Analysis of Financial Time Series

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, andFrench 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
    (The file contains the simple returns in Column 1.
      The sample period is from 1946/6 to 1999/12.)

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.