@article{Taylor_SV_1982,
author="Taylor, S. J.",
title="Financial returns modelled by the product of two stochastic processes-A study of the daily sugar prices 1961-75",
journal="Time Series Analysis : Theory and Practice",
ISSN="",
publisher="North-Holland",
year="1982",
month="",
volume="1",
number="",
pages="203-226",
URL="https://ci.nii.ac.jp/naid/10018822959/en/",
DOI="",
}

@article{TMB_2016,
   author = {Kasper Kristensen and Anders Nielsen and Casper Berg and Hans Skaug and Bradley Bell},
   title = {TMB: Automatic Differentiation and Laplace Approximation},
   journal = {Journal of Statistical Software, Articles},
   volume = {70},
   number = {5},
   year = {2016},
   keywords = {automatic differentiation; AD; random effects; latent variables; C++ templates; R},
   abstract = {TMB is an open source R package that enables quick implementation of complex nonlinear random effects (latent variable) models in a manner similar to the established AD Model Builder package (ADMB, http://admb-project.org/; Fournier et al. 2011). In addition, it offers easy access to parallel computations. The user defines the joint likelihood for the data and the random effects as a C++ template function, while all the other operations are done in R; e.g., reading in the data. The package evaluates and maximizes the Laplace approximation of the marginal likelihood where the random effects are automatically integrated out. This approximation, and its derivatives, are obtained using automatic differentiation (up to order three) of the joint likelihood. The computations are designed to be fast for problems with many random effects (≈ 106 ) and parameters (≈ 103 ). Computation times using ADMB and TMB are compared on a suite of examples ranging from simple models to large spatial models where the random effects are a Gaussian random field. Speedups ranging from 1.5 to about 100 are obtained with increasing gains for large problems. The package and examples are available at http://tmb-project.org/.},
   issn = {1548-7660},
   pages = {1--21},
   doi = {10.18637/jss.v070.i05},
   url = {https://www.jstatsoft.org/v070/i05}
   }
   
   @Article{kastner2016,
  title = {Dealing with Stochastic Volatility in Time Series Using the {R} Package {stochvol}},
  author = {Gregor Kastner},
  journal = {Journal of Statistical Software},
  year = {2016},
  volume = {69},
  number = {5},
  pages = {1--30},
  doi = {10.18637/jss.v069.i05},
}