@Article{Escobar:2015:AES,
  author =       "Marcos Escobar and Benedikt Rudolph and Rudi Zagst",
  title =        "Algorithm xxx: Estimation of Stochastic Covariance Models
                  using a Continuum of Moment Conditions",
  journal =      "{ACM} Transactions on Mathematical Software",
  volume =       "",
  number =       "",
  accepted =     "2 August 2015",
  upcoming =     "true", 
  abstract =     "
                  We describe the implementation of a parameter estimation
                  method suitable for models commonly used in quantitative
                  finance. The Continuum-Generalized Method of Moments (CGMM)
                  is a Generalized Method of Moments (GMM) type of methodology
                  that applies a continuum of moment conditions to achieve the
                  efficiency of a Maximum Likelihood method. Instead of the
                  transition density, the more commonly available conditional
                  characteristic function is used for estimation. We test the
                  CGMM and a simpler version, called the CMM, on simulated
                  time series to check the recovery of the parameters.
                  We also applied CMM to two stochastic covariance models,
                  the Wishart Affine Stochastic Correlation (WASC) model
                  and the Principal Components Stochastic Volatility (PCSV)
                  model. This illustrates the power of CGMM as stochastic
                  covariance models are generally hard to estimate. The
                  estimation method is fully implemented in Matlab.",
}