ACF
AIC
using for model selection
aicbic
Akaike information criteria. See AIC
AR model
converting from ARMA model
ARCH/GARCH effects
hypothesis test
archtest
ARMA model
converting to AR model
converting to MA model
asymptotic behavior
for long-range forecast horizons
autocorr
auto-correlation function. See ACF
auto-regressive model. See AR model
Bayesian information criteria. See BIC
BIC
using for model selection
compounding
continuous and periodic
conditional mean models
with regression components
conditional standard deviations
inferred from observed return series
of forecast errors
simulating
conditional variances
constant
of the innovations process
constraints
boundary <1> <2>
equality
fixing model parameters
stationarity and positivity
conventions in GARCH Toolbox <1> <2>
convergence
considerations
determining status
crosscorr
cross-correlation function. See XCF
default model
estimation example
forecasting example
defining a model
using a GARCH specification structure
estimating initial parameters
estimation
count of coefficients <1> <2>
incorporating a regression model
of GARCH process parameters
summary information
estimation example
estimating the model parameters
post-estimation analysis
pre-estimation analysis
using the default model
fixing model parameters
forecast
how to compute
forecast errors
conditional standard deviations <1> <2>
forecasted explanatory data
forecasting
asymptotic behavior
computing RMSE
conditional mean
conditional standard deviation
incorporating a regression model
minimum mean square error volatility. See MMSE volatility
MMSE volatility <1> <2>
plotting results
using the default model
GARCH
limitations
overview
uses for
GARCH model
default
GARCH process
forecasting
inferring innovations
objective function
parameter estimation
count of coefficients
displaying results
plotting results
simulation
GARCH specification structure
contents
creating and modifying parameters <1> <2>
definition of fields
fixing model parameters
parameters that affect convergence
retrieving parameters
use of parameters in simulation
using as function input and output
using to define a model
GARCH Toolbox
conventions and clarifications
array definitions
compounding
precision of calculations
row and column conventions
stationarity
overview
recommendations and suggestions
garchar
garchcount
garchdisp
garchfit
garchget
garchinfer
garchllfn
garchma
garchplot
garchpred
garchset
garchsim
homoskedasticity
unconditional variance
hypothesis tests
ARCH/GARCH effects
likelihood ratio
Ljung-Box lack-of-fit
inference
using a regression model
inferring
conditional standard deviations
GARCH innovations
innovations
distribution
inferring from observed return series
serial dependence
simulating
lack-of-fit hypothesis test
lagged time series matrix
lagmatrix
lbqtest
likelihood ratio hypothesis test
likelihood ratio tests
using for model selection
Ljung-Box lack-of-fit hypothesis test
log-likelihood objective function
computing values
gradient values
maximization
optimized value
lratiotest
MA model
converting from ARMA model
maximum likelihood estimation
model parameters
boundary constraints
equality constraints
estimating
initial estimates
model selection and analysis
using AIC and BIC
using likelihood ratio tests
Monte Carlo simulation
moving average model. See MA model
PACF
parameter estimation of GARCH process
parcorr
parsimonious parameterization <1> <2>
partial auto-correlation function See PACF
plotting
auto-correlation function
cross-correlation function
forecasting results
parameter estimation results
partial auto-correlation function
simulation results
prerequisites
price series
converting from return series
converting to return series
price2ret
regression
in a Monte Carlo framework
regression components
in estimation
in forecasting
in inference
in simulation
of conditional mean models
ret2price
return series
converting from price series
converting to price series
data size and quality
simulating
RMSE
computing for forecasted data
root mean square error. See RMSE
selecting a model
shifted time series matrix
simulating sample paths
simulation
of GARCH process
plotting results
using a regression model
using ordinary least squares regression
simulation example
using a higher order model
using the default model
specification structure. See GARCH specification structure
stationary and nonstationary time series
time series
correlation of observations
stationary and nonstationary
time series matrix
lagged or shifted
transient effects
minimizing
overview
transients
in the simulation process
typographical conventions (table)
unconditional variances
of the innovations process
variances
conditional and unconditional
volatility clustering
XCF