Information-theoretic optimality of observation-driven time series models for continuous responses

@article{Blasques2015InformationtheoreticOO,
  title={Information-theoretic optimality of observation-driven time series models for continuous responses},
  author={Francisco Blasques and Siem Jan Koopman and Andr{\'e} Lucas},
  journal={Biometrika},
  year={2015},
  volume={102},
  pages={325-343}
}
We investigate information-theoretic optimality properties of the score function of the predictive likelihood as a device for updating a real-valued time-varying parameter in a univariate observation-driven model with continuous responses. We restrict our attention to models with updates of one lag order. The results provide theoretical justification for a class of score-driven models which includes the generalized autoregressive conditional heteroskedasticity model as a special case. Our main… 

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References

SHOWING 1-10 OF 44 REFERENCES
Information Theoretic Optimality of Observation Driven Time Series Models
TLDR
The results provide a new theoretical justification for the class of generalized autoregressive score models, which covers the GARCH model as a special case and shows that only parameter updates based on the score always reduce the local Kullback-Leibler divergence between the true conditional density and the model implied conditional density.
Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models
This discussion paper led to a publication in the Review of Economics and Statistics . We study whether and when parameter-driven time-varying parameter models lead to forecasting gains over
A General Framework for Observation Driven Time-Varying Parameter Models
We propose a new class of observation driven time series models referred to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled score of the
GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS
TLDR
A unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models, referred to as Generalized Autoregressive Score (GAS) models, which encompasses other well-known models such as the generalized autoregressive conditional heteroskedasticity.
Time-Varying Systemic Risk: Evidence from a Dynamic Copula Model of CDS Spreads
This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of systemic risk. Our
A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts
Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data
This paper proposes a new statistical model for the analysis of data which arrive at irregular intervals. The model treats the time between events as a stochastic process and proposes a new class of
Dynamic Copula Models and High Frequency Data
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
Traditional econometric models assume a constant one-period forecast variance. To generalize this implausible assumption, a new class of stochastic processes called autoregressive conditional
New Frontiers for Arch Models
In the 20 years following the publication of the ARCH model, there has been a vast quantity of research uncovering the properties of competing volatility models. Wide-ranging applications to
...
1
2
3
4
5
...