Reviewgreen2021

Score-Driven Time Series Models

16 citations0/0 files storedAnnual Review of Statistics and Its Application

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Financial Risk and Volatility Modeling Target Tracking and Data Fusion in Sensor Networks Statistical and numerical algorithms

Abstract

The construction of score-driven filters for nonlinear time series models is described, and they are shown to apply over a wide range of disciplines. Their theoretical and practical advantages over other methods are highlighted. Topics covered include robust time series modeling, conditional heteroscedasticity, count data, dynamic correlation and association, censoring, circular data, and switching regimes.

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References

1991Conditional Heteroskedasticity in Asset Returns: A New Approach 1989A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle 1993On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks 1997Modelling Extremal Events 1987A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return 1987Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model 2004CAViaR 2013Handbook of Economic Forecasting

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