Are One Factor Logarithmic Volatility Models Useful to Fit the Features of Financial Data? An Application to Microsoft Data
AbstractThis paper provides empirical evidence that continuous time models with one factor of volatility, in some conditions, are able to fit the main characteristics of financial data. It also reports the importance of the feedback factor in capturing the strong volatility clustering of data, caused by a possible change in the pattern of volatility in the last part of the sample. We use the Efficient Method of Moments (EMM) by Gallant and Tauchen (1996) to estimate logarithmic models with one and two stochastic volatility factors (with and without feedback) and to select among them.
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Bibliographic InfoPaper provided by Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC) in its series UFAE and IAE Working Papers with number 585.03.
Date of creation: 21 Sep 2003
Date of revision:
Efficient Method of Moments; One (Two) Factor Volatility Logarithmic Model; Mean-Reversion; Persistent Volatility; Feedback; Projection; Seminonparametric (SNP); Reprojection.;
Find related papers by JEL classification:
- G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-CFN-2003-09-28 (Corporate Finance)
- NEP-ETS-2003-09-28 (Econometric Time Series)
- NEP-FIN-2003-09-28 (Finance)
- NEP-FMK-2003-09-28 (Financial Markets)
- NEP-RMG-2003-09-28 (Risk Management)
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