Companion to Tsay (2005) Analysis of Financial Time Series
FinTS-package.RdR companion to Tsay (2005) Analysis of Financial Time Series, second edition (Wiley). Includes data sets, functions and script files required to work some of the examples. Version 0.3-x includes R objects for all data files used in the text and script files to recreate most of the analyses in chapters 1-3 and 9 plus parts of chapters 4 and 11.
Details
| Package | FinTS |
| Version | 0.4-7 |
| Date | 2022-11-14 |
| Author | Spencer Graves [aut], Georgi N. Boshnakov [cre, ctb] |
| Maintainer | Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk> |
| License | GPL (>= 2) |
| URL | https://geobosh.github.io/FinTSDoc/ (doc), https://r-forge.r-project.org/projects/fints/ (devel) |
| Depends | zoo, graphics |
| Suggests | moments, distrEx, tseries, urca, lmtest, sandwich, psych, GPArotation, chron, polynom, e1071 |
| Built | R 4.2.2; ; 2022-11-14 17:40:38 UTC; unix |
Index:
ARIMA Arima with Ljung-Box
Acf Autocorrelation Function
ArchTest ARCH LM Test
AutocorTest Box-Ljung autocorrelation test
FinTS-package Companion to Tsay (2005) Analysis of Financial
Time Series
FinTS.stats Financial Time Series summary statistics
TsayFiles List of the names of files downloaded from the
"Analysis of Financial Data" web site.
apca Asymptotic PCA
as.yearmon2 Conditionally convert x to yearmon if the
conversion is unique, retaining x as names.
ch01data Financial time series for Tsay (2005, ch. 1)
ch02data Financial time series for Tsay (2005, ch. 2)
ch03data Financial time series for Tsay (2005, ch. 3)
ch04data Financial time series for Tsay (2005, ch. 4)
ch05data Financial time series for Tsay (2005, ch. 5)
ch06data Financial time series for Tsay (2005, ch. 6)
ch07data Financial time series for Tsay (2005, ch. 7)
ch08data Financial time series for Tsay (2005, ch. 8)
ch09data Financial time series for Tsay (2005, ch. 9)
ch10data Financial time series for Tsay (2005, ch. 10)
ch11data Financial time series for Tsay (2005, ch. 11)
ch12data Financial time series for Tsay (2005, ch. 12)
compoundInterest Compute compound interest
findConjugates Find complex conjugate pairs
package.dir Directory of a package
plot.loadings Plot loadings
plotArmaTrueacf Plot the theoretical ACF corresponding to an
ARMA model
read.yearmon Reading Monthly zoo Series
runscript Run a package script
url2data Create local copies of files read from urlsSee the scripts subdirectory of the FinTS installation
directory = system.file(package='FinTS').
Corrections to the script files provided and contributions to script files for other chapters will be graciously accepted.
Examples
# Where is the 'FinTS' directory?
system.file(package='FinTS')
#> [1] "/tmp/RtmpX14IGL/temp_libpathe16b71875ebc/FinTS"
# View the script file 'ch01.R', which is in the 'scripts'
# subdirectory of the system.file(package='FinTS') directory:
runscript(1, 'view')
#> ### ch. 1. Financial Time Series and Their Characteristics
#> ###
#> ###
#> ### Ruey S. Tsay (2005)
#> ### Analysis of Financial Time Series, 2nd ed.
#> ### (Wiley)
#> ###
#> ###
#> library(FinTS)
#>
#> ##
#> ## sec. 1.1. Assett Returns
#> ##
#>
#> # p. 4
#> # Table 1.1. Illustration of the effects of compounding
#> freqs <- c(1, 2, 4, 12, 52, 365, Inf)
#> cI <- compoundInterest(0.1, frequency=freqs)
#> (Table1.1 <- data.frame(Type=c(
#> "Annual", "Semiannual", "Quarterly",
#> "Monthly", "Weekly", "Daily", "Continuously"),
#> Number.of.payments=freqs,
#> Interest.rate.per.period=0.1/freqs,
#> Net.Value=cI) )
#>
#> # p. 6
#> # Example 1.1.
#> logRtn <- .0446
#> 100*expm1(logRtn)
#>
#> mo.logRtns <- c(.0446, -.0734, .1077)
#> (q.logRtns <- sum(mo.logRtns))
#>
#> ##
#> ## sec. 1.2. Distributional Properties of Returns
#> ##
#> # p. 7
#> # sec. 1.2.1. Review of Statistical Distributions and their Moments
#>
#> # p. 10
#> # Example 1.2.
#> data(d.ibmvwewsp6203)
#> s.ibm <- sd(d.ibmvwewsp6203[, "IBM"])
#> (skew.ibm <- e1071::skewness(d.ibmvwewsp6203[, "IBM"]))
#> (n.ibm <- dim(d.ibmvwewsp6203)[1])
#> (skew.sd <- sqrt(6/n.ibm))
#> (t.sk <- (skew.ibm/skew.sd))
#> pnorm(-t.sk)
#>
#> # A slightly more accurate version of this is
#> # agostino.test
#> library(moments)
#> (skew.test <- agostino.test(as.numeric(d.ibmvwewsp6203[, "IBM"])))
#>
#> # p. 11
#> # Table 1.2. Descriptive Statistics for Daily and Monthly
#> # Simple and Log Returns for Selected Indexes and Stocks
#> data(d.ibmvwewsp6203)
#> data(d.intc7303)
#> data(d.3m6203)
#> data(d.msft8603)
#> data(d.c8603)
#> (Daily.Simple.Returns.pct <- rbind(
#> FinTS.stats(100*d.ibmvwewsp6203[, "SP"]),
#> FinTS.stats(100*d.ibmvwewsp6203[, "VW"]),
#> FinTS.stats(100*d.ibmvwewsp6203[, "EW"]),
#> FinTS.stats(100*d.ibmvwewsp6203[, "IBM"]),
#> FinTS.stats(100*d.intc7303[,"Intel"]),
#> FinTS.stats(100*d.3m6203[, "MMM"]),
#> FinTS.stats(100*d.msft8603[, 'MSFT']),
#> FinTS.stats(100*d.c8603[, "C"])
#> ) )
#>
#> (Daily.log.Returns.pct <- rbind(
#> FinTS.stats(100*log(1+d.ibmvwewsp6203[, "SP"])),
#> FinTS.stats(100*log(1+d.ibmvwewsp6203[, "VW"])),
#> FinTS.stats(100*log(1+d.ibmvwewsp6203[, "EW"])),
#> FinTS.stats(100*log(1+d.ibmvwewsp6203[, "IBM"])),
#> FinTS.stats(100*log(1+d.intc7303[,"Intel"])),
#> FinTS.stats(100*log(1+d.3m6203[, "MMM"])),
#> FinTS.stats(100*log(1+d.msft8603[, 'MSFT'])),
#> FinTS.stats(100*log(1+d.c8603[, "C"]))
#> ) )
#>
#> data(m.ibmvwewsp2603)
#> data(m.intc7303)
#> data(m.3m4603)
#> data(m.msft8603)
#> data(m.c8603)
#> (Monthly.Simple.Returns.pct <- rbind(
#> SP=FinTS.stats(100*m.ibmvwewsp2603[, "SP"]),
#> VW=FinTS.stats(100*m.ibmvwewsp2603[, "VW"]),
#> EW=FinTS.stats(100*m.ibmvwewsp2603[, "EW"]),
#> IBM=FinTS.stats(100*m.ibmvwewsp2603[, "IBM"]),
#> Intel=FinTS.stats(100*m.intc7303[,"Intel"]),
#> MMM=FinTS.stats(100*m.3m4603[, "MMM"]),
#> Microsoft=FinTS.stats(100*m.msft8603[, 'MSFT']),
#> CitiGroup=FinTS.stats(100*m.c8603[, "C"])
#> ) )
#>
#> (Monthly.log.Returns.pct <- rbind(
#> SP=FinTS.stats(100*log(1+m.ibmvwewsp2603[, "SP"])),
#> VW=FinTS.stats(100*log(1+m.ibmvwewsp2603[, "VW"])),
#> EW=FinTS.stats(100*log(1+m.ibmvwewsp2603[, "EW"])),
#> IBM=FinTS.stats(100*log(1+m.ibmvwewsp2603[, "IBM"])),
#> Intel=FinTS.stats(100*log(1+m.intc7303[,"Intel"])),
#> MMM=FinTS.stats(100*log(1+m.3m4603[, "MMM"])),
#> Microsoft=FinTS.stats(100*log(1+m.msft8603[, 'MSFT'])),
#> CitiGroup=FinTS.stats(100*log(1+m.c8603[, "C"]))
#> ) )
#>
#> dimnames(Daily.Simple.Returns.pct)[[1]] <-
#> c("SP", "VW", "EW", "IBM", "Intel", "3M", "MSFT", "Citi")
#> dimnames(Daily.log.Returns.pct)[[1]] <-
#> c("SP", "VW", "EW", "IBM", "Intel", "3M", "MSFT", "Citi")
#> dimnames(Monthly.Simple.Returns.pct)[[1]] <-
#> c("SP", "VW", "EW", "IBM", "Intel", "3M", "MSFT", "Citi")
#> dimnames(Monthly.log.Returns.pct)[[1]] <-
#> c("SP", "VW", "EW", "IBM", "Intel", "3M", "MSFT", "Citi")
#>
#> #write.table(rbind(Daily.Simple.Returns.pct, Daily.log.Returns.pct,
#> # Monthly.Simple.Returns.pct, Monthly.log.Returns.pct),"Table1-2.csv", sep=",")
#>
#> # p. 12
#>
#> # Comparable to S-Plus Demonstration
#> # module(finmetrics)
#> # x = matrix(scan(file = 'd-ibmvwewsp6203.txt'), 5) % load the data
#> # ibm = x[2,]*100 % compute percentage returns
#>
#> data(d.ibmvwewsp6203)
#> quantile(100*as.numeric(d.ibmvwewsp6203[, "IBM"]))
#> FinTS.stats(100*d.ibmvwewsp6203[, "IBM"])
#>
#> # p. 15
#> # Sec. 1.2.2. Distributions of Returns
#> # p. 16
#> # Figure 1.1. Comparisons of Finite Mixture, Stable
#> # and Standard Normal Distributions
#> library(distrEx)
#>
#> N01 <- Norm()
#> N04 <- Norm(0, 4)
#>
#> contamNorm <- ConvexContamination(N01, N04, size = 0.05)
#>
#> plot(dnorm, xlim=c(-4, 4))
#> text(.75, .39, "Normal")
#> x <- seq(-4, 4, len=201)
#> lines(x, d(contamNorm)(x), lty="dotted", col="red", lwd=2)
#> text(0, 0.351, "Mixture", col="red")
#> lines(x, dcauchy(x), lty="dashed", col="green", lwd=2)
#> text(0, 0.25, "Cauchy", col="green")
#>
#> # p. 17
#> # Sec. 1.2.5. Empirical properties of returns
#>
#> # p. 18
#> # Figure 1.2. Time plots of monthly returns of IBM stock
#>
#> op <- par(mfrow=c(2,1))
#> plot(m.ibmvwewsp2603[, "IBM"], xlab="year", ylab="s-rtn")
#> plot(log(1+m.ibmvwewsp2603[, "IBM"]), xlab="year", ylab="log-rtn")
#> par(op)
#>
#>
#> # Figure 1.3. Time plots of monthly returens of the value-weighted index
#>
#> op <- par(mfrow=c(2,1))
#> plot(m.ibmvwewsp2603[, "VW"], xlab="year", ylab="s-rtn")
#> plot(log(1+m.ibmvwewsp2603[, "VW"]), xlab="year", ylab="log-rtn")
#> par(op)
#>
#> # p. 19
#> # Figure 1.4. Comparison of empirical and normal densities
#> # for the monthly simple and log returns of IBM stock
#>
#> if(require(logspline)){
#>
#> m.ibm <- m.ibmvwewsp2603[, "IBM"]
#> dens.ibm.rtns <- logspline(100*as.numeric(m.ibm))
#> m.log.ibm <- 100*log(1+m.ibm)
#> dens.ibm.logrtns <- logspline(as.numeric(m.log.ibm))
#>
#> op <- par(mfrow=c(1,2))
#> plot(dens.ibm.rtns, xlim=c(-40, 40), xlab="simple returns", ylab="density")
#> x.ibm <- seq(-40, 40, len=201)
#> lines(x.ibm, dnorm(x.ibm, mean=100*mean(m.ibm), s=100*sd(m.ibm)),
#> lty="dotted", col="red", lwd=2)
#>
#> plot(dens.ibm.logrtns, xlim=c(-40, 40), xlab="log returns", ylab="density")
#> lines(x.ibm, dnorm(x.ibm, mean=mean(m.log.ibm), s=sd(m.log.ibm)),
#> lty="dotted", col="red", lwd=2)
#> par(op)
#> }
#> qqnorm(100*m.ibm, datax=TRUE)
#> qqnorm(m.log.ibm, datax=TRUE)
#>
#> # normal plots may provide a more sensitive evaluation
#> # of skewness and kurtosis than density plots
#>
#> # The kurtosis here does not look extreme.
#> # Similar plots of the daily returns show
#> # much more extreme kurtosis.
#>
#> # p. 20
#> ##
#> ## 1.3. Processes Considered
#> ##
#> data(m.gs10)
#> data(m.gs1)
#> # Figure 1.5. Time plot of US monthly interest rates
#> op <- par(mfrow=c(2,1))
#> plot(m.gs10, xlab="year", ylab="rate", type="l")
#> title("Monthly 10-yr Treasury constant maturity rates")
#> plot(m.gs1, xlab="year", ylab="rate", type="l")
#> title("Monthly 1-yr Treasury constant maturity rates")
#> par(op)
#>
#> # p. 21
#> # Figure 1.6. Time plot of daily exchange rate
#> # between US dollara and Japanese Yen
#> data(d.fxjp00)
#> op <- par(mfrow=c(2,1))
#> plot(d.fxjp00, xlab="year", ylab="Yens", type="l")
#> plot(diff(d.fxjp00), xlab="year", ylab="Change", type="l")
#> par(op)
#>
#> # p. 22
#> # Table 1.3. Descriptive Statistics of Selected US Financial Time Series
#> data(m.fama.bond5203)
#> (m.bondRtns <- rbind(
#> "1-12 months"=FinTS.stats(100*m.fama.bond5203[, "m1.12"]),
#> "24-36 months"=FinTS.stats(100*m.fama.bond5203[, "m24.36"]),
#> "48-60 months"=FinTS.stats(100*m.fama.bond5203[, "m48.60"]),
#> "61-120 months"=FinTS.stats(100*m.fama.bond5203[, "m61.120"]) ))
#>
#> data(m.gs1)
#> data(m.gs3)
#> data(m.gs5)
#> data(m.gs10)
#> (m.treasuryRtns <- rbind(
#> "1 year"=FinTS.stats(m.gs1),
#> "3 years"=FinTS.stats(m.gs3),
#> "5 years"=FinTS.stats(m.gs5),
#> "10 years"=FinTS.stats(m.gs10) ))
#>
#> data(w.tb3ms)
#> data(w.tb6ms)
#> (w.treasuryRtns <- rbind(
#> "3 months"=FinTS.stats(w.tb3ms),
#> "6 months"=FinTS.stats(w.tb6ms) ) )
#>
#> #write.table(rbind(m.bondRtns, m.treasuryRtns, w.treasuryRtns),
#> # "Table1-3.csv", sep=",")
# SP statistics in Table 1.2 of Tsay
data(d.ibmvwewsp6203)
FinTS.stats(100*d.ibmvwewsp6203[, "SP"])
#> Start Size Mean Standard.Deviation Skewness Excess.Kurtosis
#> 1 1962-07-03 10446 0.03312081 0.9453075 -0.9449986 25.76459
#> Minimum Maximum
#> 1 -20.47 9.1