Introduction to financial time series analysis, getting financial time series data through yahoo finance API with R, time series visualization, risk and return calculation for financial time series data, autoregressive integrated moving average models with R code and applications in financial time series.

1. Financial Time Series Analysis
ผศ.ดร. อานนท์ ศักดิ์วรวิชญ์
ผู้อานวยการหลักสูตร Ph.D. and M.Sc. (Business Analytics and Data Science)
อาจารย์ประจาสาขาวิชา Actuarial Science and Risk Management
คณะสถิติประยุกต์ สถาบันบัณฑิตพัฒนบริหารศาสตร์

2. 1. Getting financial time series data
from yahoo finance API

4. install.packages("quantmod", dependencies=TRUE)
install.packages("TTR", dependencies=TRUE)
install.packages("xts", dependencies=TRUE)
install.packages("zoo", dependencies=TRUE)
library(quantmod)
library(TTR)
library(xts)
library(zoo)

9. 2. Basic financial time series visualization

10. #Time series visualization
install.packages("ggplot2",dependencies=TRUE)
install.packages("dplyr",dependencies=TRUE)
install.packages("tibble",dependencies=TRUE)
library(dplyr)
library(tibble)
library(ggplot2)
# https://plot.ly/ggplot2/time-series/
chartSeries(TNX,theme="white",TA=NULL) # Obtain plot without
volume.
chartSeries(AAPL,theme="white") # Plot the daily price and
volume
addBBands()
chartSeries(INTC,theme="white") # Plot monthly
unemployment rates
chartSeries(UNRATE,theme="white") # Plot monthly
unemployment rates
chart_Series(DJI,theme="white")
chartSeries(na.omit(DJI),theme="white")
chart_Series(PTT.BK)
chartSeries(PTT.BK,theme="white")
chartSeries(XAUTHB,theme="white")
chart_Series(XAUTHB)

11. chartSeries(TNX,theme="white",TA=NULL)
# Obtain plot without volume.
chartSeries(AAPL,theme="white")
# Plot the daily price and volume
addBBands()
John Bolinger

12. Bolinger
chartSeries(UNRATE,theme="white")
# Plot monthly unemployment rates
> chart_Series(DJI,theme="white")
Error: $ operator is invalid for atomic vectors
> chartSeries(na.omit(DJI),theme="white")

13. > chart_Series(PTT.BK) > chartSeries(PTT.BK)

14. 3. Risk and return calculation for financial time series

16. > AAPL.logrtn=diff(log(AAPL$AAPL.Close)) # Compute log returns
> chartSeries(AAPL.logrtn,theme="white")
> AAPL.rtn=diff(AAPL$AAPL.Close) # Compute simple returns
> chartSeries(AAPL.rtn,theme="white")
> head(AAPL)
AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
2005-01-03 4.627143 4.650714 4.471428 4.520714 172998000 4.006509
2005-01-04 4.556428 4.676429 4.497857 4.567143 274202600 4.047657
2005-01-05 4.604286 4.660714 4.575000 4.607143 170108400 4.083108
> library(moments)
> mean(na.omit(AAPL.logrtn))
[1] 0.001116748
> sd(na.omit(AAPL.logrtn))
[1] 0.02049932
> skewness(na.omit(AAPL.logrtn))
AAPL.Close
-0.2582925
> kurtosis(na.omit(AAPL.logrtn))
AAPL.Close
9.059003
> mean(na.omit(AAPL.rtn))
[1] 0.06189744
> sd(na.omit(AAPL.rtn))
[1] 1.293545
> skewness(na.omit(AAPL.rtn))
AAPL.Close
0.05156874
> kurtosis(na.omit(AAPL.rtn))
AAPL.Close
12.94876

17. hist(na.omit(AAPL.logrtn),breaks=15)
hist(na.omit(AAPL.rtn),breaks=15)
hist(na.omit(AAPL.AAPL.Close),breaks=15)

18. 4. Autoregressive Integrated Moving Average
Model: ARIMA

19. 1. Stationarity

20. D=Differencing
Yt-Y(t-1)
Return = Differencing

21. 2.Autocorrelation and Partial autocorrelation function

22. acf(na.omit(AAPL.logrtn),lag=20)
pacf(na.omit(AAPL.logrtn),lag=20)
> Box.test(na.omit(AAPL.logrtn),lag=20,type="Ljung")
Box-Ljung test
data: na.omit(AAPL.logrtn)
X-squared = 42.948, df = 20, p-value = 0.002077

24. > head(hkex)
DATE EXHKUS EXEUROUS
1 1-Apr-05 7.7989 0.7754
2 4-Apr-05 7.7991 0.7789
3 5-Apr-05 7.7995 0.7787
> attach(hkex)
> date=DATE
> exrate=EXHKUS
>t=1:dim(hkex)[1]
> qtrend=lm(exrate~t)
> summary(qtrend)
Call:
lm(formula = exrate ~ t)
Residuals:
Min 1Q Median 3Q Max
-0.024280 -0.014618 -0.003042 0.010123 0.037104
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.762e+00 1.518e-03 5113.540 <2e-16 ***
t 4.702e-05 5.230e-06 8.991 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.01698 on 500 degrees of freedom
Multiple R-squared: 0.1392, Adjusted R-squared: 0.1375
F-statistic: 80.84 on 1 and 500 DF, p-value: < 2.2e-16

25. plot( (1:length(exrate)), qtrend$resid, type="o",
xlab="Index", ylab="Residuals")

26. n=length(exrate)
diffrate=exrate[-1]-exrate[-n]
# Differencing for stationarity
acf(qtrend$resid,lag.max=10,plot=T)
acf(exrate, lag.max=10,plot=T)
acf(diffrate, lag.max=10,plot=T)

27. pacf(exrate,lag.max=10,plot=T)
pacf(diffrate,lag.max=10,plot=T)

28. > AR2=arima(diffrate, order = c(2,0,0))
> AR2
Call:
arima(x = diffrate, order = c(2, 0, 0))
Coefficients:
ar1 ar2 intercept
0.0899 -0.1574 0e+00
s.e. 0.0441 0.0441 1e-04
sigma^2 estimated as 3.714e-06: log likelihood = 2421.16, aic = -4834.32
> MA2=arima(diffrate, order = c(0,0,2))
> MA2
Call:
arima(x = diffrate, order = c(0, 0, 2))
Coefficients:
ma1 ma2 intercept
0.0918 -0.1611 0e+00
s.e. 0.0444 0.0465 1e-04
sigma^2 estimated as 3.711e-06: log likelihood = 2421.39, aic = -4834.77

29. > mod
Call:
arima(x = exrate, order = c(2, 1, 0))
Coefficients:
ar1 ar2
0.0901 -0.1572
s.e. 0.0441 0.0441
sigma^2 estimated as 3.715e-06: log likelihood = 2421.09, aic = -4836.18
> acf(mod$resid,lag.max=10,plot=T)
> Box.test(mod$resid,lag=10,type="Ljung")
Box-Ljung test
data: mod$resid
X-squared = 15.312, df = 10, p-value = 0.1211

30. pred10=predict(mod,20)
pred=c(exrate, pred10$pred)
plot(c(1:length(pred)), pred,
xlim=c(0,510),ylim=c(7.74,7.832),
xlab="Time", ylab="EXHKUS",type="l")