Roman Liesenfeld

Biographic Data

ID8880122
NAMERoman Liesenfeld
GIVEN NAMESRoman
FAMILY NAMELiesenfeld
SIGNATURELIESENFELD R
AFFILIATIONSUniversity of Tübingen
ORCID0000-0001-6996-6215
VERIFIEDYes
TOTAL WORKS7
TOTAL CITATIONS0
AUTHOR COUNT7
EDITOR COUNT0
FIRST PUBLICATION YEAR1998
LATEST PUBLICATION YEAR2023
H-INDEX0

  • Predicting the Global Minimum Variance Portfolio

    Laura Reh, Fabian Krüger et al.ARTICLEJournal of Business and Economic…2023

    We propose a novel dynamic approach to forecast the weights of the global minimum variance portfolio (GMVP) for the conditional covariance matrix of asset returns. The GMVP weights are the population coefficients of a linear regression of a benchmark return on a vector of return differences. This representation enables us to derive a consistent loss function from which we can infer the GMVP weights without imposing any distributional assumptions …

  • Monte Carlo Methods and Bayesian Computation

    Open AccessRoman Liesenfeld, Jean-Francois Richard et al.CHAPTERInternational Encyclopedia of the…2015

  • Dynamic Factor Models for Multivariate Count Data

    Robert C Jung, Roman Liesenfeld et al.ARTICLEJournal of Business and Economic…2011

    We propose a dynamic factor model for the analysis of multivariate time series count data. Our model allows for idiosyncratic as well as common serially correlated latent factors in order to account for potentially complex dynamic interdependence between series of counts. The model is estimated under alternative count distributions (Poisson and negative binomial). Maximum likelihood estimation requires high-dimensional numerical integration in or…

  • A Nonlinear Forecasting Model of GDP Growth

    David N DeJong, Roman Liesenfeld et al.ARTICLEThe Review of Economics and…2005

    We develop a model of GDP growth under which regime changes are triggered stochastically by an observable tension index, constructed as the geometric sum of deviations of actual GDP growth from a corresponding sustainable rate. Within expansionary regimes, the tension index tends to increase, which heightens the probability of a regime change. Given a regime change, the process becomes reversed, and the tension index begins to decline along a new…

  • Estimation of Dynamic Bivariate Mixture Models

    Roman Liesenfeld, Jean-Francois Richard et al.ARTICLEJournal of Business and Economic…2003

    This note compares a Bayesian Markov chain Monte Carlo approach implemented by Watanabe with a maximum likelihood ML approach based on an efficient importance sampling procedure to estimate dynamic bivariate mixture models. In these models, stock price volatility and trading volume are jointly directed by the unobservable number of price-relevant information arrivals, which is specified as a serially correlated random variable. It is shown that t…

  • Dynamic BivarSate Mixture Models

    Roman LiesenfeldARTICLEJournal of Business and Economic…1998

    Bivariate mixture models have been used to explain the stochastic behavior of daily price changes and trading volume on financial markets. In this class of models, price changes and volume follow a mixture of bivariate distributions with the unobservable number of price-relevant information serving as the mixing variable. The time series behavior of this mixing variable determines the dynamics of the price-volume system. In this article, bivariat…

  • Dynamic Bivariate Mixture Models

    Roman LiesenfeldARTICLEJournal of Business and Economic…1998

    Bivariate mixture models have been used to explain the stochastic behavior of daily price changes and trading volume on fmancial markets. In this class of models price changes and volume follow a mixture of bivariate distributions with the unobservable number of price relevant information serving as the mixing variable. The time series behavior of this mi-xing variable determines the dynamics of the price-volume system. In this paper, bivariate m…