Time-varying NoVaS versus GARCH: point prediction, volatility estimation and prediction intervals

DOI10.1515/jtse-2019-0044OpenAlexW3040792161MaRDI QIDQ2019875

Jie Chen, Dimitris N. Politis

Publication date: 22 April 2021

Published in: Journal of Time Series Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1515/jtse-2019-0044

zbMATH Keywords

structural breaksrealized volatilitynon-stationarityinterval predictionlocally stationary datatime-varying data

Mathematics Subject Classification ID

Applications of statistics to economics (62P20)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Neglecting parameter changes in GARCH models
• Model-free prediction and regression. A transformation-based approach to inference
• Predicting volatility: getting the most out of return data sampled at different frequencies
• Consistent ranking of volatility models
• Realized volatility forecasting and option pricing
• Bootstrap prediction intervals for linear, nonlinear and nonparametric autoregressions
• Time series analysis: Methods and applications
• Volatility forecasting using threshold heteroskedastic models of the intra-day range
• Fitting time series models to nonstationary processes
• Change-point estimation in ARCH models
• Bootstrap prediction intervals for Markov processes
• Testing for parameter constancy in GARCH\((p,q)\) models
• Generalized autoregressive conditional heteroscedasticity
• Normalized least-squares estimation in time-varying ARCH models
• Forecasting volatility under fractality, regime-switching, long memory and Student-\(t\) innovations
• A recursive online algorithm for the estimation of time-varying ARCH parameters
• Statistical inference for time-varying ARCH processes
• Least absolute deviations estimation for ARCH and GARCH models
• A Haar–Fisz technique for locally stationary volatility estimation
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities
• Diagnostic Checking in ARMA Models With Uncorrelated Errors

This page was built for publication: Time-varying NoVaS versus GARCH: point prediction, volatility estimation and prediction intervals