Market risk versus credit risk of the selected countries in the trans pacific partnership agreement

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 INTRODUCTION 1.1 Problem statement 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 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 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 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 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 Comparison of VaR methods 20 Table Matrix Variance-Covariance Calculation for a Two-Asset Portfoli 33 Table Spearman Rank Correlation Test 35 Table Sector Breakdown 37 Table Daily commodity market price movements in Vietnam and Malaysia (2007– 2016) 39 Table Daily commodity market price movements in Australia and New Zealand (2007–2016) 40 Table 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 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 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 Distribution of daily returns of NASDAQ 100 – Ticker: QQQ 17 Figure Monte Carlo simulation 100 random trials 18 Figure Distribution of daily returns of NASDAQ 100 – Ticker: QQQ 19 Figure VaR, CVaR, Deviations 22 Figure VaR Values Changes in Vietnam 41 Figure CVaR Values Changes in Vietnam 43 vi Table 11 VaR Ranking Shifts in Vietnam VaR GFC VaR post-GFC Change VaR Rank GFC VaR Rank post-GFC Diff in Rank Diff in Rank2 Utilities 0.0503 0.0367 0.0137 -3 Real Estate 0.1083 0.0446 0.0637 10 Materials 0.0551 0.0411 0.0139 -3 IT 0.0677 0.0344 0.0333 64 Industrials 0.0566 0.0470 0.0097 10 -7 49 Health Care 0.0595 0.0416 0.0178 1 Financials 0.0618 0.0366 0.0252 25 Energy 0.0583 0.0447 0.0137 -2 Cons Stap 0.0579 0.0358 0.0220 Cons Disc 0.0571 0.0460 0.0111 -5 25 204 n 10 r -0.2364 t -0.6880 degree of freedom critical value 90% 1.86 critical value 95% 2.306 critical value 99% 3.355 significance - Note: Rankings are from (lowest risk) to 10 (highest risk) Figure VaR Values Changes in Vietnam VaR - Vietnam 0.12 0.1 0.08 0.06 0.04 0.02 VaR GFC VaR post-GFC 41 Figure illustrate actual VaR as per Table 11 The bar indicates a completely pattern, illustrating the difference between the GFC and post-GFC period A Spearman Rank Correlation Test is applied to determine correlation between preGFC and GFC with VaR rankings The difference is not significant, and thus we reject the null Hypothesis (H1: There is association between GFC and post-GFC in VaR ranking) and conclude that there is no association in industry VaR Ranking between GFC and postGFC in Vietnam The market risk level using VaR, the potential losses in the future over the given time period (day or month) at a given confidential level, presented in Table above indicate that industries have enjoyed a sharp reduction in the post-GFC period (2010-2016) in comparison with the GFC period (2007-2009) Industrials industry is an interesting industry to be considered While the market risk level has reduced in the post-GFC period, market risk level of this industry is relatively smaller than other industries As a consequence, while the industry is ranked third in the GFC period, it is now ranked 10th, the riskiest industry among all 10 industries in Vietnam, in the post GFC period Another extreme, IT is ranked 9th in the GFC period, the industry has jumped into the ladder of the market, being the “safest” industry in Vietnam after the GFC These findings highlight the reality that the market risk level may have reduced, there is no guarantee the ranking, representing how relative the marker risk level of the particular industry in comparison with other industries in the same market, to remain unchanged or improved In addition, Table below presents the ranking shifts in term of the market risk of various industries in Vietnam between the GFC period and the post-GFC period Using CVaR, the extreme loss has been substantially reduced across industries between the GFC period and the post GFC period, the ranking among industries in Vietnam appear to be stable Figure illustrate actual CVaR as per Table 12 The bar indicates a completely pattern, illustrating the difference between the GFC and post-GFC period 42 Table 12 CVaR Ranking Shifts in Vietnam CVaR GFC CVaR post-GFC Change VaR Rank GFC VaR Rank post-GFC Diff in Rank Diff in Utilities 0.0934 0.0501 0.0433 1 0 Real Estate 0.5449 0.1205 0.4244 10 1 Materials 0.1327 0.0783 0.0544 -4 16 IT 0.1643 0.0663 0.0980 4 0 Industrials 0.1706 0.1072 0.0634 -3 Health Care 0.2839 0.1210 0.1628 10 -2 Financials 0.2128 0.0558 0.1570 16 Energy 0.3014 0.0649 0.2365 36 -4 16 Cons Stap 0.2441 0.0726 0.1715 Cons Disc 0.1429 0.1061 0.0368 Rank2 102 0.6 0.5 0.4 0.3 0.2 0.1 CVaR GFC CVaR post-GFC 43 r 0.382 t 1.168 critical value 90% 1.860 critical value 95% 2.306 critical value 99% 3.355 significance - CVaR Values Changes in Vietnam CVaR - Vietnam 10 degree of freedom Note: Rankings are from (lowest risk) to 10 (highest risk) Figure n A Spearman Rank Correlation Test is applied to determine correlation between preGFC and GFC with CVaR rankings The difference is not significant, and thus we reject the null Hypothesis (H2: There is association between GFC and post-GFC in CVaR ranking) and conclude that there is no association in industry CVaR Ranking between GFC and post-GFC in Vietnam 4.3 Credit Risk by Distance to Default Results for Vietnam The results below present DD and industry ranking changes in Vietnam The two sub periods are presented for post-GFC (2010 – 2012) and post-post GFC (2013 - 2016) in Table 13 Table 13 DD Ranking Shifts in Vietnam DD Post-GFC DD post-postGFC Change DD PostGFC DD post-postGFC Diff in Rank Utilities 6.41 9.98 3.57 10 16 Real Estate 8.68 10.11 1.43 1 Materials 10.06 10.58 0.52 1 IT 9.07 11.22 2.15 Industrials 8.08 8.77 0.69 Health Care 9.07 10.38 1.31 1 Financials 7.59 10.53 2.94 36 Energy 7.84 8.41 0.57 10 Cons Stap 9.51 9.94 -0.43 25 Cons Disc 8.99 9.46 0.47 Diff in Rank2 101 Note: Rankings are from (lowest risk) to 10 (highest risk) 44 n 10 r 0.382 t 1.168 degree of freedom critical value 90% 1.860 critical value 95% 2.306 critical value 99% 3.355 significance - A Spearman Rank Correlation Test is applied to determine correlation between postGFC and post-post-GFC in DD rankings The difference is not significant, and thus we reject the null Hypothesis (H3: There is association between post-GFC and post-post-GFC in DD ranking) and conclude that there is no association in industry DD Ranking between post-GFC and post-post-GFC in DD rankings in Vietnam The credit risk is proxied by the DD in Table 8, which is designed to estimate the Default occurring when the value of company's asset falls below the value of debt In the post-GFC, Materials, IT and Consumer Discretionary belongs to the safe group and Utilities, Financials and Energy are in highest risk group in Vietnam However, in the postpost-GFC, estimations provide inversely result: Materials, IT and Financial are in the lowest risk group, and Industrial, Consumer Discretionary and Energy belongs to the highest risk group It is a surprise for Industrials, Energy and Consumer Discretionary had the worst ranking movement Utilities, Financials and IT achieve the substantial enhance after in the post-post GFC periods 4.4 Market risk versus Credit risk outcomes This section considers is whether there is any association between market risk and credit risk ranking in Vietnam To test for association, DD ranking are correlated with two approaches of the Market risk (Parametric and Historical) for the post-GFC period (2010 – 2016) A Spearman Rank Correlation Test is applied to determine correlation between market risk and credit risk ranking in Vietnam The difference in Table 14 is significant at 90%, and thus we accept the null Hypothesis (H4: There is association between market risk and credit risk ranking) and conclude that there is association between market risk proxied by Parametric and credit risk proxied Distance to Default in Vietnam 45 Table 14 Market Risk proxied by Parametric and Credit Risk proxied by DD Comparison in post-GFC (2010 – 2016), Vietnam Parametric VaR DD Post-GFC Ranking VaR Ranking DD Diff in Rank Diff in Rank2 Utilities 0.0367 8.27 25 Real Estate 0.0446 9.56 1 Materials 0.0411 10.35 16 IT 0.0344 9.84 Industrials 0.0470 7.92 10 10 0 Health Care 0.0416 10.29 16 Financials 0.0366 9.71 Energy 0.0447 9.36 8 0 Cons Stap 0.0358 9.74 4 Cons Disc 0.0460 9.40 74 n Note: Rankings are from (lowest risk) to 10 (highest risk) 46 r 10 0.55 t 1.87 degree of freedom critical value 90% 1.860 critical value 95% 2.306 critical value 99% 3.355 significance * Table 15 Market Risk proxied by Historical and Credit Risk proxied by DD Comparison in post-GFC (2010 – 2016), Vietnam Historical VaR DD Ranking VaR Ranking DD Diff in Rank Diff in Rank2 Utilities 0.0363 8.27 9 Real Estate 0.0347 9.56 Materials 0.0385 10.35 1 36 IT 0.0304 9.84 Industrials 0.0427 7.92 10 Health Care 0.0344 10.29 1 Financials 0.0356 9.71 5 Energy 0.0442 9.36 10 Cons Stap 0.0308 9.74 4 Cons Disc 0.0419 9.40 1 64 n r 10 0.61 t 2.19 degree of freedom critical value 90% 1.860 critical value 95% 2.306 critical value 99% 3.355 significance * Note: Rankings are from (lowest risk) to 10 (highest risk) The difference is significant at 90%, and thus we accept the null Hypothesis (H4: There is association between market risk and credit risk ranking) and conclude that there is association between market risk proxied by Historical and credit risk proxied Distance to Default in Vietnam The results are demonstrated in Table 14 and 15 reveling that correlation is found at the 90% level It means there is a degree of similarity among those industries and those industries are risky from a market risk also risky from a credit risk in Vietnam 47 CHAPTER CONCLUDING REMARKS AND POLICY IMPLICATIONS In this chapter, we will summarize the key conclusions We will then accommodate practitioners, investors, policy makers with considerable contributions and policy implications from the empirical results Finally is limitations and recommendations for further research 5.1 Concluding remarks 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 In order to maximize the potential benefits from the partnership with any country around the world, it is time to recognize the important role of sectorial risk, in particular, for key sectors (industries) relatively to similar sectors from Malaysia, Australia and New Zealand This study is conducted to measure the level of the market risk at the sectoral levels which has attracted great attention from academia, investment bankers, and policymakers for 10 industries/sectors in Vietnam, Malaysia, Australia and New Zealand (selected countries in the Asia Pacific Region) Two periods are considered, 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 at a given confidential level, and Conditional Value at Risk (CVaR), which is designed to estimate the risk of extreme loss This includes enhancing understanding of VaR, CVaR and industry risk, as well as providing its methodologies In addition, the credit risk is also considered using the distance to default approach As a case study, Vietnam is the only country from the sample of nations to have been selected for this purpose The study is then to provide a link between credit risk, proxied by Distance to Default; and market risk, proxied by VaR and CVaR This study achieves some key findings can be summarized as below: 48  First, the market risk level using VaR for Vietnam’s industries has exhibited a sharp reduction in the post-GFC period in comparison with the GFC period These findings highlight the reality that the market risk level may have reduced, there is no guarantee the ranking, representing how relative the marker risk level of the particular industry in comparison with other industries in the same market, to remain unchanged or improved However, CVaR has been substantially reduced across industries between the GFC period and the post GFC period, the ranking among industries appear to be stable  Second, findings from this study confirm that Vietnam’s sectors are relatively riskier than their counterparts in Malaysia, Australia and New Zealand and that the market risk level across sectors in these 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 Moreover, 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  Third, the credit risk is measured using Distance to Default for various industries in Vietnam The empirical results from this study indicate that Industrials, Energy and Consumer Discretionary have had the worst ranking movement in relation to their distance to default in comparison with other sectors of the Vietnamese economy Utilities, Financials and IT have achieved the substantial improvement in the post-post GFC periods  Fourth, this study also demonstrated an important link between credit risk, proxied by distance to default, and mảket risk, proxied by VaR, at least in the context of Vietnam 5.2 Policy implications The previous section had provided various important findings in relation to market risk and credit risk According to these findings, some policy implications are provided for investors as well as for the Vietnamese Government 49 5.2.1 The implications for practitioners and investors  This research may provide some substantial benefits to practitioners This also contains enhancing knowledge and understanding of VaR, CVaR, DD as well as its methodologies  Besides, based on the empirical results of this study, investors are able to consider their investment strategy in developing countries as Vietnam, Malaysia as well as developed countries Australia and New Zealand 5.2.2 The implications for Vietnamese government  First, this particular industry is considered relatively risky in Vietnam whereas it is ranked as a very safe sector in Malaysia in the GFC period 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 in the near future is strongly recommended  Second, the empirical results indicate that IT which indicate that it is a safe industry in Vietnam whereas this sector belongs to a group of high market risk in others and highest risk in Industrial in the post-GFC Therefore, Government should consider, recognize weaknesses and strengths of Vietnam, after that adopt strategies to adapt the revolution and modify the economy  Third, the Government should develop a new approach such as creating motivation, reasonable conditions, and legal frameworks for enterprises to highly make our national economy adaptable to The Fourth Industrial Revolution with the strength in IT industry demonstrated in the empirical results  Fourth, because of the correlation between market risk and credit risk Therefore, any decision influencing the market risk, the credit risk also receives the potential impact So that, the Government should raise consideration when determine policies to manage the risk in market 50 5.3 The limitations and further research Although this research has deliberated about market risk proxied VaR, CVaR and credit risk proxied DD This section will present some limitations and further studies are also discussed First, data employed to provide the estimation for market risk in four selected countries and one country for credit risk In some circumstances, it cannot provide critical analysis for the long-term decision Although the assessing data is very complicated and difficult to obtain, this research is produced with the hope that the investors and Government are aware of the important role of risk Second, the limitation belongs to the methodology VaR is not coherent risk measurement, but this problem is not apparent in CVaR The parametric approach basing on a normal distribution, this reason may underestimate the risk of portfolio In additional, the assumption on time 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