fSeriesData {fSeries} | R Documentation |
A collection of Data Sets used in the examples of the fSeries
library.
recession
Data sets used in the regression analysis.
Recession data from the US,
3 Month Tbills data from US FED,
10 Year Tbonds data from US FED,
Stock-Watson experimental recession index.
spc1970
and
spcindis
These data are indicators for daily SP500 Stock Data together
with an set of trading indicators for technical analysis.
dem2gbp
The file "dem2gbp" contains daily observations of the
Deutschmark / British Pound foreign exchange log returns.
This data set has been promoted as an informal benchmark
for GARCH time-series software validation. See McCullough and
Renfro [1991], and Brooks, Burke, and Persand (2001) for details.
The nominal returns are expressed in percent, as published in
Bollerslev and Ghysels (2001). The data set is available from
the Journal of Business and Economic Statistics, (JBES),
ftp://www.amstat.org. A text file has one column of
data listing the percentual log-returns of the DEM/GBP exchange
rates. The sample period is from January 3, 1984, to December
31, 1991, for a total of 1975 daily observations of FX exchange
rates.
cac40
Daily CAC40 index returns with realized volatility. The data
cover the period January 1995 until December 1999, and have 1249
observations. The first column of the file CAC40
lists
the averaged return of the Index, and the second column
CAC40VOL
lists the realized volatility. The data are
used a s a benchmark for GARCH modeling by Laurent and Peters
(2002).
All files are in CSV Excel spreadsheet format.
Brooks C., Burke S.P, Persand G. (2001); Benchmarks and the Accuracy of GARCH Model Estimation, International Journal of Forecasting 17, 45–56.
McCullough B.D., Renfro C.G. (1998); Benchmarks and Software Standards: A Case Study of GARCH Procedures, Journal of Economic and Social Measurement 25, 59–71.
Laurent S., Peters J.P. (2002), G@RCH 2.2: An Ox Package for Estimating and Forecasting Various ARCH Models, Journal of Economic Surveys 16, 447–485.
## NYSE Residuals: data(nyseres) ts.plot(nyseres, xlab = "Index", ylab = "log-Returns", main = "NYSE: log-Returns")