RỦI RO BIẾN ĐỘNG GIÁ và HIỆU ỨNG lây LAN TRÊN THỊ TRƯỜNG XĂNG dầu VIỆT NAM (tt)

SUMMARY REPORT ON OIL PRICE VOLATILITY RISK AND THE CONTAGION EFFECT IN THE OIL MARKET OF VIETNAM INTRODUCTION Motives and Objectives for Research ♦ Research motives Nowadays, oil markets have become relatively free in operation, resulting in a high degree of oil-price volatility; Consequently, oil markets are naturally vulnerable to significant high price changes Those are significant extreme market risks that have led oil market participants and Governments to endure heavy potential losses Currently, according to Decree 83/2014 /NĐ-CP, the results of key traders (petroleum-oil exporters, importers and producers) of Vietnam heavily rely on volatilities of Platts Singapore price and it, of course, has resulted in a consequence of oil market risks, especially for financial risks to key traders of Vietnam, leading to volatilities in oil prices in Vietnam Platts Singapore price is also one of the bases for the State to regulate import duty on petroleumeum In addition, the operation to set up and use oil price stabilizing fund, one of the two petroleum price regulators, is also tied to the price volatilities of Platts Singapore price In addition to the two above parties, concerns about oil price volatility also come from consumers and other involved organizations such as banks and other financial institutions Therefore, the research on price volatilities of oil products based on Platts Singapore price as a basis for forecasting of oil price volatility in future and assessing Value-at-Risk that all involved parties can accept, is very necessary The Singapore market is a fully-integrated market with the world economy Thus, the thesis will also examine how market risk depends on Platts Singapore price and the world's crude oil (WTI and Brent) prices; between Platts Singapore price and Vietnam stock market Until now, in the world, there are many studies that mention the risks of the oil market that have been discussed with a many topics under qualitative perspective At the same time, other studies from the quantitative perspective, especially after the 2007-2008 global financial crisis, on the risks and the measurement of the oil market risk and forecasting of oil price volatilities have been robust over the past decade (Agnolucci, 2009; Aghayev and Rizvanoughlu, 2014) However, for Vietnam, with the exception of a few studies in qualitative perspective, not many research on the above issues from the quantitative perspective have been done To measure market risk, application of the Value-at-Risk (VaR) methodology offers comprehensive and brief advantages (Jorion, 2007, James, 2003) In addition, to assess the dependence structure of financial markets in general, the oil market in particular, analysts are particularly interested in risk contagion effects (Forbes and Rigobon, 2002; Reboredo, 2011) by using the Copula estimation method (Embrechts et al., 1999; Grégoire et al., 2008) for testing ♦ Studies Objective There are numerous methods to calculate the VaR and each of them is usually linked with certain assumptions A good estimation method is to satisfy the actual conditions of the market The oil market is characterized by high volatility and continuous-time mechanism, distribution of the return series have a fat tail distribution; the oil market is of the general nature as other markets have different psychological reactions when crude oil price returns rise and returns slump (Zakoian, 1994; Fan et al., 2008) Thus, from a practical perspective and empirical study documents show with two evaluation criteria: forecasting accuracy and reasonable loss function (Abad and Benito, 2013), VaR estimation has been carried out using GARCH - type models, based on the Generalized Error Distribution (GED) which can catch the volatility characteristics of the oil market Therefore, the thesis raises the issue of research: “Oil price volatility risk and the contagion effect in the oil market of Vietnam” with the expectation that the research results of the project will help petroleum-oil key traders, Government, and anyone needs to assess and forecast risk in the Vietnam's oil market based on Platts Singapore assessment The thesis will carry out the following research objectives: i) Measuring the volatility of petroleum price in Vietnam based on Platts Singapore price using GED-TGARCH-based VaR approach ii) Testing contagion effects among WTI, Brent and Platts Singapore markets; and between Platts Singapore market and the Vietnam stock market iii) Suggest implications for applying to the practice of risk management of petroleum price in Vietnam Studies method The research methodology is mainly quantitative applied in financial sector to clarify research issues At the same time, the thesis also uses qualitative methods through the use of analytical and synthetic methods of reasoning and practical issues to make suggestions for practical application of research results Specifically, the research methods is used as follows: - Using parameter estimation method of the TGARCH model with the Generalized Error Distribution (GED) before and after the structural breaks in the VaR estimation to measure the petroleum price volatility in Vietnam based on Platts Singapore price - Use MGARCH and Copula models to test contagion effects among WTI, Brent and Platts Singapore markets; and between Platts Singapore market and the Vietnamese stock market - Based on the results of quantitative analysis of the above models, the dissertation has used the qualitative method to analyze and synthesize in order to make suggestions on the practical implementation of petroleum price risk management in Vietnam Studies scope Data is collected from Platts Singapore price by the daily spot price of finished oil products such as: RON 92 petroleum; Naphtha petroleum; DO 0,05; FO180; and crude oil prices on the WTI and Brent markets; and VN-Index The scientific and practical significance of the topic  Scientific significance - The topic has systematized studies of risk and uncertainty, the comprehensive new awarenesses of risks, of the Value-at-Risk (VaR) in the world after the 2007-2008 global financial crisis - The topic has proposed to measure the risk of oil price volatilities in Vietnam oil market based on Platts Singapore price using GED-TGARCH-based VaR approach - At the same time, the topic has proposed to apply Copula method to analyze contagion effects among the oil markets and the oil market with the stock market  Practical significance The dissertation has contributed new points of awareness, assessment and proposing solutions to manage risks of oil business in line with Vietnam's realities as follows: - For the first time the VaR Model is used to measure risk in Vietnam oil market based on Platts Singapore price using GED-TGARCH-based VaR approach - For the first time GED-TGARCH model with structural breaks is used to calculate VaR in the Vietnam oil market based on Platts Singapore price using Bai and Perron (2003) testing; - For the first time the Copula model is used to analyze contagion effects among the WTI, Brent and Platts Singapore markets; and between the oil market based on Platts Singapore price and the Vietnam stock market; - From the results of the research models, the thesis has provided practical suggestions for key oil traders, the Government and anyone needs to assess and forecast risk in Vietnam's oil market based on Platts Singapore price Structural thesis In addition to the introduction, conclusion, commitment of the author, appendices, references, the content of the thesis is structured in chapters CHAPTER 1: THEORETICAL FRAMEWORK OF RISKS AND MEASUREMENT OF OIL PRICE RISK 1.1 Theoretical Framework of Risks 1.1.1 Risk and uncertainty The topic of risk has been raised since ancient times, from the time of the ancient Babylonians, 3,200 BC The history of human development also demonstrates that, without people daring to welcome risks, society will find it difficult to make progress Academic researchers have different views on how to define risk and measure risk From a modern financial perspective, two risky approaches have developed throughout the twentieth century Those are the traditional financial perspective and behavioral financial perspective From a traditional financial standpoint, represented by Knight, the risk is a measurable uncertainty or a presence of risk when future events occur with measurable probability Behavioral finance perspective argues that risk in addition to being linked to an objective and quantifiable factors, is also linked to subjective and qualitative factors Both uncertainty and risk implies suspicion and ambiguity about the outcome of an event, but there are different reasons Knight defines risk as imperfect knowledge where probabilities of occurrence can be known, the uncertainty exists when the probabilities are unknown According to Knight, risk involved in objective probabilities; uncertainty involved in subjective probability It is possible to generalize the risks and uncertainties of current views and perceptions Risk and uncertainty both related to the same underlying concept, that’s a randomness Both risks and uncertainties have the following similarities: (i) Rely on current uncertainties regarding reality, event, outcome, or underlying scenario ; (ii) Determined by probability or probability distribution; (iii) Include the potential to increase or decrease (increase or decrease prices); (iv) Subjectivity: Both depend on who knows what In addition to the similarities, both risks and uncertainties are fundamentally different: (i) Unlike uncertainty, risks include losses due to impact: potential consequences of the issue to a subject/object; (ii) Risk is therefore more subjective: depending on the potential consequences of the issue to whom Classification of uncertainty: There are many ways to classify uncertainty for different purposes and for different researchers From a financial viewpoint, the classification of the following researchers may be considered as follows: According to Giovanni Dosi and Massimo Egidi (1991), there are two types of uncertainty: substantive uncertainty and procedural uncertainty According to David Dequech (2011) the uncertainty is divided into three groups: (i) The first distinction, consisting of two types: substantive uncertainty and procedural uncertainty; (ii) The second distinction, consisting of two types: weak uncertainty and strong uncertainty; (iii) The third distinction, consisting of two types: ambiguity uncertainty and fundamental uncertainty Black Swan Events: The term Black Swan, created by Nassim Nicholas Taleb, addresses an unlikely event with three principal characteristics: First, it is an outlier and an unpredictable, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility Second, it carries a massive impact and extreme effect Third, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was Risk and Financial Risks Classification: Based on different criteria, it can be categorized as: pure risks and speculative risks; systematic risk and non-systematic risk; and business risk and financial risk  Business risk: The risk is related to the nature of the business and production activities The risks of a business nature are due to factors such as the business cycle, price volatility, price instability, competition, investment over time Business risk is a systematic risk, as the risk can not be diversified, unless the business risk arises from specific business decisions (where to buy and which currency to use in international trade…) can be diversified or can be avoided  Financial risks: including risk is related to the companies employ financial leverage and financial distress risk Financial distress risk is the risk that comes from market price factors affecting corporate outcomes Risk management professionals often use the concept of financial risk to address both types of risk when there is no need to separately emphasize the financial distress risk Financial risk (in the sense of financial distress risk) includes exchange rate risk, interest rate risk, commodity price risk, stock price risk With the inclusion of these four types of risk, many financial risk researchers are understood as market risk 1.1.2 Financial risk management According to Phillipe Jorion, financial risk management is the process by which financial risks are identified, assessed, measured, and managed in order to create economic value In centralized risk management tools such as Value-at-Risk (VaR) has emerged as a tool of particular importance in the early 1990s The emergence of risk management in general and financial risk management in particular as a discipline is explained by several factors The first factor is the massive increase of securities and foreign exchange trading since the late 1960s The second factor is this growth in trading activity has taken place against an environment that was often very volatile The volatility of the economic environment is reflected in four various ways: (i) stock market volatility; (ii) exchange rate volatility; (iii) interest-rate volatility; (iv) commodity market volatility The third factor is the development of risk management has also been spurred on by concerns with the dangers of improper derivatives use The fourth factor is that the development of risk management was the rapid advance in the state of information technology 1.2 Measurement of oil price risk 1.2.1 Overview of the formation of oil prices A market is a group of buyers and sellers of a particular good or service The buyers as a group determine the demand for the product, and the sellers as a group determine the supply of the product (Mankiw, 2012) On the market, supply and demand interact with each other and their interaction will lead to the formation of market prices Price volatilities are due to changes from supply or demand or from both of them This volatility causes the price of goods in the market to change, which may have two-way impact on the producer or the consumer When supply and demand are combined, they will affect the selling price and volume of a particular commodity in the market The intersection of the supply curve and the demand curve determines the equilibrium of the market At the equilibrium price, demand equals supply The behavior of buyers and sellers orients the market to equilibrium in a natural way When the market price is higher than the equilibrium price, there will be a surplus of goods, causing the market price to fall Conversely, when the market price is below the equilibrium price, there will be a shortage of goods, which will increase market prices In a market economy, prices are the signal that drives economic decisions and therefore allocates scarce resources For commodities in the economy, prices ensure a balance between supply and demand Currently, the oil market is a deregulated market due to the limited role of OPEC as the volume only accounts for about 30% of global trading volume The world's oil market is seen as a perfectly competitive market that consists of two component markets: the physical product market and the derivatives market Derivatives market has a greater impact on the formation of oil prices 1.2.2 Risk of oil price volatility Oil price shocks (that is, sudden change) can be transmitted to the macro economy via different channels Within the economy, a positive oil price shock (increase) will increase the cost of production and thus limit output This increase will, at least in part, affect the consumer Moreover, as gasoline prices rise, households face higher living costs These effects can continue to have significant price appreciation effects and affect the entire economy, affecting macroeconomic indicators such as employment, trade balance, inflation and public accounts As well as stock market prices and exchange rates Thereby, the nature and extent of such appreciation effects depend on the structural characteristics of the economy For example, the more a country engages in oil trade, the more it is likely to face price shock on the global commodity market 1.2.3 Risk measurement and measurement of oil price risk Risk Metrics (VaR): Includes methods such as gap analysis, duration analysis, scenario analysis, portfolio theory, derivatives risk measurement, and volatility analysis Value-at-Risk (VaR) - Risk measurement standard Value at Risk, or VaR, is a dollar measure of the minimum loss that would be expected over a period of time with a given probability During the 1990s, risk values were widely recognized for measuring market risk Since the early 1990s, non-bank energy traders and end users (such as airlines, carriers ) have used VaR in risk management practices finance Up to now, most major oil companies and major oil traders in the world has kept on using VaR for risk measurement (James, 2003; Burger et al., 2014) 1.3 Contagion and contagion effects 1.3.1 Overview of contagion and contagion effects There is no consensus on the definition of contagion in finance and the method of identifying the contagion Since its inception, it has generated controversy throughout the years According to Forbes and Rigobon (2002), contagion is a signification increase in cross-market linkages after a shock to one country (or group of countries) These two authors also suggest the distinction between interdependence and the contagion Although there is a lot of debate, there seems to be a broadly-accepted view to understanding of financial contagion in a comprehensive and complete way, a key to understanding of financial contagion is to have access to all three key contents These are: (i) Notion of correlation with the expression is a sudden increase in correlation consisting of two conditions: volatilities heightened in correlation and dependence linkages; (ii) Channels of contagion or transmission mechanisms such as trade link between two countries; (iii) The methods for identifying contagion manifests as the transmission speed of rapid or slow contagion (Kolb, 2011) According to Beirne et al (2008), and Masson (1998), the contagion can be divided into two types, contagion and spillover In addition, when dealing with the contagion of economists, other terms such as interdependence, comovement, and dependence structure are mentioned Contagions in finance affect the behavior of the four actors - Governments, financial institutions, investors and borrowers The contagion effect explains the contagion capability of the economic crisis or the boom across countries or regions This phenomenon can happen both at the domestic level as well as at the global level 1.3.2 Contagion Channels According to Dornbusch et al (2000) and Kolb (2011), there are two possible causes for contagion, including macroeconomic fundamentals and investors’ behavior These causes are transmitted through the contagion channels The causes belong to the macroeconomic fundamentals of transmission contagion through the following channels: (i) Trade links; (ii) Financial links/ financial ties; (iii) Competitive devaluation The contagion effect depends on the behavior of investors on four issues: (i) Liquidity and incentives problems; (ii) Information asymmetries and cooperation problems; (iii) Multiple equilibriums; (iv) Changes in the rules of the game (changes in the international financial system) 1.3.3 Measurement of Financial contagion To identify the financial contagion, the researchers conducted the measurement of contagion Measurement of contagion can be made by observing real-life contagion and can be identified by economic data Leaders who are responsible for responding to financial chaos not have any difficulty identifying the onset stages of the contagion However, sometimes they not notice until the impact is contagion For other economists, awareness contagion by economic data through econometric models is sufficiently reliable Econometric models have had a phenomenal evolution in the cognitive process of contagion 1.4 Empirical evidence from previous research 1.4.1 Evidence of the use of VaR in the measurement of oil price risk The methodology developed initially for calculating portfolio VaR is (i) Parameter method; (ii) Non-parametric method and (iii) Semiparametric method Each of these methodologies has different ways of calculating VaR Nonparametric studies on the oil market, with some authors such as Cabedo and Moya (2003), Sadeghi and Shavalponr (2006), Fan and Jiao (2006), Sadorsky (2006) 10  t2    1 t21   1U t21 The biggest limitation of the GARCH model is that they are assumed to be symmetric 2.2.5 TGARCH model (Threshold ARCH) To examine the asymmetric responses to good news and bad news, the TGARCH model was developed by Glosten, Jagannathan, and Runkle (1993) and Zakoian (1994) The TGARCH model (1.1) used in the thesis:  t2    1 t21   1U t21  1U t21dt Of which, dummy variable: dt = if Ut General form of the TGARCH model (p, q):       i    j  j dt  j U t2 j p t i 1 q t j 1 Model:       i     j  j dt  j U t2 j (10) p t i 1 q t j 1 1, if Ut-j < 0, if Ut-j > With dt-j = 2.3 Calculating VaR 2.3.1 Determination of factors influences VaR VaR of a portfolio of financial assets depends on three important factors: confidence level, interval and distribution of profit/non-profit of this interval 2.3.2 VaR approaches Mathematically, VaR is defined as: From (1), VaR would be rearranged as a rate of return of an asset: P  rt    rt     * r *  ƒ(r )dr    (2)  From (1) & (2) VaR is: 15 Model for calculating VaR have a common structure that would be summary in three points: to determine market value of the portfolio; distribution of estimated return; to calculate VaR of the portfolio 2.3.3 Generalized Error Distribution Nelson (1990) suggested using Generalize Error Distribution (GED) to describe process of error vt of  t  vt t 2.3.4 Structural break Empirical evidence shows that when structural break is taken into account in GARCH (1,1) model, long-term variance drop significantly and conditional variance of rate of return of stock, reducing the effect of the trend in the past form the observation shocks of variances 2.3.5 Calculating VaR To calculate VaR, below formula of VaR is normally applied for gas market that have price goes upward (VaRup ) and downward (VaRdown) (Liu et al, 2015) VaR up m ,t    z m, h m ,t , (m  1, 2) 2.3.6 Testing of VaR models One of the most common methods for testing a model is backtesting (or reality checks) (Jorion, 2007) The backtesting method would be classified into two groups: Unconditional coverage test and conditional coverage test 2.4 Risk Contagion Effect 2.4.1 An overview of Contagion Risk Effect Contagion would be seen as a scenario that a shock in an economy or a specific region contagions to and influence on other economies or regions through volatility of the prices In this paper, we use two methodologies The first one is MGARCH and the second one is copula theory to investigate the contagion effect 2.4.2 MGARCH Model The DCC model of Engle with matrix H is described as: H t  Dt Rt Dt ' 16 2.4.3 Underlining theory of Copula Nelsen (1999) defined copula as “joint cumulative distribution function or functions that link multivariate probability distributions into univariate marginal distribution functions” Copulas contain all information about the dependence structure of vector of random variables Copula could describe the non-linear dependency In particular, Copulas contain information about combination state of random variables with tail of a distribution This is the first thing to concern about in the papers on contagion of financial crisis Moreover, Copulas could be described state of the tail without need of using arbitrary 2.4.3.1 Basis properties of Copula The largest value of Copula is because this is a cumulative probability; The value of Copula equals zero If cumulative probability of value of x equals variable X, the variable cannot take any value less than or equal to X Therefore, copula function receives a value between and as any other probability functions 2.4.3.2 Types of Copula Including: standard Copula, Copula t-Student, Copula Archimedean (Gumbel copula, Frank copula), Copula Clayton, Symmetrized Joe-Clayton copula 2.4.3.3 Process of building a Copula function Independence testing; testing Goodness-Of-Fit, estimated parameters; building copula; building joint mass function 2.5 Research data and data processing 2.5.1 Data description 2.5.2 Data processing 17 CHAPTER 3: RESULTS AND DISCUSSION 3.1 Descriptive statistics and Stationary test 3.1.1 Descriptive statistics 3.1.2 Stationary test The result of ADF test shows that time-series variables are stationary This is the primary condition to analyze any time-series data 3.2 Estimating GARCH related models 3.2.1 Estimating GARCH related models before structure break  AR(p), MA(q) choices To eliminate the correlation of time-series data, ARMA model has been used in this paper Based on principle of Maximum likelihood estimation method and Minimum AIC and BIC standard, the result of p, q of AR(p) and MA(q) of each variables is presented in Table 3.1  Testing ARCH effect of data Because the time-series of oil price investigated in this paper have a high volatility, we examine ARCH effect (ARCH LM) By using Engles ARCH test with squared-error of returns with significant level of 1%, the outcome indicates that there is ARCH effect with time-series data (M92, NAP, DO5, F18, WTI and BRE)  Estimating GARCH related models We ran GARCH (1,1), GARCH (1,2), GARCH (2,1) and GARCH (2,2) on each series M92, NAP, D05, and F18 Based on principle of Maximum likelihood estimation method, Minimum AIC and BIC standard, we see that GARCH (1,1) is optimal Then TGARCH (1,1)-GED, GARCH-M(1,1) and GARCH-t-Student (1,1) models are developed to analyze deeper properties of volatility of returns M92, NAP, D05, and F18 The empirical result implicitly implies that TGARCH, GARCH-M, is significant In other word, properties of volatility of returns M92, NAP, D05, F18 are asymmetry and returns of series are significantly affected by its expected risk Because Ϭ2 in GARCH-M model of all variables in this research have p-value > 5%, GARCH-M model is not used GARCHt Student model purely reflects symmetry property too, so we not consider to use this model Finally, to satisfy asymmetry volatility, TGARCH(1,1)-GED is our final choice 18 This choice also fits with Fan et al (2008) when analyzing WTI and BRE; and fits with Aghayev and Rizvanoghlu (2014) when estimating VaR of rate of return of oil Azeri The summary result of each series M91, NAP, D05 and F18 is presented Table 3.11 The empirical result on time-series returns M92, NAP, D05 and F18 on Platts Singapore market reasonably fits with the outcome of Fan et al (2008) with two series returns WTI and Brent This would be because of dependency property and contagion effect between markets in globalization condition This wide guess is confirmed in the next part of the research on contagion risk by MGARCH and Copula 3.2.2 Estimating GARCH related models after structural break 3.2.2.1 Determination of structural break According to Bai and Perron (2003) research, testing structure break of returns of M92, NAP, DO5, and F18 by Least Squares with Breaks method with GIC standard and LWZ criterion, we summary period having structural break and present in Table 3.13 3.2.2.2 Estimating GARCH related models after structural break To estimate GED-TGARCH (1,1) model with structural break determined in Table 3.3, we need to create dummy variables corresponding to statistical significance structural break pointed out in Table 3.13, and we create corresponding dummies to each statistical significance structural break mentioned in Table 3.14 The calculation shows that structural break with dummies is statistically significant We will use this finding to run estimation VaR model 3.3 VaR estimate Result and Test Exactly following the last discussion, we need to measure both up-ward and down-ward prices with returns oil price to support to rationale decision of sellers and buyers involved Therefore, we admit and implement variance – covariance method to measure VaR upward and downward prices by TGARCH model (1,1) and GARCH(1,1) based on general distribution of error for returns (M92 NAP, D05, and F18 respectively) 3.3.1 Calculating TGARCH – GED model before structural break  Calculating VaR We use model with parameters displayed in Table 3.11 for estimating and predicting mean and heteroskedasticity to calculate VaR 19  Backtesting for TGARCH-GED model Backtesting results showed that TGARCH (1,1) model from the method (Method ML ARCH (Marquardt) - Generalized error distribution (GED) approved in both the 95% and 99% confidence levels This means, during the 10-day forecast, the number of times exceeded threshold loss value is zero (0%) for both the 5% level and the 1% level Therefore, estimation of 10-day-ahead VaR forecast is acccurate 3.3.2 Calculating VaR model and TGARCH-GED after structural break point  Caculating VaR The regression model use the parameters from the results shown in Table 3.15, the prediction of average value and heteroskedasticity for the calculation of VaR  Backtesting TGARCH-GED model The backtesting results showed that TGARCH models (1,1) from the method (Method ML - ARCH (Marquardt) - Generalized error distribution (GED)) approved in both the 95% and 99% confidence levels This means, during the 10-day forecast, the number of times exceeded threshold loss value is zero (0%) for both the 5% level and the 1% level Therefore, estimation of 10-day-ahead VaR forecast is accurate 3.4 Testing risk contagion effects between markets 3.4.1 Testing risk contagion effects between markets by MGARCH model DCC-GARCH model with t-student distribution is used to test the dependent relationship between the WTI, Brent and Platts Singapore crude oil markets; and between Singapore and Vietnam stock market To study the risk contagion effects, we can identify pairs of variables as Y1 and Y6, Y2 and Y6, and Y1 and Y7 In addition, to further study the impact of the WTI and Brent oil price respectively on finished gasoline M92 and semi-finished Naphtha on Platts Singapore market, we combined variables to take a more comprehensive look as Y1, Y2 and Y6; Y1, Y2 and Y5 20 3.4.2 Testing by bivariate model DCC-GARCH with t-student distribution Test results showed that all of the correlation coefficients of three pairs of variables Y1 Y7, Y2 Y6, Y1 Y6 are statistically significant with p-value