id: 06119751
dt: j
an: 06119751
au: Raknerud, Arvid; Skare, Øivind
ti: Indirect inference methods for stochastic volatility models based on
    non-Gaussian Ornstein-Uhlenbeck processes.
so: Comput. Stat. Data Anal. 56, No. 11, 3260-3275 (2012).
py: 2012
pu: Elsevier Science B.V. (North-Holland), Amsterdam
la: EN
cc: 
ut: indirect inference; quasi-likelihood estimation; stochastic volatility
    model; Ornstein-Uhlenbeck process; asymptotic variance; exchange rate
    data; simulation study; {\tt R}
ci: 
li: doi:10.1016/j.csda.2011.01.014
ab: Summary: An indirect inference method is implemented for a class of
    stochastic volatility models for financial data based on non-Gaussian
    Ornstein-Uhlenbeck (OU) processes. First, a quasi-likelihood estimator
    is derived from an approximative Gaussian state space representation of
    the OU model. Next, data are simulated from the OU model for given
    parameter values. The indirect inference estimator is then obtained by
    minimizing, in a weighted mean squared error sense, the score vector of
    the quasi-likelihood function for the simulated data, when this score
    vector is evaluated at the quasi-likelihood estimator obtained from the
    real data. The method is applied to Euro/Norwegian krone (NOK) and US
    Dollar/NOK daily exchange rate data. A simulation study reveals that
    the quasi-likelihood estimator may have a large bias even in large
    samples, but that the indirect inference estimator substantially
    reduces this bias. The accompanying R-package, which interfaces C++
    code, is documented and can be downloaded.
rv: