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 FANPAC MT

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FANPAC MT, through a cutting-edge implementation of GARCH models, provides an efficient
platform for volatility estimation in financial time series data for portfolio managers,
institutional traders and financial analysts.
GARCH and Multivariate GARCH
FANPAC MT incorporates a comprehensive suite of GARCH models for estimating volatility:
･ Multivariate Diagonal Vec and constant correlation Diagonal Vec GARCH and VAR-GARCH models
･ Multivariate BEKK models
･ Univariate OLS, ARIMA, ARCH, GARCH, ARMA-GARCH, AGARCH, exponential GARCH, integrated and
fractionally integrated GARCH
･ Univariate and multivariate models in Normal or t-distributions
･ Univariate and Diagonal Vec available ﾒin-meanﾓ; i.e., conditional variances and standard deviations may be
included in the mean equation
･ All univariate and Diagonal Vec multivariate ARCH and GARCH are available ﾒin-CVﾓ; i.e., regressors can be
added to the conditional variance equation
･ Procedures for computing standardized residuals, conditional variances, covariances matrices and forecasts
･ Confidence limits by simulation bounds (Andrews, D.W.K., 1999)
Portfolio Risk Analysis
FANPAC MT uses SQPSolveMT, a GAUSS program that solves the nonlinear programming problem. It is also
capable of solving the Markowitz mean/variance efficient frontier problem. Since it allows general nonlinear equality or
inequality constraints, it is capable of solving much more complicated mean/variance analyses.
The major shortcoming in computing the traditional efficient frontier has been in estimating the covariance matrix.
Traditional analysis assumes a constant covariance matrix across time. Experience has shown this not to be the case.
The multivariate GARCH model solves this problem. In the GARCH model, the covariance matrix is allowed to vary
with time. Research has shown that the GARCH model is quite successful fitting financial time series. With the
multivariate ARCH/GARCH models in FANPAC MT, you can forecast the covariance matrix over the next period and
use this estimate to generate investment portfolio weights that minimize your risk for a level of return and give you an stimate of your value-at-risk.

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