I picked two stocks: Gold and Silver, and want to check whether they impact on each other due themselves stock prices flutuation.

gold = `GC=F` <- read.csv("~/Desktop/GC=F.csv", stringsAsFactors=TRUE)
silver = `SI=F` <- read.csv("~/Desktop/SI=F.csv", stringsAsFactors=TRUE)
gold.ts = ts(gold[,6], frequency = 12, start = c(2016, 01, 01), end = c(2020,12,01))
silver.ts = ts(silver[,6], frequency = 12, start = c(2016, 01, 01), end = c(2020,12,01))

par(mfrow=c(1,2))
plot.ts(gold.ts)
plot.ts(silver.ts)

#corrolation between two dataset
cor(gold.ts, silver.ts)
## [1] 0.7588244
gold.diff = diff(gold.ts)
silver.diff = diff(silver.ts)

par(mfrow=c(1,2))
autoplot(gold.ts)

autoplot(silver.ts)

d1 = data.frame(gold.diff,silver.diff)

d.ts = ts(d1, start = c(2016,01,01), end = c(2020,12,01),frequency = 12)
autoplot(d.ts)

differ =diff(d.ts)
autoplot(differ)

#Build vars
library(vars)
## Loading required package: MASS
## 
## Attaching package: 'MASS'
## The following object is masked from 'package:dplyr':
## 
##     select
## The following objects are masked from 'package:fma':
## 
##     cement, housing, petrol
## Loading required package: strucchange
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## Loading required package: sandwich
## Loading required package: urca
## Loading required package: lmtest
VARselect(differ,lag.max=8,type="const")[["selection"]]
## AIC(n)  HQ(n)  SC(n) FPE(n) 
##      6      6      6      6
var1 = VAR(differ, p=1, type="const")
summary(var1)
## 
## VAR Estimation Results:
## ========================= 
## Endogenous variables: gold.diff, silver.diff 
## Deterministic variables: const 
## Sample size: 58 
## Log Likelihood: -417.884 
## Roots of the characteristic polynomial:
## 0.5353 0.5353
## Call:
## VAR(y = differ, p = 1, type = "const")
## 
## 
## Estimation results for equation gold.diff: 
## ========================================== 
## gold.diff = gold.diff.l1 + silver.diff.l1 + const 
## 
##                Estimate Std. Error t value Pr(>|t|)   
## gold.diff.l1    -0.1261     0.1678  -0.751  0.45566   
## silver.diff.l1 -14.4128     5.3216  -2.708  0.00899 **
## const            2.1136     8.1767   0.258  0.79699   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 62.25 on 55 degrees of freedom
## Multiple R-Squared:  0.32,   Adjusted R-squared: 0.2953 
## F-statistic: 12.94 on 2 and 55 DF,  p-value: 2.479e-05 
## 
## 
## Estimation results for equation silver.diff: 
## ============================================ 
## silver.diff = gold.diff.l1 + silver.diff.l1 + const 
## 
##                 Estimate Std. Error t value Pr(>|t|)    
## gold.diff.l1    0.012989   0.005478   2.371   0.0213 *  
## silver.diff.l1 -0.788102   0.173689  -4.537 3.13e-05 ***
## const           0.026112   0.266876   0.098   0.9224    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
## Residual standard error: 2.032 on 55 degrees of freedom
## Multiple R-Squared: 0.2975,  Adjusted R-squared: 0.2719 
## F-statistic: 11.65 on 2 and 55 DF,  p-value: 6.068e-05 
## 
## 
## 
## Covariance matrix of residuals:
##             gold.diff silver.diff
## gold.diff     3874.68      95.323
## silver.diff     95.32       4.128
## 
## Correlation matrix of residuals:
##             gold.diff silver.diff
## gold.diff      1.0000      0.7538
## silver.diff    0.7538      1.0000
Var1.fc = forecast(var1)
autoplot(Var1.fc)