Data sets and
commands of computer programs used in the book.
Software packages used in the book:
SCA (Scientific
Computing Associates),
RATS
(Regression Analysis of Time Series),
and S-Plus and R .
Some free softwares are available at Statlib, including neural
networks on
S-Plus and the package R.
Chapter 1: Financial Time Series and Their
Characteristics
Data used
in the text:
(1) Daily log returns of IBM (62/7/3
to 97/12): d-ibmln.dat
(2) Daily simple returns of
value-weighted and
equal-weighted indexes:
d-vwew.dat
(3) Daily simple returns of Intel stock:
d-intc.dat
(4) Daily
simple returns of 3M stock:
d-mmm.dat
(5) Daily simple returns of Microsoft stock:
d-msft.dat
(6) Daily simple returns of Citi-group stock:
d-citi.dat
(7) Monthly bond returns (30 yrs, 20 yrs,
..., 1 yr):
m-bnd.dat
(8) Monthly Treasury rates (10 yrs, 5 yrs,
..., 1 yr):
m-gs.dat
(9) Weekly Treasury Bill rates:
w-tb3ms.dat & w-tb6ms.dat
Data sets for Exercises:
1. Log returns of Alcoa stock: d-aa9099.dat
Log returns of American Express stock:
d-axp9099.dat
Log returns of Disney stock:
d-dis9099.dat
Log returns of Chicago Tribune stock:
d-trb9099.dat
Log returns of Tyco International stock:
d-tyc9099.dat
2. Monthly
log stock returns of five U.S. companies:
Alcoa: m-aa6299.dat
American Express: m-axp7399.dat
Disney: m-dis6299.dat
General
Motors: m-gm6299.dat
Hershey Foods: m-hsy6299.dat
Mellon Financial Co.:
m-mel7399.dat
3. See Alcoa stock returns in Problem 2.
4. See American Express stock returns in Problem 2.
5. See American Express stock returns in Problem 1.
6. Exchange
rates of Canadian Dollar, German Mark,
United Kingdom Pound, Japanese Yen,
and
French Franc versus U.S. Dollar:
forex-c.dat
Chapter 2: Linear Time Series Analysis and Its Applications
Data sets used in the chapter:
(1)
U.S. quarterly growth rates of GNP: q-gnp.dat
(2) Monthly
value-weighted index returns:
m-vw.dat
(3) Monthly equal-weighted index returns:
m-ew.dat
(4) Monthly log returns of 3M stock: m-3m4699.dat
(5) Quarterly earnings per share of
Johnson & Johnson: jnj.dat
(6) Weekly U.S. Treasury
1-y and 3-y constant maturity rates:
w-gs1yr.dat and w-gs3yr.dat
Data sets for Exercises:
3. Simple returns on monthly U.S. bonds: m-bnd.dat
4. Daily log returns of Alcoa stock: d-aa9099.dat
5. Daily
log returns of Hewlett-Packard, value-weighted,
equal-weighted and SP500 index: d-hwp3dx8099.dat
6. Monthly log returns of equal-weighted index: m-ew6299.dat
7. See Problem 5.
8. Daily
log returns of equal-weighted index: see Problem 5.
Calendar of 1980 on (yr,mm,dd,date): day80on.dat
Dummy
variables (M,T,W,R,yr,mm,dd,days): wkdays8099.dat
9. Log prices of futures and spot of SP500: sp5may.dat
10. U.S. quarterly unemployment rates: q-unemrate.dat
11. Quarterly GDP implicit price deflator: gdpipd.dat
Chapter 3: Conditional Heteroscedastic Models
Data sets used in the text:
(1)
Monthly simple returns of Intel stock: m-intc.dat
RATS program for an ARCH(3)
model: m-intc.rats
(2) 10-m log returns of
FX (Mark-US): exch-perc.dat
(3) Excess returns of
S&P500: sp500.dat
RATS programs for various volatility models:
(a) AR(3)-GARCH(1,1): m-sp-ar-garch11.rats
(b) GARCH(1,1): m-sp-garch11.rats
(c) GARCH(1,1) with
t_5: t5-garch11.rats
(d) GARCH(1,1) with
t: garch11-t.rats
(e) IGARCH(1,1): m-sp-igarch.rats
(f) GARCH(1,1)-M model: m-sp-garchm.rats
(g) CHARMA model: sp-charma.rats
(4) Monthly log returns of IBM
stock: m-ibmln.dat
RATS program for
EGARCH(1,0): ibm-egarch10.rats
(5) Daily log
returns of SP500 index: see d-hwp3dx8099.dat
in Chapter 2.
(6) Monthly log returns of IBM stock & SP500: m-ibmspln.dat
Data
set for Example 3.5: m-ibmsplnsu.dat
RATS program without summer effect: summer.rats
RATS
program with summer effect: summer1.rats
RATS
program for Example 3.6: charmax.rats
Data sets for exercises:
5. Monthly log returns of Intel stock: m-intc.dat
6. Monthly simple returns of Merck stock: m-mrk.dat
7. Monthly simple returns of 3M stock: m-mmm.dat
8. Monthly log returns of GM stock & Sp500: m-gmsp5099.dat
9. See problem 8.
10. Daily log returns of IBM stock: d-ibmln.dat
Chapter 4: Nonlinear Models and Their Applications
Data sets used in the text:
(1) Monthly simple returns of
equal-weighted index: m-ew.dat
(2) Daily
log returns of IBM stock: d-ibmln99.dat
RATS program for TAR-GARCH model: ibm-ar-tar.rats
(3) Monthly simple returns of 3M
stock: m-mmm.dat
RATS
program for smooth TAR: star.rats
(4) Quarterly
growth rates of U.S. gnp: q-gnp.dat
(5) Monthly log returns of IBM stock: m-ibmln99.dat
(6)
Quarterly unemployment rates: q-unemrate.dat
To run
neural networks
on S-Plus or R, visit the Modapplstat
at the
S-Archive on Statlib for free software
R and S commands for Example 4.5 are
in nnet-ibm.sor and the
data set is m-ibmln99.dat.
Data sets for exercises:
1. Monthly log returns of GE stock: m-ge2699.dat
5. Weekly U.S. interest rates:
(a) Treasury 1-year constant maturity rates: wgs1yr.dat
(b) Treasury 3-year constant maturity rates: wgs3yr.dat
Chapter 5: High-Frequency Data
Analysis and
Market
Microstructure
Data stes
used in the text:
(1) IBM transactions data (11/1/90-1/31/91): The
columns
are date/time, volume, bid quote, ask quote,
and
transaction price: ibm.txt
(large)
(2) IBM transactions data of December
1999.
(day. time, price): ibm9912-tp.dat (large)
(3) Adjusted time durations between trades
(11/01/90-
1/31/91). Positive durations only: ibmdurad.dat
(4) Adjusted durations in (3) for the
first 5 trading days:
ibm1to5-dur.dat
(5)
Data for Example 5.2 (files are relatively large)
(a) The ADS
file: ibm91-ads.dat
(b) The explanatory variables as defined: ibm91-adsx.dat
(6) Transactions data of
IBM stock on November 21, 1990
(a) original data: day15-ori.dat
(b) data for PCD
models: day15.dat
data descriptions in file day15.txt
RATS programs for estimating
duration models:
The data file used is ibm1to5-dur.dat.
(a) EACD model: eacd.rats
(b) WACD model: wacd.rats
(c) GACD model: gacd.rats
(d) Threshold-WACD model: tar-wacd.rats.
Data sets for
exercises:
3. Adjusted durations of IBM stock (11/2/90): ibm-d2-dur.dat
5. Transactions data of 3M (12/99): mmm9912-dtp.dat (large)
6. Adjusted durations of 3M (12/99): mmm9912-adur.dat
Chapter 6: Continuous-Time Models and Their Applications
Data sets used in the
text:
(1) Daily simple returns of IBM stock in 1998: ibmy98.dat
(2) Daily log returns of Cisco stock in
1999: d-cscoy99ln.dat
Source code of a Fortran
program for European call and put options
based on the simple jump
diffusion model discussed in the text:
kou.f (You need to
compile the program.)
Chapter 7: Extreme Values, Quantile Estimation, and Value at Risk
Data sets used in the text:
(1) Daily log returns
of IBM stock: d-ibmln98.dat (9190
obs)
The returns are in percentages.
(2)
RATS programs used in Example 7.3:
(Note: returns used in the example are not in
percentages.)
(a) AR(2)-GARCH(1,1): example7-3a.rats
(b) AR(2)-GARCH(1,1)-t5: example7-3b.rats
(3) Daily log returns
of Intel stock (Example 7.4): d-intc7297.dat
(4)
Data used in Subsection 7.7.6
(a) Mean-corrected daily log returns of IBM: ibmln98wm.dat
(b) The
explanatory variables on page 294: ibml25x.dat
Data sets for exercises:
1. Daily log returns (in percentages) of GE stock: d-geln.dat
2. Daily log returns (in percentages) of Cisco stock: d-csco9199.dat
3. See problem 2.
4. Daily log returns of HP and 3 indexes: d-hwp3dx8099.dat
Chapter 8: Multivariate Time Series Analysis and Its Applications
Data sets used in the
text:
(1) Monthly log returns of IBM and SP
500: m-ibmspln.dat
The SCA
commands used to analyze the series: sca-ex-ch8.txt
Source code of a Fortran
program for multivariate Q-stat: qstat.f
(2) Monthly simple returns of bond indexes:
m-bnd.dat
(3) Monthly U.S. interest
rates of Example 8.6: m-gs1n3.dat
SCA
commands used: sca-ex8-6.txt
(4) Log prices
of SP500 index futures and shares: sp5may.dat
(5)
Monthly log returns of IBM, HWP, INTC, MER & MWD: m-5cln.dat
Data sets for exercises:
1.
Monthly log returns of MRK et al.: m-mrk2vw.dat
2. Monthly U.S. interest rates (1 & 10 yrs): m-gs1n10.dat
3. See problem 2.
4. See problem 2.
Chapter 9: Multivariate Volatility Models and Their Applications
Data sets
used in the text:
(1) Daily log returns of HK and Japan market index
(Example 9.1):
Data file (491 data pts): hkja.dat
Bivariate GARCH
programs: hkja-c.rats and hkja-c1.rats
(2) Monthly log returns of IBM and SP
500: m-ibmspln.dat
Constant-correlation GARCH program: ibmsp-ex92.rats
Time-varying correlation GARCH: ibmsp-ex92q.rats
Cholesky Decomposition: ibmsp-choles.rats
(3) Daily log
returns of S&P 500, Cisco and Intel
stocks:
Data (3 columns): d-cscointc.dat
Time-varying 3-dim GARCH model: cholesky-ex93.rats
Data sets for exercises:
1. Problems 1 to 5: Monthly log returns of S&P 500, IBM
and GE stocks: m-spibmge.dat
6. Daily log returns of Dell and Cisco stocks: d-dellcsco9099.dat
Chapter 10: Markov Chain Monte Carlo Methods with Applications
Data sets
used in the text:
(1) Change series of weekly US interest rates (3-y &
1-y):
w-gs3n1c.dat
(2) Change series of weekly US 3-yr
interest rate: w-gs3c.dat
(3) Monthly log returns of
S&P 500 index: m-sp6299.dat
(4) Monthly log
returns of IBM stock & SP 500: m-ibmsp6299.dat
(5) Monthly log returns of GE
stock: m-geln.dat
Data
sets for exercises:
4. Monthly log returns of GM stock & SP500: m-gmsp5099.dat
5. Daily log returns of Cisco stock: d-csco9199.dat
6. See Problem 4.