INTRODUCTION
Problem Statements
There is much attention recently on gold, partly due to the surges in its price and the increase in its economic uses
Gold has a critical position among the major precious metals Gold is not only an industrial commodity but also an investment asset which is commonly known as a
Gold is often regarded as a "safe haven" asset to mitigate the growing risks in financial markets (Baur & Lucey, 2010; Coudert & Raymond-Feingold, 2011) Despite these risks, gold prices have not only remained stable but have also surged in recent years From the onset of the financial crisis in August 2007 to December 2012, the nominal price of gold rose by an impressive 146.65%.
For central banks, gold keeps an important position in their reserve asset Its role is increasingly enhanced from 2009 when there was so much worry about the health of
Central banks are increasingly purchasing gold to reduce their reliance on the US dollar as a reserve asset, aiming to encourage borrowing and lower interest rates on loans Recent evidence indicates a strong demand for gold among central banks, with a UBS poll revealing that while about half of the officials still view the US dollar as the most important reserve asset in 25 years, 22 percent anticipate gold will take on that role Additionally, the World Gold Council reported on August 14, 2012, that central bank purchases reached a record 157.5 tonnes in the second quarter, more than doubling the 66.2 tonnes bought during the same period in 2011, and totaling 254.2 tonnes in the first half of the year, reflecting a 25% increase from the previous year.
In Q2 2012, the official sector represented 16% of total gold demand, highlighting the increasing importance of gold for central banks This trend indicates a growing reliance on gold as a strategic asset within the financial landscape.
Central banks and nations are implementing key policies to secure gold within their borders, ensuring that it remains in the hands of citizens or central banks For instance, China has banned gold exports to retain all gold entering the country, while nations capable of producing gold, like Russia and Kazakhstan, prioritize purchasing it through their central banks Additionally, the trend of gold accumulation is primarily driven by central banks in emerging and newly wealthy nations, which hold significantly smaller gold reserves compared to their counterparts in the US and Europe.
In Viet Nam, the domestic gold prices got so much fluctuation since September
In 2012, the disparity between global and domestic gold prices significantly increased, at times exceeding VND 5 million per tael, driven by high demand outpacing supply, particularly influenced by major banks This situation posed serious challenges to the exchange rate and the overall economy Khanh (2010), General Director of Sai Gon Gold and Silver ACB-SJC Joint Stock Company, noted that rising gold prices directly and indirectly affect the USD/VND exchange rate, influence the Consumer Price Index (CPI), and impact monetary policy, the stock market, and the real estate sector.
To address the gap and stabilize the gold market, which aligns with the National Assembly of the Socialist Republic of Vietnam's Resolution No 51/2010/QH12 concerning the 2011 Socio-Economic Development Plan, several significant legal documents have been issued by the Government and the State Bank of Vietnam (SBV) since 2010, including Circular No 01/2010/TT-NHNN.
On June 1, 2010, regulations were implemented to close the gold trading floor and terminate all foreign gold trading activities Subsequent measures, including Circular No 22/2010/TT-NHNN on October 29, 2010, prohibited commercial banks from converting gold into paper currency and lending for gold trading Decree No 24/2012/ND-CP, effective April 3, 2012, declared that using gold bars as a payment method would be illegal, granting the State a monopoly on gold bar production and the import/export of raw materials through the State Bank of Vietnam (SBV) Despite these regulations limiting gold supply, demand remains high, causing volatile gold prices, especially when global prices rise In response, Decision No 16/2013/QD-TTg, issued on March 4, 2013, allowed the SBV to buy and sell gold bullion as needed to stabilize prices and manage foreign reserves This decision is crucial for the SBV to effectively control gold price fluctuations, necessitating strategic timing for gold purchases from abroad to enhance reserve assets and supply.
From the above features, the paper will focus on analyzing factors (that are addressed in the literature) affecting global gold prices.
Research objectives
This paper aims to explore the connection between local and global gold prices while identifying the key variables that influence the global price of gold in both the short and long term.
9 iii To find out whether the SBV can base on the movements of global gold price while making their decision.
Research questions
This paper aims to explore the correlation between Vietnam's gold prices and global gold prices, identify the factors influencing global gold prices, and provide recommendations to the State Bank of Vietnam (SBV).
Scope of the research
This research investigates the key factors influencing global gold prices, beginning with an overview of Vietnam's gold market and its relationship to the global market It will then analyze various variables that affect global gold prices and their directional relationships, drawing on existing literature Finally, the study will estimate a model of global gold prices, incorporating the global financial crisis as a dummy variable.
Data used for the research are secondly time series for the daily period from 2007 to
2012 due to daily data for Vietnam gold prices are not adequate.
Structure of the thesis
The paper is organized as followings:
The chapter 1 explains reason why the topic of thesis is chosen, gives main objectives, major research questions and the scope of the research
Chapter 2 provides some overview of Vietnam’s gold market, the connection between Vietnam and global gold markets
Chapter 3 covers the literature about the relationship between gold price and oil prices, US dollar exchange rate, and stock market
Chapter 4 presents the research methodology, data collection h
Chapter 5 shows statistic results from adopted model Findings are analyzed to get answers for questions mentioned in the chapter 1
Chapter 6 concludes with the main findings and gives some suggestions h
OVERVIEW OF VIETNAM’S GOLD MARKET
The national gold brand of Vietnam
Since November 25, 2011, Saigon Jewelry Company Limited (SJC) has been recognized as Vietnam's official gold brand under the control of the State Bank of Vietnam (SBV) This decision is justified by SJC's dominant 90% market share in the domestic gold bullion market, its strong brand recognition in Vietnam and the Asia-Pacific region, and its status as a 100% state-owned enterprise managed directly by the Ho Chi Minh City Party Committee.
The decision has garnered widespread agreement among analysts, highlighting several key advantages Firstly, it allows the State Bank of Vietnam (SBV) to intervene more effectively when domestic gold prices experience significant fluctuations Secondly, it eliminates the "ask and grant" mechanism, streamlining the process Lastly, this approach enhances the quality control of bullion gold, ensuring higher standards in the market.
The connection of Vietnam’s gold price to global gold price
Cost price represents all expenses to have a unit of gold This value is used as the main factor in profit and loss calculation
In Vietnam, gold is primarily imported because local mining production is minimal Consequently, the price of SJC gold in the country is largely influenced by global gold prices.
PVN = [(PTG + CVC + I) x (1 + TNK): 0.82945] x E + CGC h
- PVN is the Vietnam’s gold price (VND per tael);
- PTG is the world gold price (USD per troy ounce);
- E is USD/VND exchange rate;
- CGC is processing cost (VND per tael);
Market price is the economic price for which a good is offered in the market place (Wikipedia, 2013) In addition, it is also the intersection of supply and demand
Market prices for most goods typically exceed or match their cost prices to prevent losses However, the market prices of domestic gold can differ, as they are influenced not only by global gold prices but also by factors such as demand and purchasing power.
2.2.3 The connection between domestic and global gold markets
In general, Vietnam’s gold price tends to move along with the world gold market (Cong, 2012; Hoang, 2004)
Hoang (2004) checked the connection between Viet Nam and London markets for daily sub-sample from January, 2004 to May, 2004 by using correlation coefficient
He found that the positive correlation existed
This study analyzes the relationship between SJC prices and London gold prices from January 2007 to December 2013 SJC data was sourced from Saigon Giai Phong newspaper, while London gold prices were obtained from the World Gold Council The findings are detailed in Table 2.1.
Table 2.1: Correlation matrix between SJC and London’s gold price
The result in the Table 1 showed that: r(SJC, GOLD) equals 0.99 It means that the two market have had a strongly positively correlation
In addition, the paper also takes the following hypothesis testing:
- Null hypothesis H0: ρ = 0 (there is no actual correlation);
- Alternative hypothesis H1: ρ ≠ 0 (there is an actual correlation)
And its result is included in Table 2.2
Table 2.2: Correlation testing between SJC and London’s gold price
P-value is equal 0.0000, the null hypothesis is rejected In other words, there is an evidence that a true relationship between domestic and global gold markets
The above results confirmed the conclusion of Hoang (2004)
2.2.4 The big gap still exists between the two gold markets
The disparity between domestic and global gold markets has significantly increased, at times exceeding VND 5 million per tael, particularly in 2012 To comprehend the reasons behind this persistent gap, Bank Governor Nguyen Van Binh categorized Vietnam’s gold market into three distinct phases: 2007-2009, 2009-2012, and 2012-2013.
Between 2007 and 2009, incomplete regulations on gold management led to the rapid and spontaneous establishment of gold markets During this period, official gold imports ranged from 40 to 50 tons, while gold smuggling was estimated to be around 50 tons.
The domestic gold market has seen significant instability, with frequent gold fevers prompting people to rush into buying and selling gold This speculative behavior has adversely impacted foreign exchange markets, price indices, and overall macroeconomic stability In response, the government has banned and officially terminated the operations of gold trading floors and activities involving foreign accounts.
Between 2009 and 2012, the disparity between the two markets was relatively low, although slightly higher than in the previous stage During this period, the domestic market faced instability, which negatively affected exchange rates and the macro-economy, albeit to a lesser extent.
2012-2013 stage: New legal framework has been developed and come into effected
The State maintains a monopoly on gold imports and gold bar production, resulting in tighter controls on gold smuggling During this period, the disparity between the two markets has widened significantly, yet the domestic market remains stable Speculation has diminished, eliminating the frantic buying of gold, while the phenomenon of "goldenization" in the national economy is being restrained, contributing to a stable macro-economy.
In summary, the domestic gold price showed a correlation with international gold prices during the research period However, disparities between the two markets persisted, influenced by domestic demand and supply dynamics, as well as government policies at various times.
LITERATURE REVIEW
The relationship between gold and oil prices
There are considerable studies on the relationship between gold and oil prices However, results on the relationship are still mixed
Research highlights two main channels through which oil prices influence gold prices Firstly, an increase in oil prices leads to higher inflation, causing the general price level to rise, which in turn elevates gold prices as it serves as a hedge against inflation and currency fluctuations Secondly, rising oil prices boost export revenues, particularly for oil-producing countries, which may increase their demand for gold as part of their international reserve portfolios, further driving up gold prices.
Empirically, Narayan et al (2010), via inflation channel, proved that the relationship between gold and oil prices exists in both short-run and long-run For h
A study analyzing the relationship between gold and oil prices from 1963 to 2008 in the United States revealed that rising oil prices lead to inflation, which subsequently drives up gold prices In the long run, researchers employed a structural break cointegration test by Gregory and Hansen (1996), which is more effective than the traditional Engle and Granger (1987) method due to its ability to account for regime shifts Their analysis of data from 1995 to 2009 indicated that gold and oil markets, including spot and future prices up to a 10-month maturity, are cointegrated, suggesting that fluctuations in oil prices can predict changes in gold prices and vice versa.
Harmmoudeh et al (2008) and Sari et al (2010) conducted empirical studies that contradict the findings of Narayan et al (2010), revealing that oil prices do not significantly influence gold prices in the long run, and vice versa They noted that fluctuations in gold prices are primarily driven by factors associated with the jewelry industry and interventions by central banks aimed at managing foreign reserves and exchange rates, indicating no substantial relationship with oil prices.
Malliaris et al (2011) found no evidence of cointegration between gold and oil prices using the Johansen test and Granger causality test for the period from January 4, 2000, to December 31, 2007 However, their research indicated that gold prices can effectively predict oil prices in the short run, and vice versa, when employing neural network methodology.
The relationship between gold price and US Dollar exchange rate
The relationship between gold prices and the value of the US dollar often exhibits an inverse correlation; when gold prices rise, the dollar typically declines, and vice versa This trend is largely driven by inflation concerns, as a weaker dollar heightens worries about inflation, prompting investors to seek gold as a hedge Conversely, when the dollar strengthens, gold prices generally decrease.
Relating to the relationship, Hammoudeh et al (2008) found that an increase in the price of gold has implication for a depreciation of the US dollar versus the major h
17 currencies in both short-run and long-run, not vice versa In contrast, Kim and Dilt
(2011) found that the value of dollar and the gold price has negative relationship But value of dollar has Granger-causality to gold price, not vice versa.
The relationship between gold price and stock market
There is a logical expectation of an inverse relationship between gold prices and stock prices; as stock prices rise, investors tend to earn more from the stock market, prompting them to sell their gold to reinvest in stocks This increased selling pressure on gold ultimately leads to a decrease in its price.
Smith (2001) examined the short-term and long-term relationships between four gold price series and six different US stock price indices over the 1991-2001 period
Recent studies have revealed varying relationships between stock index returns and gold returns One study found a negative Granger causality from US stock index returns to gold returns in the short run, while another study by Gilmore et al (2009) identified a unidirectional causal effect only in the short run Additionally, Wang et al (2010) concluded that gold prices and Taiwan’s stock prices are independent, indicating no mutual influence In contrast, Bhunia & Das (2012) presented a differing perspective on these dynamics.
By applying Granger causality analysis, they found that stock market can be used to predict gold prices in India and vice versa
Research results on the relationship between gold prices and other variables remain inconclusive, with some studies indicating causality while others do not or suggest bi-directional causality These discrepancies may stem from differences in sample periods, research methodologies, and variables analyzed This paper aims to further investigate the relationship between gold prices, oil prices, stock market performance, and US dollar exchange rates using an updated dataset The study will employ the autoregressive distributed lag bound test (ARDL) and unrestricted error correction models, as these methods have proven effective in previous research by Hammoudeh et al (2008) and Sari et al (2010) Additionally, ARDL offers several advantages over the methods proposed by Engle and Granger (1987) and Johansen (1988), which will be discussed in Chapter 4.
The summary of empirical researches listed in Table 3.1
Table 3.1: Summary of empirical studies in Literature review
Methodology Key variables Period Main Results
Spot and future prices of gold and oil
1995 - 2009 Gold ↔ Oil in long-run
ARDL bound test, unrestricted error correction models, diagostic tests
Spot prices of oil, gold, silver, copper; interest rate;
US dollar index; some dummy variables
1990 - 2006 Gold → Oil in short-run;
Gold, Oil → US dollar index in long-run
ARDL bound test, unrestricted error correction models; the generalized forecast error variance decompostions, the generalized impulse response functions
Spot prices of oil, gold, silver, Palladium, Platinum, USD/EUR exchange rate
1999 - 2007 Oil → Gold in short-run;
Johansen test for long-run relationship;
Spot prices of gold, oil, euro
2000 - 2007 Gold ↔ Oil in short-run h
Pairwise Granger causality; VAR Granger
Vector autoregression (VAR); Impulse response functions and variance decomposition
Prices of gold, oil, value of dollar
Granger causality; error correction model
Four gold price series and six different US stock price indices
1991 - 2001 US stock index returns → gold returns in short- run
Vector error correction (VEC) model; variance decomposition and impulse response functions
Gold prices, stock price indices of gold mining companies, broad stock market indices
1996 - 2007 Stock prices → gold prices in short-run
Oil price; gold price; exchange
2006 - 2009 Oil price ↔ stock prices in h
20 correction model, granger causality rates of US dollar; stock price indices of United States, Germany, Japan, Taiwan, Chian
Johansen test, vector error correction model, Granger causality test
Gold price, stock returns in India
Note: ↔ means that bi-directional causality exists between two variables; → means that uni-directional causality exists between two variables h
RESEARCH METHODOLOGY
Research process
This study will be conducted through steps described in the Figure 4.1.
Model establishment
This paper utilizes the unrestricted error correction model, as successfully implemented by Hammoudeh et al (2008) and Sari et al (2010), to analyze the relationship between gold prices and various other variables Given the uncertainty surrounding the long-term relationships, the study constructs unrestricted regressions with each variable serving as a dependent variable.
Data collection
This study utilizes secondary data, comprising daily time series from January 2007 to December 2012, totaling 1,498 observations per series Consistent with prior research, the independent variables analyzed include oil prices, the US Dollar Index, and the S&P 500 Index, all of which are believed to influence gold prices Additionally, the global financial crisis, referred to as the "Crisis," spanned from August 2007 to the end of 2008, impacting these economic indicators.
(Wikipedia, 2013) will be used as a dummy variable h
- Role of gold in economy, in reserve asset of Central Banks;
- Recent policies relating to gold of some Central Banks and of the State Bank of Viet Nam;
- Target of Vietnam’s government in stabilizing the gold market
- Studies on the relationship between gold and oil prices;
- Studies on the relationship between gold price and US dollar exchange rate;
- Studies on the relationship between gold price and stock market;
- Method and model will be chosen in Thesis
- Vietnam’s national gold brand – SJC;
- Connection between Vietnam’s gold price and International gold price
Model establishment - Unrestricted error correction model
- Data of oil price, gold price, US dollar index, S&P500 index; global financial crisis occurred in 2007 and 2008 as dummy variable
- Sources: EIA, World Gold Council, Federal Reserves
- Conclusion about the relationships of gold with other variables; the impact of global financial crisis h
The data sources include the crude oil price, represented in US dollars per barrel (OIL) This paper specifically utilizes the West Texas Intermediate (WTI) crude oil spot price as a benchmark for global oil pricing, sourced from the U.S Energy Information Administration (EIA) website The selection of WTI crude oil price is based on its relevance and reliability as a representative measure of the world oil market.
Crude oil prices, particularly WTI, are typically higher than those of OPEC or Brent crude due to the United States being the largest oil consumer and WTI being its primary oil source The gold price, represented in US dollars per troy ounce, is determined by the daily average from the London afternoon fix, as London remains the foremost global center for gold trading since the 19th century The US dollar index (USDX) measures the dollar's value against major currencies such as the Euro and Japanese yen, with data available from the Federal Reserve Lastly, the S&P 500 index (SPX), which reflects the stock prices of 500 leading American companies, serves as a key indicator of the US economy.
All variables, except for the dummy variable, are modeled using natural logarithms and first differences to ensure stationary time-series data, enabling the inclusion of meaningful independent variables (Sari et al., 2010; Le et al., 2011).
Data analysis
This paper begins by conducting descriptive statistics on all series at their level, in logarithmic form, and in the first difference of logarithmic levels The aim is to identify which variable exhibits the highest volatility and average return, as well as to analyze their distribution characteristics.
Secondly, correlation matrix will be built and analyzed to know the correlation relationship among variables
Thirdly, Augmented Dickey-Fuller (ADF) test and Philips-Perron (PP) test will be used for stationary and unit root test
The autoregressive distributed lag (ARDL) bound test will be employed to examine the equilibrium relationship and causal linkage among variables Previous empirical studies, including those by Engle and Granger (1987) and Johansen (1988), have utilized co-integration tests, which necessitate that all variables be integrated of the same order, specifically I(1) Consequently, a preliminary unit root test is essential to ascertain the order of integration for the variables in the models However, in reality, variables often exhibit different integration orders; some may be stationary at level I(0), while others could be I(1) or I(2) This discrepancy can lead to spurious estimation results To address these challenges, this research will adopt the ARDL bound test approach proposed by Pesaran and Shin (1999) and Pesaran et al.
The Unrestricted Error Correction Model (UECM) offers several advantages over traditional methods, particularly through the Autoregressive Distributed Lag (ARDL) approach Firstly, ARDL accommodates variables with different orders of integration, allowing for a mix of integrated variables of order 1 and order 0 without the need for pre-testing unit roots Secondly, it demonstrates greater power even with smaller sample sizes Additionally, ARDL effectively analyzes both short-run and long-run relationships while determining causality effects Furthermore, the inclusion of dummy variables in the testing process enhances its versatility (Frimpong & Oteng-Abayie, 2006; Hoque & Yusop, 2009).
Analysis method
The flow chart of statistical analysis method showed in the Figure 4.2
4.5.1 Stationary and unit root test
The ARDL bound test approach does not necessitate unit root tests; however, conducting these tests is essential to confirm that all variables are either I(0) or I(1) This study will utilize the widely recognized ADF and PP unit root tests A variable is considered stationary after being differenced d times, while a variable integrated of order greater than or equal to 1 is deemed non-stationary Notably, most economic variables are cointegrated of order 1 (Asteriou & Hall).
ADF test based on the choosing of following three regression forms in testing for the existence of a unit root of time series Yt:
The difference between the three forms is the deterministic elements α0 and α1T In order to choosing the best equation, it is suggested that we can first plot the data of
Stationary test - Augmented Dickey-Fuller (ADF) test;
- Unrestricted error correction models (UECM). h
26 each variable and observe the graph due to it can indicate the existence or not of the deterministic trend regressors (Binh, 2011)
In the context of the Augmented Dickey-Fuller (ADF) test, the decision rule states that if the t statistic (t*) exceeds the absolute value of the ADF critical values, the null hypothesis (Ho) cannot be rejected, indicating the presence of a unit root Conversely, if the t statistic (t*) is less than the absolute value of the ADF critical values, the null hypothesis (Ho) can be rejected, suggesting that a unit root does not exist.
However, if problem of serial correlation occurs, Phillip-Perron (PP) test conducted in a similar manner of ADF test will be used alternative
Once the order of integration for each variable is determined, this study will examine the potential cointegration among the variables Cointegration suggests that a long-term equilibrium relationship and causality exist between the variables, although it does not specify the direction of this causal relationship.
The article employs the ARDL bound test to examine cointegration, utilizing unrestricted error correction models (UECM) estimated through Ordinary Least Squares (OLS) as outlined by Pesaran et al (2001) This method involves the Wald test, specifically the F-statistics version, to assess the lagged level variables on the right-hand side of the UECM.
By taking each of the variables in turn as a dependent variable, the research will estimate UECM models as followings: h
∑ p i=1 ∝ 3i ∆lnUSDX t−i + ∑ p i=1 ∝ 4i ∆lnSPX t−i + ∝ 5 lnGOLD t−1 + ∝ 6 lnOIL t−1 + ∝ 7 lnUSDX t−1 + ∝ 8 lnSPX t−1 + ε 1t
∑ k i=1 β 3i ∆lnUSDX t−i + ∑ k i=1 β 4i ∆lnSPX t−i + β 5 lnGOLD t−1 + β 6 lnOIL t−1 + β 7 lnUSDX t−1 + β 8 lnSPX t−1 + ε 2t
∑ r i=1 Υ 3i ∆lnUSDX t−i + ∑ k i=1 Υ 4i ∆lnSPX t−i + Υ 5 lnGOLD t−1 + Υ 6 lnOIL t−1 + Υ 7 lnUSDX t−1 + Υ 8 lnSPX t−1 + ε 3t
∑ n i=1 φ 3i ∆lnUSDX t−i + ∑ n i=1 φ 4i ∆lnSPX t−i + φ 5 lnGOLD t−1 + φ 6 lnOIL t−1 + φ 7 lnUSDX t−1 + φ 8 lnSPX t−1 + ε 4t
∑ p i=1 ∝ 3i ∆lnUSDX t−i + ∑ p i=1 ∝ 4i ∆lnSPX t−i + ∝ 5 lnGOLD t−1 + ∝ 6 lnOIL t−1 + ∝ 7 lnUSDX t−1 + ∝ 8 lnSPX t−1 + ∝ 9 D1G + ∝ 10 D2O + ∝ 11 D3U + ∝ 12 D4S + ε 1t
∑ k i=1 β 3i ∆lnUSDX t−i + ∑ k i=1 β 4i ∆lnSPX t−i + β 5 lnGOLD t−1 + β 6 lnOIL t−1 + β 7 lnUSDX t−1 + β 8 lnSPX t−1 + β 9 D1G + β 10 D2O + β 11 D3U + β 12 D4S + ε 2t
∑ r i=1 Υ 3i ∆lnUSDX t−i + ∑ k i=1 Υ 4i ∆lnSPX t−i + Υ 5 lnGOLD t−1 + Υ 6 lnOIL t−1 + Υ 7 lnUSDX t−1 + Υ 8 lnSPX t−1 + Υ 9 D1G + Υ 10 D2O + Υ 11 D3U + Υ 12 D4S + ε 3t h
∑ n i=1 φ 3i ∆lnUSDX t−i + ∑ n i=1 φ 4i ∆lnSPX t−i + φ 5 lnGOLD t−1 + φ 6 lnOIL t−1 + φ 7 lnUSDX t−1 + φ 8 lnSPX t−1 + φ 9 D1G + φ 10 D2O + φ 11 D3U + φ 12 D4S + ε 4t
- lnGOLDtis the log of the London gold price;
- lnOILt is the log of international crude oil prices which measures the price of West Texas Intermediate (WTI) crude oil;
- lnUSDXt is the log of US dollar index;
- lnSPXt is the log of S&P 500 index;
- ∆ is the first difference operator;
- p, k, r, n are the lag lengths and determined by the Akaike Information Criterion (AIC) (supporting by Eviews 6 software);
- αxi, βxi, 𝛶xi, φxi (x = 1 to 4) are the short-run coefficients;
- αx, βx, 𝛶x, φx (x = 5 to 8)are the long-run coefficients in Group 1;
- αx, βx, 𝛶x, φx (x = 5 to 12)are the long-run coefficients in Group 2;
- εxt (x = 1 to 4) are white noise errors;
- Crisis = 1, over the period 01/08/2007 – 31/12/2008, 0 elsewhere
The null hypothesis of no cointegration in the long run in each equation of Group 1 is that: i Equation 1: H0: α5 = α6 = α7 = α8 = 0 h
29 ii Equation 2: H0:β5 = β6 = β7 = β8 = 0 iii Equation 3: H0:γ5 = γ6 = γ7 = γ8 = 0 iv Equation 4: H0:φ5 = φ6 = φ7 = φ8 = 0
The null hypothesis for Group 2 of no cointegration will be divided into two cases:
Case 1: crisis does not affect the relationship among variables in long-run i Equation 1: H0: α9 = α10 = α11 = α12 = 0 ii Equation 2: H0:β9 = β10 = β11 = β12 = 0 iii Equation 3: H0:γ9 = γ10 = γ11 = γ12 = 0 iv Equation 4: H0:φ9 = φ10 = φ11 = φ12 = 0
Case 2: the long-run relationship among variables does not exist when crisis occurs i Equation 1: H0: α5 = α6 = α7 = α8 = α9 = α10 = α11 = α12 = 0 ii Equation 2: H0:β5 = β6 = β7 = β8 = β9 = β10 = β11 = β12 = 0 iii Equation 3: H0:γ5 = γ6 = γ7 = γ8 = γ9 = γ10 = γ11 = γ12 = 0 iv Equation 4: H0:φ5 = φ6 = φ7 = φ8 = φ9 = φ10 = φ11 = φ12 = 0
To assess the long-run relationship among variables, the general F-statistics test is employed by analyzing all variables in levels The results are then compared against critical values established by Pesaran et al (2001) There are two sets of critical value bounds for the F-statistics: if the computed F-statistic is below the lower bound, the null hypothesis of no cointegration cannot be rejected; conversely, if it exceeds the upper bound, the null hypothesis is rejected, indicating a long-run cointegration relationship exists among the variables If the F-statistic falls within the bounds, the results are inconclusive The critical values referenced are sourced from Table CI(iii) on page 300 of Pesaran et al (2001), as illustrated in Table 4.1.
Table 4.1: Asymptotic critical value bounds for the F-statistics h
30 k At 5% level of significant At 10% level of significant
Note: k is the total number of independent variables in the model
Choosing the optimal lag length for each equation is crucial in this test The VAR model will be estimated using endogenous variables in their difference form, while the first lag of all variables in log transformation will serve as exogenous variables The Akaike Information Criterion (AIC) will guide this selection process Additionally, a serial correlation test will be performed to ensure that the residuals are not serially correlated, and the inverse roots test will assess the dynamic stability of the model.
If cointegration is confirmed, the next step involves calculating long-run coefficients Subsequently, short-run dynamic parameters will be derived by estimating error correction models (ECM), with error correction terms obtained from the relationship of variables through log transformation using ordinary least squares (OLS) The ECM indicates the speed of adjustment needed to return to long-run equilibrium following short-run shocks A negative and significant ECM coefficient is essential, as a larger coefficient signifies a quicker adjustment back to equilibrium.
This chapter outlines the rationale behind variable selection and data sources, alongside the design of the econometric model To assess the stationarity and cointegration of the data, the ADF and PP tests, as well as the ARDL bound test, have been employed.
RESEARCH RESULTS
Descriptive statistics
The descriptive statistics of all series in level, log and first difference of log level is showed in Table 5.1
Table 5.1: Descriptive statistics of all series
The mean, or average, is a key indicator of the central tendency of variables In terms of stock indices, both the level and log transformations show the highest mean values Conversely, when examining the first difference of log transformations, gold prices exhibit the highest positive mean, closely followed by oil prices.
US dollar exchange rate and stock index have negative mean
The coefficient of standard deviation reveals that gold prices exhibit the highest volatility across various measures In level terms, gold leads in volatility, followed by stock indices, oil prices, and the US dollar exchange rate When applying log transformation, gold remains the most volatile, succeeded by oil prices, stock indices, and the US dollar exchange rate However, in the first difference of log, oil prices surpass both stock indices and gold prices in volatility, with the US dollar exchange rate trailing behind.
The skewness, kurtosis and Jarque-Bera, probabilities indicate that all variables are significantly non-normal distribution.
Correlation matrix
The correlation matrix of the logged variables will be presented in Table 5.2
The price of gold exhibits a moderate positive correlation with oil prices and a moderate negative correlation with the US dollar exchange rate, while showing a very weak positive correlation with stock indices Notably, oil prices and the US dollar exchange rate have a strong negative correlation of approximately -0.828, indicating potential multicollinearity However, this study will overlook the multicollinearity issue as the model is intended solely for forecasting purposes (Hoai).
Stationary and unit root test
The results of both the ADF and PP tests are reported in Table 5.3 and Table 5.4
The result in Table 5.3 indicates that most variables were not stationary in levels by both ADF and PP tests So, the null hypothesis of non-stationary cannot be rejected
It means that there is an existence of unit root in all variables at levels
Table 5.3: Unit root test for stationary at level
Intercept Intercept & Trend Intercept Intercept & Trend
Table 5.4 indicates that all variables are stationary at the first difference, as confirmed by both the ADF and PP tests Consequently, the null hypothesis of non-stationarity is rejected, suggesting that all variables are integrated of order one, denoted as I(1).
It is as the same result of Harmmoudeh et al (2008), Sari et al (2010)
Table 5.4: Unit root test for stationary at first difference
Intercept Intercept & trend Intercept Intercept and Trend
Note: * indicates significance at 1% level.
Cointegration analysis
The result of lag order selection criteria via vector autoregression estimates (VAR) is presented in Table 5.5
Table 5.5: VAR lag order selection criteria
Endogenous variables: DLNGOLD DLNOIL DLNUSDX DLNSPX
Exogenous variables: C LNGOLD(-1) LNOIL(-1) LNUSDX(-1) LNSPX(-1)
Lag LogL LR FPE AIC SC HQ
Note: * indicates lag order selected by the criterion
Basing on AIC values of the Table 5.5, the paper intends to use maximum lag of 10 in the thesis h
To assess the serial independence of model errors, the study employs serial correlation LM tests, with the null hypothesis indicating no serial correlation at a lag order of h The findings are presented in Table 5.6.
At lag 10, the p-value is 0.7836, exceeding the 5% significance level, indicating that the null hypothesis cannot be rejected This suggests that there is no serial correlation present at this lag order.
10 and the lag of 10 is suitable to use in the thesis
Table 5.6: Serial correlation test’s result
VAR Residual Serial Correlation LM Tests
Null Hypothesis: no serial correlation at lag order h
The paper involves checking dynamic stability of ARDL model by using Inverse roots graph This graph is illustrated in Figure 5.1 h
The graph shows that all roots have absolute value less than one and lie inside the unit circle This indicates that the model is stable
The calculated F-statistics and lag structure are presented in Table 5.7 and Table 5.8
Table 5.7: Bounds test procedure results without crisis interaction
Cointegration hypothesis Lag structure F-statistics Outcome
F(lngold/lnoil, lnusdx, lnspx) 9-4-5-10 0.323849 No cointegration F(lnoil/lngold, lnusdx, lnspx) 1-8-1-4 1.997150 No cointegration F(lnusdx/lngold, lnoil, lnspx) 1-5-6-10 2.205181 No cointegration F(lnspx/lngold, lnoil, lnusdx) 10-8-9-9 2.090775 No cointegration
Note: the critical value bounds at 5% and 10% level of significant are [2.86, 4.01], [2.45, 3.52] respectively for k = 4
Table 5.8: Bounds test procedure results with crisis interaction
Inverse Roots of AR Characteristic Polynomial h
Cointegration hypothesis Lag structure F-statistics Outcome Case 1
F(lngold/lnoil, lnusdx, lnspx) 9-4-5-10 4.418209 Cointegration F(lnoil/lngold, lnusdx, lnspx) 1-8-1-4 7.399536 Cointegration F(lnusdx/lngold, lnoil, lnspx) 1-5-6-10 2.744189 Inconclusion F(lnspx/lngold, lnoil, lnusdx) 10-8-9-9 1.283832 No cointegration
F(lngold/lnoil, lnusdx, lnspx) 9-4-5-10 2.37369 Inconclusion F(lnoil/lngold, lnusdx, lnspx) 1-8-1-4 4.719896 Cointegration F(lnusdx/lngold, lnoil, lnspx) 1-5-6-10 2.483260 Inconclusion F(lnspx/lngold, lnoil, lnusdx) 10-8-9-9 1.688787 No cointegration
Note: the critical value bounds at 5% and 10% level of significant are [2.86, 4.01], [2.45, 3.52] respectively for k = 4 in Case 1; [2.22, 3.39], [1.95, 3.06] respectively for k = 8 in Case 2
The findings in Table 5.7 indicate that the null hypothesis of no cointegration cannot be rejected, suggesting that a long-run equilibrium relationship does not exist in any of the equations analyzed This outcome contradicts the results presented by Harmmoudeh et al (2008) and Narayan et al (2010) Consequently, the subsequent steps for cointegration outlined in Item 4.5 will not be pursued due to the absence of cointegration.
The findings in Table 5.8 indicate that crises influence the long-term relationship between gold prices and oil prices, the US dollar exchange rate, and the stock market, though it is inconclusive whether a long-term relationship exists among these variables Additionally, during a crisis, a long-term relationship does emerge between oil prices, gold prices, the US dollar exchange rate, and the stock market, suggesting that these variables tend to move together under such conditions.
During a crisis, the US dollar exchange rate and stock market typically react first, followed by fluctuations in oil prices However, the relationship between the US dollar exchange rate and other variables in the context of a crisis remains inconclusive.
38 does not affect the relationship of stock market with remaining variables and the long-run relationship does not exists between them in case of crisis interaction
The ADF and PP tests indicate that all variables are non-stationary at level but become stationary at the first difference ARDL bound tests suggest that there is no cointegration among the variables However, during crisis interactions, these tests reveal that crises significantly impact the long-term relationships between gold prices and oil prices, the US dollar exchange rate, and the stock market, as well as between oil prices and gold prices, the US dollar exchange rate, and the stock market Furthermore, the analysis shows that gold prices, the US dollar exchange rate, and the stock market lead the movements, with oil prices following.
CONCLUSION AND POLICY IMPLICATIONS
Main findings
This study utilizes correlation and cointegration methods to analyze the relationship between Vietnam's gold prices and global gold prices, as well as the impact of global gold prices on WTI crude oil prices, the US dollar index, and the S&P 500 index from 2007 to 2012 Additionally, it explores the influence of financial crises on these dynamics.
39 occurred from 2007 to 2008 as a dummy variable to understand its effect to relationship among variables The main findings will be summarized as following:
Vietnam's gold market closely mirrors global trends, exhibiting a strong positive correlation where fluctuations in world gold prices typically influence local prices However, discrepancies persist between the two markets, driven by domestic supply and demand dynamics as well as varying state policies over time.
- World’s gold price has moderate positive correlation with oil price, moderate correlation with US dollar index, very weak positive correlation with stock index
- All variables in research are integrated of order one
- When the dummy variable ignored in models, by taking ARDL bound test, the paper does not see any evidence of cointegration existed among variables
The inclusion of dummy variables in long-run models using the ARDL bound test reveals that financial crises significantly impact the relationships between gold prices, oil prices, the US dollar index, and the S&P 500 However, the analysis does not determine which variable leads these changes Furthermore, it indicates that oil prices are influenced by gold prices, the US dollar index, and the S&P 500, with all other variables moving first, followed by oil prices.
Policy implications
The empirical findings reveal a lack of cointegration among gold prices, oil prices, the US dollar index, and the S&P 500, suggesting that these variables are significantly influenced by external factors such as government policies, inflation, and the political climate Additionally, gold prices are affected by the jewelry industry and central bank interventions aimed at managing foreign reserves and exchange rates Consequently, the State Bank of Vietnam should not rely solely on the fluctuations of these assets for decision-making.
WTI price, US dollar index and S&P 500 to forecast the movement of world gold price for making their decision in selling or buying gold
The empirical findings indicate that financial crises significantly impact the correlation between gold prices and key financial indicators such as oil prices, the US dollar index, and the S&P 500 Consequently, the State Bank of Vietnam must exercise caution in their buying or selling decisions during similar economic shocks in the future.
Limitation
Although the paper has explained carefully about reason of choosing variables, analysis method as well as economic model for research, there are some limitations
Firstly, the period from 2007 to 2012 is short due to daily data for Vietnam gold prices are not adequate
Secondly, the data set for SJC price might be unreliable due to it is manually collected via website of Saigon Giai Phong newspaper
Thirdly, the paper may omit some important factors affect to gold price due to there is no cointegration among gold price, oil price, US dollar index, S&P 500.
Future research
Future research should broaden its scope by incorporating additional factors like inflation, demand and supply dynamics of gold and oil, and exploring various cointegration methods to validate the findings.
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Standard errors in ( ) & t-statistics in [ ]
Equation 1 of Group 1: LS dlngold c dlngold(-1 to -8) dlnoil(to -4) dlnusdx(to -5) dlnspx(to -10) lngold(-1) lnoil(-1) lnusdx(-1) lnspx(-1);c(32)=c(33)=c(34)=c(35)=0 Wald Test:
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err
Equation 2 of Group 1: Ls dlnoil c dlnoil(-1) dlngold(to -8) dlnusdx(to -1) dlnspx(to -4) lngold(-1) lnoil(-1) lnusdx(-1) lnspx(-1); c(19)=c(20)=c(21)=c(22)=0 h
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err
Equation 3 of Group 1: Ls dlnusdx c dlnusdx(-1) dlngold(to -5) dlnoil(to -6) dlnspx(to -10) lngold(-1) lnoil(-1) lnusdx(-1) lnspx(-1); c(27)=c(28)=c(29)=c(30)=0
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err
Equation 4 of Group 1: dlnspx c dlnspx(-1 to -10) dlngold(to -8) dlnoil(to -9) dlnusdx(to -9) lngold(-1) lnoil(-1) lnusdx(-1) lnspx(-1); c(41)=c(42)=c(43)=c(44)=0 h
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err
Equation 1 of Group 2: LS dlngold c dlngold(-1 to -8) dlnoil(to -4) dlnusdx(to -5) dlnspx(to -10) lngold(-1) lnoil(-1) lnusdx(-1) lnspx(-1) d1g d2o d3u d4s; c(36) = c(37) = c(38) = c(39) =0
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err
Test Statistic Value df Probability h
Normalized Restriction (= 0) Value Std Err
Equation 2 of Group 2: Ls dlnoil c dlnoil(-1) dlngold(to -8) dlnusdx(to -1) dlnspx(to -4) lngold(-1) lnoil(-1) lnusdx(-1) lnspx(-1) d1g d2o d3u d4s; c(23) = c(24) = c(25) = c(26) = 0
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err
Equation 3 of Group 2: Ls dlnusdx c dlnusdx(-1) dlngold(to -5) dlnoil(to -6) dlnspx(to -10) lngold(-1) lnoil(-1) lnusdx(-1) lnspx(-1) d1g d2o d3u d4s; c(31) = c(32) = c(33) = c(34) = 0
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err
Equation 4 of Group 2: dlnspx c dlnspx(-1 to -10) dlngold(to -8) dlnoil(to -9) dlnusdx(to -9) lngold(-1) lnoil(-1) lnusdx(-1) lnspx(-1) d1g d2o d3u d4s; c(45)=c(46)=c(47)=c(48) = 0
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err
Test Statistic Value df Probability
Normalized Restriction (= 0) Value Std Err