@article{JR-Journal5u,

title = {Econometrics, Handbook of Computational Statistics},

author = {Luc Bauwens and Jeroen V. K. Rombouts},

url = {https://link.springer.com/chapter/10.1007/978-3-642-21551-3_35},

year  = {2011},

date = {2011-12-21},

journal = {Handbook of Computational Statistics },

pages = {1061-1094},

abstract = {Since the last decade we live in a digitalized world where many actions in human and economic life are monitored. This produces a continuous stream of new, rich and high quality data in the form of panels, repeated cross-sections and long time series. These data resources are available to many researchers at a low cost. This new era is fascinating for econometricians who can address many open economic questions. To do so, new models are developed that call for elaborate estimation techniques. Fast personal computers play an integral part in making it possible to deal with this increased complexity.},

keywords = {Econometrics , Statistics },

pubstate = {published},

tppubtype = {article}

}

@article{JR-Journal5k,

title = {Multivariate Option Pricing with Time Varying Volatility and Correlations},

author = {Jeroen V. K. Rombouts and Lars Stentoft},

url = {https://www.sciencedirect.com/science/article/pii/S037842661100046X},

year  = {2011},

date = {2011-09-01},

journal = {Journal of Banking & Finance},

volume = {Volume 35, Issue 9},

pages = {2267–2281},

abstract = {In this paper we consider option pricing using multivariate models for asset returns. Specifically, we demonstrate the existence of an equivalent martingale measure, we characterize the risk neutral dynamics, and we provide a feasible way for pricing options in this framework. Our application confirms the importance of allowing for dynamic correlation, and it shows that accommodating correlation risk and modeling non-Gaussian features with multivariate mixtures of normals substantially changes the estimated option prices.},

keywords = {Econometrics },

pubstate = {published},

tppubtype = {article}

}

@article{JR-Journal5i,

title = {Nonparametric Density Estimation for Multivariate Bounded Data},

author = {Taoufik Bouezmarni and Jeroen V. K. Rombouts},

url = {https://www.sciencedirect.com/science/article/pii/S0378375809002249},

year  = {2010},

date = {2010-01-01},

journal = {Journal of Statistical Planning and Inference},

volume = {Volume 140, Issue 1,},

pages = {139–152},

abstract = {We propose a new nonparametric estimator for the density function of multivariate bounded data. As frequently observed in practice, the variables may be partially bounded (e.g. nonnegative) or completely bounded (e.g. in the unit interval). In addition, the variables may have a point mass. We reduce the conditions on the underlying density to a minimum by proposing a nonparametric approach. By using a gamma, a beta, or a local linear kernel (also called boundary kernels), in a product kernel, the suggested estimator becomes simple in implementation and robust to the well known boundary bias problem. We investigate the mean integrated squared error properties, including the rate of convergence, uniform strong consistency and asymptotic normality. We establish consistency of the least squares cross-validation method to select optimal bandwidth parameters. A detailed simulation study investigates the performance of the estimators. Applications using lottery and corporate finance data are provided.},

keywords = {Econometrics },

pubstate = {published},

tppubtype = {article}

}

@article{JR-Journal5f,

title = {Mixed Exponential Power Asymmetric Conditional Heteroskedasticity},

author = {Jeroen V. K. Rombouts and Mohammed Bouaddi},

url = {https://econpapers.repec.org/paper/corlouvco/2007097.htm},

issn = {1558-3708},

year  = {2007},

date = {2007-12-01},

journal = {Studies in Nonlinear Dynamics & Econometrics},

volume = {Volume 13, Issue 3},

abstract = {To match the stylized facts of high frequency financial time series precisely and parsimoniously, this paper presents a finite mixture of conditional exponential power distributions where each component exhibits asymmetric conditional heteroskedasticity. We provide weak stationarity conditions and unconditional moments to the fourth order. We apply this new class to Dow Jones index returns. We find that a two-component mixed exponential power distribution dominates mixed normal distributions with more components, and more parameters, both in-sample and out-of-sample. In contrast to mixed normal distributions, all the conditional variance processes become stationary. This happens because the mixed exponential power distribution allows for component-specific shape parameters so that it can better capture the tail behaviour. Therefore, the more general new class has attractive features over mixed normal distributions in our application: less components are necessary and the conditional variances in the components are stationary processes. Results on NASDAQ index returns are similar.},

keywords = {Econometrics },

pubstate = {published},

tppubtype = {article}

}

@article{JR-Journal5b,

title = {Mixed Normal Multivariate Conditional Heteroskedasticity},

author = {Luc Bauwens and Christian M. Hafner and Jeroen V. K. Rombouts},

url = {https://www.sciencedirect.com/science/article/pii/S0167947306003811},

year  = {2007},

date = {2007-04-01},

journal = {Computational Statistics & Data Analysis },

volume = {Volume 51, Issue 7},

pages = {Pages 3551–3566},

abstract = {A new multivariate volatility model where the conditional distribution of a vector time series is given by a mixture of multivariate normal distributions is proposed. Each of these distributions is allowed to have a time-varying covariance matrix. The process can be globally covariance stationary even though some components are not covariance stationary. Some theoretical properties of the model such as the unconditional covariance matrix and autocorrelations of squared returns are derived. The complexity of the model requires a powerful estimation algorithm. A simulation study compares estimation by maximum likelihood with the EM algorithm. Finally, the model is applied to daily US stock returns.},

keywords = {Econometrics },

pubstate = {published},

tppubtype = {article}

}

@article{JR-Journal3,

title = {Multivariate GARCH models: a survey},

author = {Luc Bauwens and Sébastien Laurent and Jeroen V. K. Rombouts},

url = {https://onlinelibrary.wiley.com/doi/full/10.1002/jae.842},

year  = {2006},

date = {2006-02-01},

journal = {Journal of Applied Econometrics},

volume = {21, Issue 1},

pages = {Pages 79-109},

abstract = {This paper surveys the most important developments in multivariate ARCH-type modelling. It reviews the model specifications and inference methods, and identifies likely directions of future research.},

keywords = {Econometrics },

pubstate = {published},

tppubtype = {article}

}