This study aims to explore the dependency structure between the index of geopolitical risk and stock market returns.. Research objectives The research objectives: To assess the influence
For the successful completion of my thesis report, I would like to express my sincere thanks to:
I would like to express my heartfelt gratitude to my instructor, Master Nguyen Hai Nam, for his unwavering support and guidance during my scientific research project His dedication and willingness to provide valuable feedback and answer my questions were instrumental in helping me understand the topic and successfully complete this research paper.
I would like to express my gratitude to the Faculty of Finance and Banking teachers and all the educators at the University of Economics - VNU for providing an excellent learning environment that has significantly enhanced my skills and abilities, enabling me to excel in my studies.
Due to my limited knowledge and reasoning ability, | cannot avoid certain shortcomings We look forward to receiving your contributions to make this thesis topic better.
Chapter 1 introduces the research, outlining its background, objectives, and scope It details the specific subjects of the study and presents key research questions guiding the inquiry The chapter concludes with an overview of the research structure, providing a clear framework for the subsequent sections.
Chapter 2 provides a comprehensive literature review, beginning with an introduction that sets the stage for the discussion It explores the theoretical foundations of geopolitics and geopolitical risk, emphasizing their significance in understanding financial markets The section on measuring geopolitical risk outlines various methodologies used to assess its impact Additionally, the review highlights the relationship between geopolitical risk and financial assets, focusing on how such risks can positively influence asset performance Overall, this chapter synthesizes key concepts and findings from existing literature, offering valuable insights into the interplay between geopolitics and finance.
2.2.2 The negative impact of Geopolitical Risk on Financial ASSets -‹‹ 17
2.2.3 The uncertainty impact of Geopolitical Risk on Financial Assefs 19
2.3 Literature gap - - SHằ HH HH kg 20 2.4 Chapter Summ4ry - - c1 SH TH HH kg 21
CHAPTER 3: METHODOLOGY - - Q0 22011221122 11211110 1111111111111 11H 1H Hy 22 3.1 Research methods - - - - LH kg 22 K)? nh e 4d 24
Chapter 4 presents the empirical results, beginning with descriptive statistics that summarize key data insights It includes a correlation matrix that illustrates relationships among variables The section further explores the development and volatility of financial portfolios Additionally, it examines dynamic spillovers between financial assets across 12 sectors, detailing averaged dynamic connectedness, total connectedness, and net directional connectedness Finally, the chapter concludes with a summary of findings from the TVP-VAR model, highlighting significant relationships and trends within the data.
4.3 The impact of Geopolitical Risk on financial assets spillover 42 CHAPTER 5: CONCLUSION AND RECOMMENDATION ::ceccceescesseeeeeeeeeteeeeeeees 44
5.1 The research Contributions - - - - LH HH nhu 44 5.2 Policy implications LH TH HH HH 44 5.3 Limitations and recommend of the research - - nn nhe 46 ọi2n- i0 47
References in English - - - - - - - cv HH kh 47References in VietnainesSe - - - - SH HH nh 48
TVP - VAR Time - Varying Parameter VAR Model
Figure 2 1 GPR Index - Historical Chart
Figure 4.2 4 Wavelet Coherence: GPR and TCI
INTRODUCTION . - LH TH TH TH HH Hiện 8 1.1 Research background - TT HH Họ ch 8 1.2 Research objectẽVes HH HH 9 1.3 Research subjects and scope of S†Ud|y - - - - HH HH khe 9 1.4 Research Questions .- - - - - - TH TH HH TH ch 9 1.5 Structure of the research . QL ST HT n HT TH 10
In recent years, the global economic and political landscape has undergone significant changes, prompting major countries to adjust their strategies to enhance competitiveness and assert their positions internationally This shift has resulted in increased frictions and political conflicts across various regions, highlighting the importance of geopolitical risk for investors, as it indicates a country's political stability Political instability, characterized by coups, national conflicts, terrorism, or corruption, can drastically impact economic institutions and legal frameworks Geopolitical risks consistently rank among the top concerns for businesses, as conflicts can lead to property loss, workforce disruptions, and economic sanctions that threaten sustainability A notable example is the US-China trade war, which began in March 2018 when the US imposed a 25% tariff on $50 billion worth of Chinese goods, with China retaliating through its own tariffs shortly thereafter.
In the past two years, the US has imposed a 25% tax on $250 billion worth of Chinese goods, while China has reciprocated with a 25% tax on $110 billion of US imports, leading to a significant shift in the trade balance and exerting pressure on economic growth This ongoing US-China trade war is influencing macroeconomic factors, including inflation control and the potential for recession Heightened trade tensions could adversely impact various sectors, particularly the metals market, with copper facing considerable strain As geopolitical risks escalate globally, the uncertainties have intensified since the Covid-19 pandemic In Vietnam, there is now a growing awareness of these risks, especially as the market approaches a phase of inflation and economic downturn, prompting increased scrutiny of the Vietnamese market.
Geopolitical risks encompass fluctuations arising from state tensions, war threats, internal military conflicts, and terrorism, significantly impacting global prosperity The ongoing Russia-Ukraine conflict has exacerbated uncertainties in the global economy, compounding challenges already posed by the Covid-19 pandemic, which has resulted in record public debt, a cost-of-living crisis due to inflation, and labor shortages in essential sectors These risks can hinder production, disrupt business processes, and diminish operational efficiency Empirical evidence indicates that high geopolitical risk adversely affects both macroeconomic and microeconomic variables, leading to declines in real activity and lower stock returns The detrimental effects of geopolitical risks are primarily driven by the potential for negative geopolitical events, causing varied impacts on market financial indicators.
This study investigates the relationship between geopolitical risk, as measured by the Caldara and Jacoviello (2018) index, and stock market returns using the TVP-VAR method from February 2012 to April 2022 The analysis focuses on significant events, including the Russia-Ukraine war, domestic market fluctuations, and the post-Covid-19 recovery, to assess their impact on financial asset returns While existing research examines the influence of geopolitical risk on financial markets, there is a notable gap regarding its effects on the profitability of the Vietnamese securities industry Our findings provide valuable insights for policymakers and market participants in effectively managing geopolitical risks.
This study aims to evaluate the impact of Geopolitical Risk on the interconnectedness of financial assets within Vietnamese stock sectors from February 2012 to April 2022, particularly in light of significant fluctuations driven by events such as the Russia-Ukraine conflict.
- Ukraine war, a series of mistakes in the stock and bond market, the world reopens after Covid-19
1.3 Research subjects and scope of study
The research subject: Stock prices in the Vietnamese stock market.
Scope of study: Research the volatility of stock prices of sectors in Vietnam’s stock market from February 2012 to April 2022.
Specifically, this thesis intends to answer the following three research questions:How will the GPR index move?
How is the volatility correlated between the assets of the stock prices of the sectors on the Vietnamese stock market?
How much influence does GPR have on asset returns?
What is the relationship between geopolitical risk and the degree of financial asset linkage of the Vietnamese securities industry?
Are the economic effects of higher geopolitical risk due to increased threats of adverse events or to their implementation?
This research article is structured into five chapters: Chapter 1 introduces the research topics, objectives, and questions; Chapter 2 reviews the theoretical foundations and related literature; Chapter 3 outlines the research methodology, data sources, and data processing techniques; Chapter 4 presents the research findings; and Chapter 5 concludes with recommendations addressing the research problem.
LITERATURE REVIEW - ST HH HH HH HH HH 11 2.0 Chapter introduction - - - - - - - 1n ng nọ HH kg 11 2.1 Theoretical baSes . - HH HH và 11 2.1.1 Geopolitics and Geopolitical RISK cccecceeeeeteeeeneeeeeneeeteeaeeeeeneeesesaeeesenaeeeteeneeeeea 11 2.1.2 Measurement Geopolitical RISK - - - ô+ + + xEE+EkES* kh khu 14 2.1.3 Financial ASSCUS - s1 KH nh vi 15 2.2 Review of related litera†ure - - HS như 16 2.2.1 The positive impact of Geopolitical Risk on Financial Asse@fS
The negative impact of Geopolitical Risk on Financial ASSets
To assess geopolitical risk, I utilize the geopolitical risk index created by Caldara and Iacoviello (2018), which reveals a significant negative correlation between geopolitical risk and stock market participation The GPR index reflects market responses to news events, notably the COVID-19 pandemic, which saw a dramatic increase from 74 in December 2019 to 138.42 in January 2020, highlighting a surge in supply-side risks as nations enacted border closures and travel restrictions to curb the virus's spread.
The GPR index is negatively correlated with market sentiment The higher up, the more bearish market sentiment as investments, jobs and stock yields come under pressure.
Lee et al (2021a) examine the effects of Government Policy Risk (GPR) on corporate finance using data from the Chinese stock market, revealing that GPR negatively impacts financial activities of companies Similarly, Chien-Chiang Lee and Chih-Wei Wang (2021) explore GPR's influence on the real economy, particularly regarding firms' cash holdings Their research indicates that financially distressed companies are more inclined to retain cash as a protective measure against GPR Ultimately, GPR has weakened China's manufacturing sector and contributed to a slowdown in GDP growth, with evidence showing that firms in manufacturing-related industries tend to save more cash to mitigate spillover risks.
Among the BRICS stock exchanges, Balcilar et al (2018) notes that the Russian stock market bears the highest risk to GPR.
Choi (2022) investigates the influence of Geopolitical Risk (GPR) on the stock markets of Northeast Asian nations, revealing a significant interdependence between GPR and stock market volatility The findings indicate that GPR adversely affects stock returns and increases volatility Additionally, the study explores the correlation between GPR and crude oil prices, particularly in light of ongoing military tensions in oil-producing regions.
Kiryoung Lee (2023) demonstrates a significant correlation between the geopolitical risk index (GPR) and stock market participation decisions, highlighting the role of economic policy uncertainty (EPU) in this relationship The GPR index influences participation decisions for up to 12 months, with empirical evidence indicating that financial uncertainty is the key driver of these outcomes Notably, the study finds a strong negative association between the GPR index and stock market participation, affecting both broad and deep levels of engagement, as well as overall market performance.
Anupam Dutta (2022) demonstrates through a two-state Markov-mode transition model that rising geopolitical risks (GPR) significantly heighten the probability of low volatility regimes while simultaneously reducing the likelihood of high volatility conditions This correlation suggests that increased geopolitical risks influence the behavior of crude oil markets, leading to more stable price environments.
Eighteen oil users, aware of the risks associated with traditional energy sources, are increasingly viewing clean energy as a viable alternative This shift has resulted in rising share prices for new energy companies and a reduction in market volatility Furthermore, findings from generalized self-regressive conditional variance (GARCH) models indicate that a higher Green Policy Risk (GPR) correlates with reduced risk for green assets.
In their 2022 study, Sokratis Mitsas, Petros Golitsis, and Khurshid Khudoykulov utilized exponential generalized autoregressive conditional variance (EGARCH) modeling to demonstrate that GPR and GPT significantly impact and adversely affect the returns of crude oil, gold, platinum, and silver Additionally, GPA was found to negatively influence the returns of crude oil, heating oil, platinum, and sugar futures, while GPT exhibited a weakly positive effect on the volatility of corn futures.
The uncertainty impact of Geopolitical Risk on Financial Assefs
Zaghum Umar (2022) investigates the impact of geopolitical risk (GPR) from the Russia-Ukraine conflict on European and Russian global bond, equity, and commodity markets The study reveals that GPR has a more significant negative effect on European bonds compared to Russian bonds, while also showing that GPR positively influences Russian equity across all return percentiles, with a stronger relationship in higher wealth percentiles The findings indicate that most assets exhibit both positive and negative relationships with GPR, and that GPR alters asset returns under normal market conditions Additionally, the extent and direction of GPR's impact on asset returns vary based on market type and conditions.
Balcilar et al (2018) highlight that the Russian stock market poses the highest risk to geopolitical risk (GPR) among the BRICS nations, with findings by Mehmet Balcilara, Matteo Bonato, Riza Demirer, and Rangan Gupta (2018) confirming that Russia exhibits the greatest risk in terms of both returns and volatility In contrast, India is identified as the most resilient country within the BRICS group The study utilizes nonparametric quantile causality tests to analyze how geopolitical uncertainty affects the dynamics of returns and volatility in the BRICS stock markets.
The analysis reveals that the BRICS stock markets exhibit heterogeneous responses to geopolitical tensions, indicating that such news does not uniformly influence return dynamics Instead, geopolitical risk (GPR) appears to primarily affect stock market volatility rather than returns, often aligning with below-average return percentiles This suggests that GPR serves as a catalyst for negative volatility in these markets.
Hasanul Banna, Ph.D (2021), investigates the influence of geopolitical uncertainty on banking risk, revealing that this relationship holds true across various geopolitical risk (GPR) measures, including geopolitical behavior (GPRA) and US-specific GPR indices The study finds that medium and large banks, particularly those focused on commercial and savings banking, exhibit heightened risk levels during periods of high GPR, primarily due to reduced bank capital and increased profitability volatility Quantitative regression analysis indicates that the link between GPR and bank risk remains consistent regardless of the bank's existing risk profile Furthermore, banks are particularly susceptible to risks associated with war-related geopolitical uncertainties, such as military threats and escalations The negative impact of GPR is further influenced by factors like bank goodwill, capital adequacy, management quality, and specific loan types.
Recent research has examined the effects of geopolitical risk (GPR) on safe-haven assets, particularly gold and bitcoin Studies by Baur and Smales (2020) and Triki and Maatoug (2021) identify gold as a reliable safe haven and a diversification strategy against GPR Additionally, Aysan et al (2019) highlight bitcoin's potential as a hedge against GPR, suggesting its growing role in financial markets.
Literature gap - - SHằ HH HH kg 20 2.4 Chapter Summ4ry - - c1 SH TH HH kg 21
Numerous studies have examined the impacts of geopolitical risk, revealing positive, negative, and non-linear effects Unlike prior research predominantly centered on developed nations like G7 or G20 countries, this study emphasizes developing countries, particularly Vietnam Notably, there is a scarcity of studies specifically addressing the Vietnamese stock market, which remains a frontier market with significant growth potential.
Many existing studies primarily concentrate on a limited number of key industries in the stock market, typically focusing on only two or three sectors such as gold, bitcoin, and banking This study significantly contributes to the field by expanding the scope of analysis to include a broader range of industries, thereby providing a more comprehensive understanding of market dynamics.
20 stock prices of all companies listed on the stock exchange and then divide them into 10 industries.
The study period examines the timeframe encompassing the conflict between Ukraine and Russia, coinciding with the post-pandemic recovery phase This context has led to significant fluctuations in the energy market during this time.
This study aims to provide a comprehensive analysis of the relationship between geopolitical risk and stock return spillover in the Vietnamese stock market, utilizing data across various industries.
Chapter 2 presents an overview of geopolitical risk and how risk measurement can affect financial assets Chapter 2 then presents a review of the existing domestic and foreign studies on the positive, negative and non-linear effects of geolocation risk on stock market returns From there, it is possible to detect the gap in the literature as well as the potential contributions of this research.
METHODOLOGY - - Q0 22011221122 11211110 1111111111111 11H 1H Hy 22 3.1 Research methods - - - - LH kg 22 K)? nh e 4d 24
To investigate the dynamic connectedness over time, I utilize the Time-Varying Parameter Vector Autoregression (TVP-VAR) method developed by Antonakakis and Gabauer (2017) This approach integrates the connectedness framework established by Diebold and Yilmaz in their works from 2009 and 2012.
2014) and Koop and Korobilis (2014) This framework allows the variances to vary over time via a Kalman Filter estimation with forgetting factors The TVP-VAR(p) model can be expressed as:
The equations presented illustrate a model where Ve is defined as a function of previous values and disturbances, specifically Ve = BrZt-1 + & €¢1Ft-1~N(0, S;) The vector representations y; and Z;_¡ denote N x 1 and Np x 1 dimensions, respectively The time-varying coefficient matrix 6, is N x Np, while €; represents an error disturbance vector with a corresponding N x N time-varying variance-covariance matrix, S; Additionally, the vectors ứec(;), 0ec(,_;), and 4% are defined as N?p x 1 dimensional, with R; also playing a crucial role in the model's structure.
To calculate the generalized impulse response functions (GIRF) and generalized forecast error variance decomposition (GFEVD), it is essential to transform the time-varying parameter vector autoregression (TVP-VAR) into a time-varying parameter vector moving average (TVP-VMA) model, as outlined by the Wold representation theorem (Koop et al., 1996; Pesaran and Shin, 1998).
Min 37=o Ak€t_j (4) where L=[Iy, ,0p]’ is an NpXN_ dimensional matrix, W=
The matrix denoted as [:; u(p—+›Ôn(-1)xN| is an Np X Np dimensional matrix that captures the Generalized Impulse Response Functions (GIRFs) These GIRFs illustrate how all variables respond to a shock in a specific variable, denoted as variable i To analyze this effect, we calculate the differences between a forecast made J steps ahead with variable i experiencing a shock and another forecast where variable i remains unaffected This difference effectively isolates the impact of the shock in variable i.
The GIRFs of variable j, denoted as COD), are calculated using the equation Pj, #0) = = 5, TA), t,t (7), where J indicates the forecast horizon, and the selection vector ở;¿; assigns a value of one to the j-th position while being zero elsewhere Additionally, F,_, represents the information set up to time t — 1 Subsequently, we compute the GFEVD, which interprets the variance share that one variable contributes to others.
MT ent bi, D = sr gta i,t
(8) with we Ld +J) = 1 and Xij= Md.) = N Based on the GFEVD, we can build the total connectedness index (TCI) as follows: cÿ0)= ZU=ue)_ Pie Lisi Fie x 100 = Zijeuie) Pipe x 100 (9) rier O50)
The connected approach enables the analysis of how a shock in one variable affects other variables Specifically, it defines the total directional connectedness from variable i to all other variables j, illustrating the transmission of shocks across the system.
Second, the shock that variable i receives from all other variables j, i.e the total directional connectedness FROM others can be defined as:
Finally, the net total directional connectedness can be given by subtracting the total directional connectedness TO others from the total directional connectedness FROM others:
Net total directional connectedness indicates the influence of variable i within a network A positive net total directional connectedness suggests that variable i exerts more influence on the network than it receives, acting as a shock transmitter Conversely, a negative net total directional connectedness implies that variable i is primarily influenced by the network, functioning as a shock receiver.
As the net total directional connectedness is an aggregated measure and
23 sometimes masks important underlying dynamics, we want to calculate the net pairwise directional connectedness (NPDC), which informs about the bilateral transmission process between variables I and j:
A positive (negative) value of NPDŒ;;() indicates that variable iis driving (driven by) variable j.
The study utilizes combined wavelet analysis to examine the co-movement between the GPR index and individual stock returns and volatility, highlighting their development across varying frequencies and timeframes This approach is pertinent and aligns well with the stock dataset, which encompasses both returns and volatility.
The squared wavelet coherency does not reveal the direction of co-movement between GPR and stock return volatility However, the wavelet coherence phase difference, as outlined by Bloomfield et al (2004), effectively addresses this limitation through a specific phase measure.
The equation presented describes the relationship between the imaginary and real components of the smoothed cross-wavelet transform, denoted as Im and Re, respectively Here, Pij is constrained within the range of (-7, 7), while 's' represents the scale index and 'u' signifies the position index Additionally, the asterisk (*) indicates the complex conjugate, as referenced in the work of Grinsted, Moore, and Jevrejeva (2004).
Notably, the statistically significant co-movement in the time-frequency domain between GPR and stock return (volatility) is computed based on Monte Carlo simulations.
The GPR index, created by Caldara and Iacoviello (2022), serves as a valuable tool for researchers and investors to analyze the impact of geopolitical risk on financial assets This index is based on automated text-search results from the electronic archives of ten major newspapers, including the Chicago Tribune, Daily Telegraph, Financial Times, Globe and Mail, Guardian, Los Angeles Times, New York Times, USA Today, Wall Street Journal, and Washington Post Caldara and Iacoviello compute the index by measuring the monthly count of articles concerning negative geopolitical events as a proportion of the total number of news articles published in each newspaper.
The search is categorized into eight distinct areas: War Threats, which examines potential conflicts; Peace Threats, focusing on factors undermining stability; Military Buildups, assessing the increase in armed forces; Nuclear Threats, analyzing risks associated with nuclear weapons; Terror Threats, evaluating the impact of terrorism; the Beginning of War, identifying signs of imminent conflict; and the Escalation of War, exploring factors that intensify existing conflicts.
Caldara and Iacoviello developed two subindexes based on the identified search groups: the Geopolitical Threats (GPRT) index, which encompasses terms from categories 1 to 5, and the Geopolitical Acts (GPRA) index, which includes terms from categories 6 to 8, specifically focusing on terror acts as a significant element.
Users can subscribe to the Economic Policy Uncertainty (EPU) data set, which includes the GPR index among its components, available at policyuncertainty.com The data set features various indices, including the Benchmark GPR with Threats and Acts and Middle East (GPRME), GPR Index (GPRO), GPR Index excluding War and Military (GPRNOWAR), GPR Index with Terrorist Threats (GPRTERRORO), and several others that incorporate different aspects of threats, acts, and arms control, such as GPRTERROR1, GPRTERROR2, GPRTERROR3, and GPRTERROR1ARMS, along with GPRTERROR1 utilizing 7 papers instead of the usual 11.
Utilizing GPR index data allows users to conduct empirical analyses and develop investment strategies that consider the impact of geopolitical events on financial markets By tracking fluctuations in the GPR index, investors can assess the level of uncertainty in global political conditions and make informed adjustments to their portfolios.
EMPIRICAL RESULTS ĐÀ G22 11222 1221122111211 182111811 1 kg 27 4.1 Descriptive s†afiStẽCS .- Ghi 27 4.1.1 SUIMIMALY S†AfÍSHÍŒS - - SE kg 27 4.1.2 Correlation Matrix taDle - + E181 81131111 SE 1kg vn 28 4.1.3 The development and volatility of (PF - - + + +5 ++++E++xekEx+xxeexexeersexeers 30 4.2 The Dynamic spillovers between financial assets of 12 sectors 31 4.2.1 Averaged AYNAMIC conrnecl@drI@SS . - - - - ô+ kg KH khi 31 4.2.2 Dynamic total connec†edn@SS - - - ôc5 ST KH HH ki 34 4.2.3 Net directional connec†edfN@SS - - ô1H ng kh 36 4.2.4 Summary of the results from TVP-VAR modl6l -‹ô- ô+ +Ê+<+*kkkxexeeeeeexexs 39
Mean Sd Skewness Ex.Kurtosis | Se Median Trimmed Mad
Notes: Skewness: D’Agostino (1970) test; Kurtosis: Anscombe and Glynn (1983) test;
Table 4.1 presents the descriptive statistics of the transformed series, highlighting the price growth rates across various industry groups A positive mean indicates an increase in stock prices for that industry, while a negative mean signifies a decline in share prices.
27 industry has decreased in that time In general, in the table above, prices of all sectors are positive in the period from February 2012 to April 2022.
Despite stock market volatility, all industries experienced rising stock prices, with Bitcoin and Information Technology sectors leading the way at growth rates of 0.0034 and 0.0011, respectively Over the past two years, driven by the COVID-19 pandemic and the Russia-Ukraine conflict, many investors have shifted their focus from gold to Bitcoin as a preferred hedge This trend has resulted in a continuous influx of capital into Bitcoin and other cryptocurrencies, contributing to an upward price trajectory However, Bitcoin's price remains under pressure as it increasingly correlates with stocks, particularly as rising interest rates diminish the appeal of risk assets like cryptocurrencies Meanwhile, technology super stocks have shown resilience, maintaining strong performance despite economic challenges stemming from the pandemic and geopolitical tensions Other industries have also seen modest price increases, with gold experiencing the least growth due to a historic drop in demand for gold jewelry amid ongoing economic uncertainties Nevertheless, gold continues to be viewed as a safe investment option for those seeking stability in turbulent times.
The offset and kurtosis measurements reveal that nearly all sequences exhibit leptokurtic characteristics and are right-skewed In contrast to a normal distribution, which has skewness and kurtosis values of zero, the observed values indicate significant deviations from normality Notably, the proximal distribution branches show distinct differences from the normal distribution While most industries display negative skewness, indicating a rightward distribution, the health service industry demonstrates positive skewness, reflecting a leftward distribution.
Public Health Consumer Utilities Service Service om —_ om ee | 2m | em ee se an a ee ee ce em | ou ee a ee ee ee oe an a son “a
Notes: ***, **, and * denote significance at 1%, 5% and 10% significance levels respectively
The analysis reveals that the correlation between stock returns and the Global Political Risk (GPR) index is generally weak, with most stocks exhibiting a negative correlation with GPR Notably, the Information Technology sector shows no correlation at all, indicated by a correlation coefficient of 0 In contrast, both gold and Bitcoin demonstrate a positive relationship with GPR, reflecting a positive correlation.
As can be seen, the variable Information Technology is independent of 2 variables Gold and GPR.
4.1.3 The development and volatility of GPR
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The timeline of daily GPR from 2012 to late 2020 highlights significant observations, represented by green dots, alongside notable events reported in the press on those dates The selected date is marked by a spike index, indicated by prominent blue dots, showcasing peaks in data activity.
The daily GPR index highlights the impact of geopolitical tensions on its monthly counterpart, demonstrating that prolonged tensions can trigger significant spikes, exemplified by the Ukraine/Russia escalation Additionally, climate events can lead to daily increases in geopolitical risk, as seen in the aftermath of the 9/11 terrorist attacks Furthermore, ongoing geopolitical tensions can maintain elevated levels throughout the news cycle, resulting in consistently high averages.
The monthly GPR reveals 30 significant values, highlighting events such as the Syrian Civil War and the 2017-2018 Korean crisis These instances demonstrate that spikes in the daily index correlate with the onset of stress, underscoring the index's effectiveness in providing timely information.
4.2 The Dynamic spillovers between financial assets of 12 sectors
Table 4.3 presents the results of the average dynamic connectedness analysis, illustrating how each variable affects the forecast error variance of others within the network Each row highlights the influence of a specific variable on the forecast error variance of all other variables, while each column reflects the contributions of other variables to the forecast error variance of the individual variable The diagonal elements denote the self-impact of each variable, whereas the off-diagonal elements reveal the interactions between variables.
Public Consumer Consumer Utilities i Service
The Total Connectivity Index (TCI) quantifies the average impact of various factors on the forecast error of financial assets over time With a TCI score of 66.01/60.51 for 12 stock market assets, it indicates a moderate level of inter-industry connectivity This suggests that sectors experience low self-explanation for significant stock movements, highlighting the strong influence they exert on one another Notably, Gold and Bitcoin stand out as exceptions, demonstrating a capacity for self-explanation in relation to their own volatility and major price fluctuations.
All industries experience fluctuations, with the finance sector facing the highest level of shocks at 71.01%, followed closely by the industrial sector at 70.51% and materials at 70.3% Notably, industries that generate numerous shocks are also significantly impacted by shocks from other sectors, with the industrial sector enduring the most at 82.85%.
The Covid pandemic, the Ukraine-Russia conflict, and high inflation driven by the Bank's monetary tightening have significantly impacted the economy, particularly the finance sector, which influences capital-intensive industries such as manufacturing and raw materials Finance plays a crucial role across various sectors, affecting companies with high capitalization and contributing to the overall economic landscape Raw materials serve as essential inputs for industrial production and positively impact multiple industries, including manufacturing, information technology, electronics, and agriculture These material industries are vital for modern industrial development and are key to meeting integration demands, driving economic growth, and fostering the emergence of new industries and products This interconnectedness enhances resource utilization, labor productivity, product quality, and competitiveness in a globalized market.
3 industries have so much spillover to other industries.
Gold and Bitcoin are two unique industries that exhibit stability, experiencing minimal shocks and demonstrating a low susceptibility to market fluctuations.
33 from other industries These are two assets that are widely used as a hedge against the impact of recession and inflation caused by geopolitical tensions.
Research from NET indicates that certain industries significantly influence others, with medical services, gold, and bitcoin being particularly reactive These sectors tend to experience greater shocks than they impart, highlighting their interconnectedness within the economy.
Table 4.3 highlights intriguing insights into the interdependence of the 12 sectors within the Vietnamese stock market; however, the results are based on aggregate measures over the entire sampling period This reliance on averages may obscure the impact of significant economic and geopolitical events that occurred during this time, potentially leading to notable deviations from the mean TCI values Consequently, this study adopts a dynamic approach aimed at pinpointing specific periods that influence the connectivity among the variables over time.
Notes: Results are based on a TVP-VAR model with a large length of order two (BIC) and a 10-step-ahead generalised forecast error variance decomposition.
This study examines the fluctuations in average stock market connectivity over time and the impact of Global Political Risk (GPR) on this connectivity The dynamic total connectivity index (TCI) reveals significant variations throughout the sampling period, particularly during times of economic and geopolitical instability, as well as adverse natural conditions that can trigger stock market disruptions.