THE APPLICATION OF VALUEATRISK IN MEASURING RISKS OF VIETNAMESE STOCK MARKET

Dissertation submitted in partial fulfillment of the
Requirement for the MSc in Finance

FINANCE DISSERTATION ON

THE APPLICATION OF VALUE-AT-RISK
IN MEASURING RISKS OF VIETNAMESE
STOCK MARKET

NGUYEN THUY DUNG
ID No: 20000233
Intake 3

Supervisor: Dr. Tran Manh Ha

September 2020


EXECUTIVE SUMMARY
Financial market is highly important for the growth and development of any
economies globally as the facilitators of the sources of funds and the uses of
funds. However, an inherent characteristic of the financial market is its volatility
associated with a diversified set of risks. Yet, the problematic puzzle of measuring
the complex risks of the financial market remains a challenge for not only
academic scholars but also financial market players. To this end, this paper
attempted to develop closer analysis to the presence of Value-at-Risk (VaR) in
quantifying the risks associated with the financial market. Within the scope of this
paper, the focus would shed the light into VaR application in Vietnamese stock
market. With the consideration of the VN30 index as the valuable snapshot of the
stock market in Vietnam for the period from 21 April 2019 to 20 April 2020, this
paper implements four VaR approaches, including Parametric Value at Risk

(PVaR), Historical Value at Risk (HVaR), Modified Value at Risk (MVaR) and
Conditional Value at Risk (CVaR). Each method might have specific weaknesses
that can be overcome with the advantages of other ones. The main findings of this
paper are hoped to provide practical insights on the application of VaR into
measuring the risk for stock market in Vietnam based on the in-depth analysis of
previous literatures on this matter.


TABLE OF CONTENTS

EXECUTIVE SUMMARY.....................................................................................i
TABLE OF CONTENTS.......................................................................................ii
LIST OF FIGURE................................................................................................iii
I. INTRODUCTION.............................................................................................1
1. Research rationale.............................................................................................1
2. Research background........................................................................................1
3. Research objectives...........................................................................................2
4. Research contribution.......................................................................................3
5. Synopsis of research..........................................................................................3
II. LITERATURE REVIEW.................................................................................5
1. Background of financial risks...........................................................................5
2. Value-at-Risk (VaR).........................................................................................14
III. VIETNAMESE ECONOMY AND STOCK MARKET............................23
1. Overview of Vietnamese economy..................................................................23
2. The development of Vietnamese stock market.............................................26
IV. METHODOLOGY........................................................................................30
1. Research design...............................................................................................30
2. Data collection and data analysis approach..................................................31
V. FINDINGS AND DISCUSSION.....................................................................34
1. Descriptive statistics........................................................................................34

2. Risk assessment using VaR.............................................................................38
VI. CONCLUSION..............................................................................................42
REFERENCES.....................................................................................................43
APPENDIX...........................................................................................................48

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LIST OF FIGURE
Figure 1. Comparison between VaR and CVar.......................................................22
Figure 2. Vietnam’s GDP Growth from 2013 to 2019...........................................23
Figure 3. FDI growth in Vietnam from 2015-2019................................................24
Figure 4. Political Stability Index in Vietnam...........................................................25
Figure 5. Bond market growth from 2010-2015.......................................................27
Figure 6. Vietnam Stock Market Structure............................................................28
Figure 7. Stock market capitalization as % of GDP in Vietnam............................29
Figure 8. VN30 Daily Compounded Rate of Return..............................................34
Figure 9. VN30 index descriptive statistics...........................................................35
Figure 10. Descriptive statistics for individual stocks in VN30 index..................36
Figure 11. Mean Compounded Rate of Return for individual stock......................37
Figure 12. Summary of the VaR result for VN30 index........................................38
Figure 13. Individual VaR results at 95% confidence level...................................39
Figure 14. Individual losses of VN30 index components measured by VaR at
confidence level of 95%.........................................................................................41

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I. INTRODUCTION
1. Research rationale

Financial market is highly important for the growth and development of any
nation in the world. Acting as the intermediary to connect the sources of funds and
the uses of funds, one of the financial market inherent characteristics would be its
uncertainty and volatility. Risks have been among key consideration for financial
theorists since the longest time. Indeed, the movements of commodities and
financial securities deal with various uncertainties resulting from a wide range of
attributes. To this perspective, risks can be considered as an unavoidable aspect in
the financial market and in business world. As a matter of fact, the complication
of risks in the financial market can be far more complication, contributing to
further difficulties for the players in the financial market. Series of financial crisis
and scandals booming from the beginning of the century have put on the questions
of how such uncertainties and risks associated with the financial market can be
managed and supervised. The management of risks require comprehensive efforts
in identifying risks, assessing and measuring its impacts as well as determining
the proper risk mitigation method. In response to such alarming signals,
researchers and scholars have long been gravitated towards the development of a
proper sophisticated model to measure and tackle risks. One of the most important
and traditional method of risk measurements frequently utilized by the scholars is
the usage of Value at Risk model (VaR). At its core the VaR model measure the
potential losses led by unfavorable market movements. As a matter of fact, over
the past few periods, the VaR model has become a standard tool utilized in the
risks management process by not only financial market players but also
management in other business sectors.
2. Research background
Vietnam has become one of the continually growing nations in the Asian
region as well as globally. The development of Vietnam has been emerging after
Vietnamese Government’s efforts in reforming its traditional economy. Strong
growth in the national economy is largely strengthened by the contribution of the
circulation of capital between the savers and investors. Hence, the health of
financial market infrastructure become increasingly critical for the development of

the national economy. It is essential to identify that the financial market in
Vietnam is heavily relied on the facilitation of the stock market while bond market
1


is relatively inexperienced and dominated by the institutional players. The strong
composition of the stock market in Vietnam highlights the significance of
strengthening this market against the presence of increasing risks. In addition, it is
important to highlight that the stock market in Vietnam is relatively young and
inexperienced while the infrastructure and regulatory frameworks have yet been
able to keep up with the emerging expansion of the financial market and national
economy. Moreover, the ongoingly growing participants of foreign investors as
well as other impacts of globalization process enhances the complexity of the
financial market. Such attributes further contribute to higher level of volatility and
riskiness faced by investors. However, to sustain higher level of efficiency in
circulating the capital and investments of the financial market as well as attract
investors to participate in such process, it become urgent significance for Vietnam
to be able to identify proper approach towards risk identification and mitigations
within the stock market as well as other aspects of the financial market.
3. Research objectives
Although many literatures have applied the VaR models in different market,
there have been limited researches and findings associated with the development
of risk measurement through VaR model for the stock market in Vietnam.
Moreover, previous studies have the tendency to formulate the risks determinants
through Ordinary Least Square (OLS) model utilizing traditional financial ratios
while there have been limited evidences focusing on how risks are measured.
Withstanding from the above attributes, this paper is developed as a research
focused on applying the VaR model in measuring risks in Vietnamese stock
market. In accomplish this research aim, different research objectives have been
formulated:

- To implement the VaR model in measuring risks in stock market in
Vietnam;
- To understand the current risks and volatility in the Vietnamese stock
market;
- To determine the existence of diversified conclusions among different
VaR approaches;
- To further outline potential recommendations on risks management for
the stock market in Vietnam.

2


Within the scope of this paper, the study focuses on the Vietnamese stock
market only. By which it means that the risks associated with trading other form
of financial securities in Vietnamese financial market like bond trading,
derivatives markets, etc. would be neglected. On the other hand, it also identifies
that any comparative studies between Vietnamese and foreign stock markets
would not be within the scope of this paper.
4. Research contribution
This paper would be expected to contribute to the academic world in
multiple aspects. At first, there have been limited studies attempting to measuring
the level of risks in the financial market in Vietnam. In fact, such implications in
other emerging markets have been increasingly conducted by scholars and
financial theorists. Hence, this paper contributed to the discussion of risks and
risks management for financial market in Vietnam with respects to the usage of
Value at Risk model and its different approaches. On the other hand, as the
Vietnamese stock market has been growing expanding over the last few years and
contributing massively to the financial market, its stability and growth remain a
considerable concern for not only the players in these markets but also the
government and regulators. Hence, a secondary contribution and significance of

this paper would to assess the current risk level of Vietnamese stock market to
help regulators to have the clear overview of current risks scheme in the financial
market. With the growing consideration of Value at Risk model in risk
management, which was further emphasized by the Basel III, this paper would
further engage in a large picture of risks management in financial world and the
applicability of Value at Risk (VaR) model in the stock market.
The main findings of this paper are hoped to provide practical insights on
the application of VaR into measuring the risk for stock market in Vietnam based
on the in-depth analysis of previous literatures on this matter. Based on which,
potential drawbacks and strengths of VaR as the measurement of stock risks in
Vietnam would be further revealed. With that being said, the contribution of this
paper would potentially help future studies as well as financial market players a
better view on the risk landscape of Vietnam’s stock market.
5. Synopsis of research
The later research would be conducted as follow. For the first chapter, the
research would give a brief introduction to the research rationale as well as the

3


determination of the research objectives and research questions. Moreover, this
first chapter also gives the overview of the current stock market background in
Vietnam as well as different concerns with respects to the usage of Value at Risk
model in financial market. Moving on to the second chapter, this chapter is
conducted as a comprehensive literature review of a wide range of issues in
relation to risks and the Value at Risk model. In other word, this second chapter
help establish a solid foundation and background for the later study and analysis.
Next, the third chapter focuses on the analysis of the current economic
background in Vietnam along with different development and characteristics of
the financial market, especially the stock market in Vietnam. Having profound

ideas on the current macroeconomic status and the growth of stock market in
Vietnam would support a strong foundation on the later analysis as these factors
are highly correlated to the movements in risks and returns of the stock market.
Later on, the fourth chapter provide a detailed information on the research design
with respects to a wide range of attributes and consideration for research
methodology ranging from the research design, sampling method, data collection
and data analysis method, etc. The next chapter summarizes and give a more
comprehensive discussion of the data collected from the Value at Risk model and
attempt to answer the research questions and achieve the research objectives.
Furthermore, in-depth descriptive statistics and discussion would be presented in
this specific chapter. For the final chapter, this paper would give the conclusion
remark sum up the entire paper and findings from previous chapters.

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II.

LITERATURE REVIEW

1. Background of financial risks
i. The concepts and measurements of financial risks
Risk, which is also referred as volatility or uncertainty are important
concept and aspect in not only the finance world but also the physical world. The
concept of risks has become one of the most popular concepts yet difficult to
capture and conceptualize in finance field. Yet, it is noteworthy that physical
world does not exist without uncertainty. Furthermore, company must take on
risky investment opportunity to grow and prosper.
In general, risks are much perceived as the degree of uncertainty which
might be experienced in almost every aspects of life (Shkolnyk, 2019). To this

extent, risk can be characterized by two components including the level of
uncertainty and the relative exposure with respects to the object facing the former
uncertainty (Pasaribu, 2010). The concept of risks was further simplified into
mathematical ideology with the work of Meyer (1985) on the uncertainty
principle. However, to ideologize the risk concept in financial market, it would be
necessary to take on a broader and more comprehensive point of view.
In finance world, risk is an inherent aspect of every financial securities and
investments. In other words, when individuals participate in the financial market
through making investments on certain financial securities, they are exposed to a
wide range of risks. As a matter of fact, all financial market players from
investors, portfolio managers, investment bankers, securities rating agency, etc.
concerned about the uncertainty of the return on their investments (Raza et al.
2014). Such uncertainty might be contributed by a wide range of factors which
can be either specific attributes associated with the financial securities themselves
or macroeconomic drivers affecting the entire financial market as a whole. To this
extent, risks in financial market might depict the potential of unfavorable gap
between the expected outcomes by the financial market players and the actual
outcomes (Shkolnyk, 2019). Thus, it can be perceived that any incident or activity
that might contribute to a possible unfavorable return might be termed as risks in
financial market.
Attempting to quantify risk, scholars often consider the historical
movements in the financial securities prices and behaviors as the basis of
measuring the extent to which the investment experience volatility (Hull, 2018).
5


Volatility in mathematical ideology measure the dispersion of stock price
movement by either variance or standard deviation. More specifically, using the
statistical approach, financial theorists measure risk level associated to the
investment by incorporating the standard deviation of the financial securities over

a specific period of time (Shkolnyk, 2019). By measuring the variation of
financial securities prices, standard deviation depicts the volatility of financial
securities prices through measuring the total gaps between the prices and the
historical means during specific period (Hull, 2018). In addition, the level of risk
might be measured with skewness reflecting the asymmetry of the financial assets’
returns distribution (Pasaribu, 2010). In the wake of calculation of skewness for
financial assets’ returns, investment with a positive skewness might depict less
risky investment while a negative skewness might reflect a higher return and
higher risk portfolio (Shkolnyk, 2019). Taking similar approach to measure risks
using the distribution of the financial assets’ return, scholars also take into
consideration the usage of Kurtosis. Another critically important literature body
on financial risks associated with the introduction of the Capital Asset Pricing
Model (CAPM) (Jurczenko, 2018). At its core, the CAPM reflects the financial
market characteristics a single factor, named market beta; to this end, the market
beta measures volatility of the overall market. On the other hand, the market beta
act as the basis for measuring volatility of other assets with specific risk premium
depending on the specific characteristics of the financial assets (Hull, 2018). In
the light of CAPM, an asset with higher beta might indicate that it might expose to
higher risk than those with lower beta. Under CAPM, beta is measured by the
division of covariance between portfolio or individual stock returns and the
relative market return and the variance in market portfolio return (Hull, 2018).
Nevertheless, such approaches in measuring risks using historical data might
violate the fundamental financial theory of random walk. To this perspective, the
random walk hypothesis explained that past information or stock prices cannot be
used to predict the future movements (Chitenderu et al. 2014). This in turn
contributes to the comprehensiveness of risk measuring in the finance world.
ii. Financial risks classification
Moreover, the comprehension of risk is further contributed by the
sophisticated classification of risks. There are various ways for scholars to
categorize different types of risks faced by financial market participants

6


depending on their characteristics. In general, risks can be classified into the
business risk, non-business risk and financial risks. Firstly, business risk refers to
the type of uncertainty that business take on to achieve higher profitability and
maximize the benefits (Hull, 2018). In contrast, non-business risks can be
identified as the types of risks that are out of the control of firms, meaning that
business has no choice on whether or not it would accept the risks. Financial risks,
on the other hand, associate with financing activities or financial transactions and
investment (Simons, 1996).
A more common approach to characterize investment risks in financial
investments would be to separate the systematic and unsystematic risk. In the
wake of such risk classification, players in the financial markets face both
systematic and unsystematic risks associated to their portfolios (Hull, 2018). The
terminology of systematic risk can be identified as those risks that are
uncontrollable by the investors and have broad effects on the vast majority of the
financial

market.

Hence,

these

systematic

risks

have

macroeconomic

characteristics in nature. While the systematic risks are broadly known as the
market risk, unsystematic risks can be broadly perceived as the specific risks
affecting a particular industry, sector or party rather than the entire market. In
other words, the core classification factors between systematic and unsystematic
risks are their impact horizon which can be either the entire financial market or
specific to certain industry or investors (Chitenderu et al. 2014).
With regards to each of the above risk classifications, scholars also further
specified different types of risks in relation to their individual characteristics
(Simons, 1996). An important contributor for the systematic risks is the presence
of market risks, which arises from the changes in the market price of the financial
securities leading to potential negative influences in the portfolio returns and
values (Hull, 2018). Adverse movements in the market might lead to higher
exposure to potential losses in values of the portfolio held by the investors. To
begin with, investors might face with risks associated with unfavorable
movements in interest rate. Such volatility in interest rate affecting the investment
is referred as the interest rate risks. At most, interest rate risks affect the interestbearing securities, especially those securities with fixed interest rate. Interest rate
risk might affect the investors’ portfolio in a wide variety of aspects. At first,
interest rate movements might affect the prices of shares, commodity, investment
7


leading to unfavorable changes in the values of the portfolios (Sinaee and Moradi,
2010). On the other hand, the variation in interest rate might also impact the
investors in terms of reinvestment opportunities (Hull, 2018). The fact that interest
and dividend earned by investors would be reinvested at an unfavorable rate in the
future might lower the return of the portfolio.

On the other hand, one important market indicator that might contribute to
the movements in portfolio returns might include the variation of inflation rate
(Quang et al. 2018). Indeed, inflation rate affects deeply the entire market and
economic leading to various changes across different sectors (Quang et al. 2018).
Inflationary risk or the purchasing power risk can affect the movements in
financial securities in various ways. Firstly, the inflation rate is highly correlated
with the health of the entire market as a whole; hence, any movement in
inflationary background might lead to weakened or strengthened economic
situation (Vu and Tran, 2019). Theoretically, in the case of weakened economic
situation, inflation might lead to lower output and reduction in profitability of
corporate resulting in unexpected negative effects on the asset returns (Sinaee and
Moradi, 2010). Increase uncertainty of inflation rate movements might also affect
the riskiness of the financial assets which indirectly lead to higher required rate of
return on the financial assets (Quang et al. 2018). The movement inflation rate
also correlates with the movements in prices of consumers goods and other
commodities (Simons, 1996). This depicts another risk faced by investors in
financial market wherein the commodity prices move in an unfavorable direction
in relation to the financial market. (Sinaee and Moradi, 2010). The movements in
prices of commodities like gold, oil, energy, etc. are inseparably associated with
the financial market movements (Sinaee and Moradi, 2010). At its core the risks
linked with unfavorable movements in commodity prices are highly impactful as
they are the principle founding block of the economy as well as the entire market
behavior (Simons, 1996). To this extent, higher commodities prices might affect
the production costs and corporation profitability while impacting on the prices of
consumer goods (Chitenderu et al. 2014). Undeniably, such movements would
eventually hit the financial market with its impacts on prices of financial
securities. On the other hand, commodities like gold might act as the alternative
investment for other financial securities.

8

In the growing presence of globalization trend in the financial market,
another crucial market risk affecting the values and returns of the portfolio might
be depict by the movement in currency or foreign exchange rate (Vu and Tran,
2019). Unfavorable movement in the exchange rate positions might lead to
depreciation in the values of the portfolio and erode the potential returns of the
investors. Foreign exchange risks might deeply impact investors whose portfolio
consist of certain range of foreign investments and securities (Pasaribu, 2010).
However, the actual effects of the exchange rate movements on the value of
financial commodities and the stock market might be far from universal as
different scholars depicted controversial and contradicted results with regards to
this specific idea (Suriani et al. 2015).
Another popular systematic risks might relate to a broader view with regards
to the political stability of a nation. Any political instability might hit hard on the
financial market as well as the entire economy as it is the basis for development of
national economy (Beaulieu et al. 2005). Politic risks might arise from a wide
range of political power execution, war, terrorism, expropriation, government
bodies’ decisions, etc. leading direct and indirect impacts on the national
economy.
iii. The association of risks and return
Another important consideration in the financial theory would be
relationship between the risk and returns. As a fundamental financial norm, it was
frequently cited and believed that a higher return on investment would more likely
to associate with an increase in risks faced by the investors (Hull, 2018). Hence,
the statutory trade-off between risk and return has become the core idea within the
financial world for making investment decision and the process of financial
securities pricing (Abdullah et al. 2011).
In explaining the risk-return relationship, a frequently associated concept
with the risk and return tradeoff is the ideology of risk aversion wherein it

depicted that risk-averse investors require for additional compensation when
investing in higher risk financial assets (Shamsabadi et al. 2012). By which it
means that for risky investment, risk-averse investors would expect higher returns
to offset the risks that they might be exposed to (Abdullah et al. 2011). In other
words, higher risks investment would result in higher return under the effects of
risk aversion. Another important theoretical concept explaining such relationship
9


can be explained with the Capital Asset Pricing Model (CAPM) (Shamsabadi et
al. 2012). The concept of the CAPM stipulate a positive relationship between
financial securities risks and return, which holds true to the fundamental trade-off
between risks and return.
While much of the theoretical approaches to explain the relationship
between risk and return has come to an agreement on the positive effects of these
two variables, the empirical evidences from the financial market world might
depict mixed and controversial results compared to existing financial theories. As
a matter of fact, while some studies found supporting evidences on the association
between higher risks and higher return, others produced number of contradict
evidences against this concept. Indeed, a considerable number of studies have
been carried out across different financial markets in order to assess the validity of
risk-return trade off.
To begin with, assessing the association of higher uncertainty with better
returns in the Indian stock market, Dhankar and Kumar (2006) employed the
Market Index Model to investigate such impacts within this market. The empirical
found strong implication supporting the risk and return tradeoff meaning that in
the Indian financial market, higher risks positively contributed to higher returns.
Taking a different approach by assessing the validity of the Capital Asset Pricing
Model in Greek financial market, Michailidis, Tsopoglou, and Papanastasiou
(2006) oversaw the weekly stock returns of more than 100 listed companies for a

5-year period from 1998 to 2002 against their corresponding beta as the measures
of risks. In alignment with previous studies, it was suggested that for financial
market in Greek also supported the core hypothesis of higher risk link to higher
return. Similar findings supporting the contributive relationship between these two
variables can also be evident by study from Leon, Nave and Rubio (2007) with
financial markets in Europe. A recent study conducted by Chiang and Zhang
(2018) employing the TARCH-M model in the case of Chinese stock market
suggested a significant and positive contribution of higher risks to higher return.
Moreover, the paper indicated that this financial tradeoff did present across both
local and global risk-return relations. In other words, a considerable number of
empirical evidences showed support towards the fundamental hypothesis of the
relationship between risks and returns persisting in the real-world financial
market. Indeed, the fact that the similar findings could be experienced going
beyond geographic boundary and timeframe might further strengthen the
10


consideration supporting such belief within the financial world and among
scholars.
Nevertheless, scholars also found contradiction evidences on the presence of
risk and return tradeoff in the real-world financial markets. Such arguments
further enhance the controversial on the discussion of this specific ideology. To
this perspective, Celik, Mandaci and Cagli (2009) investigated the presence of the
relationship between these two variables in the Istanbul Stock Market from 2002
to 2008. The findings, however, suggested that the linear relationship between risk
and return was not significant. On the other hand, studying the Tehran Stock
Market, Sinaee and Moradi (2010) indicated that the relationship between return
and risks were a non-linear relationship while there were limited evidences
supporting the positive contribution between the two variables. Similar cases
could be experienced in the study by Hasan, Kamil, Mastafa and Baten (2011)

when invsetiging the Dhaka Stock Exchange. Similarly, another study conducted
by Kayshik, Taneja and Kaur (2010) to assess the validity of risk-adjusted beta on
more than 120 companies over the period of 2004-2009 suggested that the
increase in systematic risks was not directly correlated with the higher return,
which in turn reject the fundamental relationship proposed in traditional CAPM
hypothesis. By which it means that CAPM actually did not hold true in practical
cases. A recent study presented by Rui, Rasiah, Yen, Ramasamy and Pillay (2018)
investigating the Malaysian Stock during 2007 to 2015 attempted to assess the
relationship between the two variables from the application of CAPM. Evidently,
when testing the association between these two variables, Rui et al. (2018) found
limited evidences supporting the validity of CAPM in Malaysian Stock Exchange
while higher beta value did not relate to higher returns.
In sum, it can be seen that even though the relationship between risks and
return has long been a fundamental idea in the financial world and received
continually discussion and attentions by scholars, its practical application
remained relatively vague and ambiguous. The contradiction results from a large
number of studies might provide a potential gap for future studies and findings.
iv. Risk diversification
When it comes to risk in financial market, a popular concept comes hand-inhand with risk is the diversification of risks. As indicated in the earlier section,
investors tend to categorize risks into either systematic risk or unsystematic risk;
11


wherein, the investors have limited to no power over the control of the systematic
risk (Aliu et al. 2017). While it was much agreed by finance theorists that higher
risks tend to associate with potential higher returns; however, depending on the
level of risk adverse and behavior, each investor might have different approach to
investment decision (Lekovic, 2018). To this extent, the ideology of investment
diversification has been widely accepted as a crucial investment strategy with the
attempt to reduce the investment uncertainty and risks faced by the investors

while minimizing the impacts on the expected returns of the investments (Aliu et
al. 2017). As a matter of fact, risk diversification has become the fundamental
concept in a broad range of fields beside finance and gain massive attentions from
decisional theorists, consumer scholars, genetics scholars and economists
(Mahmoud, 2017).
From a broad perspective, the ideology of risk diversification in finance
convey the attempts of investors by combining a set of financial securities with
different level of unsystematic risks to achieve superior returns compared to a
singular securities portfolio (Lekovic, 2018). As a matter of fact, the present of
portfolio diversification can be traced back to the 4 th century with an old-time
instruction on allocation of fund before it actually took shape and gained massive
attentions from scholars by the mid of 20 th century (Olibe et al. 2008). The simple
form of portfolio diversification can be depicted as the efforts of the investors to
construct their portfolio by adding different financial securities without
considering the correlational influences among these financial assets (Lekovic,
2018). With that being said, the simple form of diversification was much based on
the concept of the larger number of financial assets held the better (Medo et al.
2009). It was argued that such simple form of portfolio diversification can help
lower the risks at the cost of increasing portfolio management expenses (Lekovic,
2018). To this perspective, it also indicates that through diversification by holding
more financial securities without concerning potential effects among them would
eventually help lower the uncertainty but might also decrease the efficiency level.
By which it means that such diversification would hurt the investors in the end
with increasing costs of holding a massive portfolio while the desired risk
reduction attempts remain ambiguous (Medo et al. 2009; Lekovic, 2018).
A more advanced version of the simple portfolio diversification model was
developed around the mid of the 20th century with the introduction of Markowitz’s
12

works wherein the author recommended that portfolio diversification should be
made with financial securities of different industries and sectors (Aliu, et al.
2017). To this extent, it was explained that different industries might hold
different fundamental risks and uncertainties; hence, by combining with the
securities of another industries, the overall covariance would be less than that of
single-industry portfolio (Olibe et al. 2008). In other words, by diversification
with securities in different industry, investors would expect lower correlation
among the components of the portfolio implying the better effects from
diversifications (Aliu et al. 2017). This form of the diversification was referred as
the Modern Portfolio Theory (MPT) dated back in the 20 th century. The core idea
of theory was that by choosing to combine a broad range of low-correlated
securities, investors would be benefited from higher diversification impacts.
At most, both simple and modern approaches to portfolio diversification put
emphasis on diversifying unsystematic risks. In the presence of ongoingly
booming globalization in the financial market, investors might attempt to
diversify the systematic risks with proper approach to international diversification
(Lekovic, 2018). In other words, rather than simply investing in domestic
financial market, the investors might choose to expand the portfolio with
investment in international financial market to help overcome the systematic risks
associated with a nation (Medo et al. 2009). The high degree of cross-country and
multidirectional movements capitals in both equity and fixed income financial
securities further eased the flow in favor of international diversification
(Mahmoud, 2017). Furthermore, it was indicated that for those nations with higher
country risks and political risks, the effects of risk diversification through
international portfolio might be critically higher (Abid et al. 2014). However, it
was also highlighted that the international financial market also faced with whatso-called the home bias puzzle, wherein investors have higher interests in
domestic or local financial securities over international alternative for
diversification (Eun et al. 2010).
It can be seen from the above arguments that the majority of scholars found
favorable impacts of portfolio diversification on the reduction of risks.

Nevertheless, the arguments on the optimal risk diversification method as well as
the size of diversification can be far from universal (Eun et al. 2010, Lekovic,
2018). On the other hand, it was also indicated that the combination of different
13


diversification method might contribute to better risk diversification effects as
well as higher benefits for the investors (Aliu et al. 2017). Regardless of the
ongoing discussion, the concept of risk diversification remains as a crucial and
important idea in the financial market.
2. Value-at-Risk (VaR)
i. Overview of VaR
The ideology of risk and management of risks have always been an integral
part considered by all players in the financial market. To this extent, the vast
scholars and theorists have been ongoingly developing different risk mitigation
approach. The Value at Risk (VaR), which is commonly referred as the VaR
models, is one of them. It was argued that the pioneering pieces of work on the
VaR model dated back in the early of the 20th century with a series of intuitive
discussion on the advantageous benefits of portfolio diversification (Lidija et al.
2014). The historical development of VaR model could be much explained by the
emerging growth of volatility of the financial market as well as within the
business context. The concept of VaR model was developed by two independent
scholars during 1952 with the attempts to maximize the level of rewards against
the set level of risks (Lidija et al. 2014). Nevertheless, during this early period,
VaR remained a mere conceptual framework published and used as the supporting
literature for portfolio theory. The presence of lenders and creditors allow
organizations to borrow and have better access to financial resources as well as
capital to serve their operational needs (Bogdan et al. 2015). While firms became
highly leveraged, their volatility would also increase (Quang et al. 2018). At that
point of time, investors as well as firms were gravitated towards the specific

methodology to help them evaluate the potential losses faced. With the increasing
accessibility and availability of financial information to serve the needs of VaR
model, it became more popular within financial market as well as in business
context. The growing number of firms incorporate this model into practices
further depicted its popularity and significance. As a matter of fact, the VaR
methodology has become so popular that the Basel rules on banking governance
identified it as a key risk identification and mitigation within the depository
financial service sectors (Barjaktaroviü et al. 2013).
The method of Value at Risk (VaR) is an important tool for risks
management in the financial sectors, especially for risks assessments of financial
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securities, commodities, portfolios, etc. The core question of the VaR model
focuses on the determination of the approximation of maximum amount of
potential losses under certain market condition (Angelovska, 2013). In other
words, VaR model estimate the possible losses in relation to the confidence
interval. Another interpretation of the VaR model’s result is how much the
investor’s portfolio might lose in its value within a certain period of time at a
given level probability (Simons, 1996). This is to say the most important attributes
in the VaR model consist of the determination of potential losses which can be
expressed in either relative or absolute value, the timeframe wherein risks are
measured and the probability for such losses to occur (Gorbunova, 2016). To
employ the VaR model, one must identify three critical components including a
common measurement unit, which is usually a monetary unit, a timeframe and a
probability for potential losses to occur (Angelovska, 2013). The growing
popularity and growing perceived importance of VaR has encouraged theorists
and scholars in enhancing its comprehensiveness and adding further
considerations as well as components in the traditional VaR model to develop a
wide spectrum of variation of VaR methodology which can be used to calculate

and measure the risks. Yet, there have been no consensus on the best methodology
of VaR analysis (Al Janabi, 2007).
Researchers and managements are gravitated towards the usage of VaR
model as the critical risk assessment method for multiple reasons. To start with, a
core advantage of the VaR model is its applicability and flexibility (Al Janabi,
2007). Indeed, as it was argued by different scholars, the VaR model can be
utilized to measure the risk associated with wide spectrum of assets and projects
(Pasaribu, 2010). Going beyond its application in financial market, the usage of
VaR model can also be found in different fields like project management,
corporate finance, and so on. In addition, the VaR model does not concern only a
singular risk factor but rather providing a wholesome consideration of risks and
offering an overall risks assessment of the portfolio (Rockafellar and Uryasev,
2002). By considering the probability of potential losses, VaR generates the
likelihood of corresponding losses which might be critically essential for the
decision-making process (Angelovska, 2013). The flexibility of the VaR model
can further be indicated for its applicability in the unit of measurement (Bogdan et

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al. 2015). The model can be expressed in the monetary units, which is relatively
applicable for stock market measurements.
Nevertheless, there is no model that is completely perfect. VaR contains
certain limitations which need to be considered and addressed throughout the
conduct of this paper. It was argued that the VaR model, regardless of its
comprehensiveness, is not a coherent risk measure resulted by its lacking
subadditivity (Gorbunova, 2016). Such characteristics, however, go against a core
understanding of the portfolio management with risks diversification efforts. On
the other hand, the presence of different models and approaches to the VaR model
generating different values might lead to increasing confusion and failure of

selection for the right VaR model (Quang et al. 2018). It can be critically fatal for
the businesses and investors to be rational about choosing the right methodology
in application.
ii. Different approaches to VaR
As indicated earlier, there have been ongoing approach to develop different
approaches towards the VaR analysis. The vast approaches to VaR analysis
emphasize on the investigation of the statistical distribution of the stock market
returns (Simons, 1996). Critical methodologies on VaR analysis might consists of
three core approaches, namely Parametric VaR, Historical Simulation VaR and
Monte Carlo Simulation VaR, which would be discussed in more details
throughout the next sections.
a. Parametric VaR
To begin with, Parametric VaR might be the most common approach of VaR
adopted by VaR users. Implied by its name, the core consideration of Parametric
VaR model is founded on the estimation of the variance-covariance matrix of
returns (Simons, 1996). To this perspective, the parametric approach to VaR
analysis depicts a core assumption on the distribution of assets return. Under the
parametric VaR assumption, the returns of the commodities should follow a
normal distribution (Angelovska, 2013). As a matter of fact, it was argued that
there exists other distribution that might wholly be captured by their parameters,
the normal distribution would be more likely to be incorporated within the
parametric approach. This is a method that uses historical information to calculate
such as arithmetic mean, correlation, standard deviation, and so on, varying
differently among the methods used for capturing risk value (Angelovska, 2013).
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However, it was argued that the mean and standard deviation were more
commonly used for this approach (Bogdan et al. 2015). To capture the ideology of
the parametric VaR, Simons (1996) introduced an equation in measuring the risks

of portfolio implied by the variance of the portfolios. In which, the portfolio
variance is calculated based on the formula of individual financial assets included
in the portfolio. The equation of parametric VaR can be summarized as below.
In which,
σ2P is the volatility level of the portfolio;
ai is the amount of portfolio share of the asset i;
σ2i is the volatility level of asset i;
pi,j is the correlation between returns of i and j assets.
It is essential to highlight that the advantageous aspects of parametric VaR is
its simplicity. As a matter of fact, this approach is relatively quick and easy for
calculation with the usage of simple descriptive statistics figures (Bogdan et al.
2015). Nevertheless, as the parametric VaR assume a normal distribution for its
analysis, when the assets’ return depicts a non-normal distribution as well as the
significant outliers present, the results of parametric VaR might not be a good
reflection (Simons, 1996). It was argued that such assumption might lead to
misinterpretation of the actual returns (Pasaribu, 2010).
b. Historical VaR
Historical VaR utilizes the historical market returns or prices, represented in
the form of distribution generated from the degree of reliability selected by the
investors (Bogdan et al. 2015). As the historical VaR analysis incorporates the
market prices of the financial assets, it is referred as a non-parametric approach to
VaR model. As indicated by its name, determining the proper timeframe for the
analysis is a crucial aspect to conduct historical VaR analysis (Pasaribu, 2010).
Within the selected timeframe, the market figures obtained would be
reconstructed with respects to their size and the degree of probability calculated
risk value.
In the past, it would be difficult to acquire the information incorporated in
the historical VaR analysis making it a less popular approach to VaR; however,
over time when it becomes easier to obtain financial information and data, this
method is increasingly used thanks to its uncomplication (Rockafellar and

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Uryasev, 2002). To further extents, the presence of different data analytic tools
like SPSS, R or Stata as well as data streaming platform such Reuters, Bloomberg,
etc. further compliment the usage of historical VaR.
Moreover, the historical simulation approach to VaR model is believed to
provide superior unconditional coverage among existing method of VaR, which
help enhancing its presence in the finance field and risks management (Sollis,
2009). To this extent, in measuring the effectiveness of VaR in practice, the usage
of back testing framework relies heavily on the unconditional coverage; hence,
historical VaR by providing solid unconditional coverage would depict superior
effectiveness and validity compared to other methodology (Pasribu, 2010). Yet, in
the presence of conditional measured required for conditional coverage in the
dataset, the advantages of historical VaR might be missed. In addition, in practice,
the incorporation of wide range of sophisticated conditional risks models might
provide more comprehensiveness and validity in practice (Sollis, 2009). On the
other hand, since the historical VaR is free from distributional assumptions as a
non-parametric method, it might deliver better performance compared to
parametric VaR model (Bogdan et al. 2015). Another advantage of this method is
also its simplicity for calculation, especially in the presence of recent innovative
data streaming system (Sollis, 2009). To this extent, the historical approach would
not require the estimation of volatility or correlation among the interested stocks.
Hence, it makes the historical VaR to be highly adoptable for any portfolio and
any financial securities for full evaluation of risks.
Nevertheless, the usage of historical simulation for the VaR model might
also carry several drawbacks. At first, it is worth noting key assumption of
historical VaR is that it suggests that the future returns would reflect the past
events (Bogdan et al. 2015). The vast scholars indicated that such assumption was
relative vague, reflecting incomplete consideration of the movements in the

financial assets’ prices (Al Janabi, 2007). This in turn violates the core idea of
irrelevant theoretical bodies. To enhance the validity and effectiveness of
historical VaR model in measuring the risks faced by the investors, it becomes
essential for the model to acquire a large dataset (Rockafellar and Uryasev, 2002).
In practice, the majority of study consists of 251-day sample size, which can be
considered to be large enough dataset. Moreover, another core assumption of this
approach is that the returns calculated have constant volatility and covariance,
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which result in problematic in assessing the sensitivity of the results (Quang et al.
2018). Considering the timeseries data, the historical VaR has higher tendency to
put emphasis on the effects of any recent impactful crises while other
macroeconomic factors might be neglected (Vasileiou, 2017).
c. Monte Carlo VaR
The next method to VaR model is the Monte Carlo Simulation VaR (MC
VaR). Unlike the above methods, this approach to VaR was based on the modelcreation approach by incorporate Monte Carlo Simulation in generating the
portfolio’s changes probability distribution and mathematically representing the
effects of risks on the portfolios (Quang et al. 2018). To certain extent, the MC
VaR is relatively similar to the historical VaR as they both determines the potential
loses regarding a fixed confidence level (Bogdan et al. 2015). However, while
historical VaR requires a large dataset to enhance its effectiveness in measuring
risks, MC VaR can achieve similar effects with a relatively smaller sample size (Li
et al. 2013). Additionally, to conduct the Monte Carlo VaR, users might choose a
distributional hypothetical approach that is best describe the return and changes in
value of the portfolio and use the predetermined distribution in simulating the
returns and risks (Corkalo, 2011). By which, it means that instead of using
historical changes like the previous approach, the Monte Carlo VaR implemented
a specific distribution estimated by the users in determining the level of risks.
After simulating the risks or returns of the portfolio, the VaR calculated as a

percentile in accordance to the chosen confidence level.
Overcoming the weaknesses of the parametric VaR, MC VaR allows users to
have a broader range of distributions, including both normal and non-normal ones
(Wu et al. 2020). Compared to the previous methods, MC VaR is far more
comprehensive and sophisticated; in return, it is indicated as the most effective
and accurate measurements of risks (Chen and Chen, 2013). Hence, its main
disadvantageous against other method is the complexity and the time requirements
to conduct a proper and insightful analysis. When choosing such models, users
must considerably reflect the trade-off between costs and effectiveness among
these three models to identify a proper selection based on their needs and
resources. However, thanks to its sophistication, the evaluation of Monte Carlo
simulation might be highly adaptable to any changes in economic forecasts.
Nevertheless, by choosing the comprehensiveness, Monte Carlo simulation VaR

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requires heavy computational efforts and estimation of particular hypothetical
probability to draw on a meaning sample for the analysis (Quang et al. 2018). In
the presence of a massive portfolio, especially for internationally diversified
portfolio consisting of a wide range of multi-currency financial securities, the
intensiveness for the computations would be further enhanced. As a matter of fact,
the estimation of hypothetical probabilities is also important for the Monte Carlo
simulation; hence, if these attributes are not determined properly, it would create
meaningless information.
d. Modified VaR
Parametric traditional VaR performs well in accounting for the tails of the
distribution and precisely estimate the tail below the risk quantile determined by
the confidence interval. However, such parametric approach might provide
insufficient consideration of the risks and returns in the case of non-normal

distribution of portfolio returns. As indicated earlier, such disadvantages depict a
core weakness in using the traditional VaR approach for employing a symmetrical
distribution function. The usage of other non-parametric method like historical or
Monte Carlo simulation approach might be helpful in the light of providing more
precise prediction of risks and returns associated with the portfolio (Quang et al.
2018). Nevertheless, they remain considerably flawed in explaining and
estimating the risks associated with financial assets with intensive non-normal
distribution. In practice, the presence of normal distribution for financial assets is
considerably limited; hence, it becomes urgent to develop another approach to
address the VaR model for non-normally distributed portfolios. By taking into
consideration higher moments of the return distribution into VaR calculation
through employing skewness and kurtosis, the newly introduced VaR model
utilize the Cornish-Fisher expansion for the calculation of VaR model (Brian et al.
2020). Such approach to VaR using Cornish-Fisher expansion is referred as the
Modified VaR model, which was first introduced in the early of 2000s with the
works from Ferve and Galeano (2002).
As a matter of fact, the usage of Modified VaR model has been increasing
employed by practitioners and adacemic scholars to determine the level of
volatility in portfolio management (Cavenaile and Lejeune, 2012).
Another superiority of the Modified VaR is that in the case of normality
persist with the portfolio return distribution, the results of Modified VaR would be
20


the same as the result of traditional parametric VaR approach. The Modified VaR
model is highly applicable for portfolio return with negative skewness distribution
or a more commonly cited name as fat-tails distribution (Brian et al. 2020). By
employing the Cornish-Fisher expansion for the fat-tailed distribution in the VaR
model, the modified VaR approach would provide the higher estimated losses than
the traditional method. In contrary, if the portfolio’s return is positively skewed,

the modified VaR model would result in a lesser result than traditional method. In
other word, the modified VaR model would more likely to produce a reliable
result with the consideration of the spread of returns throughout the distribution.
Nevertheless, the modified VaR carries certain weaknesses compared to the
previously introduced method. First of all, it was indicated that the modified VaR
model would not be able to provide reflective result of risk estimation for a
complex and sophisticated returns structure and non-continuous returns (Cavenaile
and Lejeune, 2012). Unlike the historical VaR which conduct the risk estimation
based on the historical movements of returns and data, the modified VaR utilize the
Cornish-Fisher expansion to estimate risks based on the shape of the tail for the
returns (Brian et al. 2020). By which, it indicated that the modified VaR provide a
more mathematical approach to risk evaluation based on the extreme returns that
have not been observed or measured yet.
e. Conditional VaR
Another common method utilized for Value at Risk model is the Conditional
Value at Risk (CVaR), which is also known as the Expected Shortfall or the
Expected Tail Loss. The popularity of CVaR was further enhanced with the
emphasis of the Basel Committee as one of the important tools for risks
management under the Basel III standard (Sharma, 2012).
At most, rather than being considered as an approach in VaR model, the
Condition VaR tends to be separated as an individual theoretical body from the
traditional VaR (Sarykalin et al. 2008). Indeed, the debates on the right tactical
tools for risk management between VaR and Conditional VaR have been given
much attention from the vast scholars concerning the presence of risks. The
selection between these two approaches were much of the tradeoff among a wide
range of factors from mathematical computation, properties, stability of the
estimation, costs, simplicity, etc. (Sarykalin et al. 2008). Conditional VaR can be
estimated using a broad spectrum of financial engineering tools and to be
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