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Gold Science and Technology ›› 2021, Vol. 29 ›› Issue (4): 510-524.doi: 10.11872/j.issn.1005-2518.2021.04.194

• Mining Technology and Mine Management • Previous Articles     Next Articles

Research on Influencing Factors of International Gold Futures Price

Minggui ZHENG1,2(),Tianqi CAO1(),Jianlin ZENG1   

  1. 1.School of Economics and Management,Jiangxi University of Science and Technology,Ganzhou 341000,Jiangxi,China
    2.School of Management,University of Science and Technology of China,Hefei 230026,Anhui,China
  • Received:2020-11-07 Revised:2021-03-08 Online:2021-08-31 Published:2021-10-11
  • Contact: Tianqi CAO E-mail:mgz268@sina.com;15770882892@163.com

Abstract:

Based on the dual functions of commodity and currency,this paper takes the great influence of gold price fluctuation on the world economy.At present,most of the literatures analyze the influencing factors of international gold futures price fluctuation from three aspects of supply and demand,finance and index.However,due to the instability of the global political and economic environment,international gold futures prices fluctuate violently.Few scholars consider the impact of geopolitical risks and economic policy uncertainty on the price fluctuations of international gold futures.At the same time,few scholars combine the above two factors with other variables to analyze the influencing factors of international gold futures price.In order to better explore the main influencing factors of international gold futures price,this paper used the monthly price data of international gold futures from 2000 to 2019,using VAR model,VECM model,cointegration test,impulse response and variance decomposition to conduct empirical research.At the same time,Granger causality test was used to analyze the causal relationship between the variables and the international gold futures price.The analysis focuses on the results of impulse response,which is different from the method of orthogonal impulse response in traditional dynamic analysis.In this paper,the generalized impulse response analysis method was used.The results show that there is a long-term equilibrium relationship between international gold futures price and geopolitical risk,economic policy uncertainty,dollar index,interest rate level,U.S. inflation level and the D-value between global gold supply and demand,and dollar index have the most significant impact on international gold futures price.Geopolitical risks and economic policy uncertainties have a positive impact on the international gold futures price in the short term,while the positive impact of economic policy uncertainties is longer.This paper provides a better theoretical basis for predicting the trend of gold price.

Key words: international gold futures price, geopolitical risks, economic policy uncertainty, VAR model, VECM model, impulse response

CLC Number: 

  • F831.5

Fig.1

International gold futures price,geopolitical risk index and global economic policy uncertainty index from 2000 to 2019"

Fig.2

International gold futures prices and US dollar index from 2000 to 2019"

Fig.3

International gold futures prices and interest rates from 2000 to 2019"

Fig.4

International gold futures prices and US inflation rate from 2000 to 2019"

Fig.5

International gold futures prices and global gold supply and demand from 2000 to 2019"

Table 1

Description of main variables"

变量类别变量名变量符号变量度量
被解释变量国际黄金期货价格GoldPCOMEX(纽约商品交易所)每月收盘价
解释变量地缘政治风险指数GPR参考Caldara和Iacoviello(2018)
全球经济政策不确定性指数GEPU参考Davis(2016)
美元指数USDX-
美国联邦基金有效利率FFR-
美国通货膨胀水平CPI-
全球黄金供需差值SD全球黄金供应量减去需求量

Table 2

Descriptive statistics of main variables"

变量均值中位数最小值最大值标准差偏度峰度样本量
GoldP936.851 074.40257.901 828.50464.24-0.09-1.36240
GPR103.9782.7827.21545.0970.402.7411.55240
GEPU121.01109.0949.35339.8056.571.301.63240
USDX106.00108.9186.34129.6412.470.09-1.30240
FFR1.781.160.076.541.921.06-0.10240
CPI215.36217.38169.30258.4425.46-0.19-1.20240
SD315.62322.90-208.50878.6292.13-0.03-0.69240

Table 3

Correlation coefficient between variables"

GoldPGPRGEPUUSDXFFRCPISD
GoldP1.0000------
GPR-0.07581.0000-----
GEPU0.61380.33051.0000----
USDX-0.59230.40570.08581.0000---
FFR-0.6289-0.1197-0.36490.29281.0000--
CPI0.88930.08920.7013-0.3852-0.53331.0000-
SD-0.5407-0.0082-0.16080.45820.6414-0.43221.000

Table 4

Stationarity test results of variables"

检验变量检验类型(C T P)ADF1%临界值5%临界值10%临界值Prob值平稳性
LNGoldP(C 0 0)-1.3238-3.4576-2.8734-2.57320.6188不平稳
LNGPR(C 0 0)-5.2220-3.4576-2.8734-2.57320.0000平稳
LNGEPU(C 0 1)-2.6919-3.4577-2.8735-2.57320.0769不平稳
LNUSDX(C 0 1)-1.3519-3.4577-2.8735-2.57320.6054不平稳
LNFFR(C 0 1)-1.55603-3.4577-2.8735-2.57320.5035不平稳
LNCPI(C 0 2)-1.4023-3.4579-2.8735-2.57320.5809不平稳
SD(C 0 0)-2.4588-3.4576-2.8734-2.57320.1270不平稳
DLNGoldP(C 0 0)-17.5378-3.4577-2.8735-2.57320.0000平稳
DLNGPR(C 0 1)-15.3546-3.4579-2.8735-2.57320.0000平稳
DLNGEPU(C 0 1)-13.7799-3.4579-2.8735-2.57320.0000平稳
DLNUSDX(C 0 0)-11.1712-3.4577-2.8735-2.57320.0000平稳
DLNFFR(C 0 0)-9.7003-3.4577-2.8735-2.57320.0000平稳
DLNCPI(C 0 1)-10.4613-3.4579-2.8735-2.57320.0000平稳
DSD(C 0 0)-15.3766-3.4577-2.8735-2.57320.0000平稳

Table 5

Selection of lag order of VAR model"

滞后阶数LogLLRFPEAICSCHQ
0-1 497.666NA0.00101412.9712613.0752613.01320
1986.82484 797.6387.72E-13-8.024352-7.192381*-7.688827
21 067.905151.6752*5.86e-13*-8.300902*-6.740956-7.671792*
31 100.11258.305626.80E-13-8.156134-5.868214-7.233440
41 122.45639.102028.60E-13-7.926341-4.910446-6.710062
51 153.05451.701451.02E-12-7.767710-4.023840-6.257846
61 176.83138.739091.28E-12-7.550265-3.078421-5.746817
71 208.74650.073361.51E-12-7.402980-2.203160-5.305946
81 251.79864.949741.63E-12-7.351707-1.423913-4.961089

Table 6

Roots of characteristic polynomials"

0.9995900.999590
0.9857160.985716
0.9614420.961442
0.913310-0.043144i0.914329
0.913310+0.043144i0.914329
0.7975800.797580
0.7328010.732801
0.297411-0.229582i0.375715
0.297411+0.229582i0.375715
-0.073840-0.230393i0.241936
-0.073840+0.230393i0.241936
0.1887850.188785

Fig.6

VAR model tests unit circle"

Table 7

Regression parameters"

参数名称数值
R20.994400
调整后的R20.994049
AIC-3.212908
SC-2.994068
Log likelihood397.3361

Table 8

Johansen co-integration test results"

参数名称数值
假设None*
特征值0.217696
迹统计量152.8994
临界值(5%)125.6154
迹检验P值0.0004
最大特征值统计量58.43189
临界值(5%)46.23142
最大特征值检验P值0.0016

Table 9

Normalized cointegration equation coefficients"

标准化的协整方程系数(括号内为标准误差)
LNGoldPLNGPRLNGEPULNUSDXLNFFRLNCPISDC
1.0000-0.0126*-0.6468***2.6347***0.0312*-1.8781***6.03E-05*-5.7484
(0.0382)(0.0687)(0.2182)(0.0162)(0.2637)(7.7E-05)
[-1.7329][-9.4088][12.0730][1.9312][-7.1216][1.7866]

Table 10

Regression results of VECM model"

D(LNGoldP)D(LNGPR)D(LNEPU)D(LNUSDX)D(LNFFR)D(LNCPI)D(SD)
CointEq10.0130380.2269400.333250***-0.020946***0.312555***0.004046***42.29613
(0.02469)(0.17456)(0.09026)(0.00713)(0.06602)(0.00132)(38.7620)
[0.52806][1.30006][3.69197][-2.93763][4.73448][3.07548][1.09117]
D(LNGoldP(-1))-0.126788*1.654308***-0.055850-0.042757**-0.1670330.008235**-215.8712**
(0.06868)(0.48557)(0.25109)(0.01983)(0.18364)(0.00366)(107.824)
[-1.84609][3.40691][-0.22243][-2.15576][-0.90958][2.25060][-2.00207]
D(LNGPR(-1))-0.024153***-0.168239***0.0524971.62E-050.038336-0.00017513.38613
(0.00909)(0.06430)(0.03325)(0.00263)(0.02432)(0.00048)(14.2776)
[-2.65590][-2.61655][1.57897][0.00615][1.57655][-0.36061][0.93756]
D(LNGEPU(-1))0.017827-0.144160-0.114810-0.0047950.0149100.000113-3.707458
(0.02035)(0.14384)(0.07438)(0.00588)(0.05440)(0.00108)(31.9410)
[0.87624][-1.00220][-1.54357][-0.81607][0.27409][0.10419][-0.11607]
D(LNUSDX(-1))-0.0760052.4207292.245845**0.276895***-0.975038-0.027883**-100.5175
(0.23904)(1.69007)(0.87391)(0.06903)(0.63916)(0.01274)(375.286)
[-0.31796][1.43233][2.56987][4.01105][-1.52550][-2.18932][-0.26784]
D(LNFFR(-1))-0.040695*-0.1938610.0099500.0110390.284772***-0.00163877.60889**
(0.02409)(0.17032)(0.08807)(0.00696)(0.06441)(0.00128)(37.8197)
[-1.68934][-1.13823][0.11298][1.58681][4.42111][-1.27591][2.05208]
D(LNCPI(-1))-1.73738710.665752.6188250.5074744.3775560.361571***-2084.040
(1.18888)(8.40560)(4.34644)(0.34334)(3.17889)(0.06334)(1866.50)
[-1.46136][1.26889][0.60252][1.47806][1.37707][5.70814][-1.11655]
D(SD)-1.48E-05-0.000456-5.80E-051.16E-053.85E-05-2.01E-07-0.017403
(4.2E-05)(0.00030)(0.00015)(1.2E-05)(0.00011)(2.2E-06)(0.06564)
[-0.35323][-1.54354][-0.37919][0.96227][0.34459][-0.09039][-0.26512]
C0.010630***-0.0256770.003112-0.000524-0.0107120.001057***3.020616
(0.00375)(0.02654)(0.01373)(0.00108)(0.01004)(0.00020)(5.89399)
[2.83139][-0.96738][0.22676][-0.48368][-1.06709][5.28189][0.51249]

Table 11

Granger causality test results"

原假设样本量F- 统计量P值
LNGPR does not Granger Cause LNGoldP2383.668090.027
LNGoldP does not Granger Cause LNGPR4.930410.008
LNGEPU does not Granger Cause LNGoldP2380.002150.9979
LNGoldP does not Granger Cause LNGEPU2.499050.0844
LNUSDX does not Granger Cause LNGoldP2380.148990.8617
LNGoldP does not Granger Cause LNUSDX3.460140.033
LNFFR does not Granger Cause LNGoldP2381.755300.1751
LNGoldP does not Granger Cause LNFFR0.260470.7709
LNCPI does not Granger Cause LNGoldP2382.747000.0662
LNGoldP does not Granger Cause LNCPI5.819830.0034
SD does not Granger Cause LNGoldP2380.498530.6081
LNGoldP does not Granger Cause SD1.907790.1507

Fig.7

Results of generalized impulse response analysis"

Table 12

Results of variance decomposition"

期数S.E.LNGoldPLNGPRLNGEPULNUSDXLNFFRLNCPISD
10.047082100.00000.0000000.0000000.0000000.0000000.0000000.000000
20.06202797.085661.6450680.0154720.0136080.6617430.5450770.033376
30.07243595.611781.9280800.0430500.0100241.1495941.2159340.041537
40.08095294.875861.9492830.0566540.0089221.3845711.6544670.070239
50.08830494.434111.9375840.0631810.0241121.5277111.8957300.117570
60.09484194.108881.9215090.0614780.0686511.6318852.0260000.181595
70.10077993.820571.9131450.0552940.1450481.7129272.0946820.258332
80.10625293.534231.9160500.0504410.2508231.7762932.1279180.344242
90.11135593.236661.9297370.0525970.3817611.8239782.1394430.435828
100.11616392.923421.9527660.0666310.5333391.8572712.1368430.529730
110.12073092.593911.9834930.0963280.7011101.8774772.1246960.622980
120.12510092.249542.0202710.1443180.8808251.8859522.1059820.713111
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