Conditional coverage test ...14 CHAPTER 3: APPLICATION OF VALUE AT RISK MODELS IN VIETNAM STOCK MARKET ...16 3.1.. LIST OF ACRONYMS VaR Value at Risk HOSE Ho Chi Minh City Stock Excha
INTRODUCTION
The stock market has evolved into a vital channel for businesses to raise funds, acting as an intermediary in both domestic and international capital flows It connects sellers seeking profit with buyers requiring capital for their operations However, businesses must navigate the economic cycle, which consists of four key stages: start-ups, growth, maturity, and recession Fluctuations in business activities and the economy contribute to stock market volatility, impacting investment predictions and introducing risks Therefore, effective forecasting, measurement, and risk management are crucial for both institutions and individuals to minimize losses and ensure stable operations in their business and investment endeavors.
In financial risk management, relying solely on qualitative analysis is inadequate; a more effective approach involves integrating quantitative indicators to accurately assess risk and potential financial losses This integration lays the groundwork for the evolution of quantitative methods in risk management Over the years, various models, such as SIM, EPG, and VaR, have been developed to evaluate expected returns alongside the risks associated with stocks and portfolios.
Value at Risk (VaR) is the most widely used model in stock analysis, assessing portfolio risk based on the portfolio's value and the investor's risk tolerance While VaR offers advantages, it parallels traditional measures like variance and standard deviation Consequently, economists have developed advanced methods, such as ARCH and GARCH, to enhance the calculation of VaR for stocks and various categories.
Vietnam stock market is very young, has been only opened since July 2020 but this is remarkable progress It has made a great contribution to form a relatively
2 comprehensive capital market model, create and activate a mid-term and long-term capital mobilization channel for the economy
Vietnam's stock market has experienced remarkable growth since its inception, starting with just two companies listed on the Ho Chi Minh City Stock Exchange (HOSE) on its opening day As of February 2020, data from the State Securities Commission highlights significant advancements in the market's development.
Commission of Vietnam (SSC) showed that 1,628 listed companies included 378 listed companies on HOSE, 367 listed organizations on Hanoi Stock Exchange (HNX) and
As of December 29, 2020, the Unlisted Public Company Market (UPCoM) comprises 883 firms, boasting a market capitalization of VND 4,384 trillion, which reflects an 11% increase since the end of 2018 and represents 79.2% of Vietnam's GDP Over its 20-year history, Vietnam's stock market has experienced significant fluctuations, with a notable rise from 2003 to 2017, during which the VN-Index surged from 131.44 points on September 27, 2003, to a record high in March.
Since its peak of 1,179.32 points in December 2007, Vietnam's stock market faced a dramatic decline due to the economic crisis, reaching a low of 235.5 points in February 2009 From 2009 to 2016, the VN-Index experienced significant fluctuations, alternating between increases and decreases However, from 2016 onwards, the market rebounded, driven by economic recovery and strong corporate performance, establishing itself as one of the world's most promising markets Notably, in the first quarter of 2018, the VN-Index emerged as the fastest-growing index globally, hitting a peak of 1,200 points in April, surpassing its previous highs from March 2017.
In 2018, the VN-Index experienced a modest recovery; however, the emergence of the COVID-19 pandemic in 2019 led to the most significant decline in Vietnam's stock market since 2014, particularly evident in March 2020 This data highlights the substantial volatility of Vietnam's stock market during this period.
The prices of stocks are always changing over time, it means there are many risks threatened investors when they enter the market without fully preparation Thus,
Understanding and forecasting stock price volatility, as well as predicting adverse events like the 2019 coronavirus pandemic, is crucial for investors and finance managers to maximize profits and minimize losses Various methods exist for economists to forecast and manage stock market risks, each with its own advantages and disadvantages Selecting the most suitable forecast model is essential, as different markets exhibit unique performance characteristics, making a one-size-fits-all approach ineffective This need for tailored forecasting models motivates the focus of this thesis In this study, historical stock price data will be analyzed to support these findings.
January 1 st , 2015 to February 29 th , 2020 is used in order to describe the trend of
Vietnam’s stock market in several lately years All selected companies listed on Ho Chi Minh Stock Exchange (HOSE) and Hanoi Stock Exchange (HNX)
This research aims to enhance understanding of Value at Risk (VaR) models and recommend the most appropriate model for Vietnam's stock market Each risk measurement model relies on specific assumptions, including market and investor hypotheses To clarify the objectives of this study, the author concentrates on specific targets that will guide the analysis.
- Briefly overview of theoretical models used in forecasting Vietnam’s stock market in recent time
- Provide the overview of Vietnam’s stock market, with the data of some outstanding industry to describe the volatility in some lately years
- Based on numerous previous research about risk forecast model, suggest the suitable model for Vietnam market
In the literature review, quantitative methods are employed to enhance research objectives, focusing on three prevalent Value at Risk (VaR) models: Historical Simulation (HS), Exponentially Weighted Moving Average (EWMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH).
There are five chapters in this thesis In chapter 1, this is the presentation of overview and problem statement, the objectives of the research methodology Chapter
This article discusses the theory of Value at Risk (VaR) and explores three models: Historical Simulation (HS), Exponentially Weighted Moving Average (EWMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) Chapter 3 offers an overview of the Vietnam stock market, while Chapter 4 reviews prior research, detailing the data characteristics of the Vietnam Stock Market during the selected period and recommending appropriate forecasting models Finally, Chapter 5 addresses regulatory aspects of risk management and the necessity for financial institutions to employ forecasting models.
CONCEPTUAL FRAMEWORK OF VALUE AT RISK
Definition of risk and risk management
Financial risk encompasses more than just the potential outcomes of investments; it involves concepts of uncertainty, randomness, and probability While financial outcomes can lead to either gains or losses, the focus often leans towards mitigating negative events The definition of risk is multifaceted and varies by context, but for our purposes, we define financial risk as the potential for financial loss or gain due to unpredictable changes in underlying risk factors Specifically, this research addresses market risk, which refers to the possibility of loss or gain resulting from unexpected fluctuations in market prices, such as stock prices, or market rates, like interest rates.
The evolution of risk management has been driven by the dangers associated with improper derivatives use and a history of risk management failures since the early 1990s Effective risk management is essential, encompassing the separation of front and back office operations, the application of Value at Risk (VaR) models to assess firmwide risks, and the establishment of robust systems for managing derivatives risk Additionally, monitoring counterparty relationships and ensuring transparent disclosures are critical components Advances in information technology have significantly enhanced risk management practices, improving calculation speeds and reducing data transmission costs, all thanks to modernized risk management systems.
Theoretical framework of Value at Risk (VaR)
Stock prices in the market fluctuating around the average value calls the volatility
High volatility in stock prices indicates significant deviations from the average, presenting both high risk and potential for high returns, while low volatility reflects minimal price fluctuations This relationship between volatility and risk has garnered increasing attention from economists and scholars in recent years, highlighting its importance in risk management strategies.
The Value at Risk (VaR) model is essential for assessing the market risk of assets and portfolios, defining the maximum potential loss over a specified time frame at a certain confidence level (Julija et al., 2017) It serves as a critical tool for measuring and providing early warnings of potential losses in a portfolio's value, particularly when considering the volatility of individual assets (Allen, 2004) As a valuable analytical instrument in the financial sector, VaR enables investors to estimate possible financial losses and implement strategies to hedge against risks effectively.
On the history of information, VaR is built on the basis of theoretical and statistical theories for centuries and recounts from previous measurement methods of risk
Introduced by J.P Morgan in 1994, Value at Risk (VaR) has become a fundamental risk management method, as discussed by various authors including Jorion (1996), Duffie & Pan (1997), and Dowd (1998) During the late 1990s, the US Securities and Exchange Commission emphasized the importance of VaR to enhance investor confidence.
The Commission has mandated that companies disclose quantitative market risks in their financial statements, with Value at Risk (VaR) serving as the primary tool for this disclosure Concurrently, the Basel Committee on Banking Supervision has stated that companies and banks may utilize their internal VaR calculations to determine capital requirements, indicating that a lower VaR could lead to reduced funds allocated for risk coverage.
In term of VaR, 95% and 99% level of confidence is the most common in calculating for one day time horizon On the other hand, it can apply to all liquid
Liquid assets are categorized into seven types, each possessing an indefinite value that adjusts based on market conditions and probability distributions However, Value at Risk (VaR) has its limitations, primarily assuming that market forces remain relatively stable during the determination period This assumption was notably challenged during the 2007-2008 financial crisis, when unexpected market volatility led to the bankruptcy of several investment banks, highlighting the vulnerabilities in the VaR methodology.
This study explores two primary approaches to estimating Value at Risk (VaR): the parametric method and the non-parametric method The parametric method includes two specific models, namely the GARCH and EWMA models Additionally, the Historical Simulation method is presented as the non-parametric approach for VaR estimation.
Conceptual framework of Historical Simulation
The simplest way to estimate VaR is by means of historical simulation (HS) The
HS approach estimates VaR by means of ordered loss observations Suppose we have
To calculate the Value at Risk (VaR) at a 95% confidence level from 1000 loss observations, we recognize that the 5% tail corresponds to 50 observations Therefore, the VaR can be identified as the 51st highest loss observation This can be easily estimated using a spreadsheet by organizing the data in descending order and locating the 51st largest observation.
The Historical Simulation method offers a significant advantage for Value at Risk (VaR) modeling by avoiding the need for distributional assumptions, making it more effective in handling the complexities of financial return data This approach simplifies calculations and allows for the empirical integration of correlations between different assets However, a notable drawback is the uncertainty associated with extreme returns, as there are often limited observations in the tail of the VaR distribution, which can lead to volatility and erratic behavior (Meera, 2012).
Historical scenario assumptions serve as a foundation for future decisions within the Historical Simulation method However, this approach is not suitable for addressing significant market fluctuations caused by unforeseen risks such as earthquakes, terrorism, and war.
8 the other hand, assuming the past will repeat in the future not always right, so
Historical Simulation method is useless for large or complex structures
Using the Historical Simulation method in order to determine VaR need to perform the following 6 steps:
Step 1 Determining the full time series of actual returns for the period evaluated in detail At the beginning of the process, we need to identify all assets in the portfolio The nature of the full time series will depend on the length of needed time to measure VaR It is important that the time series of data is calculated on the same platform that VaR is required
Step 2 Creates a full sequence of returns of assets, while the time series gives real yields not yet completed Base on the basis where the risk value is requested and the nature of the assets in the portfolio, it may be an unavailable full time series of data
Step 3 Determine the weight of assets with the current portfolio For purpose of comparing the change in the value of the current portfolio, weighting the individual assets in the portfolio is crucial
Step 4 Creates a value for the current portfolio for every point in the time series, assuming that the current portfolio is kept unchanged over the entire time series This is the key to historical simulation In this assumption, the weight of assets in portfolio and the time series are unchanged
Step 5 Plot the value changes from one simulated time period to another on the chart After calculating the value of the portfolio at all point in the time series, we have to calculate how the value changes in 2 consecutive points in the period
Step 6 VaR is calculated by historical simulation equal to the 95% or 99% of those results (depending on the probability level desired)
The historical approach to volatility estimation has notable issues, particularly the assumption that 'true' volatility remains constant This leads to the conclusion that any variations in volatility estimates are solely due to sample error Consequently, shorter period estimates tend to yield more volatile results compared to longer period estimates, with any discrepancies attributed only to sampling error.
To accommodate the possibility of changing true volatility over time, it is essential to adopt less restrictive assumptions, moving away from the notion that true volatility remains constant.
A significant issue with this model is that it treats distant events in the sample period as equally impactful as recent ones When an unusual event occurs at time t, the model suggests that its influence on volatility estimates will persist for the subsequent n periods, despite the market returning to normal conditions This creates a "ghost effect," leading to artificially inflated or deflated volatility estimates during those periods, which only normalize after the event is no longer included in the sample Consequently, this drop-off in volatility estimates is not reflective of actual market conditions but rather a byproduct of the estimation method used.
Conceptual framework of EWMA
In term of VaR calculations, GARCH and EWMA (Exponentially Weighted
Moving Average) models assume that returns on financial assets have serial correlations EWMA model was used by J.P.Morgan for VaR calculation In the
The Exponentially Weighted Moving Average (EWMA) model assumes that the most recent day's weight is greater than that of previous days, while considering the financial asset price to remain constant over the evaluation period The general formulation of EWMA models is expressed in the following equation:
𝜎 𝑖 is the volatility of the market at day (t), as estimated at the end of day (t – 1) Squared of variation is the variance rate
𝑅 𝑖,𝑡 is the return of stock i at time t It is determined by the following formula:
𝑃 𝑖,𝑡 is the stock price at the end of the day t
Weight 𝛼 𝑖 decreases exponentially when moving backwards Specifically,
𝛼 𝑖+1 = 𝜆𝛼 𝑖 where 𝜆 is a constants in range of 0 to 1 It points out that this weight leads to a particularly simple formula for updating volatility estimates The above formula is converted to:
𝜎 𝑡 2 = (1 − 𝜆)𝑅 𝑡−1 2 + 𝜆𝜎 𝑡−1 2 Therefore, the EWMA model is quite simple with 𝜎 𝑡 2 variables, which depends on the variance of the previous period such as 𝜎 𝑡−1 2 and 𝑅 𝑡−1 2 The difference of the
EWMA model with the GARCH model is that there are no block coefficients in the model and the coefficients of 𝑅 𝑡−1 2 and 𝜎 𝑡−1 2 are equal to 1
According to Allen (2004), historical deviation is seldom utilized in practice, with EWMA and GARCH models being more prevalent The key distinction between these techniques lies in EWMA's emphasis on recent data over historical data, while GARCH operates differently EWMA requires an optimum decay factor, determined through maximum likelihood estimation, which varies across countries and portfolio types, as noted by the Risk Data Group of J.P Morgan They suggested a decay factor of 0.94 as optimal for most cases (Fan & Wei, 2001), a figure that many researchers have adopted for their own analyses However, alternative perspectives, such as those from Hendricks, also exist in the literature.
(1996) applied additional calculations to estimate the optimal decay factor.
Conceptual framework of GARCH model
Autoregressive conditionally heteroscedastic (ARCH) models introduced by Engle
(1982), extended to GARCH models, independently, by Bollerslev (1986) and Taylor
Since their introduction in 1986, these tools have played a crucial role in analyzing time series data, especially within financial contexts for volatility forecasting They effectively capture key stylized facts of stock returns, demonstrating their significance in financial analysis.
11 varying volatility, heavy-tailed distribution, volatility clustering and volatility persistence
One weaknesses of the ARCH model is that it requires too many parameters and a high order q to capture the volatility process To reduce this limitation, Bollerslev
In 1986, both Engle and Taylor independently introduced the Generalized ARCH (GARCH) model, which extends the original ARCH model by maintaining its key characteristics while reducing the number of estimated parameters through nonlinear restrictions A notable feature of the GARCH model is the inclusion of the lagged conditional variance term (𝜎 𝑡−𝑗 2), indicating that unexpected fluctuations in returns at time t can lead to increased expected volatility in subsequent periods The standard GARCH (1,1) model can be expressed as rt = μ + εt = μ + σtzt, where rt represents the daily return at time t, calculated as rt = ln(Pt / Pt-1) x 100, with Pt denoting the price at time t and Pt-1 the price at the previous time period.
At time t, the stock prices are analyzed in relation to their values at time t-1, with the conditional mean of the asset return represented as α The prediction error is denoted as εt, while σt signifies the conditional standard deviation of the asset return, commonly referred to as volatility, ensuring that σt remains greater than zero Additionally, the standardized error is represented by zt, which follows a normal independent distribution with a mean of 0 and a standard deviation of 1.
For the GARCH (1,1) model to ensure a positive conditional variance, it is essential that the parameters satisfy the conditions ω > 0, α > 0, and β ≥ 0 The process achieves stationarity when the constraint α + β < 1 is met Ling and McAleer (2002a, 2002b) established the regularity conditions for the GARCH(1,1) model, which are defined by the expectation E[𝜀²ₜ].
1− α− β < ∞ if α + β < 1 and E[𝜀 4 𝑡 ] < ∞ if kα 2 + 2αβ + β 2 𝑉𝑎𝑟 (𝛼) Kupiec (1995) pointed out that if one assumes the probability of an exception is constant, the number of exceptions 𝑥 = ∑ 𝑙 𝑡+1 follow the binomial distribution
𝐵 (𝑁, 𝛼) where N is the number of observations The Uncondititonal coverage test counts the number of VaR exceptions, which means that the number of days of loss
14 exceeds the estimate of the VaR If the exception value is more than the significance level selected, the previous VaR estimate underestimates the risk and vice versa
The Kupiec test, which follows a chi-square distribution with one degree of freedom, is known to potentially reject a model for both high and low failure rates However, as noted by Kupiec (1995), this test typically exhibits low power Therefore, it is advisable to assess conditional coverage using methods such as those proposed by Christoffersen to further evaluate the reliability of Value at Risk (VaR) models.
Checking the harmony and temporal variability of the data is the target of this test
A reliable Value at Risk (VaR) estimate indicates that exceptions are not only consistent but also distributed over time If exceptions cluster without revealing patterns, shifts in market perspectives and fluctuations may not be accurately reflected.
This research employs the Conditional Coverage Test developed by Christoffersen (1998), which enhances the evaluation framework of Kupiec by incorporating log-likelihood theories Christoffersen's method uniquely provides distinct statistics to assess the independence of exceptions, offering a more comprehensive analysis.
The conditional coverage test is essential for detecting whether exceptions occur in clusters, thereby explaining fluctuations in clustering If clusters are proven to exist, it indicates that the model is misidentified and requires adjustment From a practical perspective, assessing clustering variability is crucial A new index, derived from the exception index, identifies nij as the number of days an exception occurs following a day without an exception The probability of observing state j after state i is represented by πij.
The Christoffersen test requires several hundred observations to ensure accuracy, making it effective for rejecting Value at Risk (VaR) models that produce excessive or insufficient clustered violations This procedure's primary advantage lies in its ability to identify and eliminate faulty models through two likelihood ratio (LR) tests.
15 be combined, by this way, we can create a complete test for coverage and independence, which is also distributed as a χ 2 (2) (Julija, et al., 2017)
The Christoffersen method evaluates the predictability and accuracy of the Value at Risk (VaR) model by combining two tests: the unconditional coverage (LR UC) and the independence (LR IND) tests This approach allows for separate testing to determine whether the model's failures stem from inadequate coverage or clustered exceptions.
A full of null and alternative hypotheses for Christoffersen test:
APPLICATION OF VALUE AT RISK MODELS IN VIETNAM STOCK
Findings of prior research
The study "Value at Risk for Southeast Asian Stock Markets: Stochastic Volatility vs GARCH" by Quang, Klein, Nam, and Walther (2018) investigates the Value at Risk (VaR) of six major ASEAN markets—Indonesia, Malaysia, the Philippines, Thailand, Singapore, and Vietnam—using Historical Simulation, GARCH models, and stochastic volatility models The research focuses on six representative indices: the Jakarta Stock Exchange Composite Index (JCI), the Kuala Lumpur Stock Exchange (KLSE), the Philippines Stock Exchange Index (PCOMP), and the Stock Exchange of Thailand (SET).
Singapore Strait’s Time Index (STI), and the Vietnam Ho Chi Minh Stock Index
Between July 1, 2006, and June 30, 2017, VNI gathered data from Bloomberg, resulting in approximately 2,700 observations for each index The following charts illustrate the log returns of each index throughout this period.
2006 to June 30 th , 2017 and was provided in this research
Figure 1 Log-returns for the period July 1 st , 2006 - June 30 th , 2017
The group of authors compare the different GARCH-type and Volatility models as well as non- and semi-parametric approaches in terms of the widely-used Value-at-
In assessing risk measures for long and short trading positions, it is essential to apply different forecasting methods, as model performances vary across ASEAN stock indexes, suggesting that market volatility is influenced by diverse factors The simple GARCH and RiskMetrics frameworks generally underperform across all forecasting horizons and markets, lacking adequate coverage and clustering in their forecasts Moreover, it is highlighted that index volatilities should not be modeled using short memory and symmetric processes Instead, long memory models such as FIGARCH, APARCH, or FIAPARCH, which account for asymmetric news impact, are more appropriate While Historical Simulation yields favorable results for the multilevel unconditional coverage test, the quality of forecasts can be further enhanced using Stochastic Volatility models.
In conclusion, simple models are inadequate for generating valuable Value at Risk (VaR) forecasts, highlighting the necessity for more sophisticated models to effectively assess financial risk in both long and short positions within ASEAN stock market indexes.
The upcoming study will concentrate on the Indian market, one of the largest markets in Asia In their 2013 paper, "Modelling Conditional Volatility: A Study of the Indian Stock Market," authors Maniklal and Shyamal investigate the nature and characteristics of stock market volatility in India They estimate conditional volatility models to capture key features of this volatility, particularly focusing on the BSE Sensex index The research highlights the presence of volatility clustering in financial time series, as indicated by autoregressive conditional heteroskedasticity (ARCH), with all data collected for analysis.
January 1991 to September 2012 The daily return on BSE Sensex is described in the follow figure provided by authors
Figure 2 Daily Return on BSE Sensex (1991-2012)
Returns exhibit continuous fluctuations around a mean value close to zero, displaying both positive and negative movements Additionally, these returns demonstrate volatility clustering, characterized by significant fluctuations interspersed with periods of relative calm.
This study highlights the similarities in volatility patterns between the Indian stock market and major global markets, demonstrating characteristics such as mean reversion, autocorrelation, and negative asymmetry in daily returns.
Recent findings reveal evidence of time-varying volatility, characterized by clustering of high and low volatility periods, along with notable persistence and predictability The GARCH (1, 1) model has been identified as the most suitable for analyzing the BSE Sensex market index, confirming earlier research by Karmakar in 2015, which indicated that the GARCH (1, 1) model was also the best fit for the Indian market based on data from the S&P CNX Nifty and BSE Sensex between January 1991 and June 2003.
In a study conducted by Singh (2017), various GARCH-based symmetric and asymmetric models were employed to analyze the conditional variance of stock market returns in the Indian banking sector over a decade, starting from 2005.
This research examines the rise in conditional volatility during the global economic crisis from 2007 to 2009, focusing on the year 2015 The study employs three models: ARMA-GARCH (1,1), ARMA-TGARCH (1,1), and ARMA-EGARCH (1,1), utilizing data from the S&P BSE to analyze market behavior.
The Bankex index, provided by the Bombay Stock Exchange Ltd., tracks the performance of the Indian banking sector from January 1, 2005, to December 31, 2015 The returns of the Indian banking index during this period are illustrated in the figure below.
Figure 3 The return of S&P BSE Bankex index
The bank index returns exhibit significant fluctuations due to volatility clustering, where periods of high volatility are succeeded by further high fluctuations, while lower volatility periods are followed by decreased fluctuations.
The study reveals the presence of persistence and leverage effects within the Indian banking sector, indicating that current conditional variance is significantly influenced by past volatility Additionally, the response of conditional variance shows asymmetry towards negative market shocks The research identifies the EGARCH model as the most suitable model compared to other GARCH models, aligning with findings from Ahmed and Aal (2011) These results carry important implications for portfolio managers and various market participants.
In two studies examining the Indian stock market, it was found that high fluctuations and persistent volatility clustering are prevalent across different indices Both studies suggest that GARCH-type models are the most effective for this market, with the EGARCH (1,1) model being the optimal choice for forecasting in the Indian market.
The Chinese market stands as a significant player in Asia, as highlighted by Zhang and Cheng (2014) in their master's thesis Their research focuses on assessing the risk of the Chinese stock market through Value at Risk (VaR) methods while also evaluating whether the downside risk is reflected in the expected market returns The study estimates the VaR for six indexes, including SSEC, SSEA, SSEB, SZSEC, SZSEA, and SZSEB, across two major stock exchanges in mainland China: the Shanghai Stock Exchange (SSE) and the Shenzhen Stock Exchange (SZSE), utilizing data collected from January 1st.
2002 to December 31 st , 2013 The below table illustrates the descriptive statistics of daily return for each market index
Table 1 Summary statistics of daily returns of Chinese Indexes
Overview of Vietnam stock market
Vietnam has achieved remarkable advancements in shifting from a centrally planned economy to a market-oriented system since the initiation of Đổi Mới reforms in 1986 Once one of the poorest nations globally, Vietnam has consistently ranked among the fastest-growing economies, showcasing a significant increase in GDP from just USD 143 in 1986 to USD 8,066 in 2019.
Vietnam's banking sector has undergone significant transformation since its establishment in 1990 as a mono-banking system, evolving into a vast network of banks and financial institutions The development of the capital market has played a crucial role in facilitating ongoing reforms within the banking industry To enhance efficiency and competitiveness, the Vietnamese government has implemented numerous reforms across various banks As of 2019, the banking system comprises 7 state-owned commercial banks, 28 joint stock banks, 61 foreign bank branches, 3 joint venture banks, 5 banks with 100% foreign capital, and two development and policy banks.
In addition, another concern in Vietnam is reforming State-owned enterprises
The government must establish a legal framework for initial public offerings (IPOs) to enhance the efficiency and productivity of state-owned enterprises (SOEs) This initiative is a key factor behind the creation of Vietnam’s stock market.
On the establishment of Vietnam’s stock market, on July 11 th , 1998, the
The Government of Vietnam issued Decision No 48/CP, marking the establishment of the country's stock market This decision led to the creation of securities trading centers in Hanoi and Ho Chi Minh City Consequently, the Ho Chi Minh City Securities Trading Center commenced operations on July 28, 2000, conducting its inaugural trading session On August 8, 2007, the center was transformed into the Ho Chi Minh City Stock Exchange (HOSE).
The Hanoi Securities Trading Center was established on March 8, 2005, and later transformed into the Hanoi Stock Exchange (HNX) on January 17, 2009 With a chartered capital of VND 1,000 billion, HNX is characterized by a smaller number of listed companies compared to the Ho Chi Minh City Stock Exchange (HOSE), highlighting the distinct nature of these two exchanges.
The VN-Index reflects the fluctuations in stock prices on the Ho Chi Minh Stock Exchange (HOSE), serving as a daily indicator of market trends for all listed shares Established on July 28, 2000, when the Vietnam stock market officially launched, the index compares the current market value to that of a base stock Adjustments to the base market value are made for events such as new listings, delistings, and changes in a corporation's charter capital.
The base market value is recalibrated to account for factors such as new listings, delistings, and changes in a company's charter capital During the initial phase from 2000 to 2005, known as the formation period, only two companies—SAM and REE—were listed on the Ho Chi Minh Stock Exchange, each with a charter capital of VND 270 billion.
Exchange (HOSE) in the beginning This period witnessed a rapid growth of VN-
Index, with the highest level of 571.01 point on April 25 th , 2001 However, after almost
6 months, in October 2001, the index fell to 200 points and the listed depreciated to 70% of the value
The period 2006 - 2007 is considered as the boom period of Vietnam stock market
In 2006, the VN-Index and HNX-Index saw significant growth, rising by 144% and 152% compared to 2005 By the end of the year, the market capitalization reached nearly USD 14 billion, representing 22.8% of Vietnam's GDP Additionally, the number of listed companies surged from 41 in 2005 to 193 in 2006.
In 2006, the VN-Index reached a peak of 809.86 points The implementation of the Law on Securities in Vietnam on January 1, 2007, significantly boosted the country's securities market Consequently, both major market indexes experienced growth, with the VN-Index soaring to a record high of 1170.67 points and the HNX-Index rising to 459.36 points.
After a period of strong growth, Vietnam's stock market entered a recession In the 4-year period from 2008 to 2012, under the effect of global crisis, Vietnam’s economy
29 witnessed a sharply decline, especially in 2012, when GDP only reached 5.03%, the lowest growth rate since 1999 It also led to the depression of stock market
In early 2009, the VN-Index experienced a significant decline, reaching a low of 235.5 points on February 24th This downturn was accompanied by widespread investor pessimism, leading to a noticeable decrease in trading activity within the stock market In response to this challenging situation, the Government took measures to stimulate market participation and restore investor confidence.
Vietnam implemented quantitative easing measures by introducing stimulus packages to boost its economy, channeling significant capital into businesses through the banking system This influx of money positively impacted the market, with the VN-Index rising from 235.5 points to over 600 points by November 2009 However, the stock market's stability was short-lived, as the VN-Index dropped to 494.77 points by December 31, 2009.
Given is the line graph concerning how the VN Index changed since its establishment until April 20 th , 2020 It is based on the database of VNDIRECT JSC
Figure 8 VN-Index stock price in the period 2000 – 2020
In 2010, the VN-Index began at over 500 points, reaching a peak of 549.51 points within five months, with transaction values exceeding 2,800 billion for 81 million shares However, government policies redirected cash flow into the manufacturing sector while restricting investments in securities and real estate, leading to a decline in stock market liquidity As a result, from July to August 2010, both the VN-Index and HNX-Index experienced a downward trend, hitting their lowest points of the year.
At the same time, M&A activities of the companies in the market became attractive with the typical business such as KMR and KMF, KDC and NKD
Between 2011 and 2012, investor sentiment in the market turned pessimistic, resulting in a significant decline in stock trading and causing the VN-Index to drop nearly 34%, from a peak of 522 points on February 9, 2011, to a low of 347 points Fortunately, the following year saw a recovery in the VN-Index, aided by macroeconomic policies implemented by the State Bank of Vietnam, which included reducing the prime rate and providing credit support for the real estate sector.
From 2013 to 2015, the VN-Index experienced significant instability, fluctuating between 500 and 650 points It closed at 504.63 points in the final session of 2013, but the following year brought numerous challenges, including the East Sea conflict and volatile global oil prices The East Sea conflict began impacting the market in late April, ending a positive growth cycle from the first quarter Within just seven days, the VN-Index plummeted over 11%, hitting a low of 508.51 points The decline in global oil prices led to a reevaluation of oil stocks, further affecting the market as these large-cap stocks lost value Consequently, the VN-Index fell by 27.8% over the last 15 sessions of that month.
In 2015, the VN-Index experienced a complex development before a strong growth period from 2016 to 2018 The index rose significantly from 526 points to a peak of 602 points between January and March, driven by positive macroeconomic conditions, including the TPP and FTA agreements, which boosted investor optimism and increased foreign ownership ratios.
31 investors was raising Nevertheless, after achieving its highest level in 641 points in
201 under the effect of Chinese yuan’s devaluation, VN-Index witnessed a dramatic drop, with 511 points China officially announced devaluation of 1.9% yuan on
11/8/2015, only for 3 days, compared with USD, CNY had fallen 4.6%, forcing
Vietnam stock market’s data illustration
This thesis utilizes the VN-Index to analyze the key characteristics of Vietnam's stock market, focusing on risk management within a portfolio that includes three industry groups: banking, steel, and construction.
Market capitalization (trillion VND) Market capitalization/GDP (trillion VND)
The VN-Index reflects the fluctuations of stock prices traded on the HOSE, serving as a vital indicator of daily stock price trends for all listed shares This index measures the current market value against the base market value established on July 28, 2000, the date of its inception Adjustments to the base market value occur in instances such as new listings, delistings, or changes in a corporation's charter capital A line chart illustrates the changes in the VN-Index from early 2015 to February 2020, highlighting its performance over this period.
On the other hand, the author uses the series of stock price data and 1,285 observations of the VN-Index from January 5 th , 2015 to February 28 th , 2020
Figure 14 Time series of VN-Index in the period of 2014 – 2020
From early 2015 to April 2016, the VN-Index experienced fluctuations between 500 and 640 points, marking a period of volatility However, following this phase, Vietnam's stock market entered a significant growth period, with the VN-Index rising steadily from April 2016 to April 2018.
The VN-Index experienced remarkable growth, surging nearly 200% from 600 points in April 2016 to a peak of 1,200 points by April 2018 However, this substantial rise was accompanied by significant fluctuations, largely driven by various external global events that greatly impacted investor trading behavior.
39 stock market in Vietnam For example, the outbreak of Coronavirus in 2019 caused a huge drop in the VN-Index and harmed directly to the growth of economy
The banking sector remains the most appealing and influential category of stocks across global exchanges, including Vietnam With a diverse array of stocks available in sectors such as construction, healthcare, and real estate, banking stocks consistently attract the highest interest from investors and financiers alike.
The Vietnamese stock market has seen a year-on-year increase in the number of listed banks, driven by the need for capital mobilization and market development As of December 15, 2019, there were 18 commercial banks listed on the stock market, with notable institutions such as MBBank, Vietcombank, SHB, Sacombank, and Vietinbank having been listed prior to 2015, in accordance with the author's time series requirements.
In recent years, Vietnam's banking sector has experienced significant improvements in financial health The State Bank of Vietnam's annual report indicates that from 2010 to 2015, the average Return on Assets (ROA) for commercial banks was 0.85%, while the average Return on Equity (ROE) reached 7.96% Notably, Vietcombank has been a key player in this positive trend.
Vietinbank, Saigonbank, MBB, BIDV, and KienLong Bank exhibit strong Return on Assets (ROA), indicating their effective asset management, while VIB, Sacombank, Eximbank, and SHB show average ROA performance These banks are recognized for their significant scale and reputation within Vietnam's banking sector.
Because of the global economic recession as well as the increase of Non-
Between 2015 and 2019, the financial performance of Vietnam’s commercial banks exhibited fluctuations, following a period of robust credit growth and mobilization from 2008 to 2010 The bursting of the real estate market bubble during this time significantly impacted the banks By 2012, the frozen real estate market severely affected the banking system, resulting in a notable decline in two key indicators: Return on Assets (ROA) and Return on Equity (ROE) In response, efforts were initiated that year to comprehensively restructure credit organizations.
40 the No 254 scheme had been issued by the State Bank of Vietnam (SBV), causing a series of mergers and acquisitions between banks in later stages
This study focuses on three selected tickers—CTG, VCB, and MBB—representing the banking sector The accompanying chart illustrates the stock prices of these banks from early 2015 to February 2020.
CTG is the stock code for the Vietnam Joint Stock Commercial Bank for Industry and Trade, commonly known as Viettinbank, which was established in 1988 after separating from the State Bank of Vietnam As one of the "big four" banks in Vietnam, Viettinbank has a chartered capital exceeding 43.678 trillion dong, equating to approximately 4.367 million shares The bank's ownership structure includes 64.46% of its charter capital held by the state and 27.75% owned by foreign shareholders Viettinbank was officially listed on the stock exchange on July 16, 2009, with a total of 121.2 million shares.
VCB is a stock code of the Joint Stock Commercial Bank for Foreign Trade of Vietnam (Vietcombank) This is the largest commercial bank in Vietnam
Vietcombank was officially listed on HOSE on June 30, 2009 with a transaction price of VND 60,000 per share
MBB, the stock code for Military Commercial Joint Stock Bank (MBBank), was listed on the HOSE on November 1, 2011, during a challenging period for Vietnam’s stock market In a strategic move, MBBank opted to enter the stock exchange despite market downturns In February 2020, the State Bank of Vietnam approved MBBank's plan to increase its charter capital to 24 trillion dong, highlighting its growth potential and resilience in the financial sector.
Figure 15 Time series of stock price of VCB, MBB and CTG from 5/1/2015 to
Between late 2016 and early 2020, VCB consistently outperformed CTG and MBB in terms of stock price, which remained relatively stable for CTG and MBB The banking sector experienced improvements in business results and a reduction in non-performing loans (NPLs), contributing to an overall upward trend in the stock prices of these banks VCB's higher pricing during this period was a significant factor driving the substantial changes in stock returns, reflecting the enhanced financial health of the banks due to the positive performance of their stocks.
The steel industry is a crucial economic sector, attracting significant investment and serving as a foundation for capital, knowledge, and technology It plays a vital role in fostering innovation in both technology and management across various fields Iron and steel are essential materials for several key industries, including automotive manufacturing, construction, container production, and shipbuilding.
The Vietnamese steel industry is primarily categorized into three main groups: steel construction, galvanized steel, and civil steel trade Among these, the steel construction sector stands out as the largest in both scale and quantity, followed by the galvanized steel group.
42 civil steel trade group, divided into two branches based on technology: blast furnace and electric furnace
Self-defense tariff has been applied on steel products imported from China in 2016
VaR model suggestion for Vietnam stock market
The research indicates that the Vietnam stock market exhibits similarities to the Indian stock market, particularly in terms of daily return volatility from January 5, 2015, to February 29, 2020 The analysis reveals that the VN-Index and the indices of three key industries—banking, steel, and construction—demonstrate significant volatility, with daily returns fluctuating around a mean value close to zero Notably, periods of increased volatility are succeeded by further increases, while periods of lower volatility are followed by declines, highlighting the phenomenon of volatility clustering This study aligns with previous research on the Vietnam market, reinforcing these findings.
Numerous studies on the Vietnam and Indian stock markets have identified GARCH(1, 1) as the most effective model for forecasting indices in the Vietnam stock market While EGARCH(1, 1) demonstrates efficiency, it is more complex than GARCH(1, 1).
In the realm of forecasting, managers tend to favor simpler models, and recent research in the Vietnamese market highlights the effectiveness of the GARCH(1, 1) model, particularly during significant events such as the 2007 economic crisis and the 2019 coronavirus pandemic Therefore, the author recommends utilizing the GARCH(1, 1) model for forecasting in the Vietnam stock market from January 5, 2015, to February 29, 2020 With its proven efficiency in previous studies, the author believes that GARCH(1, 1) will effectively predict market trends and enhance risk management strategies.
CONCLUSION
The legal framework
Effective June 2004, Basel II serves as a crucial framework for managing financial risk by establishing minimum capital requirements for financial institutions It incorporates various capital calculation methods and introduces operational risk, defined as the potential loss from inadequate or failed internal processes, personnel, systems, or external events, as a key component of the risk portfolio.
Basel II framework includes three pillars Pillar I provides detailed methods for calculating minimum regulatory capital Institutions can choose one of three options to estimate credit risk exposures: a standardized approach, the foundation internal rating- based (IRB) approach, and the advanced internal rating-based (AIRB) approach In order to calculate the capital requirement, collecting credit risk, market risk and operational risk information are obligation, because this is the criteria to evaluate the quality of the calculated capital requirements
Pillar II of Basel II outlines supervisory review standards that empower regulators to oversee and enforce discipline within financial institutions To effectively mitigate information risk, it is essential for these institutions to implement robust controls that can detect and prevent issues related to information quality, thereby ensuring a sound governance system.
Pillar III refers to market disclosure, with the purpose of promoting financial stability through increased transparency and disclosure requirements Pillar III is the
53 requirement for financial organizations to public their risk management activities, risk- rating processes and risk distributions
In conclusion, financial institutions must prioritize information quality management to mitigate information risk exposure effectively Poor-quality information can lead to inaccurate capital requirements and increase operational losses when integrated into transaction systems To safeguard against these risks, institutions should adhere to the Basel II framework, ensuring the integrity of the information utilized in their risk management processes By doing so, they can protect their operations from various risks and support the dynamic and sustainable development of their companies.
In Vietnam, the State Bank has implemented Circular No 13/2018, which outlines the internal control systems for commercial banks and foreign bank branches, complementing Basel II in the realm of risk management This circular provides valuable insights into various types of risk management, detailing the risk management process and the responsibilities of each department involved Specifically, when focusing on market risk, it is essential to consider the implications and strategies discussed in subsequent articles.
Chapter 1 of Article 3 outlines essential definitions related to risk management, serving as a foundational step for organizations in this field Key terms discussed include economic capital, various types of risk such as liquidity and operational risk, risk appetite, and stress testing These definitions are crucial for organizations to enhance their understanding of risk management and develop tailored processes to mitigate potential losses.
In Section 3, Chapter 4, we explore fundamental concepts of market risk management, emphasizing strategies for risk control, measurement, and limits This study specifically focuses on market risk, detailing risk strategies and market risk limits as outlined in Article 38, along with risk measurement and control methods in Article 39 Additionally, we highlight the importance of internal reporting on market risk as discussed in the relevant articles.
Circular No 13/2018 from the State Bank of Vietnam is a crucial legal framework for commercial banks and financial institutions, aimed at mitigating losses and managing risks effectively By integrating the guidelines outlined in this circular with Basel II standards, there is a promising opportunity to refine the risk management process, ultimately reducing risks and fostering the growth of enterprises.
Preparation of risk management for institutions
To effectively manage risk, financial institutions must meticulously prepare their human resources, data, and technology A well-defined risk strategy hinges on the careful organization of these elements, ensuring that institutions are equipped to address potential challenges This section will outline the essential preparations needed in each of these areas to enhance risk management practices.
In accordance with Basel II, particularly Pillar I, financial institutions must gather data on credit risk, operational risk, and market risk, emphasizing the importance of data quality derived from reliable sources Utilizing unchecked information can result in significant operational damage, as inaccurate data may lead to errors in calculating critical economic indices such as minimum capital requirements, adversely affecting overall risk strategies Effective data management systems encompass four key components: data access, data integration, data quality, and data governance.
Effective data access is crucial for organizations to gather information from various sources, as inadequate methods can hinder data collection and negatively impact data management Data integration plays a vital role in aggregating and analyzing this information, addressing the challenges posed by data heterogeneity By consolidating diverse data types into a unified structure and format, organizations can enhance their database systems Ultimately, the goal of data integration is to streamline the storage of data from multiple sources, facilitating better management and analysis for future decision-making.
Financial institutions prioritize data quality within their data management systems to ensure the reliability and relevance of data for future analysis The significance of data quality is paramount, as it directly impacts the value of data for any project; neglecting data testing can result in failures throughout the data management process, rendering outcomes ineffective Ensuring data quality is essential not only during data access but across all aspects of the data management system Central to this is data governance, which encompasses regulations, policies, processes, strategies, and the integration of technology and human resources Effective data management serves as a comprehensive guide for future actions, while data governance plays a crucial role in linking and overseeing all functions involved in data management.
Investing in advanced technology is crucial for improving systems and enhancing efficiency A modern system allows for quicker information collection and precise calculations of economic index numbers Additionally, institutions must upgrade their systems to utilize complex risk models for market forecasting and develop effective risk management strategies By leveraging advanced technology, organizations can optimize data management functions and enhance overall operational effectiveness.
Human resources are crucial for an effective strategy, as the quality of a company's staff significantly impacts all operational factors, including risk management Even with accurate information and a robust risk calculation system, the absence of skilled personnel renders these tools ineffective Therefore, organizations must prioritize training their employees in system usage and imparting knowledge about risk and risk management This approach enables them to execute their strategies smoothly and minimize operational losses.
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