperity. The purpose of the research is to study how the introduction of digital money into the economy affects the interest of various countries in participating in trading in the cryptocurrency market. To identify the relationship between the integration of such assets into the economy and the desire of host countries to participate in cryptocurrency markets. Consequently, there is a need to analyze the mechanisms of interaction of large economic entities - states - with cryptocurrencies, as well as predict the likely responses in this context of research. Using panel data analysis, to conduct a study of the dynamics of the cryptocurrency market in the digital finance market using the example of 50 countries around the world. To identify the relationship between the attitudes of countries and the dynamics of the cryptocurrency market in order to suggest possible directions for the future development of the studied evolutionary economic sphere. Materials and methods. As a basis for the study, a balanced and informative set of indexes (17 indexes) was identified, which represents the key variables necessary for a more in-depth analysis of the dynamics of cryptocurrency markets in the context of various countries over a period of ten years (2013-2022). The “Cryptocurrency trading volume” index was chosen as the effective index. The set of indexes was selected based on their ability to reflect cryptocurrency trading volumes, investor activity, and each country’s level of involvement in cryptocurrency transactions. The impact of various factors on the volume of transactions with electronic money and digital financial assets was assessed using panel data analysis methods in the Gretl statistical analysis program. Results. As a result of the analysis using the panel data tool, three models were created: a pooled regression model, a fixed-effects model, and a random-effects model. The choice of the best model is made through testing special hypotheses - the Brisch-Pagan test and the Hausman test. The fixed effects model was preferable to the random effects model in this study. The reason is the fixed effects model’s ability to take into account the individual characteristics of each country in the sample, leading to more accurate results. Based on the study of individual fixed effects, three groups of countries were identified: those that have a positive impact on the volume of cryptocurrency trading (for example, the United States and Japan), countries with a neutral impact (for example, Germany), and countries where individual effects have a negative impact (for example, China and Russia). Conclusion. Overall results indicate that countries with advanced digital infrastructure and ease of use of electronic payments, as well as inflationary and cultural influences, may exhibit higher activity in cryptocurrency markets. Based on the fixed effects model and taking into account assumptions about the dynamics in different countries, general conclusions were formulated regarding the index analyzed in this study - the volume of cryptocurrency trading.
To what extent does the collapse of a commercial bank spread contagion across cryptocurrency markets? How do markets behave around bankruptcy if digital assets remain stuck within the bank and cannot be withdrawn? We use a BEKK model to examine contagion effects across major digital assets during the Silicon Valley Bank (SVB) collapse period in early March 2023. We find evidence of contagion across major stablecoins and Bitcoin. We also examine the price action when nearly all withdrawals at SVB were prohibited. We find substantial abnormal movements in stablecoin cumulative returns and volumes, indicating a “flight to safety” from less to more authoritative and trusted stablecoins. The implications for practitioners and policymakers are discussed.
This study examines the role of cryptocurrencies as a hedging and safe-haven instrument against stock market risk. Employing five of the largest cryptocurrencies by market capitalization: BTC, ETH, BNB, ADA, and XRP, from 2017–2022 in a variance-optimal hedging framework we investigate and compare the hedging effectiveness of cryptocurrencies for the developed G7 and emerging BRICS stock markets. Based on EVT we introduced a new approach to the assessment of hedging effectiveness. We found that the probability of at least 10-percent hedging effectiveness of Bitcoin is approximately equal to zero. The conditional probability that Bitcoin can reduce at least 10% of volatility given that index returns fall below the 1st percentile is higher and ranges from 2% to 28.4% depending on the stock market. The probabilities estimated for other cryptocurrencies are lower. We provide new and valuable knowledge for investors, who consider cryptocurrencies as a shelter for their investment portfolios.
<p class="MsoNormal" style="margin-top: 6.0pt;"><span lang="EN-US" style="mso-bidi-font-size: 10.5pt; font-family: 'Cambria',serif; mso-fareast-font-family: 宋体; mso-bidi-font-family: 'Times New Roman';">Gold has been traditionally well recognized as a safe heaven for financial markets. Lately, Bitcoin has been gradually considered as a popular alternative. Since the outbreak of COVID-19 in early 2020, it has become even more necessary and critical to examine the diversification capability of them to hedge financial risks associated with an unexpected crisis comparable to the pandemic. This paper hence employs the wavelet analysis, complemented by the multivariate DCC-GARCH approach, to measure the coherence of the gold and Bitcoin prices with six representative stock market indices, three for developed economies and three for emerging economies, all of which are heavily affected by the pandemic. To have a more balanced and comprehensive analysis, two-year data are used, spanning from 12th April 2019 to 15th April 2021, which covers approximately one year before and one year after the announcement of the COVID-19 pandemic. The results suggest that the returns of both gold and Bitcoin are generally not strongly correlated with the market returns of all six indices, particularly for short-term investment horizons. That is, investors in all six indices can benefit through gold, as well as Bitcoin, in terms of hedging. Meanwhile, compared with Bitcoin, gold shows to be less correlated with the indices, particularly for long-term investment horizons. The findings hence suggest that gold and Bitcoin offer diversification benefits to investors in the market indices during a crisis such as the COVID-19 pandemic, especially for short-term investment horizons. The study also reminds policymakers thinking beyond the pandemic about the future of the earth, including air pollution and health, for sustainable development of the whole world.</span></p>
Bu çalışmada, Bitcoin fiyatları ile ekonomik politika belirsizlik endeksi (EPU), geniş para arzı (M3) ve enflasyon arasındaki ilişki ARDL sınır testi ve Toda-Yamamoto nedensellik testleri kullanarak araştırılmak istenmiştir. Bu bağlamda söz konusu değişkenler arasındaki kısa ve uzun dönem ilişkisi BRIC (Brezilya, Rusya, Hindistan ve Çin) ülkeleri açısından Ağustos 2010-Aralık 2021 arası aylık veriler kullanılarak gerçekleştirilmiştir. Ampirik analizler sonucunda Çin’nin EPU endeksinin uzun ve kısa dönemde Bitcoin’i negatif etkilediğine ulaşılmıştır. Hindistan için EPU endeksinin uzun dönemde Bitcoin fiyatı üzerindeki etkisi negatif iken; kısa dönemli etkiye rastlanılamadığı görülmüştür. Rusya ve Brezilya içinse EPU endeksi Bitcoin üzerinde etkili bulunamamıştır. BRIC ülkelerinde enflasyonun Bitcoin üzerindeki etkisi uzun dönemde pozitiftir. M3’ün Bitcoin üzerindeki etkisi Hindistan için kısa dönemde pozitif, Brezilya için uzun dönemde negatif yönlü çıkmıştır. Son olarak nedensellik sonuçlarına göre Hindistan ve Brezilya’da enflasyondan Bitcoin’e doğru tek yönlü nedensellik mevcuttur. Çin içinse enflasyondan Bitcoin’e; Bitcoin’den de ekonomik politika belirsizliğine doğru nedensellik ilişkisi söz konusudur. Elde edilen bulgular Bitcoin yatırımcılarının ve politika yapıcıların M3, enflasyon ve EPU’nun etkilerini göz önünde bulundurarak girişimde bulunmalarına ve Bitcoin’le ilgili düzenlemeler geliştirmelerine katkıda bulunacaktır.
Pham Thi Ngoc Dung, Long Luong, Le Ngoc Thuy Trang, Do Thi Thanh Nhan
This study aims to analyze the role of bitcoin and gold as safe haven assets against Asian equity markets during periods of high market uncertainty related to the global COVID-19 pandemic, high volatility, and extreme stock market conditions. Empirical analysis employ the DCC-GARCH methodology to estimate the time-varying relationship between bitcoin/ gold and the Asian stock market from 2016 to 2023. Our findings reveal that bitcoin serves as a strong hedge for Taiwan and Pakistan, whereas gold can be considered as a strong hedge for Japan, Singapore, India, Thailand and Vietnam. Interestingly, we observed that bitcoin does not exhibit safe haven properties in any of the Asian countries observed. In contrast, gold demonstrates strong safe haven abilities for Singapore, India, and Thailand. These results remain consistent across various measures of market turmoil, including the volatility index, COVID-19-related periods, and low quantiles in the stock market. Furthermore, our results suggest that the perception and adoption of gold as a safe haven asset in Japan and Vietnam is mainly influenced by global events and uncertainties, rather than localized stock market conditions. These findings offer valuable information for investors, financial institutions, as well as policy makers and regulators, on how cryptocurrency and gold evolved as hedge and safe haven assets in Asia during uncertainty periods.