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A time varying GARCH(p,q) model and related statistical inference

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  • Rohan, Neelabh

Abstract

We propose a two-step local polynomial and a weighted bootstrapped estimator for the parameter functions of a time varying GARCH(p,q) model. We also suggest a test statistic for testing the constancy of parameter functions of the model. Asymptotic distributions of the estimators and a test statistic are derived. The validity of the bootstrapped estimator and the test is established with the help of a simulation study.

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  • Rohan, Neelabh, 2013. "A time varying GARCH(p,q) model and related statistical inference," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 1983-1990.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:9:p:1983-1990
    DOI: 10.1016/j.spl.2013.04.030
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    7. Arup Bose & Kanchan Mukherjee, 2003. "Estimating The Arch Parameters By Solving Linear Equations," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 127-136, March.
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    Cited by:

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    2. Pourkhanali, Armin & Tafakori, Laleh & Bee, Marco, 2023. "Forecasting Value-at-Risk using functional volatility incorporating an exogenous effect," International Review of Financial Analysis, Elsevier, vol. 89(C).
    3. Karmakar, Sayar & Richter, Stefan & Wu, Wei Biao, 2022. "Simultaneous inference for time-varying models," Journal of Econometrics, Elsevier, vol. 227(2), pages 408-428.
    4. Bardet, Jean-Marc & Doukhan, Paul & Wintenberger, Olivier, 2022. "Contrast estimation of time-varying infinite memory processes," Stochastic Processes and their Applications, Elsevier, vol. 152(C), pages 32-85.
    5. Armin Pourkhanali & Jonathan Keith & Xibin Zhang, 2021. "Conditional Heteroscedasticity Models with Time-Varying Parameters: Estimation and Asymptotics," Monash Econometrics and Business Statistics Working Papers 15/21, Monash University, Department of Econometrics and Business Statistics.

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