Indirect inference methods for stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck processes
AbstractThis paper aims to develop new methods for statistical inference in a class of stochastic volatility models for financial data based on non-Gaussian Ornstein-Uhlenbeck (OU) processes. Our approach uses indirect inference methods: First, a quasi-likelihood for the actual data is estimated. This quasi-likelihood is based on an approximative Gaussian state space representation of the OU-based model. Next, simulations are made from the data generating OU-model for given parameter values. The indirect inference estimator is the parameter value in the OU-model which gives the best "match" between the quasi-likelihood estimator for the actual data and the quasi-likelihood estimator for the simulated data. Our method is applied to Euro/NOK and US Dollar/NOK daily exchange rates for the period 1.7.1989 until 15.12.2008. Accompanying R-package, that interfaces C++ code is documented and can be downloaded.
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Bibliographic InfoPaper provided by Research Department of Statistics Norway in its series Discussion Papers with number 601.
Date of creation: Dec 2009
Date of revision:
stochastic volatility; financial econometrics; Ornstein-Uhlenbeck processes; indirect inference; state space models; exchange rates;
Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
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- NEP-ALL-2010-01-10 (All new papers)
- NEP-ECM-2010-01-10 (Econometrics)
- NEP-ETS-2010-01-10 (Econometric Time Series)
- NEP-ORE-2010-01-10 (Operations Research)
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