Estimation of the long memory parameter in stochastic volatility models by quadratic variations

Ionuţ Florescu; Ciprian A. Tudor

doi:10.1515/ROSE.2011.012

Estimation of the long memory parameter in stochastic volatility models by quadratic variations

Ionuţ Florescu
, Ciprian A. Tudor

School of Business

Research output: Contribution to journal › Article › peer-review

Abstract

We consider a stochastic volatility model where the volatility process is a fractional Brownian motion. We estimate the memory parameter of the volatility from discrete observations of the price process. We use criteria based on Malliavin calculus in order to characterize the asymptotic normality of the estimators.

Original language	English
Pages (from-to)	197-216
Number of pages	20
Journal	Random Operators and Stochastic Equations
Volume	19
Issue number	2
DOIs	https://doi.org/10.1515/ROSE.2011.012
State	Published - Jun 2011

Keywords

Fractional Brownian motion
Hurst parameter
Malliavin calculus
Multiple stochastic integral
Quadratic variation
Self-similarity
Statistical estimation
Stochastic volatility model

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title = "Estimation of the long memory parameter in stochastic volatility models by quadratic variations",

abstract = "We consider a stochastic volatility model where the volatility process is a fractional Brownian motion. We estimate the memory parameter of the volatility from discrete observations of the price process. We use criteria based on Malliavin calculus in order to characterize the asymptotic normality of the estimators.",

keywords = "Fractional Brownian motion, Hurst parameter, Malliavin calculus, Multiple stochastic integral, Quadratic variation, Self-similarity, Statistical estimation, Stochastic volatility model",

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year = "2011",

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AU - Florescu, Ionuţ

AU - Tudor, Ciprian A.

PY - 2011/6

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N2 - We consider a stochastic volatility model where the volatility process is a fractional Brownian motion. We estimate the memory parameter of the volatility from discrete observations of the price process. We use criteria based on Malliavin calculus in order to characterize the asymptotic normality of the estimators.

AB - We consider a stochastic volatility model where the volatility process is a fractional Brownian motion. We estimate the memory parameter of the volatility from discrete observations of the price process. We use criteria based on Malliavin calculus in order to characterize the asymptotic normality of the estimators.

KW - Fractional Brownian motion

KW - Hurst parameter

KW - Malliavin calculus

KW - Multiple stochastic integral

KW - Quadratic variation

KW - Self-similarity

KW - Statistical estimation

KW - Stochastic volatility model

UR - https://www.scopus.com/pages/publications/84858398532

UR - https://www.scopus.com/pages/publications/84858398532#tab=citedBy

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DO - 10.1515/ROSE.2011.012

M3 - Article

AN - SCOPUS:84858398532

SN - 0926-6364

VL - 19

SP - 197

EP - 216

JO - Random Operators and Stochastic Equations

JF - Random Operators and Stochastic Equations

IS - 2

ER -

Estimation of the long memory parameter in stochastic volatility models by quadratic variations

Abstract

Keywords

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