MINISTRY OF EDUCATION AND TRAINING HO CHI MINH CITY OPEN UNIVERSITY LE DINH NGHI RETURN AND VOLATILITY SPILLOVER AMONG STOCK MARKETS A DISSERTATION SUBMITTED FOR THE DEGREE OF DOCTOR OF PHYLOSOPHY MAJOR: BUSINESS ADMINISTRATION Ho Chi Minh city, 2019 i The dissertation was completed at: HO CHI MINH CITY OPEN UNIVERSITY Academic advisors: Assoc prof Nguyen Minh Kieu, PhD Reviewer 1: …………………………………………………………………… Reviewer 2: …………………………………………………………………… This dissertation will be presented at the dissertation committee at Ho Chi Minh City Open University Ho Chi Minh City, …………………………… , 2019 This dissertation can be found at ………… CHAPTER INTRODUCTION 1.1 Introduction 1.2 Research context and rationale of the study 1.2.1 The Vietnamese Economy and Vietnamese Stock Market More than thirty years after the reforms called doi moi (renovation) from 1986, the Vietnamese economy become more integrated into the world economy, by becoming a member of Free Trade Agreements (FTA), increasing import - export and Foreign Direct Investment (FDI) activities A stock market is a financial market with an important role in the economy Influencing by being more integrated economy, Vietnam’s stock market becomes more closely related to world financial markets The US is the world's largest economy, and Japan and Korea are large economies in Asia, so they can affect other countries, including Vietnam Hence, Vietnam’s stock market may be affected by the US, Japanese and Korean stock markets Because of this, investigating the influence of the US, Japanese and Korean stock markets on Vietnamese stock market is needed to help investors and policy makers have information to support their investment decisions 1.2.2 Research context Investment decision, a part of financial management, is one of the important managerial decisions Stock market is an investment channel where company and investors can invest to gain the benefits Hence, a stock market is a financial market with an important role in the economy Stock returns and risks are the factors that are the most considered by investors in their investment decisions Stock returns can be measured by price growth rate Risk can be proxied by volatility, measured by standard deviation of stock returns around the mean and can be estimated by General Autoregressive Conditional Heteroskedasticity (GARCH) model (Tim Bollerslev 1986) In this era of globalization, the financial systems of countries may be linked Hence, research on relationships between stock markets help both investors and policy makers obtain suitable information for making their decisions Spillover is a result of the interdependence among market economies This interdependence means that shocks, whether of global or local nature, can be transmitted across countries because of their financial linkages (Abou-Zaid, 2011) Therefore, research on return and volatility spillovers among stock markets plays an important role to help investors and policy makers have information for their investment decisions Although spillover effects among stock markets have been confirmed in many studies, such as Ng (2000), Miyakoshi (2003), Ergun & Nor (2010), Sakthivel, Bodkhe, & Kamaiah (2012), Kharchenko & Tzvetkov (2013), Nishimura, Tsutsui, & Hirayama (2015), Yarovaya, Brzeszczyński, & Lau (2016), Bahadur, Kothari, & Thagurathi (2016), Jebran, Chen, Ullah, & Mirza (2017), Ishfaq & Rehman (2018), to the best of our knowledge, the previous literature has not explored return and volatility spillovers from developed markets to the Vietnamese stock market Hence, this dissertation aims to test the return and volatility spillovers from the US, Japanese and Korean markets to the Vietnamese stock market In reality, short- and long-term investors may have different considerations Short-term investors focus more on the relationship at higher frequencies, that is, short-term fluctuations, whereas long-term investors focus on the relationship at lower frequencies, i.e., long-term fluctuations (Gradojevic, 2013) Hence, long- and short-term return and volatility spillovers should be analyzed separately to reveal more precise information for different investors Frequency-domain analysis, i.e., spectral analysis, can be used in this situation Despite its usefulness, frequency domain research is relatively scarce in the empirical economics and finance literatures (Gradojevic, 2013) Few studies used this approach to investigate the spillover among stock markets To our best knowledge, only Gradojevic (2013) investigated return spillover among five regional stock exchanges (Serbia, Croatia, Slovenia, Hungary, and Germany) in the frequency domain; and volatility spillover analysis using frequency domain approach was not previously reported in the literature Therefore, applying frequency domain approach to analyze spillover effects is needed to fulfill the theoretical gap and provide more precise information for both long- and short-term investors 1.2.3 Rationale of the study From above analysis, this dissertation entitled “Return and volatility spillover among stock markets” Using frequency domain analysis, this study examines return and volatility spillovers from the U.S., Japanese and Korean stock markets to the Vietnamese stock market The results will confirm the relationship among stock markets at different frequencies, and help short- and long-term investors obtain more precise information to support their investment decisions 1.3 Research problem This dissertation investigates the relationship among stock markets In particular, this study examines return and volatility spillovers from the US, Japanese and Korean markets to the Vietnamese stock market 1.4 Research aim and research question 1.4.1 Research aim - Testing the return and volatility spillovers from the U.S., Japanese and Korean stock markets to the Vietnamese stock market - Using frequency domain analysis to test the return and volatility spillovers among stock markets at different frequencies 1.4.2 Research question This study aims to answer the following research questions: - Are there return spillovers from the U.S., Japanese and Korean stock markets to the Vietnamese stock market? - Are there volatility spillovers from the U.S., Japanese and Korean stock markets to the Vietnamese stock market? - Are the statistical test values of return spillovers from the U.S., Japanese and Korean stock markets to the Vietnamese stock market not the same at different frequencies? - Are the statistical test values of volatility spillovers from the U.S., Japanese and Korean stock markets to the Vietnamese stock market not the same at different frequencies? 1.5 Research data and Research Method Daily data from the Standard & Poor’s 500 (S&P 500) Composite Index, the Nikkei 225, KOSPI and the Vietnam Stock Index (VN-Index); a proxy for the US, Japanese, Korean and Vietnamese stock indices from January 1, 2012, to December 31, 2015, is collected from Thomson Reuters Datastream The quantitative is applied in this study The GARCH model (Bollerslev, 1986) is used to estimate volatilities in these stock markets, the Granger Causality Test (Granger, 1969) is used to examine return and volatility spillovers, and the test for causality in the frequency domain (Breitung & Candelon, 2006) is used to examine return and volatility spillovers at different frequencies 1.6 Contributions of the study 1.6.1 Theoretical contribution In this era of globalization, research on return and volatility spillovers among stock markets plays an important role that provide information to investors and policy-makers Hence, there are many studies investigating return and volatility spillovers among stock markets However, most previous literatures could not analyze spillover effects at different cycles Few studies use frequency domain approach to investigate the return spillover among stock markets Especially, to our best understanding, volatility spillover analysis using frequency domain approach were not previously reported in literature Hence, using frequency domain approach, this study analyzes return and volatility spillovers at different cycles The results offer deep insight into spillover effects among stock markets at different frequencies This is the theoretical contribution of this study Moreover, although spillover effects among stock markets have been confirmed in many studies, to our best knowledge, the previous literature has not explored return and volatility spillovers from developed markets to the Vietnamese stock market Hence, this study analyzes the return and volatility spillovers from the developed markets to the Vietnamese stock market The results offer the relationship between Vietnamese stock market and developed markets 1.6.2 Empirical contributions Research on relationships between stock markets help both investors and policy makers obtain suitable information for making their decisions In particular, market index in Vietnamese stock exchange can be determined by the US, Japanese and Korean market indices if they are fully integrated In this case, investors and policy makers from Vietnam should follow information and fluctuations in overseas markets when making their corresponding decisions However, if the Vietnamese stock market not move together with foreign markets, then foreign investors will benefit from the reduction in the portfolio risk, by diversification that includes domestic stocks Moreover, the results also provide more suitable information for short- and long-term investors In particular, short-term investors and long-term investors can make their decisions based on spillover effects among stock markets at high frequencies and low frequencies, respectively 1.7 Dissertation structure To fulfill above mentioned research objectives, the dissertation is structured as follows: Chapter 1: Introduction, Chapter 2: Theoretical Frameworks, Chapter 3: Research methodology, Chapter 4: Data Analysis and Research Results, Chapter 5: Conclusions CHAPTER THEORETICAL FRAMEWORKS 2.1 Introduction 2.2 Return The return 𝑟𝑡 is computed using the following equation (Campbell, Lo, & MacKinlay, 1997): 𝑟𝑡 = 𝑙𝑛 𝑃𝑡 𝑃𝑡−1 where 𝑃𝑡 is the market index at time t, 𝑙𝑛(𝑥) is the natural logarithm of 𝑥 2.3 Volatility 2.3.1 Definition Volatility is a measure of the dispersion in a probability density (Alexander, 2001) The most common measure of dispersion is the standard deviation of a random variable Accordingly, the higher the volatility, the higher the stock risk 2.3.2 Constant and time–varying volatility Constant volatility models only refer to the unconditional volatility of a returns process Time–varying volatility models describe a process for the conditional volatility Generalized Auto Regressive Conditional Heteroscedastic (GARCH), proposed by Bollerslev (1986), is a useful model to measure time–varying volatility 2.3.3 Volatility Model The conditional mean and variance of 𝑟𝑡 are represented below (Tsay, 2005)  t  Ert | Ft 1  ,  t  Var rt | Ft 1   E rt  t  Ft 1  where 𝐹𝑡−1 denotes the information set available at time 𝑡 − Typically, 𝐹𝑡−1 consists of all linear functions of the past returns 2.3.4 The Auto Regressive Conditional Heteroscedastic (ARCH) model The ARCH model can be used for volatility modeling, proposed by Engle (1982), as below: at   t  t  t2    1at21    m at2m where  t  is a sequence of independent and identically distributed (iid) random variables with mean zero and variance 1,   and  i  for i  2.3.5 The Generalized Auto Regressive Conditional Heteroscedastic (GARCH) model Bollerslev (1986) proposes a useful extension of ARCH model, known as the generalized ARCH (GARCH) model For a return series 𝑟𝑡 , let at  rt  t be the innovation at time t Then 𝑎𝑡 follows a GARCH(m, s) model if at   t t , m s i 1 j 1  t2     i at2i    j t2 j where  t  is a sequence of independent and identically distributed random variables with mean and variance 1,   ,  i  ,  j  , and max( m ,s )   i 1 i   i   Here it is understood that i  for 𝑖 > 𝑚 and  j  for 𝑗 > 𝑠  t is often assumed to be a standard normal or standardized Student-t distribution or generalized error distribution Above equation reduces to a pure ARCH(m) model if s=0 2.4 Spillover Spillover is a result of the interdependence among market economies This interdependence means that shocks, whether of global or local nature, can be transmitted across countries because of their financial linkages (Abou-Zaid, 2011) The transmission in return and volatility are called return spillover and volatility spillover, respectively 2.5 Literature review about return and volatility spillover Engle (1982) proposes ARCH model that modified to GARCH model by Bollerslev (1986) Based on these models, many studies estimate the volatility and test return and volatility spillover among stock markets, such as Hamao, Masulis, & Ng (1990), Ng (2000), Miyakoshi (2003), Ergun & Nor (2010), Sakthivel, Bodkhe, & Kamaiah (2012), Kharchenko & Tzvetkov (2013), Nishimura, Tsutsui, & Hirayama (2015), Yarovaya, Brzeszczyński, & Lau (2016) In Vietnam, Vương Quân Hoàng (2004) is the first study investigating volatility in the Vietnamese stock market After that, some research their studies about volatility in Vietnamese stock market, such as Nguyễn Thu Hiền & Lê Đình Nghi (2010) and Nghi (2012) However, to our best knowledge, the previous literature has not explored spillover effects from developed markets to the Vietnamese stock market 2.6 Time domain and frequency domain 2.6.1 Introduction Frequency is the number of occurrences of a repeating event per unit time In other words, the number of cycles per unit of time is called the frequency Most econometrics techniques, including regression, ARCH, GARCH models, Granger Causality Test, analyze data in time domain However, using time domain analysis, it’s hard to explore different frequency components in financial time series Therefore, frequency domain analysis is needed to analyze financial data at different frequencies 2.6.2 Frequency-domain representation of the data Time series can be represented in frequency domain The frequency domain representation 𝑋(𝑓) of time series data 𝑥(𝑡) is called spectrum of 𝑥(𝑡) Therefore, it’s easy to discover different frequency components of financial data based on spectrum 𝑋(𝑓) of time series 𝑥(𝑡) 2.6.3 Fourier transform The Fourier transform is a method that transfer data from time domain to frequency domain and vice versa 2.7 Frequency domain analysis By transferring data between time domain and frequency domain, frequency domain method can analyze financial data at different frequencies This method is also useful in causality analysis Causal relations in the frequency domain were first proposed by Granger (1969) Then, some other methods were developed by Geweke (1982), Hosoya (1991), and Breitung and Candelon (2006) The Breitung and Candelon (2006)’s approach is used in this study 23 Hypothesis H0 Nikkei 225 volatility not Granger cause VN-Index volatility Frequency Cycles Test Statistic ω Conclusion 10% significance level 𝑻= 𝟐𝝅 𝝎 𝛘𝟐 (days) 1.3967 1.4597 H0 not rejected 1.7433 1.4885 H0 not rejected 2.0900 1.5031 H0 not rejected 2.4367 1.5109 H0 not rejected 2.7833 1.5148 H0 not rejected 3.1300 1.516 H0 not rejected Source: author’s calculation Table 4.8 shows the volatility spillover from Japanese stock market to Vietnamese stock market The results show that the statistical test values (χ2 distribution values) are not the same at different frequencies and support the hypothesis that causality is not the same at different frequencies (Granger and Lin 1995) However, these differences are small, and null hypothesis is not rejected at all frequencies Therefore, volatility spillovers from Japanese to Vietnamese stock markets are not found in both short- and long-term Table 4.9 shows the volatility spillover from Korean stock market to Vietnamese stock market The frequency-domain approach provides more information than a traditional Granger-causality test Although the results from a time-domain analysis indicate that, at the 5% significance level, there is significant volatility spillover from Korean stock market to the Vietnamese stock market, the frequency-domain approach shows that this conclusion is only true at high frequencies In particular, at cycles less than or equal to nine days, the null hypothesis is rejected, but at cycles longer than nine days, the null hypothesis is not rejected Therefore, short-term investors (cycles less than or equal to nine days) should take note of the KOSPI volatility to obtain more information for their investment decisions, but it is not necessary for long-term investors (cycles longer than nine days) investors 24 Table 4.9: Volatility Spillover from Korean to Vietnamese Stock Markets in the Frequency Domain Hypothesis H0 KOSPI volatility not Granger cause VN-Index volatility Frequency Cycles Test Statistic Conclusion 𝛘𝟐 5% significance level ω 𝑻= 𝟐𝝅 𝝎 (𝛘𝟐 = 𝟓 𝟗𝟗) (days) 0.0100 628 4.16145630479399 H0 not rejected 0.3567 18 4.36257440301112 H0 not rejected 0.7033 8.10960973257027 H0 rejected 1.0500 10.4353926431105 H0 rejected 1.3967 10.9876338667918 H0 rejected 1.7433 11.1274630243396 H0 rejected 2.0900 11.1686610544428 H0 rejected 2.4367 11.1818497128015 H0 rejected 2.7833 11.1860656830685 H0 rejected 3.1300 11.1870589701376 H0 rejected 4.8 Research result discussion The results show significant return spillover from the US to the Vietnamese stock markets This result is consistent with reality because the US is the world's largest economy, so it can affect other countries, including Vietnam Similarity, the results also indicate significant return spillover from the Japanese to the Vietnamese stock markets at the 10% significance level and from the Korean to the Vietnamese stock markets at the 5% significance level The return spillovers from the US, Japanese and Korean stock markets to Vietnamese stock market indicate integration of the Vietnamese economy in the world economy 25 Some factors explain this relationship between markets First, Vietnam’s integration into the global economy explains the spillover effects from world markets to the Vietnamese market Second, global economic and political events can affect to all economies and make stock markets react similarly with others Third, psychology factors can also explain the spillover effects among stock markets Investors from Vietnam buy or sell their stocks based on world and regional market indices and make Vietnamese stock market move together with other markets Moreover, the results show significant volatility spillover from the US market to the Vietnamese stock market Hence, Vietnamese stock market is strongly affected by the US stock market, i.e., both return and volatility spillovers from the US to Vietnamese stock markets are significant These results are similar with previous literatures, such as Engle, Ito, & Lin (1990), Hamao et al., (1990), Ng (2000), Miyakoshi (2003), Ergun & Nor (2010), Sakthivel et al., (2012), Kharchenko & Tzvetkov (2013), Yarovaya et al., (2016) Similarly, the results also show significant return and volatility spillover from the Korean market to the Vietnamese stock market Operating in one of the largest economies in Asia, Korean stock market can affect to Vietnamese stock market Investors in Vietnam could forecast the stock returns on the Vietnamese stock exchange based on the Korean market returns Moreover, because of the significant return spillover from the Korean to the Vietnamese stock markets, the strategy among investors in the Korea of investing in stocks in Vietnam to reduce their diversifiable risk does not work Thus, investors in the Korea should find other markets to diversify their investment portfolios However, the results also show that volatility spillover from Japanese to Vietnamese markets is not found at the 10% significance level, i.e., the transmission of shocks from Japanese to Vietnamese markets is insignificant Hence, the shocks from Japanese stock market don’t affect to Vietnamese stock market Therefore, investors and policy-makers from Vietnam should pay more attention to information on the US market than Japanese market when making decisions This study extends research results by applying frequency domain approach The results show that in all cases of testing return and volatility spillovers from US, Japanese, Korean to Vietnamese stock markets, the statistical test values (χ2 − statistic) are not the same at different frequencies These results support the hypothesis that causality is not the same at 26 different frequencies (Granger and Lin 1995) However, return and volatility spillovers from the US to Vietnamese stock markets test results indicate these differences are small, and null hypothesis is rejected at all frequencies, i.e., there are significant return and volatility spillover from the US to Vietnamese stock markets at all frequencies In other words, the US market affects Vietnamese market in both short- and long-term Therefore, both short- and long-term investors should take note of the information from US for their investment decisions The evidence of the hypothesis that causality can be different for each frequency (Granger & Lin, 1995) is clearer in the results of testing the return spillover from the Japanese and Korean to the Vietnamese stock market In testing return spillover from Japanese to Vietnamese stock market in time domain, traditional Granger causality test results indicate that null hypothesis is rejected at the 10% significance level, but not at the 5% significance level Applying frequency domain approach, the results are change More particular, at the 5% significance level, the return spillover effect is not supported at all frequencies These results are consistent with results of return spillover testing using a traditional Grangercausality test However, at the 10% significance level, the conclusions changed In this case, although the results from a time-domain analysis indicate that, at the 10% significance level, there is significant return spillover from the Japanese to the Vietnamese stock market, the frequency-domain approach shows that this conclusion is only true at high frequencies (cycles less than or equal to three days) Similarly, although the results from a time-domain analysis indicate that, at the 5% significance level, there is significant return spillover from the Korean to the Vietnamese stock market, the frequency-domain approach shows that this conclusion is only true at high frequencies (cycles less than or equal to four days) Therefore, short-term investors (cycles less than or equal to four days) should take note of the KOSPI returns to obtain more information for their investment decisions, but it is not necessary for long-term investors (cycles longer than four days) investors Policy-makers from Vietnam should pay attention to short-term fluctuations from Korean stock market to make their managerial decisions These results provide evidence that the linkages between stock market returns may vary across frequency spectrum bands Thus, because time-domain causality testing may fail to fully capture such links, frequency-domain analysis should be used to gain deep insights into spillover effects among stock markets 27 Similarly, the test of volatility spillover from Japanese stock market to Vietnamese stock market show that the statistical test values (χ2 distribution values) are not the same at different frequencies However, these differences are small, and null hypothesis is not rejected at all frequencies Therefore, volatility spillovers from Japanese to Vietnamese stock markets are not found at any frequency Finally, the test of volatility spillover from Korean stock market to Vietnamese stock market provide evidence support hypothesis that causal relationships change at different frequencies In volatility spillover from the US and Japanese to Vietnamese stock markets testing, although the statistical test values (χ2 − statistic) are not the same at different frequencies, these differences are small It makes the conclusions from frequency domain be similar with conclusions from time domain However, in volatility spillover from Korean to Vietnamese stock markets testing, frequency domain analysis provides more information than time domain analysis In particular, although the results from a time-domain analysis indicate that, at the 5% significance level, there is significant volatility spillover from the Korean to the Vietnamese stock market, the frequency-domain approach shows that this conclusion is only true at high frequencies (cycles less than or equal to nine days) These results indicate that frequency-domain analysis is needed to gain deeper insight into the relationship between financial time series Therefore, this study provides evidences support the hypothesis that causality is not the same at different frequencies (Granger and Lin 1995), in case of volatility spillover analysis To our best knowledge, volatility spillover analysis using frequency domain approach were not previously reported in literature Therefore, this is the new contribution of this dissertation 28 CHAPTER CONCLUSIONS 5.1 Introduction 5.2 Main results of the study This study uses daily data from the Standard & Poor’s 500 (S&P 500) Composite Index, the Nikkei 225, KOSPI and the Vietnam Stock Index (VN-Index); a proxy for the US, Japanese, Korean and Vietnamese stock indices from January 1, 2012, to December 31, 2015, estimates volatility by GARCH model, analyzes return and volatility spillover by Granger Causality Test and applies the frequency domain approach (Breitung & Candelon, 2006) to investigate spillover effects in frequency domain The results show that there a significant return and volatility spillover from the US to Vietnamese stock markets Moreover, frequency domain analysis show that US stock market affect to Vietnamese stock market in both short- and long-term The results also show significant return spillover from the Japanese to the Vietnamese stock markets at the 10% significance level However, this is not true at the 5% significance level, which indicates that the evidence on return spillover between these markets is not clear The frequency domain results show that at the 10% significance level, return spillover from the Japanese to the Vietnamese stock markets is only significant at high frequencies, (cycles less than or equal to three days) Therefore, short-term investors should take note of the Nikkei 225 returns to obtain more information for their investment decisions, but it is not necessary for long-term investors Other results show that volatility spillover from Japanese to Vietnamese markets is not found at the 10% significance level The frequency domain approach provides the similar results Therefore, volatility in Vietnamese stock market is not affected by Japanese volatility Finally, traditional Granger Causality Test results show significant return and volatility spillover from Korean to the Vietnamese stock markets at the 5% significance level Hence, Vietnamese stock market is affected by fluctuations in Korean stock market However, frequency domain approach show that these effects only exist at high frequencies, i.e., return and volatility spillover from Korea to Vietnamese stock market are significant at cycles less than or equal to four days and nine days, respectively Therefore, short-term investors should take note of the KOSPI returns and volatility to obtain more information for their investment 29 decisions, but it is not necessary for long-term investors Vietnam’s policy-makers should follow short-term fluctuations from Korean stock exchange to make their managerial decisions 5.3 Theoretical contribution of the study In this era of globalization, the financial systems of countries may be linked Hence, more and more studies investigate return and volatility spillover among stock markets due to its practical significance and the nature of volatility itself which varies over time (Yarovaya et al., 2016) Therefore, research on return and volatility spillover from the US, Japanese and Korean stock markets to Vietnamese stock market contributes practical evidences of spillover effects from world and regional markets to developing markets In reality, short- and long-term investors may have different considerations Short-term investors focus more on the relationship at higher frequencies, that is, short-term fluctuations, whereas long-term investors focus on the relationship at lower frequencies, i.e., long-term fluctuations (Gradojevic 2013) Moreover, causality results can differ between frequency spectrum bands (Granger & Lin 1995) Although there are many studies investigated return and volatility spillover among stock markets, most of them could not analyze spillover effects at different cycles Therefore, it’s needed to analyze spillover effects at different frequencies Frequency-domain analysis, i.e., spectral analysis, can be used in this situation Despite its usefulness in natural and technical sciences, frequency domain research is relatively scarce in the empirical finance literature Although some authors their research using frequencydomain analysis, only Gradojevic (2013) investigated return spillover among five regional stock exchanges (Serbia, Croatia, Slovenia, Hungary, and Germany) in the frequency domain Especially, to our best understanding, volatility spillover analysis using frequency domain approach was not previously reported in the literature Therefore, research on return and volatility spillover in frequency domain fulfil the gap of knowledge of spillover effects at different frequencies In case of investigating return and volatility spillover among stock markets, the frequency domain test results show that the statistical test values (χ2 distribution values) are not the same at different frequencies Therefore, the significance of spillover effects could change at 30 different cycles This is an evidence that traditional Granger Causality Test should be extended to frequency domain to gain deep insights into return and volatility spillover among stock markets These results provide evidence that the linkages between stock market returns and volatility are nonlinear Therefore, the studies investigated on return and volatility spillover using traditional Granger Causality Test should be extended to frequency domain Hence, this dissertation fulfils the gap of knowledge of return and volatility spillover among stock markets and indicates that frequency domain analysis is needed to gain deep insights into return and volatility spillover among stock markets and helps short- and long-term investors obtain more information for investment decisions based on their needs In summary, although Granger Causality Test is needed, it can’t analyze spillover effects at different frequencies Therefore, this test should be extended to frequency domain to help short- and long-term investors obtain more information for investment decisions based on their needs 5.3 Managerial Implications The results show that the US market index returns and volatility are determinants that can forecast Vietnamese market returns and volatility Because the fluctuation from the US stock market could spill over to Vietnamese stock market, investors from Vietnam should follow information in the US markets when making their corresponding decisions The results also indicate that Vietnamese policy makers need to be aware of US market returns and volatility in making their managerial decisions on the Vietnamese stock market Moreover, because of the significant return and volatility spillover from the US to the Vietnamese stock markets, the strategy among investors in the US of investing in stocks in Vietnam to reduce their diversifiable risk does not work Thus, investors in the US should find other markets to diversify their investment portfolios The results also show that there are significant return spillovers from Japanese and Korean stock markets to Vietnamese in short-term Hence, Japanese and Korean market index returns are determinants that could be used by short-term investors to forecast Vietnamese market returns Moreover, policy makers in Vietnam should pay greater attention to the short-term 31 effects of the Japanese and Korean economy when making decisions about the Vietnamese stock market 5.4 New contributions of the dissertation Besides analyzing return and volatility spillover from the US, Japanese and Korean stock markets to Vietnamese stock market, this dissertation extends these results to frequency domain The results show that the spillover effects could change at different frequencies Therefore, this dissertation contributes practical evidences that support the hypothesis that causality is not the same at different frequencies (Granger & Lin, 1995) Although this hypothesis has been confirmed with different economics and financial time series in some previous studies, such as Yanfeng (2013), Chan et al., (2008), Gradojevic (2013), Ozer & Kamisli (2016), to our best knowledge, volatility spillover analysis using frequency domain approach was not previously reported in the literature Therefore, this dissertation extends volatility spillover theory to frequency domain and provides practical evidences that the relationship between economics and financial time series could change at different frequencies Hence, almost previous studies investigating volatility spillover among stock markets should be extended to frequency domain to obtain more precise information This is the contribution of dissertation 5.5 Limitations and future research 5.5.1 Limitations Although there are some new contributions, this dissertation has some limitations - This study uses S&P 500, Nikkei 225, KOSPI and VN-Index as a proxy for the US, Japanese, Korean and Vietnamese stock indices This may reduce the results reliability because S&P 500 and Nikkei 225 is calculated based on some top stocks in markets, but KOSPI and VN-Index is calculated based on all shares in stock exchanges - Operating in the world’s second largest economy, Chinese stock market is more popular in recent studies and can affect to Vietnamese stock market However, this dissertation only examines the return and volatility from the US, Japanese, Korean to Vietnamese stock markets but not investigate these effects from Chinese to Vietnamese stock markets 32 5.5.2 Future research Because this dissertation has some limitations, further research could be done: - Use the suitable data in the research on return and volatility spillover among stock markets For examples, S&P 500, Nikkei 225, KOSPI 200 and VN30 could be used as a proxy for the US, Japanese, Korean and 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