Forecasting volatility by means of threshold models
Article Abstract:
Threshold models are compared to predict volatility and determine the best model for forecasting. Generalized Autoregressive Conditional Heteroskedastic model (GARCH) and Stochastic Volatility model (SV) are employed to study the behavior on returns and leverage effects on the mean. Based on the study, the SETAR-THSVt is the best method for IBEX35 returns since this type of series produces more accurate predictions. Application of the Monte Carlo study to estimate the quality of the different procedures is discussed.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 2007
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Creating high-frequency national accounts with state-space modelling: a Monte Carlo experiment
Article Abstract:
A simple space-state model is presented as a technique for deriving high frequency data from lower frequency measurement, and it is verified using Monte Carlo experiments.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 2001
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Traditional versus unobserved components methods to forecast quarterly national account aggregates
Article Abstract:
A comparative analysis between traditional and modern methodologies for national quarterly accounts forecasting is presented by using Monte Carlo experiment.
Publication Name: Journal of Forecasting
Subject: Mathematics
ISSN: 0277-6693
Year: 2007
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