Dynamic Latent Factor Models for Intensity Processes

Bauwens, Luc; Hautsch, Nikolaus

doi:10.2139/ssrn.691886

Cited by 25 publications

(39 citation statements)

References 29 publications

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“…The multi-state latent factor intensity (MLFI) model is a multi-state generalization for multivariate point processes of the latent factor intensity (LFI) model of Bauwens and Hautsch (2003). Consider a set of K units (or firms) whose event-histories can be adequately described by the history of transitions between a finite set of states.…”

Section: The Multi-state Latent Factor Intensity Modelmentioning

confidence: 99%

“…In this way, we are able to use all the information in the data-set (Lando and Skødeberg, 2002, provide a detailed discussion of the advantages of the continuous-time approach). Our model can be regarded as a multi-state extension of the Latent Factor Intensity (LFI) model of Bauwens and Hautsch (2003). The LFI model is a point process model for stock transactions in tick-time.…”

Section: Introductionmentioning

confidence: 99%

“…Each of these states has its own duration process and we observe only the minimum of those. This leads to a more complicated likelihood structure than considered by Bauwens and Hautsch (2003).…”

Section: Introductionmentioning

confidence: 99%

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The Multi-State Latent Factor Intensity Model for Credit Rating Transitions

Koopman

Monteiro

2005

SSRN Journal

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A new empirical reduced-form model for credit rating transitions is introduced. It is a parametric intensity-based duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semi-Markov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation is based on Monte Carlo maximum likelihood methods for which the details are discussed in this paper. A simulation experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for transitions between investment grade, subinvestment grade, and default ratings for U.S. corporates. The model strongly suggests the presence of a common dynamic component that can be interpreted as the credit cycle. We also show that the impact of this credit cycle is asymmetric with respect to downgrade and upgrade probabilities.

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Section: The Multi-state Latent Factor Intensity Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Multi-State Latent Factor Intensity Model for Credit Rating Transitions

Koopman

Monteiro

2005

SSRN Journal

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“…The e ciency of EIS in such situations is illustrated by its application for the computation of the likelihood of various dynamic latent variable models (see, e.g., in Bauwens and Hautsch, 2003, Bauwens and Galli, 2005, Jung and Liesenfeld, 2001, and Liesenfeld and Richard 2003a, 2003b, 2005a. In particular, in the context of highly correlated latent variables, EIS can usefully be interpreted as a single block MCMC step, providing an e cient solution to the slow convergence of MCMC in such context.…”

Section: Introductionmentioning

confidence: 99%

Improving MCMC, using efficient importance sampling

Liesenfeld

Richard

2008

Computational Statistics & Data Analysis

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“…Ranaldo (2003)), in which time series aspects cannot adequately be taken into account, and the drawbacks of financial duration models for which it is difficult to formulate multivariate specifications (see e.g. Bauwens and Hautsch (2003), Engle and Lunde (2003) and Russell (1999)). …”

Section: Introductionmentioning

confidence: 99%

Trading Activity and Liquidity Supply in a Pure Limit Order Book Market. An Empirical Analysis Using a Multivariate Count Data Model

2004

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In this paper we perform an empirical analysis of the trading process in a pure limit order book market, the Xetra system which operates at various European exchanges.We study how liquidity supply and demand as well as price volatility affect future trading activity and market resiliency, and discuss the results in the light of predictions implied by theoretical models of financial market microstructure. Parameter estimation and hypotheses testing is conducted using a new econometric methodology designed for the analysis of multivariate count processes.ii

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Dynamic Latent Factor Models for Intensity Processes

Cited by 25 publications

References 29 publications

The Multi-State Latent Factor Intensity Model for Credit Rating Transitions

The Multi-State Latent Factor Intensity Model for Credit Rating Transitions

Improving MCMC, using efficient importance sampling

Trading Activity and Liquidity Supply in a Pure Limit Order Book Market. An Empirical Analysis Using a Multivariate Count Data Model

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