Nonparametric volatility density estimation for discrete time models

B. Van Es; P. Spreij; J.H. van Zanten

doi:10.1080/1048525042000267752

Nonparametric volatility density estimation for discrete time models

B. Van Es
, P. Spreij
, J.H. van Zanten

Mathematics

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time instants. A Fourier-type deconvolution kernel density estimator based on the logarithm of the squared process is proposed to estimate the volatility density. Expansions of the bias and bounds on the variance are derived.

Original language	English
Pages (from-to)	237-251
Journal	Journal of Nonparametric Statistics
Volume	17
Issue number	2
DOIs	https://doi.org/10.1080/1048525042000267752
Publication status	Published - 2005

Bibliographical note

MR2112523

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Access to Document

10.1080/1048525042000267752

Persistent URL (handle)

Persistent link

Cite this

@article{03b38c9df14a4198b70a275d4b41c07e,

title = "Nonparametric volatility density estimation for discrete time models",

abstract = "We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time instants. A Fourier-type deconvolution kernel density estimator based on the logarithm of the squared process is proposed to estimate the volatility density. Expansions of the bias and bounds on the variance are derived.",

author = "\{Van Es\}, B. and P. Spreij and \{van Zanten\}, J.H.",

note = "MR2112523 ",

year = "2005",

doi = "10.1080/1048525042000267752",

language = "English",

volume = "17",

pages = "237--251",

journal = "Journal of Nonparametric Statistics",

issn = "1048-5252",

publisher = "Taylor and Francis Ltd.",

number = "2",

}

TY - JOUR

T1 - Nonparametric volatility density estimation for discrete time models

AU - Van Es, B.

AU - Spreij, P.

AU - van Zanten, J.H.

N1 - MR2112523

PY - 2005

Y1 - 2005

N2 - We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time instants. A Fourier-type deconvolution kernel density estimator based on the logarithm of the squared process is proposed to estimate the volatility density. Expansions of the bias and bounds on the variance are derived.

AB - We consider discrete time models for asset prices with a stationary volatility process. We aim at estimating the multivariate density of this process at a set of consecutive time instants. A Fourier-type deconvolution kernel density estimator based on the logarithm of the squared process is proposed to estimate the volatility density. Expansions of the bias and bounds on the variance are derived.

U2 - 10.1080/1048525042000267752

DO - 10.1080/1048525042000267752

M3 - Article

SN - 1048-5252

VL - 17

SP - 237

EP - 251

JO - Journal of Nonparametric Statistics

JF - Journal of Nonparametric Statistics

IS - 2

ER -

Nonparametric volatility density estimation for discrete time models

Abstract

Bibliographical note

UN SDGs

Access to Document

Persistent URL (handle)

Fingerprint

Cite this