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Indirect inference methods for stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck processes

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    Abstract

    This 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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    File URL: http://www.ssb.no/a/publikasjoner/pdf/DP/dp601.pdf
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    Bibliographic Info

    Paper provided by Research Department of Statistics Norway in its series Discussion Papers with number 601.

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    Date of creation: Dec 2009
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    Handle: RePEc:ssb:dispap:601

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    Keywords: stochastic volatility; financial econometrics; Ornstein-Uhlenbeck processes; indirect inference; state space models; exchange rates;

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