Stochastic Conditional Intensity Processes

By Luc Bauwens, Nikolaus Hautsch. 2006

107 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ac63ddb8c90e2a66348baee3d0ab8c05e06377a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. S. J. Koopman
2. André Lucas
3. Drew D. Creal
4. Wei Zhang
5. Jean-François Richard
6. Francisco Blasques
7. Pawel Janus
8. Manabu Asai

In Finance

1. S. J. Koopman
2. André Lucas
3. Drew D. Creal
4. Jean-François Richard
5. Wei Zhang
6. Francisco Blasques
7. Rutger Lit
8. Manabu Asai

In Management Science and Operations Research

1. Jean-François Richard
2. S. J. Koopman
3. André Lucas
4. Drew D. Creal
5. Wei Zhang
6. Jun Yu
7. Manabu Asai
8. Roman Liesenfeld

Citations in the corpus

• GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS (2013, methodology): “Typical examples are the stochastic volatility (SV) model, see Shephard (2005) for a detailed discussion, and the stochastic intensity models of Bauwens and Hautsch (2006) and Koopman, Lucas, and Monteiro (2008).”
• Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models (2016, unknown): “…Harvey, and Renault (1996); the stochastic conditional duration model of Bauwens and Veredas (2004); the stochastic conditional intensity model of Bauwens and Hautsch (2006); the stochastic copula models of Hafner and Manner (2012); and the non-Gaussian unobserved components time series models…”
• Dynamic Factor Models for Multivariate Count Data: An Application to Stock-Market Trading Activity (2011, unknown): “A multivariate approach based on the intensity that is applicable to a larger number of stocks and which is closely related to ours is proposed by Bauwens and Hautsch (2006).”
• A General Framework for Observation Driven Time-Varying Parameter Models (2008, unknown): “proposed, see Bauwens and Hautsch (2006), Koopman et al. (2008), Duffie, Eckner, Horel, and Saita (2006), and Koopman, Lucas, and Schwaab (2008). The econometric handling of these parameter driven models is intricate while parameter estimation can be computationally demanding. In particular, likelih”
• Efficient high-dimensional importance sampling (2007, methodology): “Comparisons with alternative numerical evaluations of SV models are found in Liesenfeld and Richard (2003c) and Bauwens and Hautsch (2006). The combination of high numerical accuracy and ease of implementation of EIS appears to be unmatched in that class of models.”