scispace - formally typeset
Search or ask a question
Author

Luc Bauwens

Bio: Luc Bauwens is an academic researcher from Université catholique de Louvain. The author has contributed to research in topics: Bayesian inference & Monte Carlo integration. The author has an hindex of 13, co-authored 33 publications receiving 1172 citations. Previous affiliations of Luc Bauwens include University College London & University of Johannesburg.

Papers
More filters
Book ChapterDOI
06 Jan 2000
TL;DR: Methods of numerical integration will lead you to always think more and more, and this book will be always right for you.
Abstract: Want to get experience? Want to get any ideas to create new things in your life? Read methods of numerical integration now! By reading this book as soon as possible, you can renew the situation to get the inspirations. Yeah, this way will lead you to always think more and more. In this case, this book will be always right for you. When you can observe more about the book, you will know why you need this.

784 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a class of asymmetric Autoregressive Conditional Duration models, which extends the ACD model of Engle and Russell (1997) by letting the duration process depend on the state of the price process in the beginning and at the end of each duration.
Abstract: This paper proposes a class of asymmetric Autoregressive Conditional Duration models, which extends the ACD model of Engle and Russell (1997). The asymmetry consists of letting the duration process depend on the state of the price process in the beginning and at the end of each duration. If the price has increased, the parameters of the ACD can differ from what they are if the price has decreased. Thus, the model is also a transition model for the price process, with durations following an ACD process. The logarithmic version of the model is applied to the bid/ask price revision process by the specialist for the IBM stock on the New York Stock Exchange. The empirical evidence in favour of asymmetry is compelling.

126 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the determinants of the correlation dynamics between time series of financial returns and found that the market volatility is a major determinant of the correlations, which can be transmitted through linear or nonlinear, and direct or indirect effects, and applied to different data sets to verify the presence and possible regularity of the volatility impact on correlations.
Abstract: Several models have been developed to capture the dynamics of the conditional correlations between time series of financial returns, but few studies have investigated the determinants of the correlation dynamics. A common opinion is that the market volatility is a major determinant of the correlations. We extend some models to capture explicitly the dependence of the correlations on the volatility of the market of interest. The models differ in the way by which the volatility influences the correlations, which can be transmitted through linear or nonlinear, and direct or indirect effects. They are applied to different data sets to verify the presence and possible regularity of the volatility impact on correlations.

44 citations

Journal ArticleDOI
TL;DR: An estimation and forecasting method, based on a differential evolution MCMC method, for inference in GARCH models subjected to an unknown number of structural breaks at unknown dates, and finds structural breaks in the volatility dynamics of all series and recurrent regimes in nearly all series.

37 citations

Journal ArticleDOI
TL;DR: Applications of univariate and bivariate LFI models to transaction data extracted from the German XETRA trading system provide evidence for an improvement of the econometric specification when observable as well as unobservable dynamic components are taken into account.
Abstract: This paper introduces a new framework for the dynamic modelling of univariate and multivariate point processes. The so-called latent factor intensity (LFI) model is based on the assumption that the intensity function consists of univariate or multivariate observation driven dynamic components and a univariate dynamic latent factor. In this sense, the model corresponds to a dynamic extension of a doubly stochastic Poisson process. We illustrate alternative parameterizations of the observation driven component based on autoregressive conditional intensity (ACI) specifications, as well as Hawkes types models. Based on simulation studies, it is shown that the proposed model provides a flexible tool to capture the joint dynamics of multivariate point processes. Since the latent component has to be integrated out, the model is estimated by simulated maximum likelihood based upon efficient importance sampling techniques. Applications of univariate and bivariate LFI models to transaction data extracted from the German XETRA trading system provide evidence for an improvement of the econometric specification when observable as well as unobservable dynamic components are taken into account.

36 citations


Cited by
More filters
Book
01 Jan 1999
TL;DR: This new edition contains five completely new chapters covering new developments and has sold 4300 copies worldwide of the first edition (1999).
Abstract: We have sold 4300 copies worldwide of the first edition (1999). This new edition contains five completely new chapters covering new developments.

6,884 citations

Journal ArticleDOI
08 Nov 2004
TL;DR: The motivation, development, use, and implications of the UT are reviewed, which show it to be more accurate, easier to implement, and uses the same order of calculations as linearization.
Abstract: The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.

6,098 citations

Journal ArticleDOI
TL;DR: In this article, conditions under which the numerical approximation of a posterior moment converges almost surely to the true value as the number of Monte Carlo replications increases, and the numerical accuracy of this approximation may be assessed reliably, are set forth.
Abstract: Methods for the systematic application of Monte Carlo integration with importance sampling to Bayesian inference in econometric models are developed. Conditions under which the numerical approximation of a posterior moment converges almost surely to the true value as the number of Monte Carlo replications increases, and the numerical accuracy of this approximation may be assessed reliably, are set forth. Methods for the analytical verification of these conditions are discussed

1,649 citations

Journal ArticleDOI
TL;DR: In this article, the most important developments in multivariate ARCH-type modeling are surveyed, including model specifications, inference methods, and the main areas of application in financial econometrics.
Abstract: This paper surveys the most important developments in multivariate ARCH-type modelling. It reviews the model specifications, the inference methods, and the main areas of application of these models in financial econometrics.

1,629 citations

Journal ArticleDOI
TL;DR: This work presents a stable control strategy for groups of vehicles to move and reconfigure cooperatively in response to a sensed, distributed environment and focuses on gradient climbing missions in which the mobile sensor network seeks out local maxima or minima in the environmental field.
Abstract: We present a stable control strategy for groups of vehicles to move and reconfigure cooperatively in response to a sensed, distributed environment. Each vehicle in the group serves as a mobile sensor and the vehicle network as a mobile and reconfigurable sensor array. Our control strategy decouples, in part, the cooperative management of the network formation from the network maneuvers. The underlying coordination framework uses virtual bodies and artificial potentials. We focus on gradient climbing missions in which the mobile sensor network seeks out local maxima or minima in the environmental field. The network can adapt its configuration in response to the sensed environment in order to optimize its gradient climb.

1,291 citations