UNIVERSITY OF ECONOMICS

HO CHI MINH CITY

VIETNAM

INSTITUTE OF SOCIAL STUDIES

THE HAGUE

THE NETHERLANDS

VIETNAM – THE NETHERLANDS

PROGRAMME FOR M.A. IN DEVELOPMENT ECONOMICS

MARKET RISK VERSUS CREDIT RISK OF SELECTED

COUNTRIES IN THE TRANS-PACIFIC PARTNERSHIP

AGREEMENT

BY

QUANG VAN TUAN

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY

December 2017

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

VIETNAM – THE NETHERLANDS

PROGRAMME FOR M.A. IN DEVELOPMENT ECONOMICS

MARKET RISK VERSUS CREDIT RISK OF SELECTED

COUNTRIES IN THE TRANS-PACIFIC PARTNERSHIP

AGREEMENT

A thesis submitted in partial fulfilment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

BY

QUANG VAN TUAN

Academic Supervisor

Dr. VO HONG DUC

HO CHI MINH CITY

December 201

ABSTRACT

At the time this study is finalized, the future of the so-called Trans-Pacific

Partnership Agreement (TPP) is still uncertain after the US Present Donald Trump walked

away from his predecessor Barack Obama’s commitment. A different version of TPP, or

to be called the Comprehensive and Progressive Agreement for Trans-Pacific Partnership

(CPTPP), may be formed without the US presence. Among these member countries,

Vietnam and Malaysia (in the ASEAN), together with Australia and New Zealand, in the

Pacific Ocean, are generally considered closely competitive nations for various industries,

in particular for Agriculture; Food and Beverage and Tourism.

This study is conducted to measure and rank the market risk level of 10

industries/sectors for selected courtiers in the Asia Pacific region: Vietnam, Malaysia,

Australia and New Zealand. Two periods are considered in market risk, including: (i) the

GFC period (2007-2009); and (ii) the post-GFC period (2010-2016). The market risk level

is measured using the parametric approach and the historical approach for both Value at

Risk (VaR), the potential losses in the future over the given time period (day or month) at

a given confidential level, and Conditional Value at Risk (CVaR), which is designed to

estimate the risk of extreme loss.

Findings from this study confirm that Vietnamese sectors are relatively riskier than

their counterparts in Malaysia, Australia and New Zealand. In addition, market risk level

across sectors in all countries has substantially reduced in the post-GFC period. Financials

including Banks, Diversified Financials, and Insurance have been largely ignored from the

Vietnamese Government’s focus. Interestingly, IT industry is considered very low risk in

Vietnam whereas this sector belongs to a group of high market risk in Malaysia, Australia,

and New Zealand.

This study is then extended to measure and rank the credit risk level for all industries

for Vietnam as the case study. Credit risk is generally defined as the risk that is determined

on a credit requirement from the default. Findings from this empirical study indicate that

Industrials, Energy and Consumer Discretionary sectors have had the worst ranking

performance in relation to their credit risk. Utilities, Financials and IT have achieved a

substantial improvement in the post-post GFC periods. In addition, this study also

i

demonstrated an important link between market risk and credit risk, which can provide an

important insight to develop for further issues integrating these aspects.

With the ambition to be a financial hub in the Asia Pacific region in the regional

integration and a modern industrial economy, a shift of the attention to this particular and

important sector in Vietnam is the near future is strongly recommended.

Key words:

Market risk; Credit risk; Sectors; VaR; CVaR; DD; Vietnam; Malaysia,

Australia, New Zealand

ii

DECLARATION

I hereby declare that the thesis entitled “Market risk versus credit risk of selected

countries in the Trans-Pacific Partnership Agreement” written and submitted by me in

fulfillment of the requirements for the degree of Master of Art in Development Economics

to the Vietnam – Netherlands Programme. This is also my original work and conclusions

drawn are based on the material collected by me.

I further declare that this work has not been submitted to any other university for the

award of any other degree, diploma or equivalent course.

HCMC, December 2017

Quang Van Tuan

ii

ACKNOWLEDGEMENTS

First of all, I would like to express my gratitude to my supervisor Dr. Vo Hong Duc,

for his knowledge, motivation, support and for providing me enormous, valuable

opportunities. His guidance helped me at all the time of research and writing of this thesis,

without him, this thesis would have never been completed.

In addition, I would like to thank Prof. Nguyen Trong Hoai, Dr. Pham Khanh Nam,

Dr. Truong Dang Thuy who have provided me the valuable knowledge in the first step of

research.

Furthermore, I would also like to thank all lecturers, staff and Mr. Pham Ngoc Thach

at the Vietnam Netherlands Programme.

Finally, I wish to express my greatest gratitude to my parents, my aunt and my

younger sister for their unconditional encouragement, support and love on the way I have

chosen.

Quang Van Tuan

Ho Chi Minh City, Vietnam

iii

CONTENTS

ACKNOWLEDGEMENTS ............................................................................................... iii

CONTENTS ....................................................................................................................... iv

LIST OF TABLES ............................................................................................................. vi

LIST OF FIGURES ........................................................................................................... vi

ABBREVIATIONS .......................................................................................................... vii

CHAPTER 1 ....................................................................................................................... 8

INTRODUCTION .............................................................................................................. 8

1.1.

Problem statement ................................................................................................ 8

1.2.

The research objectives ...................................................................................... 11

1.3.

Research questions ............................................................................................. 11

1.4.

A choice of the countries in the Asia Pacific Region in this study .................... 12

CHAPTER 2 ..................................................................................................................... 13

LITERATURE REVIEW ................................................................................................. 13

2.1.

Theoretical review .............................................................................................. 13

2.1.1.

Basel II ........................................................................................................ 13

2.1.1.1.

2.1.2.

Categories of risk ................................................................................. 15

Value at Risk ............................................................................................... 16

2.1.2.1.

Introduction ......................................................................................... 16

2.1.2.2.

The Historical method ......................................................................... 16

2.1.2.3.

The Monte Carlo simulation ................................................................ 17

2.1.2.4.

The Variance-Covariance method ....................................................... 18

2.1.2.5.

Comparison of VaR Methodologies .................................................... 20

2.1.2.6.

Limitations of VaR .............................................................................. 21

2.1.3.

Conditional Value at Risk ........................................................................... 22

2.1.4.

Correlation .................................................................................................. 23

2.1.5.

Distance to Default ..................................................................................... 25

2.1.5.1.

KMV-Morton Model ........................................................................... 25

iv

2.1.5.2.

2.2.

Steps in the KMV-Merton model ........................................................ 27

Empirical literature ............................................................................................. 28

2.2.1.

Empirical evidences on the market risk ...................................................... 28

2.2.2.

Empirical evidences on credit risk .............................................................. 29

CHAPTER 3 ..................................................................................................................... 31

METHEDOLOGY AND DATA ...................................................................................... 31

3.1.

Methodology ...................................................................................................... 31

Value at Risk ............................................................................................................. 31

Conditional Value at Risk ......................................................................................... 31

Equity model ............................................................................................................. 32

Distance to Default ................................................................................................... 33

Hypothesis Testing.................................................................................................... 34

Test selection ........................................................................................................ 34

Spearman Rank Correlation Test .......................................................................... 34

3.2.

Data .................................................................................................................... 36

CHAPTER 4 ..................................................................................................................... 38

EMPIRICAL RESULTS ................................................................................................... 38

4.1.

Data descriptions ................................................................................................ 38

4.2.

Market Risk by VaR and CVaR Results ............................................................ 41

4.2.1.

In the GFC period (2007 - 2009) ................................................................ 41

4.2.2.

In the post-GFC (2010 – 2016) ................................................................... 44

4.2.3.

Ranking Shifts in Vietnam .......................................................................... 40

4.3.

Credit Risk by Distance to Default Results for Vietnam ................................... 44

4.4.

Market risk versus Credit risk outcomes ............................................................ 45

CHAPTER 5 ..................................................................................................................... 48

CONCLUDING REMARKS AND POLICY IMPLICATIONS ..................................... 48

5.1.

Concluding remarks ........................................................................................... 48

5.2.

Policy implications ............................................................................................. 49

5.2.1.

The implications for practitioners and investors ......................................... 50

5.2.2.

The implications for Vietnamese government ............................................ 50

5.3.

The limitations and further research................................................................... 51

Reference .......................................................................................................................... 52

v

LIST OF TABLES

Table 1

Comparison of VaR methods ........................................................................... 20

Table 2

Matrix Variance-Covariance Calculation for a Two-Asset Portfoli ................ 33

Table 3

Spearman Rank Correlation Test ..................................................................... 35

Table 4

Sector Breakdown ............................................................................................ 37

Table 5

Daily commodity market price movements in Vietnam and Malaysia (2007–

2016) ................................................................................................................. 39

Table 6

Daily commodity market price movements in Australia and New Zealand

(2007–2016) ..................................................................................................... 40

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) ........................................................................................... 42

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) ........................................................................................... 43

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) ........................................................................................... 38

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) ......................................................................................... 39

Table 11 VaR Ranking Shifts in Vietnam ...................................................................... 41

Table 12 CVaR Ranking Shifts in Vietnam ................................................................... 43

Table 13 DD Ranking Shifts in Vietnam ....................................................................... 44

Table 14 Market Risk proxied by Parametric and Credit Risk proxied by DD

Comparison in post-GFC (2010 – 2016) .......................................................... 46

Table 15 Market Risk proxied by Historical and Credit Risk proxied by DD Comparison

in post-GFC (2010 – 2016) .............................................................................. 47

vi

LIST OF FIGURES

Figure 1

Distribution of daily returns of NASDAQ 100 – Ticker: QQQ ..................... 17

Figure 2

Monte Carlo simulation 100 random trials ..................................................... 18

Figure 3

Distribution of daily returns of NASDAQ 100 – Ticker: QQQ ..................... 19

Figure 4

VaR, CVaR, Deviations.................................................................................. 22

Figure 5

VaR Values Changes in Vietnam ................................................................... 41

Figure 6

CVaR Values Changes in Vietnam ................................................................ 43

vi

ABBREVIATIONS

Basel II

Basel II Accord 2004: BIS revised capital adequacy framework

CVaR

Conditional Value at Risk: Value at risk on condition.

DD

Distance to Default: approach developed by KMV – Merton

GICS

Global Industry Classification Standard

VaR

Value at Risk

vii

CHAPTER 1

INTRODUCTION

1.1. Problem statement

In recent years, the global financial market has undergone a huge change. At the early

stage of the 2000s, the European Union indicated an important inclination of the European

financial markets. Moreover, the international crisis of 2007 - 2008, which was originated

from the US, caused the negative effect on the global system. In addition, Duffie & Pan

(1997) presented the idea to minimalize the loss by recognizing and calculating the risk,

and certify the financial market and economy system securely.

Vietnam has emerged as a new economic engine for the Southeast Asian region with

many important industries. The three pillars contributing the most value to the Vietnamese

economy over the last decade or so are agriculture, manufacturing, and food & beverage.

Among these key pillars, for example, agriculture is a key industry, which has consistently

contributed 20 percent to the national GDP in 2015.1 In addition, Vietnam becomes an

official member of The Trans-Pacific Partnership (TPP) with 11 other countries including

Australia, Brunei, Canada, Chile, Japan, Malaysia, Mexico, Peru, New Zealand, Singapore,

and the United States. According to statistics,2 total gross domestic product (GDP) of the

current TPP parties is approximately $28 trillion, comprises 40 percent of global GDP and

one third of world trade. TPP members account for 11.3 percent of world population and

25.9 per cent world trade.

Even though the full TPP Agreement is dead under the water after the US Present

Donald Trump walked away from his predecessor Barack Obama’s commitment. A

different version of TPP, or to be called the Comprehensive and Progressive Agreement

for Trans-Pacific Partnership (CPTPP), may be formed without the US presence. This new

CPTPP agreement will bring both opportunities and challenges for Vietnam in the future.

In order to maximize the potential benefits from this new partnership, it is time to recognize

the important role of sectorial risk, in particular, for key sectors (industries) relatively to

1

2

https://www.gso.gov.vn/default.aspx?tabid=621&ItemID=15507

http://www.nytimes.com/interactive/2016/business/tpp-explained-what-is-trans-pacific-partnership.html

8

similar sectors from other members.

In addition, the so-called Asia-Pacific region has emerged as the economic and

political powerhouse for the last decade or so. It is time for Vietnam to go beyond the

borders of the ASEAN. The common market has been getting larger and larger and

competition has been becoming more challenging. Malaysia and Thailand are no longer

the only competitors for certain sectors in Vietnam. It is time for Vietnam, as well as other

identical ASEAN nations including Thailand and Malaysia, to recognize and prepare a

response for competition from other countries, who are not the members of the ASEAN,

from the Asia-Pacific region, in particular for Australia and New Zealand.

Risks may subsist in every movement of life and risk estimation is the essential

activity. Moreover, this movement may support the lenders forecasting and avoiding bad

debts. In business activities, risk may arise from various sources: the volatility of the

business environment; business cycle, changes in government policies and especially in

the financial markets. In the event, a number of companies passively accept the risk.

However, others attempt to manage risk by utilizing the proactive methods. Either of

circumstances, risk should be carefully monitored because of its potentially harmful effects

Jorion (2007).

According to Basel II Accord (Bank for International Settlements, 2004), risk may

be split into three groups including credit risk, operational risk, and market risk. First,

credit risk is generally defined as risk of loss because of the payment default of borrower.

Second, operational risk presents the risk due to internal process failures, systems and

people. Third, market risk is considered as a change in the price of financial assets due to

the changes in interest rates, stock prices, exchange rates and commodity risk.

To be more precise, one of the most effective techniques to estimate the market risk

is Value at Risk (VaR). In 1995, The Basel Accord recommended banks to calculate the

capital requirements for market risk by employing certain parameters with value at risk

model. VaR laid the groundwork for resolving a numerous aspect of financial risk. Jorion

(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.

In addition, VaR is an uncomplicated risk management and 𝛼-percentile of a distribution

9

is easily figured out. The tempting simplicity of the VaR concept can conduct its

implementation as an ordinary risk measure for financial actualities entailed in not only

operations, banks, insurances, but also an institutional investment, and nonfinancial

enterprises. As such, VaR tends to become the prevailing method for measuring risk in

majority of the industries and countries.

Despite its popularity, VaR remains unsatisfactory mathematical properties. Artzner

et al (1999) analyzed risk measures and concluded that an arrangement of axioms that

should be suitable for any risk measures, which persuades these axioms, is considered to

be “coherent”. The four axioms are Monotonicity, Translation Equivariance, Subadditivity,

and Positive Homogeneity. However, there is no precise quantity of VaR and each measure

stretch with its limitations and VaR is not able to catch the subadditivity axiom. Therefore,

VaR is not recognized for a coherent risk measure. This makes VaR optimization a

challenging computational problem.

Acerbi and Tasche (2002) demonstrated that Condition Value at Risk (CVaR) could

convince all the above axioms and especially subadditivity axiom. As such, CVaR is a

coherent risk measure. CVaR is considered as a good measurement of the extreme losses

in the tail of the distribution as it is conditioned on the returns exceeding VaR. First, CVaR

is usually measured as a percentage. If VaR is measured at a 95% confidence level, CVaR

will be the average of the worst 5% of observations. Second, CVaR has been related to

sector risk and economic periods to measure modifications in risk for extensive product

categories by Powell, Vo, and Pham (2016c); Pflug (2000) presented that CVaR is a

rational measure, not containing the disadvantageous properties of VaR such as

subadditivity. Third, CVaR does measure tail risk as it appraises those returns beyond VaR.

This research aims to emphasize on market risk and credit risk, which have attracted

great attention from academia, investment bankers, and policymakers. Although numerous

studies such as Allen at el (2014); Powell, Vo, and Pham (2016a, 2016b, 2016c)

investigated the market and credit risk for various countries over the world, there has been

no study concerning, comparing Value at Risk (VaR) and Conditional Value at Risk

(CVaR) rankings for various sectors for Vietnam and other members from the Asia-Pacific

region. As a result, this study is conducted to fill this gap. In addition, this study will

10

demonstrate the link, if any, between the market risk estimates when the VaR/CVaR and

the Distance to Default techniques are adopted in the context of selected countries in the

Asia-Pacific region including Vietnam.

A careful examination indicates that Vietnam and Malaysia (the ASEAN members)

and Australia and New Zealand (the non-ASEAN members) are relatively identical in

relation to the key industries that contribute substantially to the national economies.

According to Bloomberg where all the data required for this study are extracted, economic

activities in any economy can be allocated into 11 different sectors. These sectors include

Energy; Materials; Industrials; Consumer Discretionary; Consumer Staples; Healthcare;

Financials; Information Technology; Telecommunication Services; Utilities; and Real

estate. The choice of these four selected countries is arbitrary albeit interesting. While

Vietnam and Malaysia are developing countries, Australia and New Zealand are developed

economies. All these selected countries are members of the Asia-Pacific Economic

Cooperation.

1.2. The research objectives

This study is conducted to achieve the following research objectives:

First, estimating the market risk for all sectors for Vietnam and selected other

countries in the Asia-Pacific region where data is available. Furthermore, this

study will closely focus on the differences of the estimates between the 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 DD model.

1.3. Research questions

The following research questions have been raised in this study:

What is the currently prevailing market risk level of various sectors from

Vietnam and selected other countries in the Asia-Pacific region using VaR and

CVaR?

Is there any link between the estimates of the market risk using VaR techniques

11

and the credit risk using the DD model?

1.4. A choice of the countries in the Asia Pacific Region in this study

Due to time constraint, conducting research for all members of the Asia Pacific

Region may become excessive. As such, it is the intention of this study is to focus on the

members in the Australasian region. In this region, members include Malaysia and Viet

Nam (ASEAN) and Australia and New Zealand. A preliminary search provides evidence

to confirm that Brunei may not be on the final list because of its financial market size and

its level of economic development. It is worth noting that, for example, agriculture is one

of the top three industries for Vietnam, Malaysia, Australia and New Zealand. A scrutiny

will be conducted.

12

CHAPTER 2

LITERATURE REVIEW

This chapter is to review the theoretical and empirical literature on both market risk

and credit risk. There are two main parts in this chapter:

i.

The basis theories in market risk and credit risk.

ii.

The empirical evidences on market risk and credit risk.

2.1. 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 is one of the most effective techniques to estimate maximum

expected loss on the market risk. In addition, there are three main methods to

calculate VaR. First, the historical method is relied on the actual historical data.

Second, the Monte Carlo method simulation that randomly creates multiple

scenarios. Third, the Variance-Covariance (parametric) method estimates VaR

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 is an estimate the level of one variable’s value is related to the value

of another. It is particularly important for practitioners interested in minimize

risk to measure the correlation between variables.

2.1.1. Basel II

Basel Capital Accord (Basel I) initially indicated by the Group of Ten (G10)

countries in 1988, to retain the adequate capital that provide a support against unexpected

losses. Therefore, Value at Risk (VaR) was selected as a method proposed to predict the

maximum expected loss over a given period time and confidence level. Moreover, Basel

13

Accord stipulated a standardized approach, which is utilized to calculate the capital for

credit risk and market risk, for all institutions. However, this method may contain several

adequacies, the most important of which were its traditionalism and its failure to

remunerate organizations with higher risk administration.

The Basel Accord was enhanced in April 1995. Basel II permitted associations to

utilize internal models to specify their VaR and the required capital expenses. Nevertheless,

organizations desire to use their internal models, which were originated from regulator, to

employ back-testing method. Following the Australian Prudential Regulatory Authority

(APRA), Basel Accord was chosen as the national mechanism of financial markets. The

normalized procedure is built on ratings from qualified external rating operation.

The Basel II structure entails of three Pillars:

Pillar 1: Capital Requirements. This Pillar recommends minimum capital

obligations for market risk, operation risk and credit risk. The value of total

capital ratio to risk weighted is determined to 8%. In addition, this ratio has the

same result with the Basel Accord I, but the only difference is the modified

alternatives for measuring risk-weighted assets.

Pillar 2: Supervisory Review. Managers are involved to certify that all the

capital essential requirements, standard procedures and schemes are similarly

structured in banks to determine the capital requirements. Moreover, the

supervisor in Australian Prudential Regulatory Authority (APRA) initiates

general discussion with bank manager, and confirms that systems are utilized

in practice.

Pillar 3: Market Discipline. Those achieves the possibility to highlight capital

regulation and alternative endeavors, delivers relative safety to financial

systems and banks. In addition, banks are required to expose their risks and

systems specifically

The Basel II gratefully acknowledges the outstanding position of Value at Risk as a

standard risk measurement and capital estimation. In particular, with the appearance of

Basel II, VaR approach can clearly distinguish the different of the market risk from the

14

credit and other risks. Moreover, market VaR methodology has become very meaningful

to banks to estimate the daily basis with the Basel requirement. This considerable benefit

banks can clarify a reduced capital requirement.

2.1.1.1. Categories of risk

Total capital requirements contain the sum of the capital that is required for the

following risks:

Market Risk, also known of as “systematic risk” that is originated from elements

influencing a large indefinite number of assets in the entire market. Besides, the risk of loss

initiated from the opposite trend in market factors there are four different kinds of market

risk: foreign exchange risk, interest rate risk, equity price risk and commodity price risk.

The value of market risk for capital charge can be measured by utilizing the Regulator’s

standard method or employing the internal model method. Hogan et al (2004) demonstrated

the great advantage of utilizing internal models as the standard method to produce extra

outstanding qualities for the capital charge. Moreover, market risk is also measured by

using the Value at Risk (VaR) method.

Operational Risk is not the formal section of Basel I. This encloses processing risks,

procedural risks and transactional risks. In addition, Banks may deal with these future

scenarios by utilizing either standardized method or Internal Ratings Based (IRB) method.

Moreover, the standardized method includes segmentation of the common enterprise along

typical lines of business after that determining beta for each line, regulator measures the

beta and that beta function is represented for whole industry. By contrast, with IRB method

that bank with highly complex and advanced systems may be obtainable. In addition,

Operational risk can be estimated based on past plans and experience by bank.

Credit Risk refers to the risk that is determined on a credit requirement from the

default. It naturally arises due to the expectation of the borrowers, who desire to pay current

debt by utilizing future cash flow. It is completely impossible to certify that borrowers will

reimburse their debts. A reward of investor, which is originated from the interest payments

for debt obligation of borrower, is assuming credit risk. In addition, this research aims to

emphasize on credit risk, which has attracted great attention from academia, investment

bankers, and policymakers.

15

2.1.2. Value at Risk

2.1.2.1. Introduction

Following

the

introduction

of

RiskMetrics

Technical

paper

by

J.P.

Morgan in 1994 and updated information by J.P. Morgan & Reuters (1996) reveal that

Value at Risk (VaR) approach is a well-known and widely employed metric for estimating

the risk in recent years. VaR is significantly different to other approaches. Harper (2004)

demonstrated that VaR based on the historical information to calculate the potential losses

in the future over the given time period (day or month) at a given confidential level

(typically 95% or 99%).

For instance, the expectation of a portfolio that may lose no more than $1 million,

with 95% confidence level of the time (30 days) has a Value at Risk of $1 million. The

negative aspect is that 5% of the period time or 1 day out of 30, the portfolio could lose at

least $1 million. VaR is also utilized for estimating the governing capital investment.

Moreover, one of the most great advantages of VaR is it may compile several both related

risks and unrelated risks into general method that is expressed in currency terms of an

enterprise or portfolio.

In particular, VaR may be estimated by three methods: The Historical method, the

Monte Carlo simulation and the Variance-Covariance method (or correlation or parametric

method)

2.1.2.2. The Historical method

The historical approach that collects actual historical losses in portfolio from top

(best) to bottom (worse) after that estimating the VaR value based on the assumption of

history information tends to be occurred repeatedly. Harper (2004) denotes the QQQ began

to trade in Mar 1990, the big data from QQQ, which is collected and calculated by daily,

will show in bar chart on Figure 1 to compare and analysis.

16

Figure 1

Distribution of daily returns of NASDAQ 100 – Ticker: QQQ

Source: Investopia (2016)

The red bars spreading out, from -4% to -8%. It describes the highest amount of daily

loss will not over 4% with 95% level of confidence. Moreover, if the investors uses $1000

to invest, they expect that their will loss not higher than $40.

VaR may not calculate the absolute value, instead of estimating the probabilistic

value for the portfolio. In addition, when raising the level of confidence to 99%, the interval

from -7% to -8% will be represented for the daily returns at 1%. If the investor uses $1000

to invest, they expect that their will loss not higher than $70 with 99% level of confidence.

2.1.2.3. The Monte Carlo simulation

The Monte Carlo Simulation refers to approach that randomly creates experiments.

In addition, a random number generates the changes of portfolio value to particular

simulation conducting the Monte Carlo simulation. However, the results for particular

conduct will provide the dissimilar value despise of the small differences among values.

The result conducted 100 trials of monthly returns for QQQ that will show into a bar chart.

17

Figure 2

Monte Carlo simulation 100 random trials

Source: Investopia (2016)

According to the statistics on Figure 2, there are two results in the range from -15%

to -20% and three results in the range from -20% to -25%. This outcome reveals the worst

five results (that complies the worst 5%) may be less than -15%. Therefore, the Monte

Carlo tends to provide the VaR-type conclusion: the loss expectation during given month

for the QQQ not excess than 15% with 95% level of confidence.

2.1.2.4. The Variance-Covariance method

The returns, which are normally distributed; the correlations between risk factors are

the essential requirements for assumption of this approach. Specifically, the VarianceCovariance method requires two important factors to calculate the VaR for an asset: the

mean and a standard deviation that allow sketching the distribution curve.

18

Figure 3

Distribution of daily returns of NASDAQ 100 – Ticker: QQQ

Source: Investopia (2016)

The actual daily standard deviation for QQQ is 2.64% based on the blue curve on

Figure 3. After that, using standard distribution tables by Statsoft Inc (2003) to obtain the

value of the “worst” 5% and 1%. Obviously, the results can be achieved by plugging the

standard deviation into the formulas below:

95% confidence = -1.645 x 𝜎 = -1.645 x 2.64% = 0.043

99% confidence = -2.326 x 𝜎 = -2.326 x 2.64% = 0.061

If investing $1 million, then VaR at 95% confidential level = $ 0.43m. VaR at 99%

level of confidence = $ 0.061m.

Moreover, J.P. Morgan & Reuters (1996) indicate and popularize the VaR and the

method to estimate VaR by the following function:

𝑃𝑡

ln (

)

𝑃𝑡−1

The logarithm of the proportion of price (current price and previous price) complies

with the normal distribution assumption to deal with the financial time series observations.

On the low side, zero tends to bound in the lognormal distribution. Choudhry (2004)

reveals the appropriateness of lognormal distribution for estimating financial time series

observations including extreme negative values (values that cannot be practically observed

19

with share prices).

2.1.2.5. Comparison of VaR Methodologies

Among these methodologies, this is remained the question for VaR methodologies:

“which method is best?” As Linsmeier and Pearson (2000) indicated that it is not easy to

find out a satisfied answer for that complicated question. Each method differs in ability to

measure and capture the risk of different scenarios. The best choice may be determined by

risk managers for their organization and evaluation. Table 1 will discuss how the three

methods differ depending on precise dimensions.

Table 1

Comparison of VaR methods

Attribute

Is it able to measure the

Historical method

Yes

The Variance-

The Monte Carlo

Covariance method

simulation

No, except for short

risk including option in

holding

period

the portfolio?

with limited option in

Yes

time

the portfolio

Is it easy to apply?

Yes if the past data are

Depending

on

the

Extremely difficult to

readily available

complexity

of

the

implement

instruments and data

Are the computations

Yes

Yes

No

Yes

No

No

achieved quickly?

Is it easy to explain with

managers?

In sum, VaR may be estimated by three methods. However, this thesis tends to utilize

two methods: The Historical and Variance-Covariance method. It is because Historical

method has the same characteristics with Monte Carlo simulation, the key difference in the

step of the algorithm 3 (Historical method uses the historical data, Monte Carlo will

generate randomly number to measure). Moreover, Glasserman, Heidelberger and

3

http://financetrain.com/calculating-var-using-monte-carlo-simulation/

20

Shahabuddin (2000) argued that the estimation of VaR for Big Data (huge portfolios) faces

a tradeoff between speed and accuracy. Hence, Monte Carlo simulation spends much time

to operate and too slow to be practical.

2.1.2.6. Limitations of VaR

Although VaR has a number of advantages, this approach remains the majority of

argument and is accused of providing the considerable loss in both the pre- and postfinancial crisis. In addition, Artzner et al. (1999) demonstrated that VaR is not an

appropriate “coherent” approach of risk because it do not satisfy the following axioms:

In specific, the loss is not only represented by random variable X and Y, but also

𝑐 𝜖 ℝ that is a scalar. In addition, 𝜌 is a risk function, i.e. it demonstrates the random

variable X (or Y) to ℝ. Moreover, the risk is associated with X (or Y).

Monotonicity reveals that a lower loss asset will produce a lower risk measure.

A risk measure 𝜌 is monotone:

𝑋 ≤ 𝑌 ⇒ 𝜌(𝑋) ≤ 𝜌(𝑌)

Translation Equivariance demonstrates that if adding more one additional risk,

it may generate more risk and more constant to random variable in order to

stability in its variability. A risk is measured by 𝜌:

𝜌(𝑋 + 𝑐) = 𝜌(𝑋) + 𝑐

Subadditivity shows that combine of two positions, which risk is not equal or

greater than the sum of the individuals. A risk measure 𝜌:

𝜌(𝑋 + 𝑌) ≤ 𝜌(𝑋) + 𝜌(𝑌)

Positive Homogeneity mentions that doubling an asset should lead to double

risk. A risk measure 𝜌 is positive homogeneous, if for all X, 𝜆 ≥ 0:

𝜌(𝜆𝑋) = 𝜆𝜌(𝑋)

In this respect, VaR is not a subadditive approach. Result of VaR values when

21

HO CHI MINH CITY

VIETNAM

INSTITUTE OF SOCIAL STUDIES

THE HAGUE

THE NETHERLANDS

VIETNAM – THE NETHERLANDS

PROGRAMME FOR M.A. IN DEVELOPMENT ECONOMICS

MARKET RISK VERSUS CREDIT RISK OF SELECTED

COUNTRIES IN THE TRANS-PACIFIC PARTNERSHIP

AGREEMENT

BY

QUANG VAN TUAN

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

HO CHI MINH CITY

December 2017

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES

HO CHI MINH CITY

THE HAGUE

VIETNAM

THE NETHERLANDS

VIETNAM – THE NETHERLANDS

PROGRAMME FOR M.A. IN DEVELOPMENT ECONOMICS

MARKET RISK VERSUS CREDIT RISK OF SELECTED

COUNTRIES IN THE TRANS-PACIFIC PARTNERSHIP

AGREEMENT

A thesis submitted in partial fulfilment of the requirements for the degree of

MASTER OF ARTS IN DEVELOPMENT ECONOMICS

BY

QUANG VAN TUAN

Academic Supervisor

Dr. VO HONG DUC

HO CHI MINH CITY

December 201

ABSTRACT

At the time this study is finalized, the future of the so-called Trans-Pacific

Partnership Agreement (TPP) is still uncertain after the US Present Donald Trump walked

away from his predecessor Barack Obama’s commitment. A different version of TPP, or

to be called the Comprehensive and Progressive Agreement for Trans-Pacific Partnership

(CPTPP), may be formed without the US presence. Among these member countries,

Vietnam and Malaysia (in the ASEAN), together with Australia and New Zealand, in the

Pacific Ocean, are generally considered closely competitive nations for various industries,

in particular for Agriculture; Food and Beverage and Tourism.

This study is conducted to measure and rank the market risk level of 10

industries/sectors for selected courtiers in the Asia Pacific region: Vietnam, Malaysia,

Australia and New Zealand. Two periods are considered in market risk, including: (i) the

GFC period (2007-2009); and (ii) the post-GFC period (2010-2016). The market risk level

is measured using the parametric approach and the historical approach for both Value at

Risk (VaR), the potential losses in the future over the given time period (day or month) at

a given confidential level, and Conditional Value at Risk (CVaR), which is designed to

estimate the risk of extreme loss.

Findings from this study confirm that Vietnamese sectors are relatively riskier than

their counterparts in Malaysia, Australia and New Zealand. In addition, market risk level

across sectors in all countries has substantially reduced in the post-GFC period. Financials

including Banks, Diversified Financials, and Insurance have been largely ignored from the

Vietnamese Government’s focus. Interestingly, IT industry is considered very low risk in

Vietnam whereas this sector belongs to a group of high market risk in Malaysia, Australia,

and New Zealand.

This study is then extended to measure and rank the credit risk level for all industries

for Vietnam as the case study. Credit risk is generally defined as the risk that is determined

on a credit requirement from the default. Findings from this empirical study indicate that

Industrials, Energy and Consumer Discretionary sectors have had the worst ranking

performance in relation to their credit risk. Utilities, Financials and IT have achieved a

substantial improvement in the post-post GFC periods. In addition, this study also

i

demonstrated an important link between market risk and credit risk, which can provide an

important insight to develop for further issues integrating these aspects.

With the ambition to be a financial hub in the Asia Pacific region in the regional

integration and a modern industrial economy, a shift of the attention to this particular and

important sector in Vietnam is the near future is strongly recommended.

Key words:

Market risk; Credit risk; Sectors; VaR; CVaR; DD; Vietnam; Malaysia,

Australia, New Zealand

ii

DECLARATION

I hereby declare that the thesis entitled “Market risk versus credit risk of selected

countries in the Trans-Pacific Partnership Agreement” written and submitted by me in

fulfillment of the requirements for the degree of Master of Art in Development Economics

to the Vietnam – Netherlands Programme. This is also my original work and conclusions

drawn are based on the material collected by me.

I further declare that this work has not been submitted to any other university for the

award of any other degree, diploma or equivalent course.

HCMC, December 2017

Quang Van Tuan

ii

ACKNOWLEDGEMENTS

First of all, I would like to express my gratitude to my supervisor Dr. Vo Hong Duc,

for his knowledge, motivation, support and for providing me enormous, valuable

opportunities. His guidance helped me at all the time of research and writing of this thesis,

without him, this thesis would have never been completed.

In addition, I would like to thank Prof. Nguyen Trong Hoai, Dr. Pham Khanh Nam,

Dr. Truong Dang Thuy who have provided me the valuable knowledge in the first step of

research.

Furthermore, I would also like to thank all lecturers, staff and Mr. Pham Ngoc Thach

at the Vietnam Netherlands Programme.

Finally, I wish to express my greatest gratitude to my parents, my aunt and my

younger sister for their unconditional encouragement, support and love on the way I have

chosen.

Quang Van Tuan

Ho Chi Minh City, Vietnam

iii

CONTENTS

ACKNOWLEDGEMENTS ............................................................................................... iii

CONTENTS ....................................................................................................................... iv

LIST OF TABLES ............................................................................................................. vi

LIST OF FIGURES ........................................................................................................... vi

ABBREVIATIONS .......................................................................................................... vii

CHAPTER 1 ....................................................................................................................... 8

INTRODUCTION .............................................................................................................. 8

1.1.

Problem statement ................................................................................................ 8

1.2.

The research objectives ...................................................................................... 11

1.3.

Research questions ............................................................................................. 11

1.4.

A choice of the countries in the Asia Pacific Region in this study .................... 12

CHAPTER 2 ..................................................................................................................... 13

LITERATURE REVIEW ................................................................................................. 13

2.1.

Theoretical review .............................................................................................. 13

2.1.1.

Basel II ........................................................................................................ 13

2.1.1.1.

2.1.2.

Categories of risk ................................................................................. 15

Value at Risk ............................................................................................... 16

2.1.2.1.

Introduction ......................................................................................... 16

2.1.2.2.

The Historical method ......................................................................... 16

2.1.2.3.

The Monte Carlo simulation ................................................................ 17

2.1.2.4.

The Variance-Covariance method ....................................................... 18

2.1.2.5.

Comparison of VaR Methodologies .................................................... 20

2.1.2.6.

Limitations of VaR .............................................................................. 21

2.1.3.

Conditional Value at Risk ........................................................................... 22

2.1.4.

Correlation .................................................................................................. 23

2.1.5.

Distance to Default ..................................................................................... 25

2.1.5.1.

KMV-Morton Model ........................................................................... 25

iv

2.1.5.2.

2.2.

Steps in the KMV-Merton model ........................................................ 27

Empirical literature ............................................................................................. 28

2.2.1.

Empirical evidences on the market risk ...................................................... 28

2.2.2.

Empirical evidences on credit risk .............................................................. 29

CHAPTER 3 ..................................................................................................................... 31

METHEDOLOGY AND DATA ...................................................................................... 31

3.1.

Methodology ...................................................................................................... 31

Value at Risk ............................................................................................................. 31

Conditional Value at Risk ......................................................................................... 31

Equity model ............................................................................................................. 32

Distance to Default ................................................................................................... 33

Hypothesis Testing.................................................................................................... 34

Test selection ........................................................................................................ 34

Spearman Rank Correlation Test .......................................................................... 34

3.2.

Data .................................................................................................................... 36

CHAPTER 4 ..................................................................................................................... 38

EMPIRICAL RESULTS ................................................................................................... 38

4.1.

Data descriptions ................................................................................................ 38

4.2.

Market Risk by VaR and CVaR Results ............................................................ 41

4.2.1.

In the GFC period (2007 - 2009) ................................................................ 41

4.2.2.

In the post-GFC (2010 – 2016) ................................................................... 44

4.2.3.

Ranking Shifts in Vietnam .......................................................................... 40

4.3.

Credit Risk by Distance to Default Results for Vietnam ................................... 44

4.4.

Market risk versus Credit risk outcomes ............................................................ 45

CHAPTER 5 ..................................................................................................................... 48

CONCLUDING REMARKS AND POLICY IMPLICATIONS ..................................... 48

5.1.

Concluding remarks ........................................................................................... 48

5.2.

Policy implications ............................................................................................. 49

5.2.1.

The implications for practitioners and investors ......................................... 50

5.2.2.

The implications for Vietnamese government ............................................ 50

5.3.

The limitations and further research................................................................... 51

Reference .......................................................................................................................... 52

v

LIST OF TABLES

Table 1

Comparison of VaR methods ........................................................................... 20

Table 2

Matrix Variance-Covariance Calculation for a Two-Asset Portfoli ................ 33

Table 3

Spearman Rank Correlation Test ..................................................................... 35

Table 4

Sector Breakdown ............................................................................................ 37

Table 5

Daily commodity market price movements in Vietnam and Malaysia (2007–

2016) ................................................................................................................. 39

Table 6

Daily commodity market price movements in Australia and New Zealand

(2007–2016) ..................................................................................................... 40

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) ........................................................................................... 42

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) ........................................................................................... 43

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) ........................................................................................... 38

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) ......................................................................................... 39

Table 11 VaR Ranking Shifts in Vietnam ...................................................................... 41

Table 12 CVaR Ranking Shifts in Vietnam ................................................................... 43

Table 13 DD Ranking Shifts in Vietnam ....................................................................... 44

Table 14 Market Risk proxied by Parametric and Credit Risk proxied by DD

Comparison in post-GFC (2010 – 2016) .......................................................... 46

Table 15 Market Risk proxied by Historical and Credit Risk proxied by DD Comparison

in post-GFC (2010 – 2016) .............................................................................. 47

vi

LIST OF FIGURES

Figure 1

Distribution of daily returns of NASDAQ 100 – Ticker: QQQ ..................... 17

Figure 2

Monte Carlo simulation 100 random trials ..................................................... 18

Figure 3

Distribution of daily returns of NASDAQ 100 – Ticker: QQQ ..................... 19

Figure 4

VaR, CVaR, Deviations.................................................................................. 22

Figure 5

VaR Values Changes in Vietnam ................................................................... 41

Figure 6

CVaR Values Changes in Vietnam ................................................................ 43

vi

ABBREVIATIONS

Basel II

Basel II Accord 2004: BIS revised capital adequacy framework

CVaR

Conditional Value at Risk: Value at risk on condition.

DD

Distance to Default: approach developed by KMV – Merton

GICS

Global Industry Classification Standard

VaR

Value at Risk

vii

CHAPTER 1

INTRODUCTION

1.1. Problem statement

In recent years, the global financial market has undergone a huge change. At the early

stage of the 2000s, the European Union indicated an important inclination of the European

financial markets. Moreover, the international crisis of 2007 - 2008, which was originated

from the US, caused the negative effect on the global system. In addition, Duffie & Pan

(1997) presented the idea to minimalize the loss by recognizing and calculating the risk,

and certify the financial market and economy system securely.

Vietnam has emerged as a new economic engine for the Southeast Asian region with

many important industries. The three pillars contributing the most value to the Vietnamese

economy over the last decade or so are agriculture, manufacturing, and food & beverage.

Among these key pillars, for example, agriculture is a key industry, which has consistently

contributed 20 percent to the national GDP in 2015.1 In addition, Vietnam becomes an

official member of The Trans-Pacific Partnership (TPP) with 11 other countries including

Australia, Brunei, Canada, Chile, Japan, Malaysia, Mexico, Peru, New Zealand, Singapore,

and the United States. According to statistics,2 total gross domestic product (GDP) of the

current TPP parties is approximately $28 trillion, comprises 40 percent of global GDP and

one third of world trade. TPP members account for 11.3 percent of world population and

25.9 per cent world trade.

Even though the full TPP Agreement is dead under the water after the US Present

Donald Trump walked away from his predecessor Barack Obama’s commitment. A

different version of TPP, or to be called the Comprehensive and Progressive Agreement

for Trans-Pacific Partnership (CPTPP), may be formed without the US presence. This new

CPTPP agreement will bring both opportunities and challenges for Vietnam in the future.

In order to maximize the potential benefits from this new partnership, it is time to recognize

the important role of sectorial risk, in particular, for key sectors (industries) relatively to

1

2

https://www.gso.gov.vn/default.aspx?tabid=621&ItemID=15507

http://www.nytimes.com/interactive/2016/business/tpp-explained-what-is-trans-pacific-partnership.html

8

similar sectors from other members.

In addition, the so-called Asia-Pacific region has emerged as the economic and

political powerhouse for the last decade or so. It is time for Vietnam to go beyond the

borders of the ASEAN. The common market has been getting larger and larger and

competition has been becoming more challenging. Malaysia and Thailand are no longer

the only competitors for certain sectors in Vietnam. It is time for Vietnam, as well as other

identical ASEAN nations including Thailand and Malaysia, to recognize and prepare a

response for competition from other countries, who are not the members of the ASEAN,

from the Asia-Pacific region, in particular for Australia and New Zealand.

Risks may subsist in every movement of life and risk estimation is the essential

activity. Moreover, this movement may support the lenders forecasting and avoiding bad

debts. In business activities, risk may arise from various sources: the volatility of the

business environment; business cycle, changes in government policies and especially in

the financial markets. In the event, a number of companies passively accept the risk.

However, others attempt to manage risk by utilizing the proactive methods. Either of

circumstances, risk should be carefully monitored because of its potentially harmful effects

Jorion (2007).

According to Basel II Accord (Bank for International Settlements, 2004), risk may

be split into three groups including credit risk, operational risk, and market risk. First,

credit risk is generally defined as risk of loss because of the payment default of borrower.

Second, operational risk presents the risk due to internal process failures, systems and

people. Third, market risk is considered as a change in the price of financial assets due to

the changes in interest rates, stock prices, exchange rates and commodity risk.

To be more precise, one of the most effective techniques to estimate the market risk

is Value at Risk (VaR). In 1995, The Basel Accord recommended banks to calculate the

capital requirements for market risk by employing certain parameters with value at risk

model. VaR laid the groundwork for resolving a numerous aspect of financial risk. Jorion

(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.

In addition, VaR is an uncomplicated risk management and 𝛼-percentile of a distribution

9

is easily figured out. The tempting simplicity of the VaR concept can conduct its

implementation as an ordinary risk measure for financial actualities entailed in not only

operations, banks, insurances, but also an institutional investment, and nonfinancial

enterprises. As such, VaR tends to become the prevailing method for measuring risk in

majority of the industries and countries.

Despite its popularity, VaR remains unsatisfactory mathematical properties. Artzner

et al (1999) analyzed risk measures and concluded that an arrangement of axioms that

should be suitable for any risk measures, which persuades these axioms, is considered to

be “coherent”. The four axioms are Monotonicity, Translation Equivariance, Subadditivity,

and Positive Homogeneity. However, there is no precise quantity of VaR and each measure

stretch with its limitations and VaR is not able to catch the subadditivity axiom. Therefore,

VaR is not recognized for a coherent risk measure. This makes VaR optimization a

challenging computational problem.

Acerbi and Tasche (2002) demonstrated that Condition Value at Risk (CVaR) could

convince all the above axioms and especially subadditivity axiom. As such, CVaR is a

coherent risk measure. CVaR is considered as a good measurement of the extreme losses

in the tail of the distribution as it is conditioned on the returns exceeding VaR. First, CVaR

is usually measured as a percentage. If VaR is measured at a 95% confidence level, CVaR

will be the average of the worst 5% of observations. Second, CVaR has been related to

sector risk and economic periods to measure modifications in risk for extensive product

categories by Powell, Vo, and Pham (2016c); Pflug (2000) presented that CVaR is a

rational measure, not containing the disadvantageous properties of VaR such as

subadditivity. Third, CVaR does measure tail risk as it appraises those returns beyond VaR.

This research aims to emphasize on market risk and credit risk, which have attracted

great attention from academia, investment bankers, and policymakers. Although numerous

studies such as Allen at el (2014); Powell, Vo, and Pham (2016a, 2016b, 2016c)

investigated the market and credit risk for various countries over the world, there has been

no study concerning, comparing Value at Risk (VaR) and Conditional Value at Risk

(CVaR) rankings for various sectors for Vietnam and other members from the Asia-Pacific

region. As a result, this study is conducted to fill this gap. In addition, this study will

10

demonstrate the link, if any, between the market risk estimates when the VaR/CVaR and

the Distance to Default techniques are adopted in the context of selected countries in the

Asia-Pacific region including Vietnam.

A careful examination indicates that Vietnam and Malaysia (the ASEAN members)

and Australia and New Zealand (the non-ASEAN members) are relatively identical in

relation to the key industries that contribute substantially to the national economies.

According to Bloomberg where all the data required for this study are extracted, economic

activities in any economy can be allocated into 11 different sectors. These sectors include

Energy; Materials; Industrials; Consumer Discretionary; Consumer Staples; Healthcare;

Financials; Information Technology; Telecommunication Services; Utilities; and Real

estate. The choice of these four selected countries is arbitrary albeit interesting. While

Vietnam and Malaysia are developing countries, Australia and New Zealand are developed

economies. All these selected countries are members of the Asia-Pacific Economic

Cooperation.

1.2. The research objectives

This study is conducted to achieve the following research objectives:

First, estimating the market risk for all sectors for Vietnam and selected other

countries in the Asia-Pacific region where data is available. Furthermore, this

study will closely focus on the differences of the estimates between the 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 DD model.

1.3. Research questions

The following research questions have been raised in this study:

What is the currently prevailing market risk level of various sectors from

Vietnam and selected other countries in the Asia-Pacific region using VaR and

CVaR?

Is there any link between the estimates of the market risk using VaR techniques

11

and the credit risk using the DD model?

1.4. A choice of the countries in the Asia Pacific Region in this study

Due to time constraint, conducting research for all members of the Asia Pacific

Region may become excessive. As such, it is the intention of this study is to focus on the

members in the Australasian region. In this region, members include Malaysia and Viet

Nam (ASEAN) and Australia and New Zealand. A preliminary search provides evidence

to confirm that Brunei may not be on the final list because of its financial market size and

its level of economic development. It is worth noting that, for example, agriculture is one

of the top three industries for Vietnam, Malaysia, Australia and New Zealand. A scrutiny

will be conducted.

12

CHAPTER 2

LITERATURE REVIEW

This chapter is to review the theoretical and empirical literature on both market risk

and credit risk. There are two main parts in this chapter:

i.

The basis theories in market risk and credit risk.

ii.

The empirical evidences on market risk and credit risk.

2.1. 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 is one of the most effective techniques to estimate maximum

expected loss on the market risk. In addition, there are three main methods to

calculate VaR. First, the historical method is relied on the actual historical data.

Second, the Monte Carlo method simulation that randomly creates multiple

scenarios. Third, the Variance-Covariance (parametric) method estimates VaR

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 is an estimate the level of one variable’s value is related to the value

of another. It is particularly important for practitioners interested in minimize

risk to measure the correlation between variables.

2.1.1. Basel II

Basel Capital Accord (Basel I) initially indicated by the Group of Ten (G10)

countries in 1988, to retain the adequate capital that provide a support against unexpected

losses. Therefore, Value at Risk (VaR) was selected as a method proposed to predict the

maximum expected loss over a given period time and confidence level. Moreover, Basel

13

Accord stipulated a standardized approach, which is utilized to calculate the capital for

credit risk and market risk, for all institutions. However, this method may contain several

adequacies, the most important of which were its traditionalism and its failure to

remunerate organizations with higher risk administration.

The Basel Accord was enhanced in April 1995. Basel II permitted associations to

utilize internal models to specify their VaR and the required capital expenses. Nevertheless,

organizations desire to use their internal models, which were originated from regulator, to

employ back-testing method. Following the Australian Prudential Regulatory Authority

(APRA), Basel Accord was chosen as the national mechanism of financial markets. The

normalized procedure is built on ratings from qualified external rating operation.

The Basel II structure entails of three Pillars:

Pillar 1: Capital Requirements. This Pillar recommends minimum capital

obligations for market risk, operation risk and credit risk. The value of total

capital ratio to risk weighted is determined to 8%. In addition, this ratio has the

same result with the Basel Accord I, but the only difference is the modified

alternatives for measuring risk-weighted assets.

Pillar 2: Supervisory Review. Managers are involved to certify that all the

capital essential requirements, standard procedures and schemes are similarly

structured in banks to determine the capital requirements. Moreover, the

supervisor in Australian Prudential Regulatory Authority (APRA) initiates

general discussion with bank manager, and confirms that systems are utilized

in practice.

Pillar 3: Market Discipline. Those achieves the possibility to highlight capital

regulation and alternative endeavors, delivers relative safety to financial

systems and banks. In addition, banks are required to expose their risks and

systems specifically

The Basel II gratefully acknowledges the outstanding position of Value at Risk as a

standard risk measurement and capital estimation. In particular, with the appearance of

Basel II, VaR approach can clearly distinguish the different of the market risk from the

14

credit and other risks. Moreover, market VaR methodology has become very meaningful

to banks to estimate the daily basis with the Basel requirement. This considerable benefit

banks can clarify a reduced capital requirement.

2.1.1.1. Categories of risk

Total capital requirements contain the sum of the capital that is required for the

following risks:

Market Risk, also known of as “systematic risk” that is originated from elements

influencing a large indefinite number of assets in the entire market. Besides, the risk of loss

initiated from the opposite trend in market factors there are four different kinds of market

risk: foreign exchange risk, interest rate risk, equity price risk and commodity price risk.

The value of market risk for capital charge can be measured by utilizing the Regulator’s

standard method or employing the internal model method. Hogan et al (2004) demonstrated

the great advantage of utilizing internal models as the standard method to produce extra

outstanding qualities for the capital charge. Moreover, market risk is also measured by

using the Value at Risk (VaR) method.

Operational Risk is not the formal section of Basel I. This encloses processing risks,

procedural risks and transactional risks. In addition, Banks may deal with these future

scenarios by utilizing either standardized method or Internal Ratings Based (IRB) method.

Moreover, the standardized method includes segmentation of the common enterprise along

typical lines of business after that determining beta for each line, regulator measures the

beta and that beta function is represented for whole industry. By contrast, with IRB method

that bank with highly complex and advanced systems may be obtainable. In addition,

Operational risk can be estimated based on past plans and experience by bank.

Credit Risk refers to the risk that is determined on a credit requirement from the

default. It naturally arises due to the expectation of the borrowers, who desire to pay current

debt by utilizing future cash flow. It is completely impossible to certify that borrowers will

reimburse their debts. A reward of investor, which is originated from the interest payments

for debt obligation of borrower, is assuming credit risk. In addition, this research aims to

emphasize on credit risk, which has attracted great attention from academia, investment

bankers, and policymakers.

15

2.1.2. Value at Risk

2.1.2.1. Introduction

Following

the

introduction

of

RiskMetrics

Technical

paper

by

J.P.

Morgan in 1994 and updated information by J.P. Morgan & Reuters (1996) reveal that

Value at Risk (VaR) approach is a well-known and widely employed metric for estimating

the risk in recent years. VaR is significantly different to other approaches. Harper (2004)

demonstrated that VaR based on the historical information to calculate the potential losses

in the future over the given time period (day or month) at a given confidential level

(typically 95% or 99%).

For instance, the expectation of a portfolio that may lose no more than $1 million,

with 95% confidence level of the time (30 days) has a Value at Risk of $1 million. The

negative aspect is that 5% of the period time or 1 day out of 30, the portfolio could lose at

least $1 million. VaR is also utilized for estimating the governing capital investment.

Moreover, one of the most great advantages of VaR is it may compile several both related

risks and unrelated risks into general method that is expressed in currency terms of an

enterprise or portfolio.

In particular, VaR may be estimated by three methods: The Historical method, the

Monte Carlo simulation and the Variance-Covariance method (or correlation or parametric

method)

2.1.2.2. The Historical method

The historical approach that collects actual historical losses in portfolio from top

(best) to bottom (worse) after that estimating the VaR value based on the assumption of

history information tends to be occurred repeatedly. Harper (2004) denotes the QQQ began

to trade in Mar 1990, the big data from QQQ, which is collected and calculated by daily,

will show in bar chart on Figure 1 to compare and analysis.

16

Figure 1

Distribution of daily returns of NASDAQ 100 – Ticker: QQQ

Source: Investopia (2016)

The red bars spreading out, from -4% to -8%. It describes the highest amount of daily

loss will not over 4% with 95% level of confidence. Moreover, if the investors uses $1000

to invest, they expect that their will loss not higher than $40.

VaR may not calculate the absolute value, instead of estimating the probabilistic

value for the portfolio. In addition, when raising the level of confidence to 99%, the interval

from -7% to -8% will be represented for the daily returns at 1%. If the investor uses $1000

to invest, they expect that their will loss not higher than $70 with 99% level of confidence.

2.1.2.3. The Monte Carlo simulation

The Monte Carlo Simulation refers to approach that randomly creates experiments.

In addition, a random number generates the changes of portfolio value to particular

simulation conducting the Monte Carlo simulation. However, the results for particular

conduct will provide the dissimilar value despise of the small differences among values.

The result conducted 100 trials of monthly returns for QQQ that will show into a bar chart.

17

Figure 2

Monte Carlo simulation 100 random trials

Source: Investopia (2016)

According to the statistics on Figure 2, there are two results in the range from -15%

to -20% and three results in the range from -20% to -25%. This outcome reveals the worst

five results (that complies the worst 5%) may be less than -15%. Therefore, the Monte

Carlo tends to provide the VaR-type conclusion: the loss expectation during given month

for the QQQ not excess than 15% with 95% level of confidence.

2.1.2.4. The Variance-Covariance method

The returns, which are normally distributed; the correlations between risk factors are

the essential requirements for assumption of this approach. Specifically, the VarianceCovariance method requires two important factors to calculate the VaR for an asset: the

mean and a standard deviation that allow sketching the distribution curve.

18

Figure 3

Distribution of daily returns of NASDAQ 100 – Ticker: QQQ

Source: Investopia (2016)

The actual daily standard deviation for QQQ is 2.64% based on the blue curve on

Figure 3. After that, using standard distribution tables by Statsoft Inc (2003) to obtain the

value of the “worst” 5% and 1%. Obviously, the results can be achieved by plugging the

standard deviation into the formulas below:

95% confidence = -1.645 x 𝜎 = -1.645 x 2.64% = 0.043

99% confidence = -2.326 x 𝜎 = -2.326 x 2.64% = 0.061

If investing $1 million, then VaR at 95% confidential level = $ 0.43m. VaR at 99%

level of confidence = $ 0.061m.

Moreover, J.P. Morgan & Reuters (1996) indicate and popularize the VaR and the

method to estimate VaR by the following function:

𝑃𝑡

ln (

)

𝑃𝑡−1

The logarithm of the proportion of price (current price and previous price) complies

with the normal distribution assumption to deal with the financial time series observations.

On the low side, zero tends to bound in the lognormal distribution. Choudhry (2004)

reveals the appropriateness of lognormal distribution for estimating financial time series

observations including extreme negative values (values that cannot be practically observed

19

with share prices).

2.1.2.5. Comparison of VaR Methodologies

Among these methodologies, this is remained the question for VaR methodologies:

“which method is best?” As Linsmeier and Pearson (2000) indicated that it is not easy to

find out a satisfied answer for that complicated question. Each method differs in ability to

measure and capture the risk of different scenarios. The best choice may be determined by

risk managers for their organization and evaluation. Table 1 will discuss how the three

methods differ depending on precise dimensions.

Table 1

Comparison of VaR methods

Attribute

Is it able to measure the

Historical method

Yes

The Variance-

The Monte Carlo

Covariance method

simulation

No, except for short

risk including option in

holding

period

the portfolio?

with limited option in

Yes

time

the portfolio

Is it easy to apply?

Yes if the past data are

Depending

on

the

Extremely difficult to

readily available

complexity

of

the

implement

instruments and data

Are the computations

Yes

Yes

No

Yes

No

No

achieved quickly?

Is it easy to explain with

managers?

In sum, VaR may be estimated by three methods. However, this thesis tends to utilize

two methods: The Historical and Variance-Covariance method. It is because Historical

method has the same characteristics with Monte Carlo simulation, the key difference in the

step of the algorithm 3 (Historical method uses the historical data, Monte Carlo will

generate randomly number to measure). Moreover, Glasserman, Heidelberger and

3

http://financetrain.com/calculating-var-using-monte-carlo-simulation/

20

Shahabuddin (2000) argued that the estimation of VaR for Big Data (huge portfolios) faces

a tradeoff between speed and accuracy. Hence, Monte Carlo simulation spends much time

to operate and too slow to be practical.

2.1.2.6. Limitations of VaR

Although VaR has a number of advantages, this approach remains the majority of

argument and is accused of providing the considerable loss in both the pre- and postfinancial crisis. In addition, Artzner et al. (1999) demonstrated that VaR is not an

appropriate “coherent” approach of risk because it do not satisfy the following axioms:

In specific, the loss is not only represented by random variable X and Y, but also

𝑐 𝜖 ℝ that is a scalar. In addition, 𝜌 is a risk function, i.e. it demonstrates the random

variable X (or Y) to ℝ. Moreover, the risk is associated with X (or Y).

Monotonicity reveals that a lower loss asset will produce a lower risk measure.

A risk measure 𝜌 is monotone:

𝑋 ≤ 𝑌 ⇒ 𝜌(𝑋) ≤ 𝜌(𝑌)

Translation Equivariance demonstrates that if adding more one additional risk,

it may generate more risk and more constant to random variable in order to

stability in its variability. A risk is measured by 𝜌:

𝜌(𝑋 + 𝑐) = 𝜌(𝑋) + 𝑐

Subadditivity shows that combine of two positions, which risk is not equal or

greater than the sum of the individuals. A risk measure 𝜌:

𝜌(𝑋 + 𝑌) ≤ 𝜌(𝑋) + 𝜌(𝑌)

Positive Homogeneity mentions that doubling an asset should lead to double

risk. A risk measure 𝜌 is positive homogeneous, if for all X, 𝜆 ≥ 0:

𝜌(𝜆𝑋) = 𝜆𝜌(𝑋)

In this respect, VaR is not a subadditive approach. Result of VaR values when

21

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