Numerical recipes: the art of scientific computing
Numerical recipes: the art of scientific computing
Time series: theory and methods
Time series: theory and methods
Modelling Federal Reserve Discount Policy
Computational Economics
A simple multivariate ARCH model specified by random coefficients
Computational Statistics & Data Analysis
Exploring the state sequence space for hidden Markov and semi-Markov chains
Computational Statistics & Data Analysis
Simulation-based sequential analysis of Markov switching stochastic volatility models
Computational Statistics & Data Analysis
Modelling nonlinearities and heavy tails via threshold normal mixture GARCH models
Computational Statistics & Data Analysis
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
Editorial: Special Issue on Statistical and Computational Methods in Finance
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Computing and estimating information matrices of weak ARMA models
Computational Statistics & Data Analysis
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A procedure is proposed for computing the autocovariances and the ARMA representations of the squares, and higher-order powers, of Markov-switching GARCH models. It is shown that many interesting subclasses of the general model can be discriminated in view of their autocovariance structures. Explicit derivation of the autocovariances allows for parameter estimation in the general model, via a GMM procedure. It can also be used to determine how many ARMA representations are needed to identify the Markov-switching GARCH parameters. A Monte Carlo study and an application to the Standard & Poor index are presented.