ch09data {FinTS}

Financial time series for Tsay (2005, ch. 9)

Description

Financial time series used in examples in chapter 9.

Usage

data(m.fac9003)
data(m.cpice16.dp7503)
data(m.barra.9003)
data(m.5cln)
#data(m.bnd) <- documented with ch08, also used in ch09
data(m.apca0103)

Format

m.fac9003

a zoo object of 168 observations giving simple excess returns of 13 stocks and the Standard and Poor's 500 index over the monthly series of three-month Treasury bill rates of the secondary market as the risk-free rate from January 1990 to December 2003. (These numbers are used in Table 9.1.)

AA

Alcoa

AGE

A. G. Edwards

CAT

Caterpillar

F

Ford Motor

FDX

FedEx

GM

General Motors

HPQ

Hewlett-Packard

KMB

Kimberly-Clark

MEL

Mellon Financial

NYT

New York Times

PG

Proctor & Gamble

TRB

Chicago Tribune

TXN

Texas Instruments

SP5

Standard & Poor's 500 index

m.cpice16.dp7503

a zoo object of 168 monthly on two macroeconomic variables from January 1975 through December 2002 (p. 412):

CPI

consumer price index for all urban consumers: all items and with index 1982-1984 = 100

CE16

Civilian employment numbers 16 years and over: measured in thousands

m.barra.9003

a zoo object giving monthly excess returns of ten stocks from January 1990 through December 2003:

AGE

A. G. Edwards

C

Citigroup

MWD

Morgan Stanley

MER

Merrill Lynch

DELL

Dell, Inc.

IBM

International Business Machines

AA

Alcoa

CAT

Caterpillar

PG

Proctor & Gamble

m.5cln

a zoo object giving monthly log returns in percentages of 5 stocks from January 1990 through December 1999:

IBM

International Business Machines

HPQ

Hewlett-Packard

INTC

Intel

MER

Merrill Lynch

MWD

Morgan Stanley Dean Witter

m.apca0103

data.frame of monthly simple returns of 40 stocks from January 2001 through December 2003, discussed in sect. 9.6.2, pp. 437ff.

CompanyID

5-digit company identification code

date

the last workday of the month

return

in percent

Source

https://faculty.chicagobooth.edu/ruey-s-tsay/teaching

References

Ruey Tsay (2005) Analysis of Financial Time Series, 2nd ed. (Wiley, ch. 7)

See Also

ch01data, ch02data, ch03data, ch04data, ch05data, ch06data

Examples

data(m.apca0103)
dim(m.apca0103)
# 1440 3;  1440 = 40*36
# Are the dates all the same?
sameDates <- rep(NA, 39)
for(i in 1:39)
    sameDates[i] <- with(m.apca0103,
                         all.equal(date[1:36], date[(i*36)+1:36]))
stopifnot(all(sameDates))
M.apca0103 <- with(m.apca0103, array(return, dim = c(36, 40), dimnames =
    list(NULL, paste("Co", CompanyID[seq(1, 1440, 36)], sep=""))))