Dynamic Models for Volatility and Heavy Tails: Location/Scale Models for Non-negative Variables
By Andrew S. Harvey. 2013
92 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Environmental Chemistry, General Agricultural and Biological Sciences
View Paper: https://www.semanticscholar.org/paper/8db771e3e81db28e776c7f903cc1a720aeac4fa3
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
- S. J. Koopman
- André Lucas
- Francisco Blasques
- D. Oh
- Andrew J. Patton
- Janneke van Brummelen
- Ivan Petrella
- Davide Delle Monache
In Environmental Chemistry
- André Lucas
- Janneke van Brummelen
- Andrew J. Patton
- S. J. Koopman
- Francisco Blasques
- D. Oh
- Ivan Petrella
- Davide Delle Monache
In General Agricultural and Biological Sciences
- André Lucas
- Janneke van Brummelen
- Andrew J. Patton
- S. J. Koopman
- Francisco Blasques
- D. Oh
- Ivan Petrella
- Davide Delle Monache
Citations in the corpus
- Time-Varying Systemic Risk: Evidence From a Dynamic Copula Model of CDS Spreads (2018, methodology): “(Harvey 2013 and Harvey and Sucarrat 2014 proposed a similar method for modeling time-varying parameters, which they called a “dynamic conditional score,” or “DCS,” model.)”
- Maximum likelihood estimation for score-driven models (2021, methodology): “Our global result differs from the existing literature that relies on high-level assumptions and only ensures local identification by imposing invertibility conditions on the information matrix at the true parameter value; see, for example, Straumann and Mikosch (2006) and Harvey (2013).”
- Adaptive models and heavy tails with an application to inflation forecasting (2017, background): “(2013) and Harvey (2013), we postulate the score-driven dynamics for the TVP.”