Therefore, the price of Bitcoinskyrocketed, highlighting the immediate response of the cryptocurrency market togeopolitical uncertainties On the other side of cryptocurrency market, Jama
INTRODUCTION - - Án HT TH nh TH TH HT Hi nh 7 1 Rationale 1
Research objectives and taskS -.- - ú- ô+ 4 TH TH TH nước 11 1 Research 002i lãi °, Win ẻ nẽnẽn
This study investigates the influence of geopolitical risks and economic policy uncertainty on cryptocurrency behavior, adoption, and market dynamics By utilizing the Geopolitical Risk (GPR) index alongside the Economic Policy Uncertainty (EPU) index and analyzing Bitcoin's returns, the research aims to provide valuable insights into the complex interactions between global political events, economic policies, and the evolving cryptocurrency market.
- Systematize general theoretical and practical issues about the effects of geopolitical risks, economic policy uncertainty and the reaction of cryptocurrencies.
- Examine and analyze the interaction of geopolitical risks, economic policy uncertainty and Bitcoin’s return
- Evaluating whether Bitcoin can be categorized as a safe-haven asset.
- Suggesting policy implications for investors and government organizations
How do geopolitical risks and economic policy uncertainty affect bitcoin's return?
What is the interplay between geopolitical risks, economic policy uncertainty and bitcoin returns?
Does Bitcoin function as a reliable safe-haven asset during periods characterized by heightened geopolitical risk and economic policy uncertainty?
What are the policy implications for investors and government organizations?
The study focuses on observing values about geopolitical risks, economic policy uncertainty, bitcoin’s return
A practical study of Global scale, Indexes is taken from official website names respectively: geopolitical risk (GPR) index, economic policy uncertainty, investing.com
The study uses monthly data with 115 observations between November 2013 and May 2023.
On the basis of the methodology, the following methods are used in the implementation process:
The research topic is divided into 4 chapters, in addition to the list of references, table catalogs and related annexes:
Chapter 1: Overview of research situation, theoretical and practical basis about the effects of geopolitical risks and economic policy uncertainty on cryptocurrencies
Chapter 2: Design and Research Methods
Research Structure i4
The research topic is divided into 4 chapters, in addition to the list of references, table catalogs and related annexes:
Chapter 1: Overview of research situation, theoretical and practical basis about the effects of geopolitical risks and economic policy uncertainty on cryptocurrencies
Chapter 2: Design and Research Methods
OVERVIEW OF RESEARCH SITUATION, THEORETICAL AND 9.900.008.711 5
Overview of the research SIUAfIOII - - 5 + 119v 9 ng ng nước 13 1 Literature review 2Q nh HT TH nh TH HH HH hệt 13 The impact of geopolitical risks, economic policy uncertainty on CLYPLOCUITENCIECS .ceesccesceescecsseceseeseseceseecsaeeescessceceseeesaeceseeececeaeeeseeseaeeeseeseaeeeteeseaeeeaes 13 1.2 The impact of geopolitical risks on financial aSSefS - ô<< <<+<c<s+2 15 1.3 The impact of economic policy uncertainty on financial assetfs
Geopolitical factors such as terrorism, warfare, and political tensions, along with economic policy uncertainty regarding fiscal, monetary, and regulatory measures, significantly influence the currency market Unexpected adjustments in government policies, including tax and interest rates, contribute to instability and uncertainty about future economic and political trends, adversely affecting the stock market These changes can dramatically impact business profits and increase investor anxiety about declining stock values, prompting many to seek alternative assets to mitigate portfolio risk In recent years, the cryptocurrency market, particularly bitcoin, has garnered global attention, although its price fluctuations remain a concern for investors Consequently, numerous studies have emerged, focusing on the effects of geopolitical risks and economic policy uncertainty on bitcoin's returns, making these factors a primary area of interest for researchers.
1.1.1.1 The impact of geopolitical risks, economic policy uncertainty on cryptocurrencies
In a 2023 study by Jihed Ben Nouir and Hayet Ben Haj Hamida, researchers employed an Autoregressive Distributed Lag (ARDL) model and quantile regression techniques to analyze monthly data, revealing intricate relationships between economic and geopolitical factors and Bitcoin's volatility The findings indicate that U.S economic policy uncertainty (EPU) and geopolitical risk (GPR) have short-term impacts on Bitcoin's price fluctuations, while China's uncertainty shows more prolonged effects The research highlights that Bitcoin's response to U.S EPU and GPR is notably similar, emphasizing the cryptocurrency's sensitivity to these external factors.
The research highlights the interdependence between economic policy uncertainty (EPU) and geopolitical risk (GPR) in the context of Bitcoin's volatility It reveals that Bitcoin reacts differently to these factors in China compared to the U.S., indicating varying influences on its price Ultimately, the study concludes that Bitcoin acts as a hedge against U.S economic policy uncertainty and geopolitical risks, offering investors protection during times of economic and geopolitical instability.
Sanjeet Singh et al (2022) explored the relationship between geopolitical risk, economic policy uncertainty, and Bitcoin in P5 + 1 nations, utilizing partial and multiple wavelet coherence analysis Their findings indicate a strong positive correlation in the short term among economic policy uncertainty (EPU), geopolitical risk (GPR), and Bitcoin returns, highlighting the interconnectedness of these elements within the cryptocurrency market.
A study by Francisco Colon et al (2021) examined Bitcoin's effectiveness as a hedge against geopolitical risk (GPR) among 25 cryptocurrencies The results revealed that Bitcoin generally functions as a strong hedge against GPR, although its effectiveness diminishes during bull markets, indicating that its role as a safe haven is influenced by prevailing market conditions.
Md Al Mamun (2020) examined the influence of geopolitical risk and economic policy uncertainty on Bitcoin's dynamics The study found that heightened geopolitical risk and global economic policy uncertainty increased Bitcoin's risk premium, especially in distressed market environments Additionally, Bitcoin emerged as a viable hedge during periods of significant policy uncertainty and worsening economic conditions, complementing traditional safe havens like gold and offering investors a potential diversification strategy amidst economic and geopolitical turmoil.
A recent study by Ngo Thai Hung et al (2023) analyzed the impact of six key uncertainty indices on Bitcoin, including Global Economic Policy Uncertainty, Equity Market Volatility, Twitter-based Economic Uncertainty, Geopolitical Risk Index, Cryptocurrency Policy Uncertainty Index, and Cryptocurrency Price Uncertainty Index The research utilized monthly data from January, providing insights into how these indices influence Bitcoin's performance.
From 2014 to December 2022, a study using an alternative methodology identified a negative correlation between Bitcoin prices and specific uncertainty factors The findings indicated that increased uncertainty levels corresponded with decreased fluctuations in Bitcoin's value across various time and frequency domains.
In a recent study, Toan Luu Duc Huynh et al (2023) explored the impact of economic policy uncertainty on Bitcoin's returns, trading volumes, and price volatility, analyzing data from May 2013 to June 2023.
2019, and this examination was subsequently extended to include data up until May
In 2020, the COVID-19 pandemic significantly influenced global markets, prompting a study that employed the transfer entropy model to analyze Bitcoin's behavior under two regimes: stationary and nonstationary The findings demonstrated a negative correlation, revealing that global Economic Policy Uncertainty negatively affected both Bitcoin trading volumes and price volatility.
A study by Binh Nguyen Quang et al (2020) analyzed the effects of world uncertainty (WUD), global economic policy uncertainty (GEPU), and geopolitical uncertainty (GUI) on the returns and liquidity of 964 cryptocurrencies from April 28, 2013, to July 14, 2018 The research revealed that increased GEPU negatively impacted cryptocurrency portfolio returns, while higher WUI significantly reduced liquidity, particularly in medium sub-portfolios The study highlighted distinct differences in how GEPU and WUI affected cryptocurrency returns and liquidity, but found no substantial influence of GUI on these financial metrics.
Geopolitical risks significantly influence financial assets, as highlighted by Caldara and Jacoviello (2022), who emphasize their crucial role in investment decisions and stock market dynamics Their study, utilizing vector autoregressive (VAR) models from 1985 to 2019 in the United States, reveals that shocks from geopolitical risks lead to extended declines in investment, employment, and stock prices, with these negative impacts enduring over time.
15 to both the anticipation and realization of unfavorable geopolitical events, indicating the profound impact of these risks.
In their 2022 study, Georgios P Kouretas and colleagues investigate the impact of geopolitical risks on stock returns across 22 countries from 1985 to 2020 The research incorporates macroeconomic factors and market structures, while also considering the effects of the 2007-2009 financial crisis The findings indicate a strong, statistically significant negative relationship between geopolitical risks and stock returns, suggesting that a one-unit standard deviation increase in geopolitical risks could lead to a decrease in stock returns ranging from 10.53% to 42.14% of the average return in the sample.
Ender Demir et al (2022) investigate the hedging properties of various asset classes amid the geopolitical tensions stemming from the Russian invasion of Ukraine Using wavelet coherence analysis, the study reveals that different securities respond uniquely to geopolitical risk, with bonds and stocks showing strong coherence over multi-week periods, while currencies react more sensitively to short-term changes The research highlights green bonds, gold, silver, the Swiss franc, and real estate as resilient assets that effectively hedge against geopolitical uncertainties.
The relationship between geopolitical risks and financial assets is further explored in Thomas C Chiang's 2021 research, which focuses on the Chinese market Chiang's study highlights a significant correlation between absolute changes in geopolitical risk (|AGPRt|) and the returns of stocks, bonds, and gold Notably, the findings indicate a positive relationship between stock-gold returns and increased |AGPRt|, emphasizing the impact of geopolitical risk on asset performance in China Additionally, Dirk G Baur and Lee A Smales (2020) examine the unique role of precious metals, revealing that geopolitical risk is distinct from traditional economic and financial indicators.
SUMMARYYY .- Gv HH TH TH nh HH Hết 24
This chapter examines the impact of Geopolitical Risk (GPR) and Economic Policy Uncertainty (EPU) on financial assets, particularly focusing on Bitcoin It identifies these factors as crucial drivers of cryptocurrency market dynamics and reviews existing literature while highlighting the need for further research into the intricate relationships between GPR, EPU, and Bitcoin Key concepts such as cryptocurrencies, geopolitical risk, and economic policy uncertainty are defined to provide a foundation for subsequent analyses Additionally, the chapter showcases innovative methodologies from previous studies that enhance our understanding of how the cryptocurrency market responds to these influences Ultimately, it underscores the significance of investigating these relationships within the context of a rapidly changing financial landscape.
DESIGN AND RESEARCH MODEL, - 5c svseeseeses 25 2.1 Data AeScription nh ốc 25 2.2 The specification of the var MOdel] -.- c2 + 33+ 3333 E**EEEvEEEeeeeeereeesereerreeee 26
Unit root tests SỪỪỪỪỔỮÚỒẶ Á
The stationarity of data is an essential aspect in time series data analysis In Phillips'
In 1986, research emphasized that non-stationary data series, which exhibit changing statistical properties over time, can result in unreliable or misleading analyses and modeling outcomes This finding highlights the critical need to assess the stationarity of time series data before conducting any analyses.
27 unit root tests to ensure data stationarity, providing a foundation for more accurate analyses and modeling (Phillips, 1986).
The Augmented Dickey-Fuller (ADF) unit root test is a standard method for assessing the stationarity of time series data, having evolved from the original Dickey-Fuller test as described by Engle and Granger (1987) ADF is essential for determining whether variables are stationary, which is crucial for accurate modeling and forecasting The test compares a t-statistic to critical values to evaluate stationarity This study utilizes the ADF test to analyze the stationarity of three variables: GEPU_current, GPR, and BTC's returns The results provide valuable insights into the stationarity properties of these time series, establishing a reliable foundation for concluding the stationarity of GEPU_CURRENT, GPR, and BTC's return data.
Choosing the right lag length in a time-series model is crucial for ensuring optimal model performance and reliable results The selected lag length influences how well the model reflects the data's dynamics According to Song and Witt (2006), a short lag period may fail to adequately represent the data generating process, risking inaccuracies, while an excessively long lag period can diminish degrees of freedom, making parameter estimation unreliable.
Lag length selection is crucial in time-series modeling, often utilizing statistical criteria like the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Schwarz Information Criterion (SIC) to find the optimal balance between model complexity and goodness of fit, as noted by Hamilton (1994) These criteria assist researchers in determining the lag length that best represents the underlying relationships in the data, ensuring accurate model performance.
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Series Correlation fSfS 0 nghệ 29 2.6 Heteroskedasticity fŒSS LH TH HH HH HH HH 29 2.7 Inverse Roots of AR Characteristic Polynomial .- - ô ô<< s+++se++sexssess 30 P.00) s6i- 309): s00) i1) in
David A Dickey and Wayne A Fuller (1979) investigated the effects of serial correlation on the distribution of estimators in autoregressive time series models with a unit root, highlighting that serial correlation can lead to inefficient estimations and biased estimator statistics They emphasized the necessity of testing for serial correlation to maintain the integrity of these models and their related estimations, laying a vital theoretical groundwork for addressing this issue in statistical research and economics Furthermore, Graham Elliott, Thomas J Rothenberg, and James H Stock (1996) reinforced the importance of tackling serial correlation to ensure accurate model estimates and reliable inferences.
This research employs VAR Residual Serial Correlation LM Tests to investigate the presence of serial correlation in the residuals of the Vector Autoregression (VAR) model Detecting serial correlation in residuals suggests potential patterns or relationships within the unexplained data, which may compromise the reliability of the model's estimates.
Ralf Briiggemann (2016) highlighted that many econometric models for financial and macroeconomic time series often show innovations with serial lack of correlation, yet they are not identically and independently distributed A key deviation from independence is conditional heteroskedasticity, a finding echoed by Gongalves and Kilian (2004), who identified similar deviations in various empirical studies These inconsistencies in innovations can invalidate standard inference methods, potentially leading to misleading conclusions about underlying dynamics Briiggemann's research provides theoretical insights for making inferences in stable VAR (Vector Autoregression) models that accommodate uncorrelated innovations under a mixing assumption, effectively addressing general deviations from independence, particularly the critical case of unknown conditional heteroskedasticity.
2.7 Inverse Roots of AR Characteristic Polynomial
The stability of the VAR (q) model, as outlined by G O Nwafor et al (2016), is confirmed when the eigenvalues fall within the unit circle, indicating that their absolute values are less than one The AR root table, referenced by Liitkepohl (1991), illustrates the inverse roots of the characteristic AR polynomial, with stability achieved when all roots maintain this condition If any roots exceed the unit circle, the VAR (q) is deemed unstable This research emphasizes the importance of conducting the AR root test to ensure the VAR model's stability, as model misspecification or parameter non-constancy can severely impact statistical inferences and lead to misleading outcomes (Xu and Lin, 2016).
Lawrence J Christiano's paper affirms that Sims' research on VAR-based impulse response functions has significantly influenced economic thought, particularly in understanding the aggregate economy These functions have been instrumental in assessing the impacts of economic shocks on crucial indicators such as GDP, inflation, and unemployment, guiding economists in model selection and shaping debates on topics like government spending and monopolies As a vital analytical tool, impulse response functions enhance comprehension of variable interactions and economic disruptions Building on these foundational insights, this study utilizes impulse response functions to analyze the interrelationships among GPR, GEPU_current, and Bitcoin return values.
SUMMARY: 11 33454
Chapter 2 outlines the methodology for examining the relationships among geopolitical risk (GPR), global economic policy uncertainty (GEPU_current), and Bitcoin's return (BTC’s return) using a Vector Autoregressive (VAR) model This approach captures the dynamic interactions and shock influences on BTC’s return Lag lengths are determined by the Akaike Information Criterion (AIC) for precise modeling The chapter details a dataset of 115 monthly observations from November 2013 to May 2023, sourced from Bitcoin price records, the GPR index, and the global EPU index Stationarity is evaluated using unit root tests, confirming Bitcoin's return is stationary, while further analysis is warranted for GEPU_current The logarithm of GPR is stationary, suitable for time series analysis Additionally, the chapter addresses serial correlation, heteroskedasticity tests, and the AR root test to ensure model stability, and explores impulse response functions to analyze the impact of economic shocks on these variables These findings provide a foundation for understanding how global events affect the cryptocurrency market.
Chapter 2 explores the complexities of research methodology and data collection, emphasizing the importance of data stationarity Employing statistical tests to confirm stationarity lays a solid foundation for time series analysis, guaranteeing reliable and meaningful outcomes.
EMPIRICAL RESULTS & DISCUSSION - -<c<c<<+ 32 3.1 Specification tests 0.0
Unit root tests for Bitcoin’s T€ẨUTT .- - Gv HH ng ni, 32 3.1.2 Unit root tests for GEPÙ_ €urr€n( ô=5 + + + ***Ê+++2225 5 eeeeeeeee 32 3.1.3 Unit root tests for CIP - - 6 c cv 1k TH TH TH ng ngà 34 3.2 Experimental Results and VAR Model Discussion -ôô++- ô<< s+<ss2 36 3.2.1 Lag length selection
The analysis of Bitcoin's return reveals a significant rejection of the null hypothesis regarding the presence of a unit root, indicating that the return series is stationary with an exceptionally low p-value close to zero This crucial finding confirms that the Bitcoin return series does not display long-term trends or structural instability, paving the way for more in-depth analyses.
Table 1: Unit root tests for Bitcoin’s return
Null Hypothesis: BITCOIN_RETURN has a unit root
Lag Length: 0 (Automatic - based on AIC, maxlag) t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -8.993586 0.0000
3.1.2 Unit root tests for GEPU_current
The unit root tests for GEPU_current reveal a t-statistic of -3.449895 and a p-value of 0.0500, alongside critical values at 1%, 5%, and 10% significance levels for comparison Since the t-statistic is higher than the critical values at both the 1% (-4.041280) and 5% (-3.450073) levels, we fail to reject the null hypothesis, indicating that the data for GEPU_current is non-stationary at these significance levels.
Table 2: Unit root tests for GEPU_CURRENT
Null Hypothesis: GEPU_CURRENT has a unit root
Lag Length: I (Automatic - based on AIC, maxlag)
Augmented Dickey-Fuller test statistic t-Statistic Prob.*
Augmented Dickey-Fuller Test Equation
The current series of GEPU yields inconclusive results, indicating a need for further analysis to establish stationarity definitively Utilizing the first-order differenced time series serves as a representation of stationarity The ADF test results demonstrate a rejection of the null hypothesis.
The ADF test statistic of -8.802335 is well below the critical values at the 1%, 5%, and 10% levels, indicating that the series is stationary Additionally, the coefficients for lagged values of DGEPU, particularly the constant coefficient for DGEPU(-1), demonstrate high significance, contributing to the overall model fit, as reflected in the R-squared statistics.
The analysis reveals a mean dependent variable of 0.985737 and a standard deviation of 41.12268, indicating variability in the dataset The Akaike Information Criterion (AIC) is calculated at 10.11878, while the Schwarz Criterion (SC) stands at 10.21533, and the Hannan-Quinn Criterion is 10.15796 Additionally, the Durbin-Watson statistic is 1.979510, suggesting a lack of autocorrelation in the residuals The variable DGEPU demonstrates strong evidence of stationarity, as it includes a constant and a linear trend in the unit root tests conducted.
33 squared, adjusted R-squared, AIC, and an F-statistic, further support the stationarity of DGEPU, making it suitable for reliable time series analysis and forecasting.
Table 3: DGEPU_ First-order difference
Null Hypothesis: DGEPU has a unit root
Lag Length: 2 (Automatic - based on AIC, maxlag) t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -8.802335 0.0000
Augmented Dickey-Fuller Test Equation
Variable Coefficient Std Error t-Statistic Prob.
S.E of regression 38.45114 Akaike info criterion 10.18065
Sum squared resid 156720.0 Schwarz criterion 10.30270
Log likelihood -560.0262 Hannan-Quinn criter 10.23017
3.1.3 Unit root tests for GPR
The unit root test results for the logarithm of GPR (LOGGPR) indicate strong evidence of stationarity in the series The Augmented Dickey-Fuller (ADF) test successfully rejected the null hypothesis of a unit root, confirming that LOGGPR is stationary.
The test statistic of -4.735093 is significantly lower than the critical value at the 1% level, indicating statistical significance, while being slightly above the 5% level The presence of a constant and a linear trend in the test equation, along with highly significant coefficients for the lagged value of LOGGPR and the constant term, reinforces the stationary nature of the series This finding suggests that the logarithm of GPR is appropriate for detailed time series analysis and forecasting, demonstrating a stable and predictable pattern, as supported by the model fit statistics and overall significance.
Table 4: Unit root tests for LogGPR
Null Hypothesis: LOGGPR has a unit root
Lag Length: 0 (Automatic - based on AIC, maxlag) t-Statistic Prob.*
Augmented Dickey-Fuller test statistic -4.735093 0.0010
Augmented Dickey-Fuller Test Equation
Variable Coefficient Std Error t-Statistic Prob.
S.E of regression 0.196802 Akaike info criterion -0.387270
Sum squared resid 4.299162 Schwarz criterion -0.315265
Log likelihood 25.07438 Hannan-Quinn criter -0.358047
3.2 Experimental Results and VAR Model Discussion
This study follows the traditional methodology established in Hamilton (1994), utilizing a lag of 1 determined through various statistical criteria, including the sequential modified LR test statistic, final prediction error (FPE), Akaike information criterion (AIC), Schwarz information criterion (SC), and Hannan-Quinn information criterion (HQ), as presented in Table 5.
VAR Lag Order Selection Criteria
Endogenous variables: BITCOIN_-_RETURN DGEPU LOGGPR
Lag LogL LR FPE AIC SC HQ
* indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
HQ: Hannan-Quinn information criterion
The findings highlight the relationship between variables and include statistical indicators of model fit Although the R-squared values are modest, the model demonstrates statistical significance, and the information criteria indicate an appropriate balance between complexity and fit.
Standard errors in () & t-statistics in [ ]
Determinant resid covariance (dof adj.) 2.963006
Table 7: VAR Residual Serial Correlation LM Tests
VAR Residual Serial Correlation LM Tests
Null hypothesis: No serial correlation at lag h
Lag LRE* stat df Prob Rao F-stat df Prob.
Null hypothesis: No serial correlation at lags 1 toh
Lag LRE*stat df Prob Rao F-stat df Prob.
*Edgeworth expansion corrected likelihood ratio statistic.
The tests conducted for lag 1 and lag 2 reveal no significant serial correlation, indicated by high p-values of 0.8530 and 0.5911, respectively Furthermore, the cumulative assessment of serial correlation between lags 1 and 2 also shows a lack of strong evidence, with a probability of 0.5994 These results suggest that the VAR model effectively captures temporal dependencies in the data, thereby improving the reliability of parameter estimates and overall model performance.
The VAR Residual Heteroskedasticity Tests indicate the importance of addressing heteroskedasticity in the residuals of the Vector Autoregression (VAR) model Heteroskedasticity, as noted by Jim Frost, refers to the inconsistency in the variance of error terms across observations, which can significantly affect the reliability of model estimates and inferences.
VAR Residual Heteroskedasticity Tests (Levels and Squares)
Dependent R-squared (6,106) Prob Chi-sq(6) Prob. res1*res1 0.093471 1.821582 0.1017 10.56220 0.1029 res2*res2 0.03873I 0.711818 0.6408 4.376606 0.6259 res3*res3 0.074075 1.413352, 0.2163 8.370475 0.2122 res2*res | 0.062624 1.180279 0.3224 7.076560 0.3138 res3*res 1 0.074377 1.419583 0.2139 8.404633 0.2099 res3*res2 0.1044ó6 = 2.060855 0.0639 II1.804ó6 0.0665
The research investigates the Inverse Roots of the AR Characteristic Polynomial to assess the stability of the VAR model The results indicate that all characteristic roots lie within the unit circle, suggesting that a VAR model with a lag of 1 is both stable and suitable for application.
Inverse Roots of AR Characteristic Polynomial
Figure 1: Inverse Roots of AR Characteristic Polynomial
Impulse response function enables the study to identify and measure the impact of a sudden change, referred to as a shock, in one variable on other variables in a VAR model.
Monitoring the time changes in responsive variables following a shock provides crucial insights into the interactions and relationships within the system, as illustrated in Figure 2.
Response of BITOOIN_RETURN to BI TCOIN_ RETURN Response of BITCOIN_RETURN to DGEPU Response of BITCOIN_RETURN to LOGGPR
Response of DGEPU to LOGGPR
Response of LOGGPR to BITCOIN_RETURN Response of LOGGPR to DGEPU
The results obtained from the var model and impulse response function analysis show:
Response of Bitcoin_return to DGEPU
The response graph of Bitcoin_return to DGEPU examines how Bitcoin reacts to fluctuations in the DGEPU index, highlighting the impact of global economic policy uncertainty on its returns Findings indicate that following an economic policy uncertainty shock, Bitcoin typically experiences an immediate decline in profits over the first two weeks, as investors perceive heightened risk and seek to protect their investments by reducing exposure However, after this short-term dip, Bitcoin returns to its initial level, demonstrating market adaptability and investors' ability to readjust This suggests that while Bitcoin may not act as a safe-haven asset during periods of economic uncertainty, it has the potential to rebound once the initial shock subsides, indicating that it may not be a stable option amidst rapid changes and uncertainty.
Recent studies highlight the complex relationship between cryptocurrency performance and economic policy uncertainty (EPU) Binh Nguyen Quang et al (2020) found that increasing geopolitical risk has a negative impact on cryptocurrency portfolios Similarly, Shan Wu et al (2019) noted that Bitcoin does not effectively hedge against EPU, showing heightened sensitivity to EPU shocks, while both Bitcoin and gold are generally weak safe havens during extreme market conditions In contrast, Jessica Paule-Vianez et al (2020) observed that Bitcoin's returns and volatility increase during uncertain times, suggesting it possesses investment asset qualities This aligns with Gang-Jin Wang et al (2019), who reported that Bitcoin can serve as a safe haven or diversification tool during EPU-related shocks, indicating its potential utility beyond just a medium of exchange.
Response of Bitcoin_return to LOG GPR
Impulse response 00000005000: 18⁄4
Chapter 3 provides the empirical results and discussions It covers unit root tests, the critical choice of lag length in time-series modeling, presents VAR model results, serial correlation and heteroskedasticity tests, stability assessments, and the impulse response function analysis The chapter offers valuable insights into Bitcoin's return response to global economic policy uncertainty and geopolitical risk, underlining Bitcoin is not an alternative asset or a safe haven asset for investors during periods of geopolitical risk and economic policy uncertainty.
SUMMARY Gà 45
Chapter 3 presents empirical findings and discussions, including unit root tests and the critical selection of lag length in time-series modeling It showcases VAR model results, tests for serial correlation and heteroskedasticity, stability assessments, and impulse response function analysis The chapter provides key insights into Bitcoin's return behavior in relation to global economic policy uncertainty and geopolitical risk, emphasizing that Bitcoin does not serve as an alternative or safe haven asset for investors during times of geopolitical instability and economic uncertainty.
POLICY IMPLICATIONS ng HH ri, 46 4.1 Implications for inVeStors - + 1111991991119 ng 46 4.2 Implications for government OrỉaTI1ZAfIOTIS 5 c +< + +*kESeseeeeereereeers 46
Recent research indicates that, contrary to its promotion as a safe haven asset during political uncertainty, Bitcoin does not possess the stability required for such a role Despite its popularity and the enthusiasm it generates among investors, Bitcoin fails to serve as a reliable hedge in times of global upheaval The cryptocurrency market's significant volatility can result in unpredictable fluctuations, posing risks to investors Consequently, the study advises caution for those considering Bitcoin as a protective investment during turbulent periods.
Research suggests that investors should avoid investing in Bitcoin during global turmoil Instead, they should diversify their portfolios by exploring traditional assets that offer reliable investment opportunities in unstable times This approach is designed to minimize risks and safeguard financial assets during periods of uncertainty.
The government must swiftly establish regulations for Bitcoin investment to enhance safety and transparency in the cryptocurrency market These regulations should prioritize investor protection by ensuring that Bitcoin transactions are conducted transparently and legally It's essential that citizens receive accurate information about the risks involved in digital asset investments Moreover, the government should promote educational initiatives to help newcomers understand the risks and benefits of investing in digital currencies.
In the dynamic realm of global financial markets, the interplay between traditional assets, global political risk (GPR), and economic policy uncertainty (EPU) has garnered significant attention Cryptocurrencies, particularly Bitcoin, have emerged as a noteworthy asset class for risk management and safe-haven investments A comprehensive study led by esteemed scholars reveals that Bitcoin, often viewed as a hedge, may not fulfill this role during times of heightened GPR and EPU Utilizing rigorous empirical analysis and methodologies like unit root tests, Augmented Dickey-Fuller tests, and Vector Autoregression (VAR) models, the research offers critical insights into Bitcoin's performance amid geopolitical and economic uncertainties Unlike previous studies, such as Jihed Ben Nouir's work in 2023, these findings suggest that Bitcoin may not serve as a reliable safe haven or alternative asset in the face of adverse political and economic conditions.
Recent research highlights significant implications for the cryptocurrency market and investors facing today's complexities The findings indicate that Bitcoin is not a consistent safe haven, emphasizing the importance of diversification in investment strategies While it may be viewed as a speculative asset, Bitcoin is less effective as a reliable hedge against geopolitical uncertainty and policy ambiguities.
A recent study by a reputable research team emphasizes the importance for investors to adopt a careful and informed strategy Understanding Bitcoin's dynamics during global financial instability is essential for creating robust and diversified investment portfolios.
As the cryptocurrency market matures, this expert-led study lays the groundwork for future research, revealing the complex interactions between cryptocurrencies, global economic factors, and the dynamic landscape of financial markets.
In summary, recent research indicates that Bitcoin's status as a safe-haven asset is more complex than previously believed It emphasizes the importance of understanding Bitcoin's behavior amid geopolitical and economic uncertainties As the cryptocurrency landscape evolves, investors will need a more nuanced view of Bitcoin's role in portfolios The ongoing search for a dependable safe haven, highlighted by these scholars, will continue to influence investment strategies in our interconnected world.
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