Problem statement
In recent years, the global financial market has experienced significant changes, particularly following the European Union's pivotal shift in the early 2000s The 2007-2008 international crisis, which originated in the US, had detrimental effects on the global financial system To mitigate losses, Duffie & Pan (1997) proposed strategies for recognizing and calculating risk, thereby enhancing the security of financial markets and the overall economic system.
Vietnam has become a vital economic driver in Southeast Asia, showcasing significant growth in various key industries Over the past decade, agriculture, manufacturing, and food production have served as the three main pillars that have greatly enhanced the value of the Vietnamese economy.
Vietnam's agriculture sector plays a vital role in the national economy, contributing 20% to the GDP as of 2015 Additionally, Vietnam joined the Trans-Pacific Partnership (TPP) alongside 11 other countries, including the United States and Japan Collectively, TPP members represent around 40% of global GDP, totaling approximately $28 trillion, and account for 11.3% of the world's population, significantly influencing global trade dynamics.
Despite the collapse of the original TPP Agreement following President Donald Trump's withdrawal from Barack Obama's commitment, a revised version known as the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) may emerge without the United States This new CPTPP presents both opportunities and challenges for Vietnam, highlighting the need to acknowledge the significant impact of sectorial risks, particularly in key industries, to fully leverage the benefits of this partnership.
1 https://www.gso.gov.vn/default.aspx?tabidb1&ItemID507
2 http://www.nytimes.com/interactive/2016/business/tpp-explained-what-is-trans-pacific-partnership.html
13 similar sectors from other members.
The Asia-Pacific region has become a significant economic and political force over the past decade, prompting Vietnam to expand its focus beyond ASEAN borders As the common market grows and competition intensifies, Vietnam faces new challenges from non-ASEAN countries, particularly Australia and New Zealand It is essential for Vietnam, along with similar ASEAN nations like Thailand and Malaysia, to acknowledge this evolving landscape and prepare strategic responses to maintain their competitive edge.
Every aspect of life carries inherent risks, making risk assessment a crucial activity, particularly for lenders aiming to predict and mitigate bad debts In the business realm, risks can stem from diverse factors such as market volatility, economic cycles, shifts in government policies, and fluctuations in financial markets While some companies may passively accept these risks, others take a proactive approach to manage them Regardless of the strategy employed, it is vital to monitor risks closely due to their potential adverse effects (Jorion, 2007).
The Basel II Accord categorizes risk into three primary types: credit risk, operational risk, and market risk Credit risk specifically refers to the potential loss that arises when a borrower fails to make required payments.
Operational risk arises from failures in internal processes, systems, and personnel, while market risk refers to fluctuations in the prices of financial assets influenced by changes in interest rates, stock prices, exchange rates, and commodity prices.
Value at Risk (VaR) is a highly effective method for estimating market risk, as highlighted by its recommendation in the 1995 Basel Accord for banks to determine capital requirements using this model VaR has established a foundational approach for addressing various aspects of financial risk management.
(1996) and Pritsker (1997) indicated that VaR, as a risk management method, can estimate the maximum expected loss that may occur over a given period, at a given confidence level.
Value at Risk (VaR) is a straightforward risk management tool that easily calculates the p-percentile of a distribution Its appealing simplicity makes VaR a popular choice for assessing risk across various sectors, including banking, insurance, institutional investments, and non-financial enterprises Consequently, VaR has emerged as the dominant method for measuring risk in numerous industries and countries.
Despite its widespread use, Value at Risk (VaR) exhibits unsatisfactory mathematical properties Artzner et al (1999) proposed a set of axioms for risk measures deemed "coherent," which include Monotonicity, Translation Equivariance, Subadditivity, and Positive Homogeneity However, VaR fails to meet the subadditivity axiom, leading to its classification as an incoherent risk measure, as it lacks a precise quantification and has inherent limitations.
This makes VaR optimization a challenging computational problem.
Acerbi and Tasche (2002) established that Condition Value at Risk (CVaR) satisfies all axioms of coherent risk measures, particularly the subadditivity axiom As a coherent risk measure, CVaR effectively captures extreme losses in the tail of the distribution, as it is specifically conditioned on returns that surpass the Value at Risk (VaR).
Conditional Value at Risk (CVaR) is typically expressed as a percentage, representing the average of the worst 5% of observations when Value at Risk (VaR) is assessed at a 95% confidence level Research by Powell, Vo, and Pham (2016c) has linked CVaR to sector risk and economic conditions, enabling the evaluation of risk changes across various product categories Additionally, Pflug (2000) highlighted that CVaR is a rational risk measure, overcoming the limitations of VaR, such as its lack of subadditivity Importantly, CVaR effectively quantifies tail risk by evaluating returns that exceed the VaR threshold.
This research focuses on market risk and credit risk, which are critical areas of interest for academia, investment bankers, and policymakers While several studies, including those by Allen et al (2014) and Powell, Vo, and Pham (2016), have explored market and credit risk across different countries, there is a notable gap in the literature regarding the comparison of Value at Risk (VaR) and Conditional Value at Risk (CVaR) rankings across various sectors in Vietnam and other countries.
16 the Asia-Pacific region As a result, this study is conducted to fill this gap In addition, this study will
This article explores the relationship between market risk estimates derived from Value at Risk (VaR) and Conditional Value at Risk (CVaR) methodologies, alongside the Distance to Default techniques, specifically within selected Asia-Pacific countries, including Vietnam.
The research objectives
This study is conducted to achieve the following research objectives:
This study aims to assess market risk across various sectors in Vietnam and other accessible countries in the Asia-Pacific region, with a particular emphasis on comparing estimates during crisis and non-crisis periods.
• Second, estimating the credit risk using the Distance to Default (DD) structural approach and providing the link, if any, between the VaR and the
Research questions
The following research questions have been raised in this study:
• What is the currently prevailing market risk level of various sectors fromVietnam and selected other countries in the Asia-Pacific region using VaR andCVaR?
• Is there any link between the estimates of the market risk using VaR techniques and the credit risk using the DD model?
A choice of the countries in the Asia Pacific Region in this study
Due to time constraints, this study will focus on members of the Australasian region, specifically Malaysia, Vietnam, Australia, and New Zealand, rather than the entire Asia Pacific Region Preliminary research indicates that Brunei may be excluded from the final list due to its smaller financial market size and lower level of economic development Notably, agriculture ranks among the top three industries in Vietnam, Malaysia, Australia, and New Zealand, warranting further examination.
Theoretical review
The literature review re-examines the Basel Accords II, Value at Risk methodologies, Conditional Value at Risk methodology and correlation techniques.
• The Basel II framework establishes the minimum standards for management on a sensitive risk: Market Risk, Operational Risk and Credit Risk
Value at Risk (VaR) is a powerful tool for estimating the maximum potential loss due to market risk There are three primary methods for calculating VaR: the historical method, which utilizes actual historical data; the Monte Carlo simulation, which generates numerous random scenarios; and the Variance-Covariance (parametric) method, which estimates VaR based on the assumption of a normal distribution.
• Conditional value at Risk is a coherent risk approach and satisfies the desirable characteristics that are the shortcomings of Value at Risk.
Correlation measures the relationship between the values of two variables, making it essential for practitioners focused on risk reduction to assess how these variables interact Understanding this relationship helps in making informed decisions to minimize potential risks.
The Basel Capital Accord (Basel I), introduced by the Group of Ten (G10) countries in 1988, aimed to ensure that banks maintain sufficient capital to absorb unexpected losses To achieve this, Value at Risk (VaR) was chosen as a method to estimate the maximum expected loss over a specified time frame and confidence level.
The Accord established a standardized framework for calculating capital requirements related to credit and market risk across all institutions However, this approach has notable limitations, primarily its traditional nature and its inability to adequately reward organizations that effectively manage higher levels of risk.
In April 1995, the Basel Accord was enhanced with the introduction of Basel II, allowing organizations to use internal models for calculating Value at Risk (VaR) and determining required capital expenses Companies aimed to implement their internal models, initially developed by regulators, through back-testing methods Following the lead of the Australian Prudential Regulatory Authority (APRA), the Basel Accord was adopted as the national framework for financial markets, relying on standardized procedures based on ratings from accredited external rating agencies.
The Basel II structure entails of three Pillars:
Pillar 1 establishes minimum capital requirements for market, operational, and credit risks, mandating a total capital ratio of 8% to risk-weighted assets While this ratio aligns with Basel Accord I, it introduces revised methods for calculating risk-weighted assets.
Pillar 2 of the supervisory review process emphasizes the role of managers in ensuring that banks meet essential capital requirements and adhere to standardized procedures The Australian Prudential Regulatory Authority (APRA) facilitates discussions with bank managers to verify the effective implementation of these systems in practice.
Pillar 3: Market Discipline emphasizes the importance of capital regulation and alternative strategies, which enhance the safety of financial systems and banks Additionally, it mandates that banks transparently disclose their risks and operational frameworks.
Basel II recognizes the significant role of Value at Risk (VaR) as a key standard for measuring risk and estimating capital This framework allows for a clear differentiation of market risk, enhancing the effectiveness of risk management practices in financial institutions.
The market Value at Risk (VaR) methodology is essential for banks to assess daily credit and other risks while complying with Basel requirements, ultimately leading to reduced capital requirements.
Total capital requirements contain the sum of the capital that is required for the following risks:
Market risk, or systematic risk, arises from factors that impact a vast number of assets across the market This type of risk encompasses potential losses due to adverse market trends and is categorized into four main types: foreign exchange risk, interest rate risk, equity price risk, and commodity price risk To assess the capital charge associated with market risk, one can use either the regulator’s standard method or an internal model approach.
In 2004, it was shown that employing internal models significantly enhances the quality of capital charge calculations Additionally, market risk assessment is effectively conducted through the Value at Risk (VaR) method.
Operational Risk, while not formally defined in Basel I, encompasses processing, procedural, and transactional risks Banks can address these risks using either the standardized method or the Internal Ratings Based (IRB) method The standardized approach involves segmenting the enterprise by typical business lines and determining a beta for each, which regulators then measure to represent the entire industry In contrast, the IRB method is available to banks with complex and advanced systems Additionally, banks can estimate operational risk based on historical data and experiences.
Credit risk is the potential loss that arises when borrowers fail to repay their debts, stemming from their reliance on future cash flows to meet current obligations Investors assume this risk in exchange for interest payments on the borrower's debt This article highlights the growing focus on credit risk among academics, investment bankers, and policymakers, underscoring its significance in the financial landscape.
Since its introduction in J.P Morgan's 1994 RiskMetrics Technical paper and subsequent updates by J.P Morgan & Reuters in 1996, Value at Risk (VaR) has become a prominent metric for risk estimation Unlike other methods, VaR utilizes historical data to project potential future losses over specified timeframes, typically at confidence levels of 95% or 99%, as highlighted by Harper (2004).
Empirical literature
to obtain the reasonable results provided by equation (2) from the market leverage inpractice.
To deal with this issue, the author will apply a complicated interactive procedure by KMV.
To be precise, author makes a proposal for an initial value of � = � (�
�+�) and employs the value of � � to calculate the market value of each firm’s assets every day in equation
To estimate the daily log return on assets, we will generate a series of returns that will be used to calculate new values iteratively This process will continue until convergence is achieved, indicated by the absolute difference between consecutive values being less than a specified threshold.
2.2.1 Empirical evidences on the market risk
Several studies have focused on the Value at Risk (VaR) and Conditional Value at Risk (CVaR) methodologies Mausser and Rosen (1999) highlighted instruments that compute VaR contributions, marginal VaR, and risks associated with portfolios of European options The authors evaluated these tools using parametric and delta-normal models, subsequently enhancing them with non-parametric and simulation approaches.
In a study conducted in 2000, the effectiveness of Value at Risk (VaR) was analyzed using data from two companies listed on the Paris Bourse, comprising 546 observations The findings underscored the importance of statistical inference in the analysis of VaR and expected loss, highlighting that actual losses can exceed the specified loss quantile.
Powell, Vo, and Pham (2016a) conducted a comprehensive study on a specific agricultural product that consistently outperformed others over a twelve-year period, divided into four stages: pre-crisis, crisis, post-crisis, and post-post crisis The research identified a winning product based on three key factors: returns, resilience (the ability to manage risks), and teamwork (its contribution to portfolio optimization) To assess extreme risk and determine the overall winner, the authors employed Conditional Value at Risk (CVaR) However, the study revealed inconsistencies in return rankings across different periods.
Commodity returns can vary significantly over time, with some experiencing top performance in one period and poor returns in another However, the relative risk associated with these commodities tends to remain more consistent, as they often maintain similar risk rankings across different periods.
Numerous studies have explored the relationship between Value at Risk (VaR) and Conditional Value at Risk (CVaR) Notably, Powell (2007) analyzed these metrics across various Australian industries, comparing their VaR and CVaR values over time The research utilized both diversified and undiversified VaR, along with parametric and nonparametric CVaR approaches Additionally, the study highlighted a significant connection between credit risk and market risk, paving the way for the advancement of models that integrate these critical elements.
Gaivoronski and Pflug (2000) analyzed portfolio optimization using the Value at Risk (VaR) approach and compared it with the Conditional Value at Risk (CVaR) method, finding that historical data offers a more practical basis for portfolio calculations than CVaR or variance optimization Additionally, Allen et al (2012) utilized CVaR to assess extreme risk values in mining share portfolios across seven major mining regions, highlighting CVaR's effectiveness in optimizing portfolios and reducing extreme risk The authors noted significant disparities in risk rankings between countries when employing VaR and CVaR, concluding that investors relying solely on traditional VaR may not effectively minimize portfolio risk.
Rockafellar and Uryasev (2000) introduced a model utilizing Conditional Value at Risk (CVaR) for optimizing credit risk in bond portfolios Their research indicated that CVaR outcomes closely align with those obtained through Value at Risk (VaR), particularly in terms of expected loss and the minimum expected regret approach Furthermore, this model has been successfully applied to emerging market bonds.
The review indicates a scarcity of Value at Risk (VaR) and Conditional Value at Risk (CVaR) studies within the Vietnamese context, especially concerning industry risk The limited research conducted has primarily centered on international portfolios or explored different dimensions unrelated to this specific topic.
2.2.2 Empirical evidences on credit risk
Many researchers focus on the critical role of default risk by utilizing option models, as demonstrated by Patel and Vlamis (2006), who estimate default distances (DD) and risk-neutral default probabilities Their findings indicate that high leverage and asset volatility are significant factors influencing default Similarly, Bystrom (2006) identifies key drivers of default, which can aid in assessing the default probabilities of firms.
Bharath and Shumway (2008) compare the DD model with an alternative method using a functional form, demonstrating that while the DD model may not yield accurate statistics for defaults, it is effective in constructing a valuable default prediction model.
Koutsomanoli-Filippaki and Mamatzakis (2009) highlighted the significant relationship between risk and efficiency, demonstrating through panel analysis that small shocks to the distance to default (DD) negatively impacted inefficiency Following the Global Financial Crisis, Huang and He (2010) confirmed the DD model's effectiveness in assessing default rates among major Chinese banks Additionally, Allen and Powell (2012) revealed that the DD model indicated greater and more volatile default risk in Australian banks compared to traditional book-based asset valuations.
A minority of researchers have adapted the original Merton Distance to Default (DD) model, notably Crosbie and Bohn (2003), who developed the Moody's KMV model This model provides a robust method for estimating DD but relies on a proprietary database of firms for default calculations rather than a standard normal distribution The findings indicate that high asset volatility and leverage are critical factors influencing default risk.
Argrawal and Maheshwari (2016) evaluated the effectiveness of the Merton Distance to Default (DD) in forecasting defaults among listed Indian companies, including both defaulting and non-defaulting firms Their study utilized logistic regression and multiple discriminant analysis as alternative models Additionally, an option-based DD was developed, demonstrating a significantly negative correlation with default likelihood Notably, the DD maintained its predictive significance even after incorporating Altman’s Z-score, reinforcing its reliability as a key indicator of default risk.
Methodology
This research aims to leverage Value-at-Risk (VaR) functions to assess the market risk across different industries within the selected countries In this context, let X denote a random variable that represents potential losses, with a specific parameter guiding the VaR estimation process.
In this paper, the author utilizes Acerbi's Integral Formula for Conditional Value at Risk (CVaR), highlighting that merely assessing conditional expectations of losses exceeding Value at Risk (VaR) may not provide a coherent risk measure Furthermore, Acerbi (2002) elucidates the relationships between these definitions, indicating that the results can be derived through alternative methods that are more suitable for practical applications.
Acerbi's Integral Formula for CVaR is adopted The CVaR of a random variable X, which represents loss, at the confidence level � can be expressed as follows:
Hence, ���� � can be explained as the average ��� � for � ∈ [α, 1].
For this example, assume that the loss is distributed continuously and uniformly between 0 and 100 Thus, � (�) = 1 0 ≤ � ≤ 100 The VaR at confidence level
� � given as ��� � (X) = 100 × � Then the CVaR at confidence level α can be calculated100
Choudhry (2014) introduced a variance-covariance matrix approach for analyzing two or more assets, which significantly improves the estimation of portfolio standard deviation compared to manual methods This matrix approach effectively manages large portfolios, making it a valuable tool in risk assessment Furthermore, RiskMetrics and other computer models utilize this methodology to compute data across various assets efficiently.
For example with the two assets portfolio:
To create a Variance Matrix, begin by generating a 2 x 2 matrix that includes individual standard deviations The horizontal axis of this matrix represents the standard deviations, while the vertical axis is designated as zero.
• Step 2: Generating the Correlation Matrix” (2 x 2) with the placing the correlation of the two assets in cells corresponding to the other asset The appearance of number
“1” on the cells implies a correlation of “1” with itself.
Correlation Matrix to produce another matrix with name is
Variance Matrix to obtain the Variance covariance Matrix
(VCV) matrix Each value in the
Matrix (VCV) demonstrates the variance of each asset.
• Step 5: Generating a matrix for two assets of the portfolio weighting (basing on the market capitalization)
Weighting Matrix with VCV matrix
T hi s pr o d uc es th e W ei g ht in g V ar ia nc e C o va ri an ce M at ri x ( W
• Step 7: That all we need a single number Therefore, WVCV (obtained in Step
6) is multiplied by weighting matrix (reset as a 2 x 1 matrix) to provide the portfolio variance.
Table 2 Matrix Variance-Covariance Calculation for a Two-Asset Portfoli
Step 1 to Step 3 Variance matrix Correlation matrix VC matrix
VC matrix Variance matrix VCV matrix
Weighting matrix VCV matrix WVCV matrix
Weighting matrix VCV matrix WVCV matrix
Source: Based on methodology described in Choudhry (2014, p484)
However, the matrix method provides the same result as the manual approach by formula that has presented in Section 2.1.4
This study employs Merton’s Distance to Default (DD) to estimate the credit risk.
• V: The market value of the firm’s assets.
• F: The face value of the firm’s debt, r is the instantaneous risk-free rate.
• �: An estimation of the annual return of the firm’s assets.
• T: The time to maturity of the firm’s debt.
• � � : Volatility of the firm’s equity
• E: Market capitalization of the firm.
This section focuses on test selection, highlighting the two main types: parametric and non-parametric tests Parametric tests are typically used with large datasets and rely on statistical measures such as mean and standard deviation, assuming that the observations follow a normal distribution.
In this study, author employs nonparametric test for these reasons:
Non-parametric tests are ideal for smaller data sets, making them suitable for this study, which utilizes Bloomberg data organized by GICS industries The analysis focuses on comparing Value at Risk (VaR) and Conditional Value at Risk (CVaR) across ten industries in selected countries, including Vietnam, Malaysia, Australia, and New Zealand Additionally, the study examines data over a decade, divided into two distinct sub-periods for a comprehensive comparison.
• The second reason, this study intensely focuses on the rankings among these industries rather than statistics measures.
Therefore, The Spearman Ranking Correlation Test is suitable for comparing samples depending on rankings This test is also appropriate for testing between 2 period time.
Author employs Spearman Rank Correlation Test for following hypothesis:
H 1 : There is association between GFC and post-GFC in VaR ranking.
H 2 : There is association between GFC and post-GFC in CVaR ranking.
H 3 : There is association between post-GFC and post-post-GFC in DD ranking
H 4 : There is association between market risk and credit risk ranking.
This article presents a theoretical analysis of ten industries to assess the association in industry risk rankings between two distinct periods, labeled as period A and period B The risk rankings for both periods are detailed in an accompanying table, with the final two columns showcasing the cumulative differences in ranks.
Table 3 Spearman Rank Correlation Test
The test results demonstrate the relationship between two ranking data sets, quantified by the ranking correlation coefficient (ρ) A coefficient of ρ = 1 indicates a perfect positive correlation, while a coefficient of ρ = -1 signifies a perfect negative correlation.
The t-test, as suggested by Siegel and Castellan (1988), is significantly more effective than the z-test for determining significance In this example, the significance of the t-test is calculated to yield meaningful results.
To calculate the t-value, we use the formula √(1 − 0.43²)/(10 − 2), where (n-2) represents the degrees of freedom in a t-distribution Utilizing a two-tailed test, the critical t-values at significance levels of 90%, 95%, and 99% are 1.860, 2.306, and 3.355, respectively Since our calculated t-value is 2.33, we reject the null hypothesis of no ranking correlation, concluding that there is a significant linear relationship between rankings in periods A and B at the 95% level of significance.
Data
This study utilizes daily data from all stocks listed on the Vietnam and Malaysia stock markets, encompassing 223 stocks from Vietnam and 244 from Malaysia Additionally, the final sample includes 382 stocks from Australia and 225 stocks from New Zealand, covering a total of 11 sectors Data for this analysis is sourced from Bloomberg.
This article analyzes daily data spanning a decade, segmented into two distinct periods: the Global Financial Crisis (GFC) from 2007 to 2009 and the post-GFC period from 2010 to 2016.
Utilities Gas, Electric, Multi, Water
Materials Metals & Mining, Construction Materials, Chemicals, Paper &
Information Technology Software & Services, Technology & Equipment, Semiconductors &
Industrials Transportation, Capital Goods, Commercial Services & Supplies
Health Care Equipment & Services, Pharmaceuticals & Biotechnology
Financials Banks, Diversified Financials, Insurance
Energy Oil & Gas, Energy Equipment & Services
Media, Hotels Restaurants & Leisure, Retailing, Consumer Durables & Apparel, Automobile & Components
Food Beverage & Tobacco, Food & Staples Retailing, Household & Personal Products
Data descriptions
This section summarizes daily price movements in the commodity markets of Vietnam, Malaysia, Australia, and New Zealand In Vietnam, 223 companies are analyzed to assess market and credit risk, while 244 stocks in Malaysia, 382 stocks in Australia, and 225 stocks in New Zealand are utilized for market risk estimation.
The analysis reveals that the maximum daily movements across ten industries in Vietnam and New Zealand are relatively stable, unlike the significant fluctuations observed in Malaysia and Australia Conversely, the minimum daily movements indicate considerable volatility in the same ten industries for Vietnam, Malaysia, Australia, and New Zealand.
Table 5 reveals that the average daily prices in Vietnam, Malaysia, Australia, and New Zealand are nearly zero, aligning with the assumption of a standard normal distribution.
Table 5 Daily commodity market price movements in Vietnam and Malaysia (2007–2016)
No of firm Max (%) Min (%) Aver (%) S.D (%) No of firm Max (%) Min (%) Aver (%) S.D (%)
Table 6 Daily commodity market price movements in Australia and New Zealand (2007–2016)
No of firm Max (%) Min (%) Aver (%) S.D (%) No of firm Max (%) Min (%) Aver (%) S.D (%)
Market Risk by VaR and CVaR Results
This article presents daily Value at Risk (VaR) and Conditional Value at Risk (CVaR) calculated at a 95% confidence level using both Parametric and Historical approaches It analyzes market risk levels across various sectors during two distinct periods: the Global Financial Crisis (GFC) from 2007 to 2009, detailed in Tables 3 and 4, and the post-GFC period from 2010 to 2016, outlined in Tables 5 and 6 Additionally, it compares the relative market risk levels across sectors in Vietnam, Malaysia, Australia, and New Zealand.
The analysis using Value at Risk (VaR) as a measure of market risk reveals that the parametric approach yields significantly higher estimates of market risk than the historical approach across all industries in Vietnam and Malaysia Furthermore, industries in Vietnam demonstrate a consistently higher level of market risk compared to those in Malaysia, regardless of the method used.
In Vietnam, when evaluating market risk by sector, Utilities, Industrials, and Consumer Staples emerge as "low risk" sectors, while Real Estate presents a higher risk profile.
IT, and Financials are at the other extreme of the market risk level in Vietnam For Malaysia, Utilities, Energy, and Financials are considered “low risk” in Malaysia.
In Australia Utilities, Consumer Staples, Energy belong to the “low risk” group For New Zealand, Utilities, Real estates, Consumer Discretionary can be consider “low risk”.
In Malaysia, the financial sector is regarded as low risk, contrasting sharply with its classification as high market risk in Vietnam, Australia, and New Zealand Additionally, the materials sector in Vietnam is surprisingly categorized as medium risk, while it is associated with high market risk in Malaysia, Australia, and New Zealand.
Table 7 The level of market risk proxied by VaR using Parametric and Historical approaches for Vietnam, Malaysia,
Australia and New Zealand in the GFC period (2007-2009)
VaR 95 per cent in GFC period
Viet Nam Malaysia Australia New Zealand
Values Ranking Values Ranking Valu es Ranking Values Ranking
Parametric methods are essential in statistical analysis, allowing for the modeling of data by assuming a specific distribution These techniques enable researchers to make inferences about population parameters based on sample data By utilizing parametric approaches, analysts can achieve more accurate predictions and insights, provided the underlying assumptions are met This makes parametric methods a powerful tool in various fields, including economics, psychology, and engineering.
Note: Rankings are from 1 (lowest risk) to 10 (highest risk)
Table 8 The level of market risk proxied by CVaR using Parametric and Historical approaches for Vietnam, Malaysia,
Australia and New Zealand in the GFC period (2007-2009)
CVaR 95 per cent in GFC period
Viet Nam Malaysia Australia New Zealand
Values Ranking Values Ranking Valu es Ranking Values Ranking
Parametric modeling is a powerful technique used in various fields, allowing for the manipulation of parameters to create dynamic designs This approach enhances flexibility and efficiency in design processes, enabling quick adjustments and iterations By utilizing parametric tools, designers can achieve more innovative solutions while maintaining control over their projects Overall, parametric design fosters creativity and precision, making it an essential method in contemporary design practices.
Note: Rankings are from 1 (lowest risk) to 10 (highest risk)
In Vietnam, when assessing market risk through Conditional Value at Risk (CVaR), the Utilities, IT, and Financial sectors are particularly vulnerable, experiencing significantly larger losses during extreme risk events compared to other industries.
The Financial sector in Malaysia is considered a safe industry; however, it is part of the high market group in Vietnam, Australia, and New Zealand Despite its stability, when risks do arise, the potential losses can be substantial.
In comparing market risk as measured by Value at Risk (VaR) and Conditional Value at Risk (CVaR) between Vietnam and Malaysia, it is evident from the estimations in Tables 7 and 8 that all industries in Vietnam faced higher market risk during the Global Financial Crisis (GFC) period compared to those in Malaysia In contrast, the performance of industries in Australia and New Zealand appears stable and reasonable, highlighting a significant difference in market risk exposure across these regions.
Consumer Staple, this industry looks risky in Australia in the GFC.
In the post-global financial crisis era, using Value at Risk (VaR) as a measure of market risk, findings indicate that the parametric approach yields significantly higher estimates of market risk compared to the historical approach across all industries in Vietnam and Malaysia Furthermore, industries in Vietnam exhibit a greater level of market risk than those in Malaysia, regardless of the methodology employed.
When the level of the market risk of different sectors in Vietnam is considered in isolation, it can be concluded that, across the two approaches, IT, Consumers Staples and
In Vietnam, the Financial sector is viewed as "low risk," while the Industrial, Energy, and Consumer Discretionary sectors represent higher market risk levels Similarly, in Malaysia, the Utilities, Consumer Staples, and Financial sectors are categorized as "low risk."
In Australia Utilities, Consumer Staples, Financials belong to the “low risk” group. For New Zealand, Utilities, Heath Care, Consumer Discretionary can be consider “low risk”.
Table 9 The level of market risk proxied by VaR using Parametric and Historical approaches for Vietnam, Malaysia,
Australia and New Zealand in the GFC period (2010-2016)
VaR 95 per cent in GFC period
Viet Nam Malaysia Australia New Zealand
Values Ranking Values Ranking Values Ranking Values Ranking
Parametric analysis involves using parameters to define a system or model, enabling more precise control and understanding of complex data This approach allows for the optimization of various processes by adjusting specific variables, ultimately leading to improved outcomes By focusing on key parameters, researchers can gain insights that drive innovation and efficiency in their respective fields.
Note: Rankings are from 1 (lowest risk) to 10 (highest risk)
Table 10 The level of market risk proxied by CVaR using Parametric and Historical approaches for Vietnam, Malaysia,
Australia and New Zealand in the GFC period (2010-20016)
CVaR 95 per cent in GFC period
Viet Nam Malaysia Australia New Zealand
Values Ranking Values Ranking Values Ranking Values Ranking
Parametric methods are statistical techniques that rely on assumptions about the parameters of the population distribution from which the samples are drawn These methods often involve the estimation of population parameters, such as means and variances, and are widely used in various fields for hypothesis testing and data analysis Their effectiveness hinges on the validity of the underlying assumptions, making them suitable for normally distributed data Proper application of parametric methods can lead to more powerful and efficient statistical inferences compared to non-parametric alternatives.
Note: Rankings are from 1 (lowest risk) to 10 (highest risk)
In Vietnam, the IT sector is perceived as low risk following a crisis, contrasting with its classification as high market risk in Malaysia, Australia, and New Zealand Meanwhile, the financial sector is viewed as low risk in Malaysia and Australia, while in Vietnam, it is considered medium risk, indicating a significant disparity in market perceptions across these countries.
In Table 10, the Conditional Value at Risk (CVaR) is used to assess market risk, indicating that the Utilities sector is classified as "low risk" in Vietnam, Malaysia, Australia, and New Zealand.
Credit Risk by Distance to Default Results for Vietnam
The results below present DD and industry ranking changes in Vietnam The two sub periods are presented for post-GFC (2010 – 2012) and post-post GFC (2013 -
Table 13 DD Ranking Shifts in Vietnam
Diff in Rank Diff in Rank 2
Note: Rankings are from 1 (lowest risk) to 10 (highest risk)
101 n 10 r 0.382 t 1.168 degree of freedom 8 critical value 90% 1.860 critical value 95% 2.306 critical value 99% 3.355 significance -
The Spearman Rank Correlation Test was conducted to assess the relationship between post-GFC and post-post-GFC DD rankings The results indicate no significant difference, leading to the rejection of the null hypothesis (H3), which posited an association between the two periods Consequently, it can be concluded that there is no correlation in industry DD rankings between post-GFC and post-post-GFC in Vietnam.
Credit risk is represented by the Distance to Default (DD) metric, as shown in Table 8, which estimates the likelihood of default when a company's asset value drops below its debt obligations Following the Global Financial Crisis (GFC), the Materials, IT, and Consumer Discretionary sectors have been classified as relatively safe.
In Vietnam, the Utilities, Financials, and Energy sectors are identified as the highest risk groups Conversely, post-global financial crisis analyses reveal that Materials, IT, and Financial sectors rank among the lowest risk groups, while Industrial, Consumer Discretionary, and Energy sectors are categorized as the highest risk.
It is a surprise for Industrials, Energy and Consumer Discretionary had the worst ranking movement Utilities, Financials and IT achieve the substantial enhance after in the post-post GFC periods.
Market risk versus Credit risk outcomes
This section examines the relationship between market risk and credit risk rankings in Vietnam To assess this association, the DD rankings are correlated with two market risk approaches—parametric and historical—during the post-GFC period from 2010 to 2016 A Spearman Rank Correlation Test is utilized to evaluate the correlation between market risk and credit risk rankings in Vietnam.
Table 14 shows a significant difference at the 90% confidence level, leading us to accept the null hypothesis (H4), which posits an association between market risk and credit risk ranking Consequently, we conclude that a relationship exists between market risk, as indicated by the Parametric measure, and credit risk, represented by the Distance to Default in Vietnam.
Table 14 Market Risk proxied by Parametric and Credit Risk proxied by DD
Comparison in post-GFC (2010 – 2016), Vietnam
Note: Rankings are from 1 (lowest risk) to 10 (highest risk) n 10 r 0.55 t 1.87 degree of freedom 8 critical value 90% 1.860 critical value 95% 2.306 critical value 99% 3.355 significance *
Table 15 Market Risk proxied by Historical and Credit Risk proxied by DD
Comparison in post-GFC (2010 – 2016), Vietnam
DD Ranking Ranking Diff in Diff in
Note: Rankings are from 1 (lowest risk) to 10 (highest risk) degree of freedom 8 critical value 90% 1.860 critical value 95% 2.306 critical value 99% 3.355 significance *
The difference is significant at 90%, and thus we accept the null Hypothesis (H 4 :
There is a significant association between market risk, represented by historical data, and credit risk, indicated by the Distance to Default, in Vietnam This relationship underscores the interconnectedness of these financial risks within the Vietnamese market.
Tables 14 and 15 reveal a significant correlation at the 90% level, indicating a notable similarity among the industries This suggests that these industries are exposed to both market risk and credit risk in Vietnam.
Concluding remarks
Vietnam has become a significant economic driver in Southeast Asia, primarily fueled by its robust agriculture, manufacturing, and food industries Over the past decade, these three sectors have played a crucial role in enhancing the country's economic value and growth.
To fully leverage the advantages of international partnerships, it is essential to acknowledge the significant impact of sectorial risk, particularly concerning key industries in comparison to similar sectors in Malaysia.
This study analyzes market risk at the sectoral level across ten industries in Vietnam, Malaysia, Australia, and New Zealand, focusing on two key periods: the Global Financial Crisis (2007-2009) and the post-GFC era (2010-2016) It employs both parametric and historical approaches to assess Value at Risk (VaR) and Conditional Value at Risk (CVaR), which measure potential future losses and the risk of extreme loss, respectively The research aims to enhance understanding of VaR, CVaR, and industry-specific risks while detailing the methodologies used in the analysis.
In addition, the credit risk is also considered using the distance to default approach.
This case study focuses on Vietnam, the sole nation selected from a sample of four, to explore the relationship between credit risk, indicated by Distance to Default, and market risk, represented by Value at Risk (VaR) and Conditional Value at Risk (CVaR).
This study achieves some key findings can be summarized as below:
The market risk level, measured by Value at Risk (VaR), has significantly decreased for Vietnam's industries in the post-GFC period compared to the GFC period While this reduction indicates a lower market risk, it does not ensure that the relative ranking of industries will remain consistent However, Conditional Value at Risk (CVaR) has shown a notable decline across various industries from the GFC period to the post-GFC period, with the rankings among industries appearing to remain stable.
The study reveals that Vietnam's sectors present higher risks compared to those in Malaysia, Australia, and New Zealand, with a notable decrease in market risk across these countries since the post-GFC period Additionally, the Vietnamese Government has largely overlooked the financial sector, including banks, diversified financials, and insurance Interestingly, while the IT industry is perceived as low risk in Vietnam, it is classified as high market risk in Malaysia, Australia, and New Zealand.
The credit risk in various Vietnamese industries is assessed through the Distance to Default metric Findings reveal that the Industrials, Energy, and Consumer Discretionary sectors have experienced the most significant decline in their distance to default rankings compared to other sectors Conversely, the Utilities, Financials, and IT sectors have shown notable improvement in the periods following the global financial crisis.
Fourth, this study also demonstrated an important link between credit risk, proxied by distance to default, and mảket risk, proxied by VaR, at least in the context of Vietnam.
Policy implications
The previous section highlighted key findings regarding market and credit risk, which lead to significant policy implications for both investors and the Vietnamese Government.
5.2.1 The implications for practitioners and investors
• This research may provide some substantial benefits to practitioners This also contains enhancing knowledge and understanding of VaR, CVaR, DD as well as its methodologies.
This study's empirical findings enable investors to evaluate their investment strategies in both developing countries like Vietnam and Malaysia, as well as in developed nations such as Australia and New Zealand.
5.2.2 The implications for Vietnamese government
The industry in Vietnam is viewed as relatively risky, contrasting sharply with its status as a safe sector in Malaysia during the Global Financial Crisis (GFC) To achieve its ambition of becoming a financial hub in the Asia Pacific and transitioning to a modern industrial economy, Vietnam should focus on enhancing this crucial sector in the near future.
Empirical results show that the IT sector in Vietnam is relatively safe, contrasting with its classification as a high market risk industry globally, particularly in the post-GFC era Consequently, the government should assess Vietnam's strengths and weaknesses to develop strategies that adapt to ongoing changes and enhance economic stability.
The government needs to adopt a new strategy that fosters motivation, establishes favorable conditions, and creates legal frameworks for businesses This approach will enhance the adaptability of our national economy to the Fourth Industrial Revolution, leveraging the demonstrated strengths of the IT industry.
The correlation between market risk and credit risk means that any decision affecting market risk can also impact credit risk Consequently, the government must carefully consider this relationship when formulating policies to manage market-related risks.
The limitations and further research
Although this research has deliberated about market risk proxied VaR, CVaR and credit risk proxied DD This section will present some limitations and further studies are also discussed
This research estimates market risk in four selected countries and credit risk in one country, highlighting the challenges of obtaining complex data for critical long-term decision-making Despite these difficulties, the study aims to raise awareness among investors and government officials about the significant role that risk plays in financial planning.
The methodology used in risk measurement has limitations, particularly with Value at Risk (VaR), which is not a coherent measure of risk, unlike Conditional Value at Risk (CVaR) Additionally, the parametric approach that relies on a normal distribution may lead to an underestimation of portfolio risk Furthermore, the assumption of a one-period time horizon (T=1) in the Drawdown (DD) model adds to these concerns.
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