ARFIMAX and ARFIMAX-TARCH realized volatility modeling

DOI10.1080/02664760802271017zbMath1253.62076OpenAlexW1967260842MaRDI QIDQ3183838

Stavros Degiannakis

Publication date: 21 October 2009

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://mpra.ub.uni-muenchen.de/80465/1/MPRA_paper_80465.pdf

zbMATH Keywords

volatility forecasting

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items (4)

Financial modelling, risk management of energy instruments and the role of cryptocurrencies ⋮ Modelling extreme risk spillovers in the commodity markets around crisis periods including COVID19 ⋮ Unnamed Item ⋮ Evaluation of realized volatility predictions from models with leptokurtically and asymmetrically distributed forecast errors

Uses Software

• ARFIMA

Cites Work

• Consistent ranking of volatility models
• Long memory relationships and the aggregation of dynamic models
• Estimating the dimension of a model
• The Distribution of Realized Exchange Rate Volatility

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