1 MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HOCHIMINH CITY - oOo - NGUYỄN THÚY VÂN THE IMPACT OF MACROECONOMIC FACTORS ON CONDITIONAL STOCK MARKET VOLATILITY IN VIETNAM MAJOR: BANKING AND FINANCE MAJOR CODE: 60.31.12 MASTER THESIS INSTRUCTOR: Doctor TRƯƠNG QUANG THÔNG Ho Chi Minh City – 2011 ACKNOWLEDGEMENT Firstly, I would like to express my sincerest gratitude to my supervisor, Dr Truong Quang Thong for his valuable guidance and helpful comments during the course of my study I also would like to thank all of my lecturers at Faculty of Banking and Finance, University of Economics Hochiminh City for their English program, knowledge and teaching during my master course at school I would like to specially express my thanks to my classmates, my friends for their support and encouragement Special thanks should go to my family for their love and support during my life ABSTRACT The study looks at the relationship between macroeconomic factors and and stock market, and determined whether inflation, movements in exchange rate, interst rate have an effect on stock market return volatility in Vietnam The Generalised Autoregressive Conditional Heteroskedascity (GARCH) models are used in establishing the relationship between these variables and stock market volatility The results confirms presence of GARCH (1,1) effect on stock return time series of Vietnam stock market It is also found that there is strong and positive relationship between inflation and stock market return volatility It means that an increase in inflation leads to an increase in stock market return volatility in the long run However, there is no enough proof to conclude that change in interest rate and exchange rate can influence market return volatility Keywords: volatility, leverage, interest rate, inflation, exchange rate, returns, Hochiminh Stock Exchange i Table of contents  CHAPTER Introduction 1.1 Introduction 1.2 Research problem 1.3 Research objectives .3 1.4 Research methodology and scope .3 1.5 Structure Of The Study CHAPTER Literature review 2.1 Introduction 2.2 ARCH and GARCH model 2.2.1 Autoregressive Conditional Heteroskedasticity (ARCH) 2.2.2 Generalized (GARCH) Autoregressive Conditional Heteroskedasticity 2.3 The impact of macroeconomic variables on stock market volatility 2.3.1 Inflation .10 2.3.2 Interest rate 11 2.3.3 Exchange rate .13 2.4 Application of Garch model in Vietnam 14 2.5 Conclusion 15 CHAPTER Research Methodology 16 3.1 Introduction .16 3.2 Research data and construction of variables: 16 3.2.1 Research data 16 ii 3.2.2 Construction of variables for the models: 23 3.3 DF unit root test: 25 3.4 Hypotheses and empirical models .26 3.4.1 Model 1: The standard GARCH (1,1) model .26 3.4.2 Applying GARCH (1,1) models to find out the impact of macroeconomic variables on stock return volatility 27 3.5 Conclusion 28 CHAPTER Empirical Results of the Research 29 4.1 Introduction .29 4.2 Descriptive statistics 29 4.3 DF unit root test 30 4.4 Correlation Matrix of the variables 30 4.5 Emprical result of model 31 4.5.1 Model 1: Standard GARCH (1,1) .31 4.5.2 Model .32 4.5.3 Model .34 4.5.4 Model .35 4.5.5 Model .37 CHAPTER Conclusions, Limitations and recommendations .39 5.1 Introduction 39 5.2 Conclusions and Implications 39 5.3 Limitations and recommendations: 40 REFERENCES .42 APPENDIX 45 iii Descriptive Statistics of variables .45 Monthly CPI from 2000 – 2010 (Source: GSO) .47 Unit root test .48 Data 50 Figures  Figure 3.1 The performance of VN-Index from 07/2000 – 12/2010 .17 Figure 3.2 Inflation in Vietnam and selected countries 2000 - 2009 .19 Figure 3.3 Vietnam‟s nominal exchange rate (VND/USD) and inflation rate 1992-2010 20 Tables  Table 3.1 Vietnam exchange rate arrangement 2000 - 2010 22 Table 4.1 Descriptive statistics of variables (07/2000 – 12/2010) 29 Table 4.2 ADF UNIT ROOT TEST 30 Table 4.3 Correlation Matrix of the variables 31 Table 4.4 Result of model 31 Table 4.5 Result of model 33 Table 4.6 Result of model 34 Table 4.7 Result of model 36 Table 4.8 Result of model 37 iv Glossary CPI: consumer price index SBV: State Bank of Vietnam GARCH: Generalized AutoRegressive Conditional Heteroskedasticity ARCH: Autoregressive Conditional Hetroskedasticity GDP: Gross Domestic Product HOSE: Hochiminh Stock Exchange CHAPTER Introduction 1.1 Introduction Stock return volatility refers to the variation in stock price changes during a period of time Normally investors and agents perceive this variation as a measure of risk The policy makers use estimate of volatility as a tool to measure the vulnerability of the stock market Since understanding the nature of stock market volatility gives important implications for policy makers and investors, movements in stock prices volatility have been the central variable of many researches There have been numerous of studies trying to answer an interesting question: what are the factors that derive stock market volatility Researchers have analyzed the relative importance of economy-wide factors, industry-specific factors, and firm-specific factors stock volatility One of the earliest studies was of Officer (1973) which related changes in stock market volatility to changes in real economic variables He noted that variability in stock prices was unusually high during the period of great depression i e 1929-1939 compared with pre-and post-depression periods Schwert (1989) was a classic study which intended to verify Officer‟s (1973) findings and explored the relationship between stock prices volatility and macroeconomic variables This issue has been studied by numerous researches and their findings are not the same Many papers of Engle and Rangel (2005), Campbell (1987) and Shanken (1990)…confirmed that macroeconomic factors had significant effect on stock market volatility Contrary to this, Davis and Kutan (2003), Schwert (1989) evidenced that macroeconomic variables had weak predictive power for explaining variability of stock market prices and returns volatility Inconsistent results depend on different characteristics of every countries as well as different time periods Since ARCH model was proposed by Engle (1982) and generalized by Bollerslev (1986) and Taylor (1986), the models have been proved to be sufficient in capturing properties of time varying stock return volatility Literatures have found evidence in support the capability of GARCH models in volatility estimation as well as volatility forecast Vietnam stock market was newly established in 2000 in Ho Chi Minh City on 28 July 2000 (Hochiminh Stock Exchange – HOSE) In the first trading session there were only two stocks with a total market capitalization of 270 billion VND Although the market has significantly grown over ten years of operation (until at the end of 2010), it is still rather small and incomplete in comparison to other stock markets in the Asian region Moreover, interest rate, inflation, exchange rate and stock market are hot subjects attracting attention of the government, investors and corporations in recent years Relationship among these macroeconomic variables as well as their effect on stock market has been discussed every day In fact, in Vietnam, inflation, interest rate and exchange rate impact on stock market? Can we measure this impact? 1.2 Research problem Research and practice have proved the important role of macroeconomic variables on the economy Stock market volatility is known as one of the most important phenomena that determine the amount of risk faced by investors The impact of macroeconomic factors on stock market including market volatility is a major question to be posed and tested in many countries around the world However, as far as the author is concerned, in Vietnam there were not many researches exploring this issue In addition, unlike the stock market in the developed countries, Vietnam's stock market is not really operating under the law of supply and demand but it is influenced by herb behavior and "crowd effect" Therefore, no one can confidently confirm that changes in macroeconomic factors impact to the entire stock market Moreover, inflation, exchange rate, interest rate and stock market are hot topics in recent years As the importance of volatility as a proxy of risk, the advantages of GARCH family and Vietnam stock market‟s particular situation mentioned above, the paper chooses to study the impact of inflation, exchange rate and interest rate to stock market volatility by applying GARCH models My study will try to answer the following questions: What macroeconomic determinants of stock market volatility in Vietnam are? And how they specifically affect the stock market? 1.3 Research objectives The main purpose of this study is to identify factors that impact stock market conditional volatility using the data from Hochiminh Stock Exchange The present study contributes to the literature in three ways Firstly, the present study will shed some light on the depth of the stock market activities especially in emerging market in addition to identifying and relating the changes in economic factors to the changes in stock market movements It is necessary to have more and more researches about Vietnam stock market so that we can understand and develop our immature stock market Secondly, the findings of this investigation should enable the investors to know about stock market volatility as a measure of risk and make their decision Finally, the study will help the policy makers in seeing the effect of their policy to stock market and choosing in which way they should adjust their policy 1.4 Research methodology and scope To achieve the above mentioned objectives, the author employs quantitative research by using data of Hochiminh Stock Exchange Index (VNIndex), inflation, 39 CHAPTER Conclusions, Limitations and recommendations 5.1 Introduction Based on empirical results presented in Chapter 4, I would like to summarize the main conclusions and implications of this research The limitations and recommendations for further researches are also included 5.2 Conclusions and Implications This study aims to examine the impact of inflation, interest rate and exchange rate on stock market return volatility The dependent variable is monthly Hochiminh stock market return and the independent variables are inflation, interest rate and exchange rate The study employed five models based on GARCH (1,1) Model one without variables is standard GARCH (1,1) and remaining models incorporated with independent variables The study period is from 08/2000 until 12/2010 Data for the estimation of GARCH (1,1) models was obtained from General Statistics Office of Vietnam, IMF and Hochiminh stock exchange official website Firstly, results show evidence of time varying volatility in stock market returns and effect of GARCH effect of Vietnam stock market This is consistent with finding of previous researches as Vuong Quan Hoang (2002), A Farber, Nguyen V.H and Vuong Q.H (2006) and Manh Tuyen Tran (2009) The difference between this research and other last studies is that the author uses monthly data instead of daily data Moreover, the period time for testing is much longer than older papers – this is the strength of my research Secondly, it is clearly that the inflation significantly and positive impacts on conditional variance of stock market return The finding suggests that the higher rate of inflation is, the greater stock market volatility becomes, that is, higher rate of inflation 40 is coincident with greater stock market risk This is consistent with the previous researches in other developing countries Thirdly, we not find enough evidence to conclude that interest rate change, exchange rate change have impacts on conditional variance of stock market return The reasons led to this result are that maybe interest rate and exchange rate are official figures that did not reflect all sensitive change in market or monthly data is not suitable Implications: This study provides better understanding on the depth of the stock market activities by identifying the changes in economic factors with the changes in stock market movements For investors, this investigation should enable the investors to know more about stock market volatility as a measure of risk and make their decision They should therefore pay attention to macroeconomic factors, especially for inflation as a mean for choosing and adjusting their investment The study will help the policy makers in seeing the effect of their policy to stock market and choosing which way they should adjust their policy Academics-wise, this is the first step to explore the relationship between macroeconomic factors and stock market return volatility It is suggested base on this study that other researchers can use other data and periods of time to study other the macroeconomic determinants of stock market volatility My expectation is that there will have more further researches relating to this topic 5.3 Limitations and recommendations: Although I try my best, my study still has some limitations: Firstly, the research just uses data of Hochiminh stock market and not consider data of Ha Noi Stock Exchange (HNX) 41 Secondly, this paper is analyzed based on monthly data and results reflect that there is no relationship between stock market return volatility and interest rate as well as exchange rate Maybe daily data is more suitable For future researches, I recommend the following subjects: - Will this result conducting by data of HOSE be consistent with data of Ha Noi Stock Exchange (HNX)? - What are other determinants of stock market return volatility such as other macroeconomic variables or corporation characteristics? - Can we find any relationship between stock market return volatility and exchange rate, interest rate by changing methods of data collecting such as daily? - How these variables effect to individual stocks and to sectorial portfolios? 42 REFERENCES Adjasi, C., Harvey, S K., & Coast, C (2008) EFFECT OF EXCHANGE RATE VOLATILITY ON THE GHANA STOCK EXCHANGE Banking, 3(3), 28-47 Affairs, S (2009) IMPACT OF MACROECONOMIC INDICATORS ON VIETNAMESE STOCK PRICES Finance, Aliyu, S U R (2012) Does inflation have an impact on stock returns and volatility? Evidence from Nigeria and Ghana Applied Financial Economics, 22(6), 427-435 doi:10.1080/09603107.2011.617691 Aggarwal, R, C Inclan, and R Leal, 1999 "Volatility in Emerging Markets", Journal of Financial and Quantitative Analysis 34, pp 33-55 Bahmani-Oskooee and Sohrabian (1992) Stock prices and the effect exchange rate of the dollar, Applied Economics 24, 459-464 Beltratti, A., & Morana, C (2006) Breaks and persistency: macroeconomic causes of stock market volatility Journal of Econometrics, 131(1-2), 151-177 Elsevier Binti, H., Kadir, A., Selamat, Z., Masuga, T., & Taudi, R (2011) Predictability Power of Interest Rate and Exchange Rate Volatility on Stock Market Return and Volatility : Evidence from Bursa Malaysia Finance, 4, 199-202 Bollerslev, T (1986) Generalized autoregressive conditional heteroskedasticity Journal of econometrics, 31(3), 307-327 Campbell, J Y., 1987, «Stock Returns and the Term Structure», Journal of Financial Economics 18, 373-399 Chang, H.-ling (2009) Asymmetric Price Transmissions between the Exchange Rate and Stock Market in Vietnam Finance and Economics, 23(23) Choi, J.J., Elyasiani, E., and Kopecky, K.J., 1992 “The sensitivity of bank stock returns to market, interest and exchange rate risks”, Journal of Banking Finance 16, pp 983–1004 Davis, N., & Kutan, A M (2003) Inflation and output as predictors of stock returns and volatility: international evidence Applied Financial Economics, 13(9), 693-700 Taylor & Francis 43 Engle, R F (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation Econometrica: Journal of the Econometric Society, 987-1007 JSTOR Engle, R F., & Patton, A J (2001) What good is a volatility model? 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Evidence and Analysis Umr, C (2008) Modelling Volatility Using GARCH Models : Evidence from Vietnam Africa, (2005) Gulin Vardar (2008) Effect of Interest and Exchange Rate on Volatility and Return of Sector Price Indices at Istanbul Stock Exchange, European Journal of Economics, Finance and Administrative Sciences Issue 11 (2008) Zafar, N., Urooj, F S., & Durrani, K T (2008) Interest Rate Volatality and stock Return and Volatality European Journal of Economics,Finance and Administratie Science 45 APPENDIX Descriptive Statistics of variables 20 Series: R Sample 2000M08 2010M12 Observations 125 16 Mean Median Maximum Minimum Std Dev Skewness Kurtosis 12 1.250777 0.044043 32.57549 -42.07289 12.23161 -0.222739 3.786231 Jarque-Bera Probability 4.253176 0.119243 -40 -30 -20 -10 10 20 30 80 Series: IR Sample 2000M08 2010M12 Observations 125 70 60 50 40 30 20 10 -40 -30 -20 -10 10 20 Mean Median Maximum Minimum Std Dev Skewness Kurtosis 1.086111 0.000000 24.18357 -41.93405 7.283562 -1.112734 14.69327 Jarque-Bera Probability 737.9436 0.000000 46 80 Series: ER Sample 2000M08 2010M12 Observations 125 70 60 Mean Median Maximum Minimum Std Dev Skewness Kurtosis 50 40 30 20 Jarque-Bera Probability 10 -1 30 Series: IF Sample 2000M08 2010M12 Observations 125 25 20 15 10 -1 Mean Median Maximum Minimum Std Dev Skewness Kurtosis 0.641619 0.399202 3.825871 -0.803217 0.865995 1.321997 4.982351 Jarque-Bera Probability 56.87720 0.000000 0.236140 0.091948 5.412291 -1.019314 0.715633 4.556076 28.25407 3754.165 0.000000 47 Monthly CPI from 2000 – 2010 (Source: GSO) 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 January 100.40 100.3 101.1 100.9 101.1 101.1 101.2 101.1 102.400 100.3 101.4 February 101.60 100.4 102.2 102.2 103.0 102.5 102.1 102.2 103.6 101.2 102.0 March 98.90 99.3 99.2 99.4 100.8 100.1 99.5 99.8 103.0 99.8 100.8 April 99.30 99.5 100.0 100.0 100.5 100.6 100.2 100.5 102.2 100.4 100.1 May 99.40 99.8 100.3 99.9 100.9 100.5 100.6 100.8 103.9 100.4 100.3 June 99.50 100.0 100.1 99.7 100.8 100.4 100.4 100.9 102.1 100.6 100.2 July 99.40 99.8 99.9 99.7 100.5 100.4 100.4 100.9 101.1 100.5 100.1 August 100.10 100.0 100.1 99.9 100.6 100.4 100.4 100.6 101.6 100.2 100.2 September 99.80 100.5 100.2 100.1 100.3 100.8 100.3 100.5 100.2 100.6 101.3 October 100.10 100.0 100.3 99.8 100.0 100.4 100.2 100.7 99.8 100.4 101.1 November 100.90 100.2 100.3 100.6 100.2 100.4 100.6 101.2 99.2 100.6 101.9 December 100.10 101.0 100.3 100.8 100.6 100.8 100.5 102.9 99.3 101.4 102.0 Last month = 100% 48 Unit root test + STOCK RETURN Null Hypothesis: R has a unit root Exogenous: Constant Lag Length: (Automatic based on SIC, MAXLAG=12) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -7.047958 0.0000 Test critical values: 1% level -3.483751 5% level -2.884856 10% level -2.579282 *MacKinnon (1996) one-sided p-values + EXCHANGE RATE Null Hypothesis: ER has a unit root Exogenous: Constant Lag Length: (Automatic based on SIC, MAXLAG=12) tStatistic Augmented Dickey-Fuller test statistic 1% Test critical values: level 5% level 10% level *MacKinnon (1996) one-sided p-values + INFLATION 11.27080 3.483751 2.884856 2.579282 Pro b.* 0.00 00 49 Null Hypothesis: IF has a unit root Exogenous: Constant Lag Length: 12 (Automatic based on SIC, MAXLAG=12) tStatistic Augmented Dickey-Fuller test statistic 1% Test critical values: level 5% level 10% level 2.632604 3.500669 2.892200 2.583192 Pro b.* 0.09 01 *MacKinnon (1996) one-sided p-values + INTEREST RATE: Null Hypothesis: IR has a unit root Exogenous: Constant Lag Length: (Automatic based on SIC, MAXLAG=12) tStatistic Augmented Dickey-Fuller test statistic 1% Test critical values: level 5% level 10% level *MacKinnon (1996) one-sided p-values 6.547614 3.483751 2.884856 2.579282 Pro b.* 0.00 00 50 Data Column1 Official exchange rate (VND/USD) month deposit interest rate (%) VN Index M7 2000 14093 3.540 101.5 M8 2000 14121 3.540 115.2 M9 2000 14215 3.540 120.7 M10 2000 14378 3.720 140.8 M11 2000 14499 3.720 168.7 M12 2000 14514 4.240 206.8 M1 2001 14546 5.400 245.8 M2 2001 14565 5.160 252.4 M3 2001 14545 5.160 269.3 M4 2001 14567 5.400 321.1 M5 2001 14662 4.680 404.3 M6 2001 14845 4.800 500.3 M7 2001 14941 4.800 422.5 M8 2001 14994 5.400 277.4 M9 2001 15003 5.400 244.5 M10 2001 15033 5.900 260.3 M11 2001 15068 5.850 288.5 M12 2001 15084 5.700 235.4 M1 2002 15117 5.850 207.6 M2 2002 15192 5.850 191.1 M3 2002 15250 6.000 200.2 M4 2002 15249 6.390 208.5 M5 2002 15261 6.390 207.1 M6 2002 15321 6.390 202.1 M7 2002 15321 6.540 197.6 M8 2002 15331 6.780 191.7 M9 2002 15347 6.780 182.1 M10 2002 15364 6.780 177.6 M11 2002 15385 6.780 177.9 M12 2002 15403 6.840 183.3 M1 2003 15411 6.780 172.4 M2 2003 15415 6.840 164.6 M3 2003 15443 6.990 145.4 M4 2003 15459 6.990 152.5 M5 2003 15476 6.990 152.2 M6 2003 15499 7.140 152.3 51 M7 2003 15517 7.140 146.3 M8 2003 15522 6.560 142.7 M9 2003 15557 6.120 139.3 M10 2003 15645 5.970 136.2 M11 2003 15630 5.970 163.9 M12 2003 15646 5.970 166.9 M1 2004 15696 5.970 214.3 M2 2004 15758 5.970 260.6 M3 2004 15724 5.970 277.4 M4 2004 15721 5.970 264.4 M5 2004 15745 5.970 252 M6 2004 15723 5.970 249.7 M7 2004 15752 5.970 238.4 M8 2004 15764 6.210 232.4 M9 2004 15755 6.480 233.2 M10 2004 15748 6.510 232.6 M11 2004 15762.5 6.525 229.6 M12 2004 15777 6.540 239.3 M1 2005 15832 6.540 233.3 M2 2005 15803 6.540 235.1 M3 2005 15823 6.540 246.5 M4 2005 15832 7.200 246.2 M5 2005 15851 7.200 244.2 M6 2005 15857 7.200 246.8 M7 2005 15884 7.200 245.5 M8 2005 15878 7.200 254.5 M9 2005 15895 7.530 289.3 M10 2005 15905 7.530 307.4 M11 2005 15916 7.530 311.3 M12 2005 15916 7.530 307.5 M1 2006 15922 7.530 312.3 M2 2006 15910 7.650 390.6 M3 2006 15927 7.650 503.6 M4 2006 15934 7.650 595.5 M5 2006 15959 7.530 538.9 M6 2006 15996 7.650 515.6 M7 2006 16007 7.650 422.4 M8 2006 16014 7.650 491.2 M9 2006 16055 7.650 526.7 52 M10 2006 16083 7.650 511.5 M11 2006 16089 7.650 633 M12 2006 16054 7.650 751.8 M1 2007 16036 7.680 1041.3 M2 2007 15990 7.680 1137.7 M3 2007 16024 7.650 1071.3 M4 2007 16047 7.650 923.9 M5 2007 16087 7.650 1081.5 M6 2007 16125 7.440 1024.7 M7 2007 16147 7.440 908 M8 2007 16270 7.440 908.4 M9 2007 16105 7.440 1046.9 M10 2007 16100 7.440 1065.1 M11 2007 16125 7.200 972.4 M12 2007 16114 7.200 927 M1 2008 16091 7.200 844.1 M2 2008 16050 8.970 663.3 M3 2008 15960 11.190 516.9 M4 2008 15967 11.520 522.4 M5 2008 16086 13.250 414.1 M6 2008 16514 16.635 399.4 M7 2008 16495 16.890 451.4 M8 2008 16495 17.160 539.1 M9 2008 16517 16.920 456.7 M10 2008 16511 15.240 347.1 M11 2008 16481 10.020 314.7 M12 2008 16977 7.770 315.6 M1 2009 16978 6.990 303.2 M2 2009 16972 6.540 245.7 M3 2009 16954 7.110 280.7 M4 2009 16937 7.170 321.6 M5 2009 16938 7.320 411.6 M6 2009 16953 7.500 448.3 M7 2009 16967 7.620 466.8 M8 2009 16974 7.950 546.8 M9 2009 16991 8.130 580.9 M10 2009 17010 8.400 587.1 M11 2009 17956 9.960 504.1 M12 2009 17941 10.230 494.8 53 M1 2010 17941 10.230 482 M2 2010 18544 10.230 496.9 M3 2010 18544 10.320 499.2 M4 2010 18544 10.965 542.4 M5 2010 18544 11.175 507.4 M6 2010 18544 11.223 507.1 M7 2010 18544 11.100 493.9 M8 2010 18932 11.100 455.1 M9 2010 18932 11.090 454.5 M10 2010 18932 11.000 452.6 M11 2010 18932 12.530 451.6 M12 2010 18932 13.760 484.7 (Source: IFS and Hochiminh stock exchange website) ... Model The impact of inflation on stock market returns volatility is investigated through the estimation of equation (4) The coefficient of inflation in GARCH (1,1) measures the effect of inflation... economy Stock market volatility is known as one of the most important phenomena that determine the amount of risk faced by investors The impact of macroeconomic factors on stock market including... Measuring the Impact of Inflation on Conditional Stock Market Volatility in Pakistan : An Application of IGARCH Model Finance and Economics, 13(13) 44 Sadorsky, P (2003) The macroeconomic determinants