Recursive computation of piecewise constant volatilities

DOI10.1016/j.csda.2010.06.027zbMath1254.91751OpenAlexW2117875334MaRDI QIDQ1927142

Christian Höhenrieder, Laurie Davies, Walter Kramer

Publication date: 30 December 2012

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2010.06.027

zbMATH Keywords

stock returnsvolatilityheteroskedasticity

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Software, source code, etc. for problems pertaining to statistics (62-04) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Economic time series analysis (91B84) Derivative securities (option pricing, hedging, etc.) (91G20)

Related Items (9)

Multiscale Quantile Segmentation ⋮ A binary search algorithm for univariate data approximation and estimation of extrema by piecewise monotonic constraints ⋮ Fourier methods for analyzing piecewise constant volatilities ⋮ Separation theorems for the extrema of best piecewise monotonic approximations to successive data ⋮ Multiscale blind source separation ⋮ On the online estimation of local constant volatilities ⋮ FDR-control in multiscale change-point segmentation ⋮ Multiscale change-point segmentation: beyond step functions ⋮ Multiscale Change Point Inference

Uses Software

• ftnonpar

Cites Work

• Detection of multiple changes in a sequence of dependent variables
• Nonparametric regression, confidence regions and regularization
• Modeling stock markets' volatility using GARCH models with normal, Student's \(t\) and stable Paretian distributions
• An adaptive algorithm for least squares piecewise monotonic data fitting
• Consistencies and rates of convergence of jump-penalized least squares estimators
• Local extremes, runs, strings and multiresolution. (With discussion)
• Statistical inference for time-inhomogeneous volatility models.
• Detection of multiple change-points in multivariate time series
• Least‐squares Estimation of an Unknown Number of Shifts in a Time Series
• Estimating and Testing Linear Models with Multiple Structural Changes

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