# 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…

## 115 Citations

Finite Sample Optimality of Score-Driven Volatility Models

- Economics, Mathematics
- 2017

We study optimality properties in finite samples for time-varying volatility models driven by the score of the predictive likelihood function. Available optimality results for this class of models…

A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-Frequency Covariance Dynamics

- Computer Science
- 2018

An interesting outcome of this approach is that intra-day patterns are recovered without the need of any cross-sectional averaging, allowing, for instance, to estimate the real-time response of the market covariances to macro-news announcements.

Modeling Time-Varying Tail Dependence, with Application to Systemic Risk Forecasting*

- Mathematics, Economics
- 2021

A dynamic model for bivariate extremes that allows for smooth transitions between regimes of asymPTotic independence and asymptotic dependence is proposed and is found to outperform those of a benchmark dynamic t-copula model.

On observation-driven time series modeling

- Computer Science, Mathematics
- 2017

This thesis addresses different aspects of observation-driven time series modeling by deriving feasible conditions that ensure the consistency of the maximum likelihood estimator for a wide class of models and shows how the model can be easily estimated by maximum likelihood and proves the consistencyof the estimator.

Dynamic term structure models with score-driven time-varying parameters: estimation and forecasting

- Mathematics
- 2017

We consider score-driven time-varying parameters in dynamic yield curve models and investigate their in-sample and out-of-sample performance for two data sets. In a univariate setting, score-driven…

Spillover Dynamics for Systemic Risk Measurement Using Spatial Financial Time Series Models

- Economics
- 2014

A Score-Driven Conditional Correlation Model for Noisy and Asynchronous Data: An Application to High-Frequency Covariance Dynamics

- EconomicsJournal of Business & Economic Statistics
- 2020

Abstract The analysis of the intraday dynamics of covariances among high-frequency returns is challenging due to asynchronous trading and market microstructure noise. Both effects lead to significant…

## References

SHOWING 1-10 OF 44 REFERENCES

Information Theoretic Optimality of Observation Driven Time Series Models

- Computer Science
- 2014

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

- Mathematics
- 2012

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

- Mathematics
- 2008

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

- Mathematics, Computer Science
- 2013

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

- Economics
- 2013

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

- Mathematics
- 2011

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

- Economics
- 1998

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…

Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation

- Mathematics
- 1982

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

- Economics
- 2002

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…