Using COGARCH-Filtered Volatility in Modelling Within ARDL Framework

DOI10.1007/978-3-030-54108-8_13OpenAlexW3133028528MaRDI QIDQ5049440

Yakup Arı

Publication date: 11 November 2022

Published in: Handbook of Research on Emerging Theories, Models, and Applications of Financial Econometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-030-54108-8_13

zbMATH Keywords

ARDLcointegrationvolatilityCOGARCHbounds testingRyuima

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05)

Uses Software

• quantmod
• Rugarch
• Microfit
• dLagM

Cites Work

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• Continuous-time GARCH processes
• GARCH modelling in continuous time for irregularly spaced time series data
• Statistical analysis of cointegration vectors
• Spurious regressions in econometrics
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• Testing for a unit root in time series regression
• Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root
• A continuous-time GARCH process driven by a Lévy process: stationarity and second-order behaviour
• Discrete‐Time Approximation of a Cogarch(p,q) Model and its Estimation
• Co-Integration and Error Correction: Representation, Estimation, and Testing
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
• Method of moment estimation in the COGARCH(1,1) model
• Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models

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