Volatility Forecast Toolbox 1.0

The Toolbox forecasts the volatility of a (mxn) vector of data and from a variety of in-built / non-in-built GARCH models with various distributions, as well as the univariate RiskMetrics. The toolbox also estimates a number of Volatility Forecast Loss Functions and if selected Value-at-Risk for both short and long positions, along with a number of back-tests.The three main functions are the following:VFMatlab.m: In-built Matlab functions (i.e. models: GARCH, GJR-GARCH and EGARCH, distributions Normal and Studentd-deOaos t-Distribution)VFGarch.m: Forecasts the volatility of the following models and distributions:The supported models are:GARCH, GJR-GARCH, EGARCH,NARCH: Nonlinear ARCH: Engle and Bollerslev (1986) NGARCH: Nonlinear GARCH: Higgins and Bera (1992) AGARCH: Asymmetric GARCH: Engle (1990) APGARCH: Asymmetric Power GARCH: Ding, Granger and Engle (1993) NAGARCH: Nonlinear Asymmetric GARCH: Engle and Ng (1993)The supported distributions are:GAUSSIAN: Gaussian / Normal Distribution T: Student t-Distribution GED: Generalized Error Distribution CAUCHY: Cauchy-Lorentz Distribution HANSEN: Hansen's Skew-t Distribution GC: Gram-Charlier Expansion SeriesFinally, VFRiskMetrics.m forecasts the volatility of RiskMetrics model.Furthermore, not only the rolling window (P) and maximum number of forecasts (max_forecast) can be specified but the number of forecasts that are of interest (int_forecasts). This means, for example, that one forecasts the volatility of daily returns for up to 22 days, but is interested only for the 1-day, 1- and 2-weeks and 1-month periods. Finally, a number of volatility forecast evaluation metrics are estimated and are given in: LossFunctions.m. The forecast evaluation metrics are: Mean Square Error, Median Absolute Error, Median Absolute Percentage Error, Mean Absolute Deviation, Heteroskedasticity-adjusted Mean Square Error, Heteroskedasticity-adjusted Mean Absolute Error, R2LOG and QLIKE.If it is enabled, Value-at-Risk for both long and short positions is estimated as well as a number of back-tests, which are given in VaRTest.m. The VaR are computed according to the distribution specified for the estimation of the GARCH model.Value-at-Risk Backtesting for Long or/and Short Positions entail the estimation of the following: Percentage of Failures, Time Until First Failure, Christoffersen (2003) test for correct conditional coverage, which covers: unconditional coverage, independence test, and conditional coverage, and finally Basel II Accord evaluation criteria

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	Absolute,  Deviation,  Estimated,  Evaluation,  Finally,  Forecast,  Interested,  Lossfunctionsm,  Median,  Metrics,  Modelfurthermore,  Month,  Percentage,  Periods,  Returns,  Rolling,  Square,  Weeks,  Window
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