Least tail-trimmed squares for infinite variance autoregressions

DOI10.1111/jtsa.12005zbMath1273.62064OpenAlexW3121882644MaRDI QIDQ2852489

Jonathan B. Hill

Publication date: 9 October 2013

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

Full work available at URL: https://doi.org/10.1111/jtsa.12005

zbMATH Keywords

heavy tailsleast squaresrobust inferencetail trimming

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (11)

Sieve-based inference for infinite-variance linear processes ⋮ Robustness of Bootstrap in Instrumental Variable Regression ⋮ Robust parameter estimation of regression model with AR(p) error terms ⋮ GEL estimation for heavy-tailed GARCH models with robust empirical likelihood inference ⋮ Robust score and portmanteau tests of volatility spillover ⋮ On the measurement and treatment of extremes in time series ⋮ Least tail-trimmed absolute deviation estimation for autoregressions with infinite/finite variance ⋮ Robust generalized empirical likelihood for heavy tailed autoregressions with conditionally heteroscedastic errors ⋮ Robust estimation and inference for heavy tailed GARCH ⋮ The eigenstructure of the sample covariance matrices of high-dimensional stochastic volatility models with heavy tails ⋮ Convergence in mean and central limit theorems for weighted sums of martingale difference random vectors with infinite rth moments

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