Efficient nonparametric estimation and inference for the volatility function

DOI10.1080/02331888.2019.1615066zbMath1440.62384arXiv1607.08033OpenAlexW2963736116WikidataQ127851225 ScholiaQ127851225MaRDI QIDQ5384667

Francesco Giordano, Maria Lucia Parrella

Publication date: 24 June 2019

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1607.08033

zbMATH Keywords

confidence intervalstesting for symmetrynonparametric volatility estimationoptimal bandwidth estimation

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Nonparametric regression and quantile regression (62G08) Nonparametric hypothesis testing (62G10) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric tolerance and confidence regions (62G15) Stochastic models in economics (91B70)

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Cites Work

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• Statistical inference for nonparametric GARCH models
• Global adaptive smoothing regression
• Local polynomial estimators of the volatility function in nonparametric autoregression
• An algorithm for nonparametric GARCH modelling.
• Efficient semiparametric garch modeling of financial volatility
• Efficient estimation of conditional variance functions in stochastic regression
• Testing symmetry in nonparametric regression models
• Estimating market risk with neural networks

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