| GARCH Toolbox |
 |
Index
- 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
| Bibliography | |