INTRODUCTION
Background and problem statement
Stock prices serve as key economic indicators that reflect the overall health and growth of the economy, making it crucial to analyze their relationship with various economic factors This study explores the long and short-run dynamics between Vietnam's VN-Index and the S&P 500 Index, as well as the influence of exchange rates between the US Dollar and the Vietnam Dong, gold prices, and crude oil prices The analysis covers a five-year period before and after the 2008 Global Economic Crisis, providing valuable insights for both government policymakers and investors.
The Standard and Poor's 500 Index (S&P 500) is a capitalization-weighted index comprising 500 stocks, aimed at gauging the performance of the U.S economy through the aggregate market value of these stocks across major industries Established with a base level of 10 during the 1941-43 periods, the S&P 500 serves as a benchmark for numerous mutual funds, exchange-traded funds, and pension funds, attracting hundreds of billions of dollars in investments Additionally, it is recognized as a leading indicator for stock price indices globally, influencing investors and trade magazines in Vietnam, who utilize the S&P 500 to predict fluctuations in the VN-Index Despite its significance, there is a lack of published research exploring the relationship between the S&P 500 and the VN-Index, with most existing analyses limited to basic graphical representations.
Exchange rates significantly influence stock prices and vice versa, making their relationship crucial for governments, multinational corporations, and investors Understanding this relationship aids governments in shaping monetary and fiscal policies, as decisions to adjust these policies can impact the stock market and domestic currency value Additionally, multinational corporations can leverage insights from this relationship to anticipate exchange rate fluctuations based on stock price changes, allowing them to manage foreign contract exposure, mitigate exchange rate risk, and stabilize earnings.
In recent years, investors have increasingly incorporated currency as an asset in their portfolios, recognizing that accurate assessments of portfolio variability can enhance their benefits Analysts suggest that understanding the relationship between currency and stock markets is crucial for predicting potential crises, such as the Asian Financial Crisis of 1997 This crisis was largely attributed to the sharp depreciation of the Thai baht, which subsequently led to declines in other regional currencies and stock markets.
Gold has become a key component in investors' strategic asset allocations due to its diversification benefits and potential as a hedge against inflation, deflation, political unrest, and currency risk Research indicates that gold often outperforms stocks and bonds, serving as a safe haven during market crashes.
The price of crude oil significantly influences a country's economic costs and is often analyzed as a key indicator of economic growth Numerous studies have explored the relationship between crude oil prices and various economic indicators and commodities, highlighting the effects of oil price fluctuations on these metrics.
2004) The results show that there are the long-run and two-way feedback relations between the crude oil prices and stock prices (Wang et al., 2010).
Research objectives
This study aims to explore the relationships between the VN-Index and various financial indicators, including the S&P 500 Index, gold prices, the US Dollar to VN Dong exchange rates, and crude oil prices Additionally, it investigates the lead-lag relationships between the VN-Index and these variables through pairwise analysis.
Research methodology
This research analyzes the co-movement of the VN-Index with the S&P 500 Index, gold prices, US Dollar - VN Dong exchange rates, and crude oil prices using various econometric and financial modeling techniques To assess short-term relationships among these variables, correlation methods are utilized Additionally, Granger causality tests are conducted to determine if changes in one variable precede those in another For long-term relationships, cointegration techniques are employed.
Research structure
The remainder of this study is structured as follows:
Chapter 3 describes the methodology employed in the study and represents the data descriptive statistics
Chapter 4 reports the empirical results
LITERATURE REVIEW
Numerous studies have explored the economic factors influencing stock prices, utilizing econometric models such as correlation, cointegration, and Granger causality to analyze relationships within international stock markets and between stock prices and other economic variables, including bond prices However, these studies often yield mixed and conflicting results due to variations in the groups of stocks examined across different regions Additionally, most research tends to focus on the relationship between stock prices and a single economic variable, with limited investigations into the interplay among stock prices, oil prices, gold prices, and exchange rates.
Research on the integration of international equity markets reveals a mixed landscape; while some studies suggest a stable correlation structure over time (Panton et al., 1976; Watson, 1980), the majority highlight significant instability in these relationships (Makridakis and Wheelwright, 1974; Maldonado and Sounders, 1981; Meric and Meric, 1989; Fischer and Palasvirta, 1990; Madura and Soenen, 1992; Wahab and Lashgari, 1993; Longin and Solnik, 1995; Kearney and Lucey, 2004) This instability is primarily influenced by real economic linkages among countries (Bodurtha et al., 1989; Campbell and Hamao, 1992; Roll, 1992; Arshanapalli and Doukas, 1993; Bachman et al., 1996; Bracker and Koch, 1999).
Kasa (1992) utilized the Engle–Granger cointegration methodology to analyze major equity markets from 1974 to 1990, revealing a single cointegrating vector that suggests a low level of market integration This finding is supported by subsequent studies, including those by Chan et al (1992), Arshanapalli and Doukas (1993), Gallagher (1995), Allen and MacDonald (1995), and Chan et al (1997), which also report similar results indicating low integration among equity markets.
Kanas (1998a) utilized multivariate trace statistics, the Johansen method, and the Bierens nonparametric approach to examine pairwise cointegration between the US market and six major European equity markets—UK, Germany, France, Switzerland, Italy, and the Netherlands—over the period from 1983 to 1996 The findings indicated a lack of pairwise cointegration, suggesting low integration levels and potential long-run diversification benefits for US investors in European markets Vo and Daly (2005b) analyzed daily return data from Asian equity market indices and advanced nations from 1994 to 2003, employing correlation, cointegration, and Granger causality tests, which revealed a weak causal relationship between Asian and developed markets, indicating diversification opportunities for Australian and US investors in Asian equities Similarly, Vo and Daly (2005a) found minimal linkages among European equity markets, suggesting potential diversification advantages However, other studies argue that long-run covariances between markets are greater than short-run covariances, implying reduced benefits of international diversification (Grubel and Fadner, 1971; Panton et al., 1976; Taylor and Tonks, 1989) In contrast, research employing the Johansen multivariate approach has reported strong integration among markets (Chou et al., 1994; Hung and Cheung, 1995; Kearney).
1998, Gilmore and McManus, 2002, Manning, 2002, Ratanapakorn and Sharma,
Research indicates that a decline in domestic currency value adversely affects aggregate domestic stock prices in both the short and long term, while currency appreciation tends to boost stock prices Conversely, an increase in aggregate domestic stock prices may lead to a temporary drop in currency value, followed by a long-term appreciation Ajayi and Mougoue (1996) found that rising stock indices in advanced economies signal economic expansion and heightened inflation expectations, which can lead to decreased foreign demand for the currency and subsequent depreciation This depreciation raises concerns about future corporate performance, resulting in lower stock prices Dimitrova (2005) highlights that the relationship between stock prices and currency is positive when equity markets experience prior changes, but negative when currency prices fluctuate first, although the empirical evidence remains weak Additionally, she notes that a joint linkage between the two markets can facilitate recovery during financial crises, where sharp currency depreciation leads to a gradual decline in stock prices, and the collapse of the stock market can trigger currency appreciation, ultimately causing stock prices to rise again.
In 2010, researchers aligned with the hypothesis of Ajayi and Mougoue (1996) but offered a distinct explanation, suggesting that fluctuations in exchange rates significantly impact international trade and, consequently, stock markets When the exchange rate declines, leading to an appreciation of the domestic currency, the cost of imports decreases This reduction allows businesses to maintain their selling prices while increasing profits, ultimately driving stock prices higher.
When domestic currency depreciates, exporters initially face decreased revenues and profits, leading to a drop in stock prices However, as the prices of domestic products become cheaper, competitiveness increases, allowing exporters to sell more volume and ultimately boost revenues While Wang et al (2010) suggest a fixed volume of sales, other researchers, such as Granger et al (2000), argue that stock price reactions to currency changes are ambiguous Their analysis of multinational corporations during the 1997 Asian Crisis indicates that the impact of currency depreciation on company value depends on whether the firm primarily imports or exports, making it challenging to predict the overall effect on stock market indices.
Recent research on gold has focused on its role as a diversifier and a hedge against inflation, highlighting its low or negative correlation with other assets and its high positive skewness Baur and Lucey (2010) found that gold serves as a hedge against stock market fluctuations and acts as a safe haven during periods of market stress, although this safe haven effect is not applicable to bonds They caution investors against holding gold for extended periods, as its protective qualities are temporary Additionally, numerous studies emphasize the importance of including gold in investment portfolios to mitigate risks and enhance returns.
Research shows conflicting views on the impact of oil price fluctuations on stock prices Mussa (2000) notes that while rising oil prices significantly reduce consumer and business confidence, the subsequent decline in stock prices is primarily driven by factors unrelated to oil.
Several authors have explored the relationship between crude oil prices and equity values, yielding varying conclusions El-Sharifa et al (2005) conducted a study using data from the oil market, while Arouri (2011) and Filis et al (2011) also contributed to this discourse, highlighting the complexity of the interactions between oil prices and stock market performance.
The United Kingdom stands as the largest oil producer in the European Union, with a consistently positive and often significant relationship between crude oil prices and share values However, research by Filis et al (2011) indicates a generally negative correlation between these two markets, irrespective of the source of oil price shocks, except during the Global Financial Crisis of 2008, when oil prices positively influenced stock prices across six oil-exporting countries.
Canada, Mexico, Brazil and Oil-importing: USA, Germany, Netherlands
Filis et al (2011) assert that the relationship between oil prices and stock markets is primarily affected by demand-side shocks, such as fluctuations in the global business cycle or geopolitical events, rather than supply-side shocks They argue that oil markets do not serve as a "safe haven" for stock markets Additionally, Arouri (2011) highlights significant variability in the strength of this relationship across different European sector stock markets, noting an asymmetry in how stock returns respond to oil price changes A particularly noteworthy study by Narayan and Narayan (2009) further explores these dynamics.
Narayan and Narayan (2009) examine the impact of the US Dollar - VN Dong exchange rate on Vietnam’s stock prices using daily data from 2000 to 2008 Their findings reveal significant relationships between Vietnam’s stock prices, oil prices, and nominal exchange rates Specifically, an increase in oil prices and a depreciation of the Vietnamese currency lead to a notable rise in Vietnam’s stock prices.
The findings reveal a discrepancy with theoretical expectations, suggesting that Vietnam's stock market is more influenced by internal and domestic factors than by rising oil prices Alongside examining the correlation between oil prices and stock prices, several studies have also analyzed the effect of oil price risk on stock returns (Sadorsky, 1999; Basher and Sadorsky, 2006; Nandha and Faffa).
2008, Fayyad and Daly, 2011) By applying a vector autoregression (Sadorsky,
1999), an international multi-factor model on emerging equity markets (Basher and Sadorsky, 2006), they find that oil price risk has significant effect on stock returns
Nandha and Faffa (2008) conducted an empirical investigation on 35 DataStream global industry indices from April 1983 to September 2005, revealing that rising oil prices negatively impact equity returns across all sectors except for mining and oil and gas industries They recommend that international portfolio investors hedge against oil price risk Additionally, Fayyad and Daly employed Vector Auto Regression (VAR) analysis using daily data from September 2005 to February 2010, focusing on seven countries: Kuwait, Oman, UAE, Bahrain, Qatar, the UK, and the USA.
METHODOLOGY
Methodology
The correlation coefficient is a key tool for assessing the relationship between two variables based on historical data, particularly in analyzing the VN-Index's correlation with the S&P 500 Index, gold prices, US Dollar - VN Dong exchange rates, and crude oil prices This analysis helps investors determine potential gains from diversifying their portfolios However, it's important to note that the correlation coefficient primarily reflects short-term relationships, which can lead to misleading conclusions, as economic variables may diverge in the short term yet converge over the long term To address this limitation, cointegration tests are utilized to identify long-term relationships between pairs of economic variables, providing a more accurate understanding of their interactions.
Cointegration is a crucial technique for analyzing whether economic and financial time series are interconnected Its relevance spans various areas of finance, where cointegration is often anticipated Consequently, the methodology has gained increasing popularity in empirical research.
The exploration of international stock market cointegration reveals that perfect market integration, indicated by cointegrated stock prices, suggests minimal benefits from international diversification Numerous studies, including those by Taylor and Tonks (1989) and Chan et al (1992), have employed cointegration techniques to assess the long-term co-movement of stock market prices and the potential advantages of international equity diversification A lack of cointegration among national stock and bond markets has been interpreted by various authors, such as Kasa (1992), as evidence of long-run gains from international portfolio diversification.
This thesis utilizes the Johansen cointegration technique to explore the relationships between the VN-Index and S&P 500 Index, as well as the US Dollar - VN Dong exchange rates, gold prices, and crude oil prices, both before and after the 2008 Global Financial Crisis The findings from this analysis have significant implications for diversification strategies, particularly for long-term investments Additionally, understanding these relationships will enable investors to predict VN-Index movements based on changes in the S&P 500 Index, US Dollar - VN Dong exchange rates, gold prices, and crude oil prices.
International investors typically seek to reduce risk through diversification by holding various products across multiple national markets This study focuses on the diversification benefits for Vietnamese investors considering these products If the VN-Index is highly correlated with other markets in the long run, the effectiveness of diversification diminishes Conversely, if the Vietnamese stock market operates independently, Vietnamese investors can achieve significant diversification benefits Therefore, assessing the integration of stock, currency, gold, and oil markets is crucial for determining the long-run diversification opportunities available to investors.
To test for cointegration, the initial step involves verifying that each series is integrated of the same order In financial markets, it is typical for most macroeconomic and financial time series to be integrated of order one, indicating they follow an I(1) process.
To ensure accurate analysis, it is crucial to determine if a time series is stationary, as using non-stationary data can result in spurious regressions When two time series exhibit trends over time, regressing one on the other may yield a high R² value, despite a lack of actual relationship, leading to misleading conclusions A time series is considered non-stationary if it contains a unit root, indicating it is integrated of order one, while its first difference is stationary, or integrated of order zero Consequently, this study employs the Dickey–Fuller (DF) and Augmented Dickey-Fuller (ADF) tests to assess the presence of a unit root, with the methodologies for these tests briefly outlined.
An AR(1) process is defined by the equation y_t = ρy_{t-1} + δx_t + ε_t, where x_t includes optional exogenous regressors, ρ and δ are parameters, and ε_t is white noise The stationarity of the series y depends on the value of ρ; if |ρ|≥1, y is non-stationary, leading to an increasing variance over time, while |ρ|