R: MSCI Switzerland Index

MSCISwitzerland {AER} | R Documentation

## MSCI Switzerland Index

### Description

Time series of the MSCI Switzerland index.

### Usage

    data("MSCISwitzerland")

### Format

A daily univariate time series from 1994-12-30 to 2012-12-31 (of class `"zoo"` with `"Date"` index).

### Source

Online complements to Franses, van Dijk and Opschoor (2014).

http://www.cambridge.org/us/academic/subjects/economics/econometrics-statistics-and-mathematical-economics/time-series-models-business-and-economic-forecasting-2nd-edition

### References

Ding, Z., Granger, C. W. J. and Engle, R. F. (1993). A Long Memory Property of Stock Market Returns and a New Model. Journal of Empirical Finance, 1(1), 83–106.

Franses, P.H., van Dijk, D. and Opschoor, A. (2014). Time Series Models for Business and Economic Forecasting, 2nd ed. Cambridge, UK: Cambridge University Press.

### Examples

    data("MSCISwitzerland", package = "AER")

    ## p.190, Fig. 7.6
    dlmsci <- 100 * diff(log(MSCISwitzerland))
    plot(dlmsci)

    dlmsci9501 <- window(dlmsci, end = as.Date("2001-12-31"))

    ## Figure 7.7
    plot(acf(dlmsci9501^2, lag.max = 200, na.action = na.exclude),
      ylim = c(-0.1, 0.3), type = "l")

    ## GARCH(1,1) model, p.190, eq. (7.60)

    ## standard errors using first derivatives (as apparently used by Franses et al.)
    library("tseries")
    msci9501_g11 <- garch(zooreg(dlmsci9501), trace = FALSE)
    summary(msci9501_g11)

    ## standard errors using second derivatives
    library("fGarch")
    msci9501_g11a <- garchFit( ~ garch(1,1), include.mean = FALSE,
      data = dlmsci9501, trace = FALSE)
    summary(msci9501_g11a)

    round(msci9501_g11a@fit$coef, 3)
    round(msci9501_g11a@fit$se.coef, 3)

    ## Fig. 7.8, p.192
    plot(msci9501_g11a, which = 2)
    abline(h = sd(dlmsci9501))

    ## TGARCH model (also known as GJR-GARCH model), p. 191, eq. (7.61)
    msci9501_tg11 <- garchFit( ~ aparch(1,1), include.mean = FALSE,
      include.delta = FALSE, delta = 2, data = dlmsci9501, trace = FALSE)
    summary(msci9501_tg11)

    ## GJR form using reparameterization as given by Ding et al. (1993, pp. 100-101)
    coef(msci9501_tg11)["alpha1"] * (1 - coef(msci9501_tg11)["gamma1"])^2  ## alpha*
    4 * coef(msci9501_tg11)["alpha1"] * coef(msci9501_tg11)["gamma1"]      ## gamma*

    ## GARCH and GJR-GARCH with rugarch
    library("rugarch")
    spec_g11 <- ugarchspec(variance.model = list(model = "sGARCH"),
      mean.model = list(armaOrder = c(0,0), include.mean = FALSE))
    msci9501_g11b <- ugarchfit(spec_g11, data = dlmsci9501)
    msci9501_g11b

    spec_gjrg11 <- ugarchspec(variance.model = list(model = "gjrGARCH", garchOrder = c(1,1)),
      mean.model = list(armaOrder = c(0, 0), include.mean = FALSE))
    msci9501_gjrg11 <- ugarchfit(spec_gjrg11, data = dlmsci9501)
    msci9501_gjrg11

    round(coef(msci9501_gjrg11), 3)

[Package AER version 1.2-5 Index]