Oil Market Analysis as of March 14, 2017

The following chart displays the historical oil prices and the prices of oil declined by 10% recently.

Rplot04

The late drop in the oil price was little too much and it is believed to rebound soon.

Rplot01

However, the forecasting model projects that the oil prices will increase gradually for next 12 month.

 

Rplot07

The following chart displays the historical oil import by U.S. and the import has been increasing again.

Rplot02

The following chart shows the oil export by U.S. and it has been significantly increasing lately.

Rplot03

 

The following chart displays the net export (Export -Import) of petroleum for U.S. the next export was decreasing, but it begins to increase again due to improvement in U.S. economy.

Rplot05

The following R codes produces the charts above:
library(Quandl)
library(ggplot2)
library(forecast)
library(‘quantmod’)

mydata1=Quandl(“EIA/PET_MCRIMUS1_M”)
mydata2=Quandl(“EIA/PET_MCREXUS1_M”)

oil_price=Quandl(“OPEC/ORB”)

oil=Quandl(“OPEC/ORB”, api_key=”ucUWoKV_gnNZAgY8AQvL”, type=’xts’)

# Quantmod for oil
chartSeries(oil, subset=’last 12 months’)
addMACD()
addBBands()
addCCI()
names(mydata1)[2]<-“Import”
names(mydata2)[2]<-“Export”
names(oil_price)[2]<-“oil_price”

Mergedata<-merge(mydata1, mydata2)

summary(Mergedata)
head(Mergedata)
tail(Mergedata)

Mergedata$netexport<-Mergedata$Export-Mergedata$Import

head(Mergedata)

ggplot(Mergedata,aes(x=Date))+geom_line(aes(y=Import, Color=”Import”))+
geom_line(aes(y=Export, color=”Export”))

ggplot(Mergedata,aes(x=Date))+geom_line(aes(y=Import, Color=”blue”))
ggplot(Mergedata,aes(x=Date))+geom_line(aes(y=Export, Color=”blue”))
ggplot(oil_price,aes(x=Date))+geom_line(aes(y=oil_price, Color=”blue”))
ggplot(Mergedata,aes(x=Date))+geom_line(aes(y=netexport, Color=”blue”))
fit <- arima(oil_price, order=c(2, 0, 1))

fit

summary(oil_price)

Diff365
# predictive accuracy
library(forecast)
accuracy(fit)

# predict next 5 observations
library(forecast)

forecast(fit, 365)

plot(forecast(fit,365))

 

Baltic Dry Index – with R

Baltic Dry Index. Source: Lloyd’s List. The Baltic Dry Index (BDI) is a measure of the price of shipping major raw materials such as metals, grains, and fossil fuels by sea. It is created by the London Baltic Exchange based on daily assessments from a panel of shipbrokers. The BDI is a composite of 3 sub-indices, each covering a different carrier size: Capesize, Panamax, and Supramax. Capesize carriers are the largest ships with a capacity greater than 150,000 DWT. Panamax refers to the maximum size allowed for ships traveling through the Panama Canal, typically 65,000 – 80,000 DWT. The Supramax Index covers carriers with a capacity of 50,000 – 60,000 DWT.

Source: http://www.lloydslist.com/ll/sector/markets/market-data.htm

The following R codes would generate the following charts:
library(Quandl)
library(ggplot2)
library(forecast)
library(‘quantmod’)
Baltic_Index=Quandl(“LLOYDS/BDI”, type=”xts”)

plot(Baltic_Index)

Rplot01

 

 

 

chartSeries(Baltic_Index, subset=’last 12 months’)
addMACD()
addBBands()

Baltic Index in last 12 months – Baltic Index is rising.

Rplot

 

Diff365<-Delt(Baltic_Index, k=365)*100

plot(Diff365)

The following chart shows the changes from previous year (365 days ago)

Rplot03

 

fit <- arima(Baltic_Index, order=c(2, 0, 1))

 

# predictive accuracy
library(forecast)
accuracy(fit)

# predict next 5 observations
library(forecast)
forecast(fit, 365)
plot(forecast(fit, 360))

The following chart shows the projection for next 365 days with ARIMA (2,0,1) model

Rplot02

Japanese Yen to South Korean Won, Daily, Not Seasonally Adjusted

아래 차트는 일본의 물가 상승률 과 한국의 물가 상승률 (빨간색) 비교한것인데 일본은 오랜기간동안 물가가 정체되어있었지만 한국은 경제가 빨르게 성장하면서 비율이 지속적으로 하락하다가 아베노믹스로 인해서 잠시 역전되었습니다. (2015년기준)

또한 일본의 엔화 에 대한 한국의 원화는 장기적으로 지속적으로 하락세를 보였지만 중간중간 원화가 엔화에 대해서 강세를 보인시점은 한국에 외환위기기 일어나기전 시점입니다. (1997 , 2007) 아베노믹스 이후 일본중앙은행의 양적완화로 원화는 엔화에 대해서 강세를 보이다가 2015 그리고 2016년에는 다시 원화는 약세를 보였습니다. 하지만 올해들어서 원화는 엔화에 대해서 강세를 보이고 있습니다.

fredgraph-17

 


오랜기간 (1995-1996 의 강세는 1997년 아시아 외환위기  그리고 2004-2006 강세는 2007년 외환위기) 동안 원화의 엔화의 강세는 한국의 외환위기를 초래하였습니다. 다행이 2012-2015년의 원화의 강세가 한국의 외환위기를 불러오지는 않았습니다. 2017년에 들어와서 다시 원화는 엔화에 대해서 강세를 보이고 있습니다.



아래차트는 월별 통계입니다. 대체로 평균적으로 전반부에는 원화의 강세를 보이다가 후반에들어서는 원화가 약세를 보입니다.