THE APPLICATION OF VALUEATRISK IN MEASURING RISKS OF VIETNAMESE STOCK MARKET

THE APPLICATION OF VALUEATRISK IN MEASURING RISKS OF VIETNAMESE STOCK MARKET THE APPLICATION OF VALUEATRISK IN MEASURING RISKS OF VIETNAMESE STOCK MARKET THE APPLICATION OF VALUEATRISK IN MEASURING RISKS OF VIETNAMESE STOCK MARKET THE APPLICATION OF VALUEATRISK IN MEASURING RISKS OF VIETNAMESE STOCK MARKET THE APPLICATION OF VALUEATRISK IN MEASURING RISKS OF VIETNAMESE STOCK MARKET

Dissertation submitted in partial fulfillment of the Requirement for the MSc in Finance FINANCE DISSERTATION ON THE APPLICATION OF VALUE-AT-RISK IN MEASURING RISKS OF VIETNAMESE STOCK MARKET NGUYEN THUY DUNG ID No: 20000233 Intake Supervisor: Dr Tran Manh Ha September 2020 EXECUTIVE SUMMARY Financial market is highly important for the growth and development of any economies globally as the facilitators of the sources of funds and the uses of funds However, an inherent characteristic of the financial market is its volatility associated with a diversified set of risks Yet, the problematic puzzle of measuring the complex risks of the financial market remains a challenge for not only academic scholars but also financial market players To this end, this paper attempted to develop closer analysis to the presence of Value-at-Risk (VaR) in quantifying the risks associated with the financial market Within the scope of this paper, the focus would shed the light into VaR application in Vietnamese stock market With the consideration of the VN30 index as the valuable snapshot of the stock market in Vietnam for the period from 21 April 2019 to 20 April 2020, this paper implements four VaR approaches, including Parametric Value at Risk (PVaR), Historical Value at Risk (HVaR), Modified Value at Risk (MVaR) and Conditional Value at Risk (CVaR) Each method might have specific weaknesses that can be overcome with the advantages of other ones The main findings of this paper are hoped to provide practical insights on the application of VaR into measuring the risk for stock market in Vietnam based on the in-depth analysis of previous literatures on this matter TABLE OF CONTENTS EXECUTIVE SUMMARY i TABLE OF CONTENTS .ii LIST OF FIGURE iii I INTRODUCTION 1 Research rationale Research background Research objectives Research contribution .3 Synopsis of research II LITERATURE REVIEW .5 Background of financial risks Value-at-Risk (VaR) .14 III VIETNAMESE ECONOMY AND STOCK MARKET 23 Overview of Vietnamese economy 23 The development of Vietnamese stock market 26 IV METHODOLOGY 30 Research design .30 Data collection and data analysis approach 31 V FINDINGS AND DISCUSSION 34 Descriptive statistics 34 Risk assessment using VaR 38 VI CONCLUSION 42 REFERENCES 43 APPENDIX 48 LIST OF FIGURE Figure Comparison between VaR and CVar .22 Figure Vietnam’s GDP Growth from 2013 to 2019 23 Figure FDI growth in Vietnam from 2015-2019 24 Figure Political Stability Index in Vietnam 25 Figure Bond market growth from 2010-2015 .27 Figure Vietnam Stock Market Structure 28 Figure Stock market capitalization as % of GDP in Vietnam 29 Figure VN30 Daily Compounded Rate of Return 34 Figure VN30 index descriptive statistics 35 Figure 10 Descriptive statistics for individual stocks in VN30 index 36 Figure 11 Mean Compounded Rate of Return for individual stock 37 Figure 12 Summary of the VaR result for VN30 index 38 Figure 13 Individual VaR results at 95% confidence level 39 Figure 14 Individual losses of VN30 index components measured by VaR at confidence level of 95% .41 I INTRODUCTION Research rationale Financial market is highly important for the growth and development of any nation in the world Acting as the intermediary to connect the sources of funds and the uses of funds, one of the financial market inherent characteristics would be its uncertainty and volatility Risks have been among key consideration for financial theorists since the longest time Indeed, the movements of commodities and financial securities deal with various uncertainties resulting from a wide range of attributes To this perspective, risks can be considered as an unavoidable aspect in the financial market and in business world As a matter of fact, the complication of risks in the financial market can be far more complication, contributing to further difficulties for the players in the financial market Series of financial crisis and scandals booming from the beginning of the century have put on the questions of how such uncertainties and risks associated with the financial market can be managed and supervised The management of risks require comprehensive efforts in identifying risks, assessing and measuring its impacts as well as determining the proper risk mitigation method In response to such alarming signals, researchers and scholars have long been gravitated towards the development of a proper sophisticated model to measure and tackle risks One of the most important and traditional method of risk measurements frequently utilized by the scholars is the usage of Value at Risk model (VaR) At its core the VaR model measure the potential losses led by unfavorable market movements As a matter of fact, over the past few periods, the VaR model has become a standard tool utilized in the risks management process by not only financial market players but also management in other business sectors Research background Vietnam has become one of the continually growing nations in the Asian region as well as globally The development of Vietnam has been emerging after Vietnamese Government’s efforts in reforming its traditional economy Strong growth in the national economy is largely strengthened by the contribution of the circulation of capital between the savers and investors Hence, the health of financial market infrastructure become increasingly critical for the development of the national economy It is essential to identify that the financial market in Vietnam is heavily relied on the facilitation of the stock market while bond market is relatively inexperienced and dominated by the institutional players The strong composition of the stock market in Vietnam highlights the significance of strengthening this market against the presence of increasing risks In addition, it is important to highlight that the stock market in Vietnam is relatively young and inexperienced while the infrastructure and regulatory frameworks have yet been able to keep up with the emerging expansion of the financial market and national economy Moreover, the ongoingly growing participants of foreign investors as well as other impacts of globalization process enhances the complexity of the financial market Such attributes further contribute to higher level of volatility and riskiness faced by investors However, to sustain higher level of efficiency in circulating the capital and investments of the financial market as well as attract investors to participate in such process, it become urgent significance for Vietnam to be able to identify proper approach towards risk identification and mitigations within the stock market as well as other aspects of the financial market Research objectives Although many literatures have applied the VaR models in different market, there have been limited researches and findings associated with the development of risk measurement through VaR model for the stock market in Vietnam Moreover, previous studies have the tendency to formulate the risks determinants through Ordinary Least Square (OLS) model utilizing traditional financial ratios while there have been limited evidences focusing on how risks are measured Withstanding from the above attributes, this paper is developed as a research focused on applying the VaR model in measuring risks in Vietnamese stock market In accomplish this research aim, different research objectives have been formulated: - To implement the VaR model in measuring risks in stock market in Vietnam; - To understand the current risks and volatility in the Vietnamese stock market; - To determine the existence of diversified conclusions among different VaR approaches; - To further outline potential recommendations on risks management for the stock market in Vietnam Within the scope of this paper, the study focuses on the Vietnamese stock market only By which it means that the risks associated with trading other form of financial securities in Vietnamese financial market like bond trading, derivatives markets, etc would be neglected On the other hand, it also identifies that any comparative studies between Vietnamese and foreign stock markets would not be within the scope of this paper Research contribution This paper would be expected to contribute to the academic world in multiple aspects At first, there have been limited studies attempting to measuring the level of risks in the financial market in Vietnam In fact, such implications in other emerging markets have been increasingly conducted by scholars and financial theorists Hence, this paper contributed to the discussion of risks and risks management for financial market in Vietnam with respects to the usage of Value at Risk model and its different approaches On the other hand, as the Vietnamese stock market has been growing expanding over the last few years and contributing massively to the financial market, its stability and growth remain a considerable concern for not only the players in these markets but also the government and regulators Hence, a secondary contribution and significance of this paper would to assess the current risk level of Vietnamese stock market to help regulators to have the clear overview of current risks scheme in the financial market With the growing consideration of Value at Risk model in risk management, which was further emphasized by the Basel III, this paper would further engage in a large picture of risks management in financial world and the applicability of Value at Risk (VaR) model in the stock market The main findings of this paper are hoped to provide practical insights on the application of VaR into measuring the risk for stock market in Vietnam based on the in-depth analysis of previous literatures on this matter Based on which, potential drawbacks and strengths of VaR as the measurement of stock risks in Vietnam would be further revealed With that being said, the contribution of this paper would potentially help future studies as well as financial market players a better view on the risk landscape of Vietnam’s stock market Synopsis of research The later research would be conducted as follow For the first chapter, the research would give a brief introduction to the research rationale as well as the determination of the research objectives and research questions Moreover, this first chapter also gives the overview of the current stock market background in Vietnam as well as different concerns with respects to the usage of Value at Risk model in financial market Moving on to the second chapter, this chapter is conducted as a comprehensive literature review of a wide range of issues in relation to risks and the Value at Risk model In other word, this second chapter help establish a solid foundation and background for the later study and analysis Next, the third chapter focuses on the analysis of the current economic background in Vietnam along with different development and characteristics of the financial market, especially the stock market in Vietnam Having profound ideas on the current macroeconomic status and the growth of stock market in Vietnam would support a strong foundation on the later analysis as these factors are highly correlated to the movements in risks and returns of the stock market Later on, the fourth chapter provide a detailed information on the research design with respects to a wide range of attributes and consideration for research methodology ranging from the research design, sampling method, data collection and data analysis method, etc The next chapter summarizes and give a more comprehensive discussion of the data collected from the Value at Risk model and attempt to answer the research questions and achieve the research objectives Furthermore, in-depth descriptive statistics and discussion would be presented in this specific chapter For the final chapter, this paper would give the conclusion remark sum up the entire paper and findings from previous chapters II LITERATURE REVIEW Background of financial risks i The concepts and measurements of financial risks Risk, which is also referred as volatility or uncertainty are important concept and aspect in not only the finance world but also the physical world The concept of risks has become one of the most popular concepts yet difficult to capture and conceptualize in finance field Yet, it is noteworthy that physical world does not exist without uncertainty Furthermore, company must take on risky investment opportunity to grow and prosper In general, risks are much perceived as the degree of uncertainty which might be experienced in almost every aspects of life (Shkolnyk, 2019) To this extent, risk can be characterized by two components including the level of uncertainty and the relative exposure with respects to the object facing the former uncertainty (Pasaribu, 2010) The concept of risks was further simplified into mathematical ideology with the work of Meyer (1985) on the uncertainty principle However, to ideologize the risk concept in financial market, it would be necessary to take on a broader and more comprehensive point of view In finance world, risk is an inherent aspect of every financial securities and investments In other words, when individuals participate in the financial market through making investments on certain financial securities, they are exposed to a wide range of risks As a matter of fact, all financial market players from investors, portfolio managers, investment bankers, securities rating agency, etc concerned about the uncertainty of the return on their investments (Raza et al 2014) Such uncertainty might be contributed by a wide range of factors which can be either specific attributes associated with the financial securities themselves or macroeconomic drivers affecting the entire financial market as a whole To this extent, risks in financial market might depict the potential of unfavorable gap between the expected outcomes by the financial market players and the actual outcomes (Shkolnyk, 2019) Thus, it can be perceived that any incident or activity that might contribute to a possible unfavorable return might be termed as risks in financial market Attempting to quantify risk, scholars often consider the historical movements in the financial securities prices and behaviors as the basis of measuring the extent to which the investment experience volatility (Hull, 2018) Volatility in mathematical ideology measure the dispersion of stock price movement by either variance or standard deviation More specifically, using the statistical approach, financial theorists measure risk level associated to the investment by incorporating the standard deviation of the financial securities over a specific period of time (Shkolnyk, 2019) By measuring the variation of financial securities prices, standard deviation depicts the volatility of financial securities prices through measuring the total gaps between the prices and the historical means during specific period (Hull, 2018) In addition, the level of risk might be measured with skewness reflecting the asymmetry of the financial assets’ returns distribution (Pasaribu, 2010) In the wake of calculation of skewness for financial assets’ returns, investment with a positive skewness might depict less risky investment while a negative skewness might reflect a higher return and higher risk portfolio (Shkolnyk, 2019) Taking similar approach to measure risks using the distribution of the financial assets’ return, scholars also take into consideration the usage of Kurtosis Another critically important literature body on financial risks associated with the introduction of the Capital Asset Pricing Model (CAPM) (Jurczenko, 2018) At its core, the CAPM reflects the financial market characteristics a single factor, named market beta; to this end, the market beta measures volatility of the overall market On the other hand, the market beta act as the basis for measuring volatility of other assets with specific risk premium depending on the specific characteristics of the financial assets (Hull, 2018) In the light of CAPM, an asset with higher beta might indicate that it might expose to higher risk than those with lower beta Under CAPM, beta is measured by the division of covariance between portfolio or individual stock returns and the relative market return and the variance in market portfolio return (Hull, 2018) Nevertheless, such approaches in measuring risks using historical data might violate the fundamental financial theory of random walk To this perspective, the random walk hypothesis explained that past information or stock prices cannot be used to predict the future movements (Chitenderu et al 2014) This in turn contributes to the comprehensiveness of risk measuring in the finance world ii Financial risks classification Moreover, the comprehension of risk is further contributed by the sophisticated classification of risks There are various ways for scholars to categorize different types of risks faced by financial market participants relationship from the raw data (Bordens and Abbott, 2017) The process gives a preliminary analysis of the dataset before detail data analysis and risk assessment with the VaR model Moreover, this research would provide the descriptive analysis for VN30 index as well as its components To begin with, this paper would take a close look into the movement in the VN30 index compounded rate of return VN30 Daily Compounded Rate of Return 0.08 0.06 0.04 0.02 9 9 9 9 9 9 9 9 9 9 0 0 0 0 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 01 02 02 02 02 02 02 02 02 -2 y-2 y-2 n -2 n-2 n -2 l-2 l-2 g-2 g-2 g-2 p-2 p-2 t-2 t-2 v-2 v-2 v-2 c -2 c -2 n-2 n -2 b-2 b-2 r-2 r-2 r-2 r-2 r -0.02 p a a c c o o o u u a a a p -A -M -M -Ju -Ju -Ju 1-J 4-J -Au -Au -Au -Se -Se -O -O -N -N -N -De -De -Ja -Ja -Fe -Fe -M -M -M -A 09 2 04 17 06 19 13 09 2 04 15 11 07 07 04 17 13 -0.04 -0.06 -0.08 #REF! VN30 Daily Compounded Rate of Return Figure VN30 Daily Compounded Rate of Return It can be seen that VN30 have the tendency to vary considerable over the period More importantly, by the end of the discussed period, the daily return remained relatively negative suggesting considerable losses of VN30 To this extent, the negative return of VN30 during the end of the period can be much explained by the effects of Coronavirus outbreak in the early of 2020 leading serious impacts on the stock market globally and in Vietnam as well Excluding the period impacted by the pandemic in the early of 2020, it can be drawn from the above figure that the daily compounded rate of returns of VN30 tend to be relatively minor with a range from -2% to 2% The severe impacts of Coronavirus outbreak led to immediate downturn and considerably dramatic variations of the VN30 daily returns To draw on more insights from the dataset, the descriptive statistics analysis of VN30 index can be outlined in the table below 35 Figure VN30 index descriptive statistics On average, VN30 index has the daily compounded rate return of -0.0708%, depicting the loss over the discussed period The standard deviation of the compounded rate of return is relatively low suggesting a less varied daily rate of return for the dataset Comparing to the mean, larger standard deviation might support the evidence that the daily returns are dominated by randomness (Edmonds and Kennedy, 2017) The range of the VN30 daily returns is considerably large which is around 11.59% varying from -6.5652% to 5.0318% over the period These extreme observations are most allocated in the period after Coronavirus outbreak in Vietnam leading to considerable variation within the entire stock market It is worth noting that the similar trends can also be witnessed for individual stocks included in the VN30 index 36 Figure 10 Descriptive statistics for individual stocks in VN30 index 37 With regards to the performance of individual stocks in the VN30 index, it can be seen that the vast stocks have negative average compounded rate of return over the period Mean Compounded Rate of Return 0 S RO W PO SN M H BV GA S J PN M VH CT D BB M B VC B EI T SB NV L G HP B ST 0 -0.01 -0.01 -0.01 Figure 11 Mean Compounded Rate of Return for individual stock Except for MWG, VCB, VJC, REE, BID, FPT and VPB, the other stocks included in the VN30 index suffers from negative average compounded rate of returns, which were contributed largely by the high variation of stock returns during the Coronavirus outbreak by the end of the discussed period From the figure above, it can be clearly seen that ROS were the stock with the lowest mean returns, which are considerably lower than the other stocks included in the VN30 index with -0.84% and the highest daily returns among the 30 observations was FPT stocks with 0.08% for the interested period Similar to the VN30 index returns, it can be witnessed that the majority of stock have relatively low standard deviation, indicating a lesser variation over time Another striking factor in the investigation of descriptive statistics highlighting that the vast stock return has the tendency to be negatively skewed with lower than zero skewness As a rule of thumps, if the negative skewness is lower than -0.05, it might indicate significantly left skewed distribution, meaning that the large proportion of the observations are distributed to the left of the mean or lower than the mean value (Salkind, 2010) For daily stock return, it is expected that a right-skewed distribution would be more preferable with the majority of the return to be higher than the average returns From the skewness, it might depict that the majority of stock in the VN30 index might not provide a 38 desirable return Nevertheless, for stocks like CTD, EIB, CTG and STB, the skewness figures are positive meaning that they might contribute to a preferable right-skewed distribution for the daily return Risk assessment using VaR From the calculated returns, the data are processed with R Programming language to conduct the VaR model and analysis The following table illustrate the riskiness of the VN30 index at certain confidence level collected from four VaR approaches, including Parametric Value at Risk (PVaR), Historical Value at Risk (HVaR), Modified Value at Risk (MVaR) and Conditional Value at Risk (CVaR) Figure 12 Summary of the VaR result for VN30 index The figure above showcase the VaR value calculated with different methodology at confidence level ranging from 90%, 95% and 99% for the risk value over the period of 21 April 2019 to 20 April 2020 with 250 trading days representing 249 daily returns calculated A striking point is that the higher the confidence level, the larger the VaR value acquired, which persist across all VaR methodology At lower level of confidence, CVaR produced the lowest VaR value; however, as for 99% confidence level, HVaR and MVaR produced a considerably higher value than CVaR In practice, it was argued that to ensure the highest level of effectiveness for the CVaR model, it is preferable to choose a higher than 95% confidence interval For VN30, it can be seen that HVaR model generated the lowest results compared to other models with respects to confidence interval of 90% and 95% while at the confidence level of 99% PVaR provided the lowest figure Considering the above value, it can be interpreted that at 10% probability, VN30 index could not lose more than 2.22% while for 5% and 1% probability, the maximum potential loss could not exceed 2.6% and 5.75% in one day Such value might be considerably significant especially for large transaction in one day To further analyze the risk level associated with the Vietnamese stock market, the research conducts the VaR analysis on individual stock included in the VN30 index 39 Figure 13 Individual VaR results at 95% confidence level 40 Under the 95% confidence level, it can be seen that CVaR model produces the highest level of VaR To this end, CVaR is perceived as the expected value of losses that go beyond VaR; hence, CVaR would have the tendency exceed VaR if measured following the same methodology In terms of the lowest VaR recorded, there have been limited agreements across different methods varying across different stocks On the other hand, for specific stock, ROS remained as the one with the highest potential losses at the confidence level of 95% Another striking feature is that ROS also have the lowest compounded rate of return, which might help explaining the tendency for losses with VaR model It is also notable that the VaR collected from all four VaR models are considerably higher than the VaR of other stocks The lowest potential losses belong to VJC with the historical VaR model ROS VRE POW SSI MSN TCB BVH SAB GAS HDB PNJ VIC VHM PLX CTD MWG MBB VNM VCB VJC EIB REE SBT BID NVL CTG HPG Parametric Historical Modified Conditional Weigh VaR 0.36% 1.99% 1.05% 1.22% 3.26% 10.74% 0.78% 1.49% 1.28% 3.36% 2.62% 6.47% 5.00% 0.88% 0.43% 5.95% 5.54% 8.46% VaR 0.18% 1.64% 0.82% 1.00% 3.31% 10.27% 0.41% 1.06% 0.98% 2.95% 2.41% 5.09% 4.66% 0.71% 0.24% 7.18% 5.69% 7.58% VaR 0.39% 1.89% 1.05% 1.12% 2.61% 11.41% 0.73% 1.56% 1.21% 3.37% 2.50% 5.24% 4.43% 1.00% 0.42% 5.71% 5.39% 8.98% VaR 0.34% 1.99% 1.04% 1.21% 3.21% 10.71% 0.77% 1.48% 1.27% 3.35% 2.62% 6.46% 4.98% 0.87% 0.43% 5.99% 5.54% 8.42% t 0.30% 1.70% 0.90% 0.90% 4.70% 8.00% 0.60% 1.80% 1.10% 2.90% 2.10% 7.70% 4.80% 0.90% 0.40% 4.60% 4.50% 10.10 3.90% 3.69% 0.84% 0.88% 0.51% 1.36% 0.79% 1.62% 6.35% % 3.70% 5.50% 2.90% 1.00% 0.80% 1.10% 3.40% 1.30% 6.20% 3.87% 3.66% 0.85% 0.88% 0.51% 1.35% 0.80% 1.61% 6.34% 4.79% 3.15% 1.48% 0.93% 0.58% 1.62% 1.47% 1.75% 6.47% 4.10% 5.28% 0.61% 0.92% 0.67% 1.38% 0.85% 1.52% 6.13% 41 FPT STB VPB Parametric Historical Modified Conditional Weigh VaR 5.35% 4.49% 9.42% VaR 6.42% 3.80% 11.34% VaR 5.35% 4.24% 9.90% VaR 5.42% 4.49% 9.50% t 5.20% 3.60% 7.30% Figure 14 Individual losses of VN30 index components measured by VaR at confidence level of 95% The above figure outlines individual losses of each stock included in the VN30 index calculated according to the weight of the stocks within the index It is notable that across all four VaR models, TCB is the most significant potential losses measured by VaR within VN30 index With higher percentage holding within the VN30’s stocks, TCB might be the individual stock with considerable contribution to the volatility of VN30 Following closely by TCB were VPB and VNM with a slightly lower individual loss across each VaR model On the other hand, it is worth noting that investors would like to take consideration of those stocks with VaR contribution exceed their weight For the case of TCB and VPB, their individual losses exceeded their weight in the VN30 stock portfolio This might potentially depict the risks associated with losses from VN30 index might be intensively affected and contribute by the losses experience with TCB and VPB suggesting relatively higher risks and volatility of these two stocks in relation with their composition in the VN30 index Notably, these two stocks are both in the group of depository financial institutions Going back to the above figures, it can be seen that some banks’ stocks included in the VN30 index; namely STB, TCB, VPB, MBB, HDB, their individual losses tend to be relatively significant This might suggest a potential imbalance in the risk portfolio of VN30 led by high level of banks’ stocks holding VI CONCLUSION Overall, this paper has developed the analysis of the risks assessment for Vietnamese stock market using the VaR model As volatility has become one of the most important consideration in the financial market as well as the stock market, the study of risks assessment and management become increasingly important for the development and survival of investors and players in the stock markets One of the most important tools in assessing such risk in financial market might be the usage of VaR model Moreover, the process of conducting VaR 42 model might comprehend a wide spectrum of consideration in terms of the VaR methodology applied to specific concerns and risks assessment To complement the limited literature foundation on risks assessment for the financial market in Vietnam, this paper is designed and conducted as the assessment of risks for investing in stock market in Vietnam However, to conduct the risk assessment on the entire market might be unnecessary and inefficient With the consideration of the VN30 index as the valuable snapshot of the stock market in Vietnam for the period from 21 April 2019 to 20 April 2020, this paper implements four VaR approaches, including Parametric Value at Risk (PVaR), Historical Value at Risk (HVaR), Modified Value at Risk (MVaR) and Conditional Value at Risk (CVaR) Each method might have specific weaknesses that can be overcome with the advantages of other ones Rather than looking at a single index, which is the VN30 index, this paper also takes a further step in analyzing the contribution of individual stocks to the volatility of the stock market in Vietnam The present paper help deepening the understanding of the stock market in Vietnam Yet, it might carry certain weaknesses that might be open for future studies and opportunities To start with, the dataset consists of 250-day period observation over year, which was argued in previous literature as a standard timeframe for analysis However, it is worth noting that for method like Historical VaR, the larger the dataset and timeframe the more likely that the results would be deepened Secondly, over the analyzed period, it is important to indicate that prior to the end of this timeframe, there was an emerging outbreak of the Coronavirus leading to serious impacts on the financial market as a whole Hence, the dataset might be affected considerably by the effects of Coronavirus outbreak To conclude, in further research, there might be chance for expansion of the dataset as well as further consideration of any potential sociopolitical events that might contribute to the unexpected performance in the stock market 43 REFERENCES Abid, F., Leung, P L., Mroua, M & Wong, W K (2014), ‘International Diversification Versus Domestic Diversification: Mean-Variance Portfolio Optimization and Stochastic Dominance Approaches’, Journal of Risk and Financial Management, Vol 2, pp 45-66 Al Janabi, M A A (2007), ‘A Value At Risk Approach to Measuring Equity Trading Risk Exposure in Emerging Stock Markets’, Asian Academy of Management Journal of Accounting and Finance, Vol 3, No 1, pp 1-19 Angelovska, J (2013), ‘Managing Market Risk with VaR (Value at Risk)’, Journal of Contemporary Management Issues, Vol 18(2), pp 81-96 Barjaktaroviü L et al (2013) ‘Concentration level of the banking industry in CEE countries’, Industrija, Vol 41, No 3, pp 39-53 Beaulieu, M., Cosset, J & Essaddam, N (2005), ‘The impact of political risk on the volatility ofstock returns: the case of Canada’, Journal of International Business Studies, Vol 36, pp 701-718 Bogdan, S., Suzana B., & Zoran I (2015), ‘Estimating Risk on the Capital Market with VaR Method’, UTMS Journal of Economics, Vol (1), pp 165–175 Bordens, K S & Abbott, B B (2017), Research Design and Methods: A Process Approach (10th ed.), McGraw-Hill Chen, Q & Chen, R (2013), ‘Method of Value-at-Risk and empirical research for Shanghai stock Celik, S., Mandaci, P E & Cagli, E C (2009) ‘An Examination of Risk and Return Trade off in Manufacturing Industry: An Asset Pricing Approach’, Middle Eastern Finance and Economics, 4, 5-27 Chiang, T C & Zhang, Y (2018), ‘An Empirical Investigation of RiskReturn Relations in Chinese Equity Markets: Evidence from Aggregate and Sectoral Data’, International Journal of Financial Studies, Vol 6, pp 35-56 Chitenderu, T T., Maredza, A & Sibanda, K (2014), ‘The Random Walk Theory And Stock Prices: Evidence From Johannesburg Stock Exchange’, International Business & Economics Research Journal, Vol 13(6), pp 1241-1250 Christensen, L B., Johnson, R B & Turner, L A (2014), Research Methods, Design, and Analysis (12th ed.), Pearson 44 Corkalo, S (2011), ‘Comparison of Value at Risk Approaches on a stock portfolio’, Croatian Operational Research Review, Vol 2, pp 81-90 Dargiri, M.N., Shamsabadi, H.A., Thim, C.K., Rasiah, D & Sayedy, B (2013), ‘Value-at-risk and Conditional Value-at-risk Assessment and Accuracy Compliance in Dynamic of Malaysian Industries,’ Journal of Applied Sciences, Vol 13, pp 974-983 Deloitte, (2019), Doing Business in Vietnam 2019 - Investing in Vietnam, Engaging the world, [online] Available at https://www2.deloitte.com/vn/en/pages/tax/articles/vietnam-doing-business2019.html Deloitte, (2020), Doing Business in Vietnam 2020 - Investing in Vietnam, Engaging the world, [online] Available at https://www2.deloitte.com/vn/en/pages/tax/articles/vietnam-doing-business2020.html Edmonds, W A & Kennedy, T D (2017), An Applied Guide to Research Designs: Quantitative, Qualitative, and Mixed Methods (2nd ed.), Sage Publications Eun, C., Lai, S., Roon, F & Zhang, Z (2010), ‘International diversification with factor funds’, Management Science, Vol 56, pp 1500–1518 Fraenkel, J., Wallen, N & Hyun, H (2011), How to Design and Evaluate Research in Education (8th ed.), McGraw-Hill Gorbunova, N A (2016), ‘Methods of Analysis of Equity Securities Risk and Return: Issues and Prospects’, European Research Studies, Vol XIX(3), pp 228-249 Hasan, M Z., Kamil, A A., Mustafa, A., & Baten, M A (2011), ‘A Validity Test of Capital Asset Pricing Model for Dhaka Stock Exchange,’ Journal of Applied Sciences, 11(20), 3490-3496 Hull, J C (2018), Risk Management and Financial Institutions (5th ed.), Wiley IMF (July 16, 2019), Five Charts Explain Vietnam's Economic Outlook, IMF, [online] Available at https://www.imf.org/en/News/Articles/2019/07/11/na071619-five-charts-explainvietnams-economic-outlook 45 Kemp, S (February 18, 2020), DIGITAL 2020: VIETNAM, Data Reportal, [online] Available at https://datareportal.com/reports/digital-2020-vietnam Jurczenko, E (2018), Factor Investing - From Traditional to Alternative Risk Premia , Elsevier Ltd Lekovic, M (2018), ‘Investment Diversification as a strategy for reducing investment risk’, Economic Horizon, Vol 20(2), pp 169-184 Leon, A., Nave, J M., & Rubio, G (2007), ‘The relationship between risk and expected return in Europe,’ Journal of Banking and Finance, 31, 495-512 Li, P., Wang, X & Wang, H (2013), ‘A Factor Model for the Calculation of Portfolio Credit VaR’, Procedia Computer Science, Vol 17, pp 611-618 Lidija, B., Ivica, T & Marko, M (2014), ‘Testing Applicability of Value at Risk Models in Stocks Markets’, Mediterranean Journal of Social Sciences, Vol 5, No 9, pp 297-306 Mahmoud, O (2017), The Origin of Diversification: An Evolutionary Theory Available at: https://ssrn.com/abstract=3004574 MBS, (2019), Vietnam Capital Market Report, [online] Available at https://www.mbs.com.vn/uploads/files/Bao-cao-IB/2_-Vietnam-Capital-MarketReport_032019.pdf Medo, M., Yeung, C H., & Zhang, Y C (2009) How to quantify the influence of correlations on investment diversification International Review of Financial Analysis, 18(1), 34-39 Michailidis, G., Tsopoglou, S., & Papanastasiou, D (2006), ‘Testing the Capital Asset Pricing Model (CAPM): The Case of the Emerging Greek Securities Market’, International Research Journal of Finance and Economics, 4, 78-91 Nguyen, H T T & Nguyen, T T T (2016), Vietnam’s Securities Market 16 years of establishment and development (2000-2016), [online] Available at http://www.nomurafoundation.or.jp/en/wordpress/wpcontent/uploads/2016/10/NJACM1-1AU16-09_Vietnam.pdf Olibe, K O., Michello, F A., & Thorne, J (2008) ‘Systematic risk and international diversification: An empirical perspective’ International Review of Financial Analysis, Vol 17(4), 681-698 Pasaribu, R B F (2010), ‘Value At Risk Portfolio Dan Likuidit Assaham’, Journal of Accounting and Management, Vol 21(2), pp 105-127 46 PwC (2019), Doing Business in Viet Nam - A reference guide for entering the Viet Nam market, PwC, [online] Available on https://www.pwc.com/vn/en/publications/vietnam-publications/dbg-2019.html Quang, P B et al (2018), ‘Value-at-Risk for South-East Asian Stock Markets: Stochastic Volatility vs GARCH’, Journal of Risk and Financial Management, Vol 11, pp 18-37 Rockafellar, R T., & Uryasev, S (2002) ‘Conditional value-at-risk for general loss distributions’, Journal of Banking & Finance, Vol 26(7), pp 14431471 Rui, K X., Rasiah, D., Yen, Y X., Ramasamy, S & Pillay, S D (2018), ‘An Analysis of the Relationship between Risk and Expected Return in Malaysia Stock Market: Test of the CAPM’, International Journal of Engineering & Technology, Vol 7(3), pp 161-170 Salkind, N J (2010), Encyclopedia of Research Design (1st ed.), SAGE Publications, Inc Shamsabadi, H A., Dargiri, M N & Rasiah, D (2012), ‘A Review Study of Risk-Return Relationship and Performance Measures Comparing Different Industry Sectors’, Australian Journal of Basic and Applied Sciences, Vol 6(12), pp 14-22 Sharma, M (2012), ‘Evaluation of Basel III revision of quantitative standards for implementation of internal models for market risk’, IIMB Management Review, Vol 24(4), pp 234-244 Simons, K (1996), Value at Risk – New Approaches to Risk Management Simons, K (1996), ‘Value at Risk – New Approaches to Risk Management’, New England Economic Review (September/October 1996), pp 1-13 Sinaee, H., & Moradi, H (2010) ‘Risk-Return Relationship in Iran Stock Market’ International Research Journal of Finance and Economics, Vol 41, 150162 Sobreira, N & Louro, R (2020), ‘Evaluation of volatility models for forecasting Value-at-Risk and Expected Shortfall in the Portuguese stock market’, Finance Research Letters, Vol 32, pp 35-41 Sollis, R (2009) ‘Value at Risk: A Critical Overview’, Journal of Financial Regulation and Compliance, Vol 17(4), pp 398-414 47 Suriani, S., Kumar, M D., Jamil, J & Munner, S (2015) ‘Impact of Exchange Rate on Stock Market’, International Journal of Economics and Financial Issues, Vol 5, pp 385-388 Thanh, V T (January 07, 2020), Viet Nam's economy shines in 2019 but concerns exist for 2020, Vietnam News, [online] Available on https://vietnamnews.vn/economy/570785/viet-nams-economy-shines-in-2019-butconcerns-exist-for-2020.html Vasileiou, E (2017), ‘Value at Risk (VaR) Historical Approach: Could It Be More Historical and Representative of the Real Financial Risk Environment?’, Theoretical Economic Letters, Vol 7, pp 951-974 Vu, T T V & Tran, D K (2019), ‘Systematic Risk in Vietnam Stock Market’, Asian Economic and Financial Review, Vol 9(3), 339-352 VNS, (December 28, 2019), Viet Nam’s GDP jumps 7.02 per cent in 2019, Vietnam News, [online] Available on https://vietnamnews.vn/economy/570467/viet-nams-gdp-jumps-702-per-cent-in2019.html Wu, X., Xia, M & Zhang, H (2020), ‘Forecasting VaR using realized EGARCH model with skewness and Kurtosis’, Finance Research Letters, Vol 32, pp 1-7 48 APPENDIX Quotation ROS VRE POW SSI MSN TCB BVH SAB GAS HDB PNJ VIC VHM PLX CTD MWG MBB VNM VCB VJC EIB REE SBT BID NVL CTG HPG FPT STB VPB Full Name FLC Faros Construction Joint Stock Company Vincom Retail Joint Stock Company PetroVietnam Power Corporation SSI Securities Corporation Masan Group Corporation Vietnam Technological and Commercial Joint Stock Bank Bao Viet Holdings Saigon Beer - Alcohol - Beverage Corporation PetroVietnam Gas Joint Stock Corporation Ho Chi Minh Development Joint Stock Commercial Bank Phu Nhuan Jewelry Joint Stock Company Vingroup Joint Stock Company Vinhomes JSC Viet Nam National Petroleum Group Coteccons Construction Joint Stock Company Mobile World Investment Corporation Military Commercial Joint Stock Bank Viet Nam Dairy Products Joint Stock Company Bank for Foreign Trade of Vietnam Vietjet Aviation Joint Stock Company Vietnam Commercial Joint Stock Export Import Bank Refrigeration Electrical Engineering Corporation Thanh Thanh Cong - Bien Hoa Joint Stock Company JSC Bank For Investment And Development Of Vietnam No Va Land Investment Group Corporation Vietnam Joint Stock Commercial Bank for Industry and Trade Hoa Phat Group Joint Stock Company FPT Corporation Sai Gon Thuong Tin Commercial Joint Stock Bank Vietnam Prosperity Joint Stock Commercial Bank 49 Weight (%) 0.3 1.7 0.9 0.9 4.7 0.6 1.8 1.1 2.9 2.1 7.7 4.8 0.9 0.4 4.6 4.5 10.1 3.7 5.5 2.9 0.8 1.1 3.4 1.3 6.2 5.2 3.6 7.3 ... the scope of this paper, the focus would shed the light into VaR application in Vietnamese stock market With the consideration of the VN30 index as the valuable snapshot of the stock market in. .. with the advantages of other ones The main findings of this paper are hoped to provide practical insights on the application of VaR into measuring the risk for stock market in Vietnam based on the. .. composition of the stock market in Vietnam highlights the significance of strengthening this market against the presence of increasing risks In addition, it is important to highlight that the stock market