Luc Bauwens

Luc Bauwens
Université Catholique de Louvain - UCLouvain | UCLouvain · Center for Operations Research and Econometrics

Ph. D. in Economics

About

167
Publications
33,391
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7,592
Citations
Citations since 2016
12 Research Items
2429 Citations
20162017201820192020202120220100200300
20162017201820192020202120220100200300
20162017201820192020202120220100200300
20162017201820192020202120220100200300
Introduction
Luc Bauwens (born in 1952) received the Ph. D. degree in economics from the Université catholique de Louvain (UCL), Louvain-la-Neuve, Belgium, in 1983. He received the 1984 Leonard J. Savage Thesis Award for his thesis. Since 1991 he is professor of economics at UCL, where he is affiliated with the Center for Operations Research and Econometrics (CORE), has been chairman of the Department of Economics (2000-2003), research director of the CORE (2006-2009), and President of the CORE (2010-2013).
Additional affiliations
January 1991 - present
Université Catholique de Louvain - UCLouvain
Position
  • Professor Emeritus

Publications

Publications (167)
Article
This paper introduces the scalar DCC-HEAVY and DECO-HEAVY models for conditional variances and correlations of daily returns based on measures of realized variances and correlations built from intraday data. Formulas for multi-step forecasts of conditional variances and correlations are provided. Asymmetric versions of the models are developed. An...
Article
Time series of realized covariance matrices can be modeled in the conditional autoregressive Wishart model family via dynamic correlations or via dynamic covariances. Extended parameterizations of these models are proposed, which imply a specific and time-varying impact parameter of the lagged realized covariance (or correlation) on the next condit...
Article
New parameterizations of the dynamic conditional correlation (DCC) model and of the regime-switching dynamic correlation (RSDC) model are introduced, such that these models provide a specific dynamics for each correlation. They imply a nonlinear autoregressive form of dependence on lagged correlations and are based on properties of the Hadamard exp...
Article
Full-text available
We develop novel multivariate state-space models wherein the latent states evolve on the Stiefel manifold and follow a conditional matrix Langevin distribution. The latent states correspond to time-varying reduced rank parameter matrices, like the loadings in dynamic factor models and the parameters of cointegrating relations in vector error-correc...
Article
Full-text available
A new process — the factorial hidden Markov volatility (FHMV) model — is proposed to model financial returns or realized variances. Its dynamics are driven by a latent volatility process specified as a product of three components: a Markov chain controlling volatility persistence, an independent discrete process capable of generating jumps in the v...
Article
The Multiplicative MIDAS Realized DCC (MMReDCC) model simultaneously accounts for short and long term dynamics in the conditional (co)volatilities of asset returns, in line with the empirical evidence suggesting that their level is changing over time as a function of economic conditions. Herein the applicability of the model is improved along two d...
Article
Full-text available
Novel model specifications that include a time-varying long-run component in the dynamics of realized covariance matrices are proposed. The modeling framework allows the secular component to enter the model either additively or as a multiplicative factor, and to be specified parametrically, using a MIDAS filter, or non-parametrically. Estimation is...
Technical Report
Full-text available
The Multiplicative MIDAS Realized DCC (MMReDCC) model of Bauwens et al.(2016) decomposes the dynamics of the realized covariance matrix of returns into short-run transitory and long-run secular components where the latter reflects the effect of the continuously changing economic conditions. The model allows to obtain positive-definite forecasts of...
Article
Full-text available
Markov-switching models are usually specified under the assumption that all the parameters change when a regime switch occurs. Relaxing this hypothesis and being able to detect which parameters evolve over time is relevant for interpreting the changes in the dynamics of the series, for specifying models parsimoniously, and may be helpful in foreca...
Conference Paper
The Multiplicative MIDAS Realized DCC (MMReDCC) model of Bauwens et al. (2014) decomposes the dynamics of the realized covariance matrix of returns into long-run secular and short-run transitory components that reflect the continuously changing economic conditions. The model provides positive defi�nite forecasts of the realized covariance matrices...
Article
This paper presents a method capable of estimating richly parametrized versions of the dynamic conditional correlation (DCC) model that go beyond the standard scalar case. The algorithm is based on the maximization of a Gaussian quasi-likelihood using a Bregman-proximal trust-region method to handle the various non-linear stationarity and positivit...
Article
We present 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. We treat break dates as parameters and determine the number of breaks by computing the marginal likelihoods of competing models. We allow for both recurre...
Article
We develop a new and improved method for forecasting a stationary autoregressive frac- tionally integrated moving average (ARFIMA) process subject to structural breaks, via an autoregressive (AR) approximation. We show that an ARFIMA process subject to breaks can be approximated well by an AR model. We use Mallows' criterion to choose the order of...
Article
Full-text available
This paper illustrates some computationally efficient estimation procedures for the estimation of vast dimensional realized covariance models. In particular, we derive a Composite Maximum Likelihood (CML) estimator for the parameters of a Conditionally Autoregressive Wishart (CAW) model incorporating scalar system matrices and covariance targeting....
Article
The deregulation of European electricity markets has led to an increasing need in understanding the volatility and correlation structure of electricity prices. We model a multivariate futures series of the European Energy Exchange (EEX) index, using an asymmetric GARCH model for volatilities and augmented dynamic conditional correlation (DCC) model...
Preprint
Full-text available
corrections to the book 'Bayesian Inference in Dynamic Econometric Models'
Technical Report
Full-text available
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 th...
Article
We develop an easy-to-implement method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks with unknown break dates. We show that an ARFIMA process subject to a mean shift and a change in the long memory parameter can be well approximated by an autoregressive (AR) model an...
Article
A jump robust positive semidefinite rank-based estimator for the daily covariance matrix based on high-frequency intraday returns is proposed. It disentangles covariance estimation into variance and correlation components. This allows us to account for ...
Chapter
IntroductionGARCHStochastic VolatilityRealized VolatilityAcknowledgments
Article
A complete guide to the theory and practice of volatility models in financial engineering Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Application...
Article
Full-text available
We present an algorithm, based on a differential evolution MCMC method, for Bayesian inference in AR-GARCH models subject to an unknown number of structural breaks at unknown dates. Break dates are directly treated as parameters and the number of breaks is determined by the marginal likelihood criterion. We prove the convergence of the algorithm an...
Article
GARCH volatility models with fixed parameters are too restrictive for long time series due to breaks in the volatility process. Flexible alternatives are Markov-switching GARCH and change-point GARCH models. They require estimation by MCMC methods due to the path dependence problem. An unsolved issue is the computation of their marginal likelihood,...
Article
This chapter presents an introductory review of Bayesian methods for research in empirical macroeconomics.
Book
We model the dynamic volatility and correlation structure of electricity futures of the European Energy Exchange index. We use a new multiplicative dynamic conditional correlation (mDCC) model to separate long-run from short-run components. We allow for smooth changes in the unconditional volatilities and correlations through a multiplicative compo...
Article
This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empirical evaluation involving...
Article
This paper compares the forecasting performance of different models which have been proposed for forecasting in the presence of structural breaks. These models differ in their treatment of the break process, the parameters defining the model which applies in each regime and the out-of-sample probability of a break occurring. In an extensive empiric...
Article
The first three appendices contain details about the implementation of the estimation and forecasting of the structural break models named PPT and KP in the paper. These models are explained in Section 2 of the paper and information about the forecasting implementation of these models is presented in Section 4 of the paper. The fourth appendix cont...
Article
A portfolio selection model which allocates a portfolio of currencies by maximizing the expected return subject to Value-at-Risk (VaR) constraint is designed and implemented. Based on an econometric implementation using intradaily data, the optimal portfolio allocation is forecasted at regular time intervals. For the estimation of the conditional v...
Article
The general-to-specific (GETS) methodology is widely employed in the modelling of economic series, but less so in financial volatility modelling, due to its computational complexity when many explanatory variables are involved. This study proposes a simple way of avoiding this problem when the conditional mean can appropriately be restricted to zer...
Article
Summary We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood...
Article
Modelling multivariate time series of possibly high dimension calls for appropriate dimension-reduction, e.g. by some factor modelling, additive modelling, or some simplified parametric structure for the dynamics (i.e. the serial dependence) of the time series. This workshop aimed to bring together experts in this field in order to discuss recent m...
Article
Change-point models are useful for modeling time series subject to structural breaks. For interpretation and forecasting, it is essential to estimate correctly the number of change points in this class of models. In Bayesian inference, the number of change points is typically chosen by the marginal likelihood criterion, computed by Chib’s method. T...
Article
The evaluation of the likelihood function of the stochastic conditional duration (SCD) model requires to compute an integral that has the dimension of the sample size. ML estimation based on the efficient importance sampling (EIS) method is developed for computing this integral and compared with QML estimation based on the Kalman filter. Based on M...
Article
Le déclin résistible de la science en Europe Using a new data set that allows us to analyze precisely the research output in all fields of science, we show that the gap in scientific performance between Europe, especially continental Europe, and Anglo-Saxon countries, especially the USA, is large. We measure research quality by the number of highly...
Article
The evaluation of the likelihood function of the stochastic conditional duration (SCD) model requires to compute an integral that has the dimension of the sample size. ML estimation based on the ecient importance sampling (EIS) method is developed for computing this integral and compared with QML estimation based on the Kalman filter. Based on Mont...
Book
This exciting volume presents cutting-edge developments in high frequency financial econometrics, spanning a diverse range of topics: market microstructure, tick-by-tick data, bond and foreign exchange markets and large dimensional volatility modelling. The chapters on market microstructure deal with liquidity, asymmetries of information, and limit...
Article
The general-to-specific (GETS) approach to modelling is widely employed in the modelling of economic series, but less so in financial volatility modelling due to computational complexity when many explanatory variables are involved. This study proposes a simple way of avoiding this problem and undertakes an out-of-sample forecast evaluation of the...
Chapter
Full-text available
This paragraph is a virtual copy of the one in p. 2 of Frisch's Editor Note on Econometrica Vol. 1, No. 1. The only difference is that economics has been replaced by finance, economic by financial, econometrics by financial econometrics.
Book
In this paper, we give an overview of the state-of-the-art in the econometric literature on the modeling of so-called financial point processes. The latter are associated with the random arrival of specific financial trading events, such as transactions, quote updates, limit orders or price changes observable based on financial high-frequency data....
Article
The choice of an appropriate social rate of discount is critical in the decision-making process on public investments. In this paper we review the literature on social discounting, and address in particular a recently growing field of related research, that is, individual time preferences. We argue that an explicit consideration and analysis of the...
Article
The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economist...
Article
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 stati...
Article
We present a novel GARCH model that accounts for time varying, state dependent, persistence in the volatility dynamics. The proposed model generalizes the component GARCH model of Ding and Granger (1996). The volatility is modelled as a convex combination of unobserved GARCH components where the combination weights are time varying as a function of...
Article
We consider the estimation of a large number of GARCH models, of the order of several hundreds. Our interest lies in the identification of common structures in the volatility dynamics of the univariate time series. To do so, we classify the series in an unknown number of clusters. Within a cluster, the series share the same model and the same param...
Article
We propose a Bayesian approach for inference in a dynamic disequilibrium model. To circumvent the difficulties raised by the Maddala and Nelson (1974) specification in the dynamic case, we analyze a dynamic extended version of the disequilibrium model of Ginsburgh et al. (1980). We develop a Gibbs sampler based on the simulation of the missing obse...
Article
We estimate by Bayesian inference the mixed conditional heteroskedasticity model of (Haas, Mittnik, and Paolella 2004a). We construct a Gibbs sampler algorithm to compute posterior and predictive densities. The number of mixture components is selected by the marginal likelihood criterion. We apply the model to the SP500 daily returns.
Article
Full-text available
This study sheds new light on the mixture of distribution hypothesis by means of a study of the weekly exchange rate volatility of the Norwegian krone. In line with other studies we find that the impact of information arrival on exchange rate volatility is positive and statistically significant, and that the hypothesis that an increase in the numbe...
Article
Full-text available
The empirical evidence from financial markets suggests that the pattern of response of market volatility to shocks is highly dependent on the magnitude of shocks themselves. Markov-Switching GARCH (MS-GARCH) models are a valuable tool for modelling state dependence in the dynamics of the volatility process. However, their application is still limit...
Article
Full-text available
We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past information. We provide sufficient conditions for stationarity and existence of moments. Because of path dependen...
Article
Full-text available
We review Bayesian inference for dynamic latent variable models using the data augmentation principle. We detail the difficulties of stimulating dynamic latent variables in a Gibbs sampler. We propose an alternative specification of the dynamic disequilibrium model which leads to a simple simulation procedure and renders Bayesian inference fully op...
Article
Full-text available
In this article, we introduce the so-called stochastic conditional intensity (SCI) model by extending Russell's (1999) autoregressive conditional intensity (ACI) model by a latent common dynamic factor that jointly drives the individual intensity components. We show by simulations that the proposed model allows for a wide range of (cross-)autocorre...
Article
We develop univariate regime-switching GARCH (RS-GARCH) models wherein the conditional variance switches in time from one GARCH process to another. The switching is governed by a time-varying probability, specified as a function of past information. We provide sufficient conditions for stationarity and existence of moments. Because of path dependen...
Article
We design and implement optimal foreign exchange portfolio allocations. An optimal allocation maximizes the expected return subject to a Value-at-Risk (VaR) constraint. Based on intradaily data, the optimization procedure is carried out at regular time intervals. For the estimation of the conditional variance from which the VaR is computed, we use...
Article
Full-text available
The general-to-specific (GETS) approach to modelling is widely employed in the modelling of economic series, but less so in financial volatility modelling due to computational complexity when many explanatory variables are involved. This study proposes a simple way of avoiding this problem and undertakes an out-of-sample forecast evaluation of the...
Article
Full-text available
The second alternative has been proposed by Andersen et al. (2003). In this case, a daily measure of variances and covariances is computed as an aggregate measure from intraday returns. More specifically, a daily realized variance for day t is computed as the sum of the squared intraday equidistant returns for the given trading day and a daily real...
Article
We propose a Bayesian approach for inference in a dynamic disequilibrium model. To circumvent the difficulties raised by the Maddala and Nelson (1974) specification in the dynamic case, we analyze a dynamic extended version of the disequilibrium model of Ginsburgh et al. (1980). We develop a Gibbs sampler based on the simulation of the missing obse...
Article
Matching university places to students is not as clear cut or as straightforward as it ought to be. By investigating the matching algorithm used by the German central clearinghouse for university admissions in medicine and related subjects, we show that a procedure designed to give an advantage to students with excellent school grades actually harm...
Article
We analyze statistically inter-trade durations of four stocks listed on the Tokyo Stock Exchange in 2003. We find that these data display the usual stylized facts (intra-daily seasonality, clustering, and overdispersion) found for similar data of the New York Stock Exchange, but with some differences. We also estimate autoregressive conditional dur...
Article
Using density forecast evaluation techniques, we compare the predictive performance of econometric specifications that have been developed for modeling duration processes in intra-day financial markets. The model portfolio encompasses various variants of the Autoregressive Conditional Duration (ACD) model and recently proposed dynamic factor models...
Article
Full-text available
This paper constructs a two-country (Home and Foreign) general equilibrium model of Schumpeterian growth without scale effects. The scale effects property is removed by introducing two distinct specifications in the knowledge production function: the permanent effect on growth (PEG) specification, which allows policy effects on long-run growth; and...
Article
Full-text available
We consider the estimation of a large number of GARCH models, say of the order of several hundreds. Especially in the multivariate case, the number of parameters is extremely large. To reduce this number and render estimation feasible, we regroup the series in a small number of clusters. Within a cluster, the series share the same model and the sam...
Article
We introduce a class of models for the analysis of durations, which we call stochastic conditional duration (SCD) models. These models are based on the assumption that the durations are generated by a dynamic stochastic latent variable. The model yields a wide range of shapes of hazard functions. The estimation of the parameters is performed by qua...
Article
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, t...
Article
Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with non-elliptical, possibly, multimodal target distributions. A key step is a radial-based transformation to directions and distances. After the transformation a Metropolis-Hastings method or, alternatively, an importance sampling method is ap...
Article
Full-text available
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 erais fascinating...
Article
Full-text available
The primary aim of the paper is to place current methodological discussions in macroeconometric modeling contrasting the ‘theory first’ versus the ‘data first’ perspectives in the context of a broader methodological framework with a view to constructively appraise them. In particular, the paper focuses on Colander’s argument in his paper “Economist...