Khóa luận tốt nghiệp Tài chính ngân hàng: The impact of political and economic events on the Vietnamese stock market

The study indicates that the impact of domestic economic events and political events on the Vietnamese stock market is not significant, while foreign economic events have astrong impact

INTRODUCTTION 5-55 525+ tt hà HH HH grrrrrirrrerrree 6 1.1 The urgency Of the tOpÌC ôtt HH HH tren 6 1.2 Research objectives and taSÌKS s +c+chnHHnHHH HH HH gà 7

Research subjects and scope 1.5 The distribution of research 1.6 Structure 000.20 n6

1.4.1, Research subject The impact of political events and both domestic and foreign economic events on the Vietnamese stock market.

1.4.2 Research scope e Spatial scope: The study focuses on domestic political events in Vietnam, as well as economic events both within and outside of the country, and their impact on several key economic variables (including consumer price index, export value, foreign direct investment, retail sales, etc.) and the related variables to the Vietnamese stock market. e Temporal scope: From 2012 to 2022

This study aims to elucidate the connections between major political and economic events, offering a theoretical framework for understanding the Vietnamese stock market and its influencing factors By establishing this foundation, the research will assess how significant political and economic occurrences impact the performance of the Vietnamese stock market.

This research provides valuable insights for investors to understand and track the factors impacting the Vietnamese stock market, enabling them to develop effective investment strategies tailored to current market conditions Additionally, it serves as a resource for policymakers to assess the influences affecting the market, allowing for timely adjustments to policies in response to future economic and political developments.

The research paper is organized into five chapters Chapter 1 introduces the study, outlining the rationale for the topic selection, the research subject, and objectives Chapter 2 provides a synthesis of the theoretical foundation regarding the stock market, focusing on the influence of economic and political events, particularly in relation to the Vietnamese stock market Chapter 3 details the data and methodology employed to evaluate the impact of these events on the Vietnamese stock market.

The article is structured into five chapters, including an introduction, reference list, and appendices, with Chapter 4 presenting the model's results and discussing the impact of the events, while Chapter 5 offers conclusions and recommendations for relevant stakeholders.

Chapter II: Research overview and theoretical basis on Vietnameses stock market, the political and economic events, and the impact of political and economic events on the Vietnam stock market.

Chapter IV: Result and discussion

RESEARCH OVERVIEW AND THEORETICAL BASIS ON VIETNAMESE STOCK MARKET, THE POLITICAL AND ECONOMIC EVENTS, AND THE IMPACT OF POLITICAL AND

Theory of the impact of political and economic events on the Vietnamese stock market "m .Ô.Ỏ 24

2.3 Theory of the impact of political and economic events on the Vietnamese stock market

The Vietnamese stock market has undergone substantial growth and transformation in recent decades, marked by volatility influenced by political and economic events Research indicates that political occurrences, such as elections and regime changes, significantly affect the market; for instance, the 2016 U.S presidential election and the ensuing trade war with China impacted Vietnam's stock market Additionally, the 2016 parliamentary elections in Vietnam, which led to a new Prime Minister, also influenced market dynamics Economic factors, including interest rates, inflation, and GDP growth, play a crucial role as well, with the COVID-19 pandemic causing notable repercussions for the Vietnamese economy and stock market Furthermore, the transition from a centrally planned to a market-oriented economy has profoundly shaped the stock market's landscape.

The 2008 global financial crisis significantly impacted the Vietnamese economy, leading to a decline in exports and GDP growth due to reduced global demand Consequently, the VN-Index plummeted over 70% from October 2007 to March 2009 Additionally, a 2011 corruption scandal involving the chairman of Vinashin eroded investor confidence, resulting in falling stock prices and decreased trading volume Conversely, the signing of the Trans-Pacific Partnership (TPP) in 2016 positively influenced the Vietnamese stock market, as it was perceived as a catalyst for economic growth.

The signing of the agreement has the potential to boost foreign investment and exports, leading to a more than 15% increase in the VN-Index in the subsequent months This highlights the substantial influence that political and economic developments can exert on the Vietnamese stock market.

Domestic and international economic and political events significantly influence the Vietnamese stock market, affecting both its scale and investor sentiment.

RESEARCH METHODOLOGY -2555-SS+S2 + HH HH re 26 3.1 Design Of reSe©aFCHh cc.nHnH HH HH H H nà g1 11101101111 1111 26 3.2 Research problem identification and research conte1n( -secsrxxsrrrrrerrrrrrrrrree 27 3.3 Research methods uc ccssssssssssecsesssseesseecsseessessseessneessueessessaeecsseecsueeesseesaessneessueessseesaeeesseesseesaneesseeesneesanees 27 k0) (0( xin, 3,270

Independent Variable 00h88 h

Independent variables are chosen based on theoretical frameworks and findings from relevant domestic and international studies These variables are then processed according to their characteristics and interrelations The MARKET variable is calculated by dividing market capitalization by Vietnam's Gross Domestic Product (GDP) on a quarterly basis, incorporating data from the Ho Chi Minh Stock Exchange (HOSE), Hanoi Stock Exchange (HNX), and UpCom, sourced from the FiinPro-X database Quarterly GDP data at current prices is also retrieved from FiinPro-X The Consumer Price Index (CPI) data is compiled from the General Statistics Office of Vietnam (GSO) and FiinPro-X, while the VND/USD exchange rate (TYGIA) data is sourced from the State Bank of Vietnam (SBV) and FiinPro-X Additionally, the Money Supply (M2) data is obtained from the FiinPro-X database.

The State Bank of Vietnam (SBV) provides essential data on key financial indicators, including the interbank interest rate (IRLNH) and the 10-year government bond interest rate (LSTPCP) This data is sourced directly from the SBV's comprehensive database, ensuring accuracy and reliability for financial analysis and decision-making.

Table 3.4: The list of the other variables relevant to Vietnamese stock market

1 MARKET Market capital/GDP FiinPro-X

2 GDP Gross Domestic Production GSO, FiinPro-X

3 CPI Consumer Price Index GSO, FiinPro-X

4 TYGIA Exchange rate SBV, FiinPro-X

6 IRLNH Interbank interest rate SBV

7 LSTPCP Government bond interest rate SBV

The data processing and Dickey-Fuller (DF) test were conducted to assess the stationarity of various variables, including MARKET, GDP, differenced GDP (d.GDP), M2, differenced M2 (d.M2), CPI, TYGIA, differenced TYGIA (d.TYGIA), IRLNH, LSTPCP, differenced LSTPCP (d.LSTPCP), DEE, FEE, and PE With a p-value of 0.0000 for each variable, which is below the 0.05 significance level, we reject the hypothesis of a random series, confirming that the variable series are stationary.

CHAPTER IV: RESULT AND DISCUSSION

This chapter analyzes the influence of political and economic events on the Vietnamese stock market, utilizing data from 2012 to 2022 The author assesses how these events affect macroeconomic indicators linked to the stock market, providing an evaluation of their impacts Furthermore, the chapter includes forecasts on the potential effects of future political and economic occurrences of similar magnitude on these factors.

The study will proceed to analyze the impact of each event sequence on the variables Specifically:

For Domestic ECONOMIC CVENCES n6 n6 n6 ốốốốốốốốố

The R2 values, ranging from 0.5464 for IRLNH to 0.9995 for M2, demonstrate that the models account for a moderate to high percentage of variance in the dependent variables Additionally, the p-values from Chi2 tests of parameter significance are nearly zero, confirming a strong model fit and the significance of the coefficients.

The VAR model demonstrates a strong fit for the current data, evidenced by significant coefficients and low RMSE and FPE values Each variable significantly influences others within the model and is also affected by its historical values, highlighting the interdependence of the variables This suggests that the VAR model is effective for analyzing the dynamic relationships among the variables.

To ensure the stability of the VAR model, the eigenvalues of the estimated coefficient matrix were analyzed, revealing a table of eigenvalues and their corresponding absolute values For the VAR model to be deemed stable and for the impulse response functions to hold significance, it is essential that all eigenvalues exhibit a modulus of less than 1 The results confirm that the coefficient matrix and VAR meet this stability condition.

Chart 4.1: The residual error plot after the model has been estimated and used to predict the data (DEE)

The results highlight the model's stability and prediction quality while suggesting areas for improvement By comparing residuals with original data values, we can assess the discrepancies between predictions and actual outcomes Additionally, the findings reveal strong interrelationships among input variables, indicating that certain variables influence one another This insight can guide model enhancement by fostering associations between relevant input variables or eliminating those that are redundant.

Chart 4.2: The estimated value ofthe response Chart 4.3: The estimated value of the response of the DEE to the MARKET remains constant, of the DEE to the GDP remains constant, while while other variables vary other variables vary

IRF, DEE, DEE IRF, MARKET, DEE IRF, DEE, DEE IRF, GDP, DEE

Step Step 95% Cl Impulse-response function 95% Cl Impulse-response function

Graphs by irfname, impulse variable, and response variable Graphs by ifname, impulse variable, and response variable

Chart 4.4: The estimated value of the response of the DEE to the M2 remains constant, while other variables vary.

Graphs by irfname, impulse variable, and response variable

Chart 4.6: The estimated value of the response of the DEE to the TYGIA remains constant, while other variables vary.

IRF, DEE, DEE IRF, TYGIA, DEE

Chart 4.5: The estimated value of the response of the DEE to the CPI remains constant, while other variables vary.

IRF, CPI, DEE IRF, DEE, DEE 10

Step 95% Cl Impulse-response function

Chart 4.7: The estimated value of the response of the DEE to the IRLNH remains constant, while other variables vary.

IRF, DEE, DEE IRF, IRLNH, DEE

Chart 4.8: The estimated value of the response of the DEE to the LSTPCP remains constant, while other variables vary.

IRF, DEE, DEE IRF, LSTPCP, DEE

The forecast error variance decomposition (FEVD) results for the variables 'DEE' and 'MARKET' reveal their reactions to both their own shocks and those from other variables The analysis is organized into four sections, each representing a unique combination of shock and response variables Each section details the percentage of variance in the response variable attributable to its own shocks and those from other variables over varying time lags Additionally, for each time lag, the table includes three columns presenting the actual FEVD alongside the lower and upper bounds of the 95% confidence interval for the estimates.

+ At step 0, before any shocks occur, all variables have zero variance.

In the initial step, the variances of both variables are exclusively linked to their individual shocks As time progresses, the influence of each variable's unique shocks diminishes, while the impact of shocks from other variables begins to rise.

In step 2 of the analysis, it was found that 84.63% of the variance in 'DEE' is due to its own shocks, whereas a mere 0.13% of the variance in 'MARKET' is influenced by shocks from 'DEE.' Additionally, shocks from 'DEE' account for 1.83% of the variance in 'MARKET.'

The long-term analysis reveals that external variables increasingly influence the variance of the response variable Specifically, at step 16, 56.21% of the variance in 'DEE' is due to its own shocks, while 4.31% is attributed to shocks from 'MARKET' Conversely, 4.18% of the variance in 'MARKET' can be traced back to shocks from 'DEE'.

Overall, the FEVD table provides detailed information about the complex interactions between the variables `DEE` and ‘MARKET’ and how shocks in each variable can affect the other variable.

It can be explained for another variables and the results can be summarized in following table:

Table 4.1: The results of VAR model for prediction in step 2 (short-term effect) Impulse Response Variance of A can Variance of B Variance of A can

(Variable A) | (Variable be contributed to can be be contributed to

B) shocks from B contributed to its own shock shocks from A DEE MARKET 0,13% 1,83% 84,63%

The VAR model results in step 16 indicate that the long-term effect of shocks from Variable A and Variable B on the DEE MARKET shows a variance contribution of 4.31% from shocks to Variable A, 4.18% from shocks to Variable B, and a significant 56.21% attributed to its own shocks.

The analysis reveals that domestic economic events significantly influence key economic variables over time In the short term, fluctuations in Vietnam's Consumer Price Index (CPI) are primarily driven by shocks from domestic economic events (DEE), accounting for a variance probability of 9.12% Conversely, the M2 variable shows a minimal influence from DEE, with a variance probability of only 0.001% This suggests that short-term changes in domestic economic conditions will predominantly affect Vietnam's CPI.

In the analysis of economic indicators, domestic economic events are projected to significantly influence the Vietnamese economy, with a 10.51% probability of impacting GDP and a 4.18% probability affecting the stock market Conversely, the M2 money supply is expected to experience minimal influence, with only a 2.15% probability attributed to these events Additionally, the stock market is anticipated to be impacted by domestic economic occurrences at a 1.83% probability, highlighting the varying degrees of influence these factors have on different economic variables.

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The R2 values range from 0.5462 for IRLNH to 0.9996 for M2, demonstrating that the models account for a moderate to high percentage of variance in the dependent variables Additionally, the Chi2 test p-values are nearly zero, confirming a strong model fit and significant coefficients.

The VAR model demonstrates a strong fit with the current data, evidenced by significant coefficients and low RMSE and FPE values Each variable notably affects other variables within the model while also being influenced by its historical values This interdependence among variables highlights the VAR model's effectiveness in analyzing their dynamic relationships.

The stability of the VAR model is assessed by examining the eigenvalues of the estimated coefficient matrix, which are presented in a table along with their absolute values For the VAR model to be deemed stable and for the impulse response functions to hold significance, it is essential that all eigenvalues have a modulus of less than 1 The results confirm that all eigenvalues of the coefficient matrix meet this stability condition, indicating a stable VAR model.

Chart 4.9: The residual error plot after the model has been estimated and used to predict the data (FEE)

The results highlight the model's stability and predictive quality while emphasizing the need for improvement By comparing residuals with original data values, we assess the discrepancies between predictions and actual outcomes Additionally, the analysis reveals a strong interrelationship among input variables, indicating that certain variables influence one another This insight can be leveraged to enhance the model by forming associations between relevant input variables or eliminating those that are unnecessary.

Chart 4.10: The estimated value of the Chart 4.11: The estimated value of the response of response of the FEE to the MARKET remains _ the FEE to the GDP remains constant, while other constant, while other variables vary variables vary.

IRF1, FEE, FEE IRF1, MARKET, FEE IRF1, FEE, FEE IRF1, GDP, FEE

Chart 4.12: The estimated value of the response of the FEE to the M2 remains constant, while other variables vary.

IRF1, FEE, FEE IRF1, M2, FEE

Chart 4.14: The estimated value of the response of the FEE to the TYGIA remains constant, while other variables vary.

IRF1, FEE, FEE IRF1, TYGIA, FEE

Chart 4.13: The estimated value of the response of the FEE to the CPI remains constant, while other variables vary.

IRF1, CPI, FEE IRF1, FEE, FEE

Chart 4.15: The estimated value of the response of the FEE to the IRLNH remains constant, while other variables vary.

IRF1, FEE, FEE IRF1, IRLNH, FEE

Chart 4.16: The estimated value of the response of the FEE to the LSTPCP remains constant, while other variables vary.

IRF1, FEE, FEE IRF1, LSTPCP, FEE

The forecast error variance decomposition (FEVD) results for the variables 'FEE' and 'MARKET' reveal their reactions to both their own shocks and those from other variables, as depicted in the accompanying charts The analysis is organized into four sections, each representing different shock-response combinations Each section details the percentage of variance in the response variable attributable to its own shocks and those from other variables across various time lags Additionally, for each time lag, the table presents the actual FEVD alongside the lower and upper bounds of the 95% confidence interval for the estimates.

+ At step 0, before any shocks occur, all variables have zero variance.

In the initial step, the variances of both variables are linked solely to their individual shocks As time progresses, the influence of each variable's unique shocks diminishes, while the impact of shocks from other variables grows.

In step 2, it is revealed that 81.86% of the variance in 'FEE' is due to its own shocks, whereas only 4.37% is influenced by shocks from the 'MARKET' Additionally, 2.83% of the variance in 'MARKET' is attributed to shocks from 'FEE'.

The long-term analysis reveals that external shocks increasingly influence the variance of the response variable At step 16, 57.12% of the variance in 'FEE' is attributed to its own shocks, while 4.47% is due to shocks from 'MARKETT' Additionally, 3.2% of the variance in 'MARKET' can be traced back to shocks from 'FEE'.

Overall, the FEVD table provides detailed information about the complex interactions between the variables ‘FEE’ and ‘MARKET’ and how shocks in each variable can affect the other variable.

It can be explained for another variables and the results can be summarized in following table:

Table 4.3: The results of VAR model for prediction in step 2 (short-term effect) Impulse Response Variance of A can Variance of B Variance of A can

B) shocks from B contributed to its own shock shocks from A FEE MARKET 4,37% 2,83% 81,86%

Table 4.4: The results of VAR model for prediction in step 16 (long-term effect) Impulse Response Variance of A can Variance of B Variance of A can

B) shocks from B contributed to its own shock shocks from A FEE MARKET 4,47% 3,2% 57,12%

The findings reveal that global economic events have a minimal short-term impact on the model's independent variables, but this effect becomes significantly pronounced in the long term In the short term, the MARKET variable's variance probability is primarily influenced by shocks from the FEE variable, exhibiting a probability of 2.83%, whereas the GDP variable shows the least influence with a probability of just 0.07%.

Short-term fluctuations in foreign economic events significantly affect the Vietnamese stock market, while having a minimal impact on GDP growth In the long run, shocks from foreign economic events are most likely to influence Vietnam's interbank interest rate, with a probability of 15.9%, compared to only 3.2% for the stock market This suggests that over time, the influence of foreign economic fluctuations on Vietnam's interbank interest rate will be more pronounced, while their effect on the stock market will gradually increase.

4.1.3 For Political events The VAR analysis results were performed on a dataset including eight variables: PE, MARKET, GDP, CPI, M2, TYGIA, IRLNH, LSTPCP The output values of each variable (L1 and L2) represent the impact of past values of the variable on its current value.

The R2 values vary from 0.5871 for IRLNH to 0.9995 for M2, demonstrating that the models account for a moderate to high percentage of variance in the dependent variables Additionally, the Chi2 test p-values are all near 0, confirming that the model fits the data effectively and that the coefficients are statistically significant.

The VAR model demonstrates a strong fit with current data, evidenced by significant coefficients and low RMSE and FPE values Each variable significantly influences others within the model while also being affected by its historical values This interdependence highlights the VAR model's effectiveness in analyzing dynamic relationships among the variables.

The stability of the VAR model is confirmed by analyzing the eigenvalues of the estimated coefficient matrix A table of eigenvalues and their absolute values reveals that all eigenvalues have a modulus less than 1 This condition is essential for the VAR model to be deemed stable and for the impulse response functions to yield meaningful results The output clearly indicates that the stability condition is satisfied for all eigenvalues of the coefficient matrix in the VAR model.

Chart 4.17: The residual error plot after the model has been estimated and used to predict the data (PE)

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4.2.1 The result and reason for this result

The VAR model proves to be suitable for analyzing the Vietnamese stock market, as evidenced by various tests and evaluations The findings reveal that domestic economic and political events have minimal impact on the market, while foreign economic events significantly influence it in both the short and long term Future fluctuations in foreign economic factors are expected to affect the Vietnamese stock market Key independent variables, including the consumer price index (CPI), gross domestic product (GDP), exchange rate, and interbank interest rate, are influenced by economic and political events, with the M2 money supply being the least affected Notably, CPI and GDP are projected to be strongly impacted by domestic economic events, while the interbank interest rate will be most affected by foreign economic events Additionally, both CPI and exchange rates will experience significant effects from future political events in Vietnam, particularly in the short and long term, with the interbank interest rate being influenced by these political events over the long term.

The research paper highlights the influence of both domestic and international economic and political events on the Vietnamese stock market and its related macroeconomic factors It also offers forecasts regarding the future effects of these events However, the study acknowledges limitations in evaluating the timing effects of independent variables before, during, and after these events, indicating a need for improvement in future research Additionally, the study faces significant constraints in its analysis.

54 information due to the presence of inaccurate time-related data, which leads to errors occurring during the data processing process.

CONCLUSION AND RECOMMENDATTION 55-2 ey 56 5.1 COMCIUSION 0 eessesessesssessstecssessseesssessssecsusecssessnsecsasecsasessusessueessuessssessaeessueeessessasessueessneesssessaersueessneesaessuessneesanees 56 5.2 RecommenndatẽOII 5c tin HH TH TH gà HH ng THHREHRREHREEEELEEELEELEEELETrerri 56 5.2.1 FOr INVCStOTS nan

The Vietnamese stock market is significantly influenced by economic and political events, facing numerous challenges throughout its development Market dynamics are often volatile and unpredictable, with fluctuations driven by both domestic and global news This thesis aims to assess the impact of these events on the stock market and forecast future trends The findings reveal that while domestic economic and political events have a limited impact, foreign economic events significantly affect the market in both the short and long term Key independent variables, such as the consumer price index (CPI), gross domestic product (GDP), exchange rate, and interbank interest rate, are also influenced by these events, with the M2 money supply being the least affected Notably, future domestic economic events will strongly impact CPI and GDP, while the interbank interest rate will be most affected by foreign economic events Political events in Vietnam are expected to significantly affect the CPI exchange rate in both the short and long term, with the interbank interest rate being influenced in the long term.

The research offers investors a crucial resource, delivering an in-depth understanding of the Vietnamese stock market's foundational elements, including its development and essential economic and political concepts.

56 events By acquiring this knowledge, investors can gain insights into the functioning of the Vietnamese stock market and the factors that influence it.

Investors in the Vietnamese stock market must analyze the influence of past economic and political events on the market and key macroeconomic indicators This assessment is vital for developing effective investment strategies that minimize unnecessary risks By examining historical impacts, investors can recognize patterns and trends, which empowers them to make informed decisions and implement measures to protect their investments.

The research findings empower investors to assess the potential impact of future economic and political events on the Vietnamese stock market and macroeconomic factors, both in the short and long term This insight enables investors to anticipate market fluctuations and uncertainties, allowing them to adjust their portfolios and implement risk management strategies to minimize losses and enhance returns.

To ensure stability in the Vietnamese stock market amid future uncertainties, it is essential for the government to enhance its ability to manage and regulate the macroeconomic environment This includes creating mechanisms that can effectively address potential market disruptions and fluctuations.

The Vietnamese government must closely examine macroeconomic factors influencing the stock market, especially in the context of recent global and domestic events like the Russia-Ukraine conflict and local economic scandals To mitigate the negative impacts of such occurrences, it is essential for the government to develop targeted macroeconomic, monetary, and fiscal policies that specifically address the unique challenges faced by the Vietnamese stock market, ensuring preparedness for future economic and political fluctuations.

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Sample: 2012q3 thru 2022q4 Number of obs = 42

Equation Parms RMSE R-sq chi2 P>chi2

LSTPCP 7 „006972 0.9524 841.1504 0.0000 irf table fevd, irf(IRF) impulse( DEE MARKET) response ( DEE MARKET )

Step fevd Lower Upper fevd Lower Upper

95% lower and upper bounds reported.

(1) irfname = IRF, impulse = DEE, and response = DEE.

(2) irfname = IRF, impulse = DEE, and response = MARKET.

(3) irfname = IRF, impulse = MARKET, and response = DEE.

(4) irfname = IRF, impulse = MARKET, and response = MARKET. irf table fevd, irf(IRF) impulse( DEE GDP ) response ( DEE GDP

Step fevd Lower Upper fevd Lower Upper m Ô 1n .Ô.Ô.Ô.ÔÔÔÔÔÔ Ỏ

(2) irfname IRF, impulse = DEE, and response = GDP II

(3) irfname IRF, impulse = GDP, and response = DEE.

(4) irfname IRF, impulse = GDP, and response = GDP. irf table fevd, irf(IRF) impulse( DEE M2 ) response ( DEE M2

Step fevd Lower Upper fevd Lower Upper ơ ễ 4 ễễễễễễễễễễ

(1) irfname IRF, impulse = DEE, and response = DEE.

(2) irfname IRF, impulse = DEE, and response II

(3) irfname = IRF, impulse = M2, and response = DEE.

(4) irfname = IRF, impulse = M2, and response = M2. irf table fevd, irf(IRF) impulse( DEE CPI ) response ( DEE CPI

Step fevd Lower Upper fevd Lower Upper ơ — ễễệễễệệễệệệệệệệệễệ'ễ

(2) irfname = IRF, impulse DEE, and response = CPI.

(3) irfname = IRF, impulse CPI, and response = DEE.

(4) irfname IRF, impulse = CPI, and response = CPI. irf table fevd, irf(IRF) impulse( DEE TYGIA ) response ( DEE TYGIA )

4 .Ô 4 ÔÔÔÔÔÔÔÔÔÔÔÔ.ÔÔÔÔÔÔÔ.Ô.ÔÔÔ ÔÔÔÔÔÔÔÔ Ô ÔỎ

(2) irfname IRF, impulse DEE, and response = TYGIA.

(3) irfname IRF, impulse TYGIA, and response DEE.

(4) irfname IRF, impulse TYGIA, and response irf table fevd, irf(IRF) impulse( DEE IRLNH ) response ( IRLNH )

(2) irfname IRF, impulse = DEE, and response = IRLNH.

(3) irfname TRF, impulse TRLNH, and response DEE.

(4) irfname = IRF, impulse IRLNH, and response = IRLNH. irf table fevd, irf(IRF) impulse( DEE LSTPCP ) response ( DEE LSTPCP)

(2) irfname IRF, impulse = DEE, and response = LSTPCP.

(3) irfname = IRF, impulse = LSTPCP, and response = DEE.

(4) irfname = IRF, impulse = LSTPCP, and response = LSTPCP.

2 For Foreign Economic events var FEE MARKET GDP M2 CPI TYGIA IRLNH LSTPCP, lags(1/2)

| lag | chi2 để Prob > chi2 |

HO: no autocorrelation at lag order

When testing for autocorrelation, the analysis is conducted for lag 1 and lag 2 For each lag, the results include the chi-squared test statistic, the degrees of freedom (df), and the probability value (Prob > chi2).

For lag 1, the chi-squared statistic is 75.6269 with 64 degrees of freedom and a probability value of 0.15164

For lag 2, the chi-squared statistic is 92.9056 with 64 degrees of freedom and a probability value of 0.01059

The null hypothesis of this test asserts that there is no autocorrelation at the designated lag order The probability values suggest that we fail to reject the null hypothesis at standard significance levels, such as 0.05 or 0.1, since these values exceed the established thresholds.

Equation Excluded chi2 df Prob > chi2

The Granger causality test for the VAR model assesses whether past values of one variable can predict another variable's future values, offering insights beyond the information from other variables in the model.

The output presents the Wald chi2 test results for each equation in the VAR model, highlighting the excluded variable and the predictors used to forecast it For each excluded variable, the report includes the name of the variable, the associated predicting variables, the chi2 test statistic, degrees of freedom (df), and the probability value (Prob > chi2) Additionally, the impulse response function (IRF) analysis is conducted using the command "irf table fevd," with the impulse set to "FEE MARKET" and the response also focused on "FEE MARKET."

(1) irfname IRF1, impulse = FEE, and response FEE.

(2) irfname = IRF1, impulse = FEE, and response = MARKET.

(3) irfname IRF1, impulse MARKET, and response = FEE.

(4) irfname = IRF1, impulse = MARKET, and response MARKET. irf table fevd, irf(IRF1) impulse( FEE GDP ) response ( FEE GDP

(1) irfname IRF1, impulse = FEE, and response = FEE II

(2) irfname IRF1, impulse = FEE, and response = GDP.

(3) irfname = IRF1, impulse = GDP, and response = FEE.

(4) irfname IRF1, impulse = GDP, and response = GDP. irf table fevd, irf(IRF1) impulse( FEE M2 ) response( FEE M2

(1) irfname = IRF1, impulse = FEE, and response = FEE.

(2) irfname = TRF1, impulse = FEE, and response = M2.

(3) irfname = IRF1, impulse = M2, and response FEE.

(4) irfname = IRF1, impulse = M2, and response = M2. irf table fevd, irf(IRF1) impulse( FEE CPI ) response( FEE CPI )

Step fevd Lower Upper fevd Lower Upper ơ ễ — ễ.ễễ ễ.ễ.ễễễễ ,.ễ.

Step | fevd Lower Upper fevd Lower Upper

(1) irfname IRF1, impulse = FEE, and response = FEE.

(2) irfname IRF1, impulse = FEE, and response = CPI.

(3) irfname = IRF1, impulse = CPI, and response = FEE.

(4) irfname = IRF1, impulse = CPI, and response = CPI. irf table fevd, irf(IRF1) impulse( FEE TYGIA ) response( FEE TYGIA )

Step £evd Lower Upper fevd Lower Upper

(1) irfname = IRF1, impulse = FEE, and response = FEE.

(2) irfname IRF1, impulse FEE, and response = TYGIA.

(3) irfname IRF1, impulse TYGIA, and response = FEE.

(4) irfname = IRF1, impulse = TYGIA, and response TYGIA. irf table fevd, irf(IRF1) impulse( FEE IRLNH ) response( FEE IRLNH )

Step fevd Lower Upper fevd Lower Upper ơ "

(2) irfname TRF1, impulse FEE, and response = IRLNH.

(3) irfname IRF1, impulse = IRLNH, and response = FEE.

(4) irfname TRF1, impulse TRLNH, and response TRLNH.

83 irf table fevd, irf(IRF1) impulse( FEE LSTPCP ) response( FEE LSTPCP

Step fevd Lower Upper fevd Lower Upper ơ ` "—-

Step fevd Lower Upper fevd Lower Upper ơ ' ễễễễễ

(2) irfname IRF1, impulse FEE, and response = LSTPCP.

(3) irfname IRF1, impulse = LSTPCP, and response = FEE II

(4) irfname IRF1, impulse = LSTPCP, and response = LSTPCP.

3 For Political events var PE MARKET GDP M2 CPI TYGIA IRLNH LSTPCP, lags (1/2)

| lag | chi2 để Prob > chi2 |

HO: no autocorrelation at lag order vargranger

Equation Excluded chi2 df Prob > chi2

LSTPCP ALL 35.14 14 0.001 irf table fevd, irf(IRF2) impulse( PE MARKET) response ( PE MARKET )

Step | fevd Lower Upper fevd Lower Upper

(1) irfname IRF2, impulse = PE, and response = PE II

(2) irfname = IRF2, impulse = PE, and response = MARKET.

(3) irfname = IRF2, impulse = MARKET, and response = PE.

(4) irfname = IRF2, impulse MARKET, and response = MARKET II irf table fevd, irf(IRF2) impulse( PE GDP ) response ( PE GDP )

Step fevd Lower Upper fevd Lower Upper ơ ễ "— ễ ễ ễ

(1) irfname IRF2, impulse = PE, and response = PE.

(2) irfname IRF2, impulse = PE, and response = GDP.

(3) irfname = IRF2, impulse GDP, and response = PE.

(4) irfname = IRF2, impulse = GDP, and response = GDP. irf table fevd, irf(IRF2) impulse( PE M2 ) response ( PE M2 )

Step | fevd Lower Upper fevd Lower Upper ơ ` — `

(2) irfname IRF2, impulse = PE, and response = M2.

(3) irfname IRF2, impulse = M2, and response = PE.

(4) irfname IRF2, impulse = M2, and response = M2. irf table fevd, irf(IRF2) impulse( PE CPI ) response ( PE CPI )

(1) irfname IRF2, impulse PE, and response PE.

(2) irfname = IRF2, impulse = PE, and response = CPI.

(3) irfname = IRF2, impulse = CPI, and response = PE.

(4) irfname = IRF2, impulse = CPI, and response = CPI. irf table fevd, irf(IRF2) impulse( PE TYGIA ) response ( PE TYGIA )

(1) irfname = IRF2, impulse = PE, and response = PE.

(2) irfname TRF2, impulse = PE, and response

(3) irfname IRF2, impulse TYGIA, and response PE.

(4) irfname = IRF2, impulse TYGIA, and response irf table fevd, irf(IRF2) impulse( PE IRLNH ) response ( PE IRLNH

(2) irfname = IRF2, impulse = PE, and response = IRLNH.

(3) irfname IRF2, impulse IRLNH, and response = PE.

(4) irfname = TRF2, impulse IRLNH, and response = IRLNH II irf table fevd, irf(IRF2) impulse( PE LSTPCP ) response ( PE LSTPCP

Tiêu đề	The Impact Of Political And Economic Events On The Vietnamese Stock Market
Tác giả	Ngo Hoang Linh
Người hướng dẫn	Ph.D. To Lan Phuong
Trường học	University of Economics and Business, Vietnam National University - Ha Noi
Chuyên ngành	Banking & Finance
Thể loại	Graduation Thesis
Năm xuất bản	2023
Thành phố	Hanoi

Định dạng
Số trang	106
Dung lượng	49,44 MB