Luc C A Bauwens
SKEMA Business School, Finance, Faculty Member
- UCLouvain (University of Louvain), CORE, Emeritusadd
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A new model – the high-dimensional Markov (HDM) model – is proposed for financial returns and their latent variances. It is also applicable to model directly realized variances. Volatility is modeled as a product of three components: a... more
A new model – the high-dimensional Markov (HDM) model – is proposed for financial returns and their latent variances. It is also applicable to model directly realized variances. Volatility is modeled as a product of three components: a Markov chain driving volatility persistence, an independent discrete process capable of generating jumps in the volatility, and a predictable (data-driven) process capturing the leverage effect. The Markov chain and jump components allow volatility to switch abruptly between thousands of states. The transition probability matrix of the Markov chain is structured in such a way that the multiplicity of the second largest eigenvalue can be greater than one. This distinctive feature generates a high degree of volatility persistence. The statistical properties of the HDM model are derived and an economic interpretation is attached to each component. In-sample results on six financial time series highlight that the HDM model compares favorably to the m...
... Sébastien Laurent 1,2,* ,; Francesco Violante 1,2. ... However, it has been shown by Hansen and Lunde,4 Patton,5 Patton and Sheppard,6 and Laurent et al.7 that the substitution of the true volatility by a proxy, by definition... more
... Sébastien Laurent 1,2,* ,; Francesco Violante 1,2. ... However, it has been shown by Hansen and Lunde,4 Patton,5 Patton and Sheppard,6 and Laurent et al.7 that the substitution of the true volatility by a proxy, by definition imperfect, may introduce serious distortions in the ...
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Public sales art catalogues include low and high pre-sale price estimates by experts. This makes it possible to analyze whether pre-sale estimates are unbiased predictors of realized prices. Unbiasedness is tested using a sample of some... more
Public sales art catalogues include low and high pre-sale price estimates by experts. This makes it possible to analyze whether pre-sale estimates are unbiased predictors of realized prices. Unbiasedness is tested using a sample of some 1,600 lots of English silver auctioned by Christie's and Sotheby's. Results show that estimates are slightly (but significantly) biased and that experts do not use all the information that is available to them when they make their estimates.
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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,... more
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-correction models. The corresponding nonlinear filtering algorithms are developed and evaluated by means of simulation experiments.
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Adaptive Polar Sampling (APS) algorithms are proposed for Bayesian analysis of models with nonelliptical, possibly, multimodal posterior distributions. A location-scale transformation and a transformation to polar coordinates are used.... more
Adaptive Polar Sampling (APS) algorithms are proposed for Bayesian analysis of models with nonelliptical, possibly, multimodal posterior distributions. A location-scale transformation and a transformation to polar coordinates are used. After the transformation to polar coordinates, a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to sample directions and, conditionally on these, distances are generated by inverting the cumulative distribution function. A sequential procedure is applied to update the initial location and scaling matrix in order to sample directions in an efficient way.
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Research Interests: Econometrics, Statistics, Time Series, Monte Carlo, Bayesian Inference, and 14 moreModels, Markov Chain Monte Carlo, GARCH, Importance Sampling, STOCK EXCHANGE, Regression Model, Option pricing, Indexation, Bayesian Probability, Degree of Freedom, Time Series Model, Gibbs Sampler, GARCH Model, and Autoregressive Conditional Heteroskedasticity
ABSTRACT 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... more
ABSTRACT 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.
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ABSTRACT 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... more
ABSTRACT 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 recurrent and non-recurrent (change-point) regime specifications. We illustrate the estimation method through simulations and apply it to seven financial time series of daily returns. We find structural breaks in the volatility dynamics of all series and recurrent regimes in nearly all series. Finally, we carry out a forecasting exercise to evaluate the usefulness of structural break models.
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Research Interests: Applied Mathematics, Economics, Econometrics, Persistence, Standard Deviation, and 13 moreSocial Science Research Network, GARCH, Value at Risk, Gibbs sampling, Financial Market, Empirical evidence, Time varying, Convex Combination, State dependence, De Gruyter, Bayesian approach, GARCH Model, and Autoregressive Conditional Heteroskedasticity
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Diffuse priors lead to pathological posterior behavior when used in Bayesian analyses of simultaneous equation models (SEM's). This results from the local nonidentification of certain parameters in SEM's. When this a priori known... more
Diffuse priors lead to pathological posterior behavior when used in Bayesian analyses of simultaneous equation models (SEM's). This results from the local nonidentification of certain parameters in SEM's. When this a priori known feature is not captured appropriately, it results in an a posteriori favoring of certain specific parameter values that is not the consequence of strong data information but of local nonidentification. We show that a proper consistent Bayesian analysis of a SEM explicitly has to consider the reduced form of the SEM as a standard linear model on which nonlinear (reduced rank) restrictions are imposed, which result from a singular value decomposition. The priors/posteriors of the parameters of the SEM are therefore proportional to the priors/posteriors of the parameters of the linear model under the condition that the restrictions hold. This leads to a framework for constructing priors and posteriors for the parameters of SEM's. The framework is u...
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Adaptive Polar Sampling is proposed as an algorithm where random drawings are directly generated from the target function (posterior) inall-but-one directions of the parameter space. The method is based on the mixed integration technique... more
Adaptive Polar Sampling is proposed as an algorithm where random drawings are directly generated from the target function (posterior) inall-but-one directions of the parameter space. The method is based on the mixed integration technique of Van Dijk, Kloek & Boender (1985) butextends this one by replacing the one-dimensional quadrature step by Monte Carlo simulation from this one-dimensional distribution function.The method
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Research Interests: Mathematics, Computer Science, Economics, Econometrics, Data Analysis, and 15 moreEconomic Growth, Bayesian Analysis, Social Science Research Network, Markov Chain Monte Carlo, Numerical Integration, Importance Sampling, Regression Model, Mixture Model, Economic Model, Covariance Matrix, Numerical Inversion, C, Elliptical Distribution Family, Monte Carlo Method, and Monte Carlo Integration
The general-to-specific (GETS) methodology 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... more
The general-to-specific (GETS) methodology 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 when the conditional mean can appropriately be restricted to zero, and undertakes an out-of-sample forecast evaluation of the methodology applied to the modelling of weekly exchange rate volatility. Our findings suggest that GETS specifications perform comparatively well in both ex post and ex ante forecasting as long as sufficient care is taken with respect to functional form and with respect to how the conditioning information is used. Also, our forecast comparison provides an example of a discrete time explanatory model being more accurate than realised volatility ex post in 1 step forecasting. JEL Classification: C53, F31
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We develop a new and improved method for forecasting a stationary autoregressive fractionally integrated moving average (ARFIMA) process subject to structural breaks, via an autoregressive (AR) approximation. We show that an ARFIMA... more
We develop a new and improved method for forecasting a stationary autoregressive fractionally 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 the approximate AR model. Our method helps to avoid the issue of spurious breaks and the confusion between long memory and structural changes. Insights from our theoretical analysis are confirmed by Monte Carlo experiments, through which we also find that our method provides a substantial improvement over existing methods. Finally, an empirical application to the realized volatility of three exchange rates illustrates the usefulness of our forecast procedure. The empirical success of the HAR-RV model is explained, from an econometric perspective, by our theoretical and simulation results.
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New dynamic models for realized covariance matrices are proposed. The expected value of the realized covariance matrix is specified in two steps: one for each realized variance, and one for the realized correlation matrix. The realized... more
New dynamic models for realized covariance matrices are proposed. The expected value of the realized covariance matrix is specified in two steps: one for each realized variance, and one for the realized correlation matrix. The realized correlation model is a scalar dynamic conditional correlation model. Estimation can be done in two steps as well, and a QML interpretation is given to each step, by assuming a Wishart conditional distribution. The model is applicable to large matrices since estimation can be done by the composite likelihood method.
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This paper introduces the DCC-HEAVY and DECO-HEAVY models, which are dynamic models for conditional variances and correlations for daily returns based on measures of realized variances and correlations built from intraday data. Formulas... more
This paper introduces the DCC-HEAVY and DECO-HEAVY models, which are dynamic models for conditional variances and correlations for 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 provideid. Asymmetric versions of the models are developed. An empirical study shows that in terms of forecoaosts the new HEAVY models outperform the BEKK-HEAVY model based on realized covariances, and the BEKK, DCC and DECO multivariate GARCH models based exclusively on daily data.
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Multivariate volatility models are widely used in the description of the dynamics of timevarying asset correlations and covariances. Among the well-known drawbacks of many of these parametric families one can name the so called curse of... more
Multivariate volatility models are widely used in the description of the dynamics of timevarying asset correlations and covariances. Among the well-known drawbacks of many of these parametric families one can name the so called curse of dimensionality and the nonlinear parameter constraints that need to be imposed at the time of estimation and that are dicult to handle. In this paper we use a Bregman divergences based optimization technique to tackle the quasi-maximum likelihood (QML) estimation of the DVEC (Diagonal VEC) family for various non-scalar specifications. Additionally, we implement a composite likelihood (CL) method to estimate several non-scalar DCC and DVEC model specifications. The use of the CL approach motivates the in-depth study of di↵erent model reduction questions and the analysis of the closedness of the considered families under the reduction operation. The availability of both the QML and CL estimation tools makes possible the empirical out-of-sample performa...
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... more
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 and we show how to compute marginal likelihoods. We allow for both pure change-point and recurrent regime specifications and we show how to forecast structural breaks. We illustrate the efficiency of the algorithm through simulations and we apply it to eight financial time series of daily returns over the period 1987-2011. We find at least three breaks in all series.
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We propose a practical and flexible solution to introduce skewness in multivariate symmetrical distributions. Applying this procedure to the multivariate Student density leads to a "multivariate skew-Student" density, for which... more
We propose a practical and flexible solution to introduce skewness in multivariate symmetrical distributions. Applying this procedure to the multivariate Student density leads to a "multivariate skew-Student" density, for which each marginal has a different asymmetry coefficient. Similarly, when applied to the product of independent univariate Student densities, it provides a "multivariate skew density with independent Student components" for which each marginal has a different asymmetry coefficient and number of degrees of freedom. Combined with a multivariate GARCH model, this new family of distributions (that generalizes the work of Fernandez and Steel, 1998) is potentially useful for modelling stock returns, which a are known to be conditionally heteroskedastic, fat-tailed, and often skew. In an application to the daily returns of the CAC40, NASDAQ, NIKKEI and the SMI, it is found that this density suits well the data and clearly outperforms its symmetric com...
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The Multiplicative Midas Realized DCC (MMReDCC) model of Bauwens et al (2014) generalizes the ReDCC model of Bauwens et al (2012) by decomposing the conditional covariance matrix of returns into long-run secular and short-run transitory... more
The Multiplicative Midas Realized DCC (MMReDCC) model of Bauwens et al (2014) generalizes the ReDCC model of Bauwens et al (2012) by decomposing the conditional covariance matrix of returns into long-run secular and short-run transitory components in the spirit of Engle & Lee (1999). Given the multiplicative component structure of the model, estimation in realistic high dimensional settings can become infeasible due to an over-parametrization problem. In order to obtain a computationally feasible estimation procedure, we propose an algorithm that relies on the maximization of an iteratively re-computed moment-based profile likelihood function. The finite sample properties of the estimator are assessed via a comprehensive simulation study. Also, the results of two empirical applications are presented. The first demonstrates the accuracy of the algorithm in a low-dimensional setting while the second illustrates its effectiveness and practical usefulness in the implementation of high d...