De-noising option prices with the wavelet method

Emmanuel Haven; Xiaoquan Liu; Liya Shen

doi:10.1016/j.ejor.2012.04.020

De-noising option prices with the wavelet method

Emmanuel Haven, Xiaoquan Liu, Liya Shen

Research output: Journal Publication › Article › peer-review

74 Citations (Scopus)

Abstract

Financial time series are known to carry noise. Hence, techniques to de-noise such data deserve great attention. Wavelet analysis is widely used in science and engineering to de-noise data. In this paper we show, through the use of Monte Carlo simulations, the power of the wavelet method in the de-noising of option price data. We also find that the estimation of risk-neutral density functions and out-of-sample price forecasting is significantly improved after noise is removed using the wavelet method.

Original language	English
Pages (from-to)	104-112
Number of pages	9
Journal	European Journal of Operational Research
Volume	222
Issue number	1
DOIs	https://doi.org/10.1016/j.ejor.2012.04.020
Publication status	Published - 1 Oct 2012
Externally published	Yes

Keywords

De-noise
Monte Carlo simulation
Option pricing
Wavelet analysis

ASJC Scopus subject areas

General Computer Science
Modelling and Simulation
Management Science and Operations Research
Information Systems and Management

Access to Document

10.1016/j.ejor.2012.04.020

Cite this

@article{a0a408b609bf4a3995c4a0b91c7c01d8,

title = "De-noising option prices with the wavelet method",

abstract = "Financial time series are known to carry noise. Hence, techniques to de-noise such data deserve great attention. Wavelet analysis is widely used in science and engineering to de-noise data. In this paper we show, through the use of Monte Carlo simulations, the power of the wavelet method in the de-noising of option price data. We also find that the estimation of risk-neutral density functions and out-of-sample price forecasting is significantly improved after noise is removed using the wavelet method.",

keywords = "De-noise, Monte Carlo simulation, Option pricing, Wavelet analysis",

author = "Emmanuel Haven and Xiaoquan Liu and Liya Shen",

year = "2012",

month = oct,

day = "1",

doi = "10.1016/j.ejor.2012.04.020",

language = "English",

volume = "222",

pages = "104--112",

journal = "European Journal of Operational Research",

issn = "0377-2217",

publisher = "Elsevier",

number = "1",

}

TY - JOUR

T1 - De-noising option prices with the wavelet method

AU - Haven, Emmanuel

AU - Liu, Xiaoquan

AU - Shen, Liya

PY - 2012/10/1

Y1 - 2012/10/1

N2 - Financial time series are known to carry noise. Hence, techniques to de-noise such data deserve great attention. Wavelet analysis is widely used in science and engineering to de-noise data. In this paper we show, through the use of Monte Carlo simulations, the power of the wavelet method in the de-noising of option price data. We also find that the estimation of risk-neutral density functions and out-of-sample price forecasting is significantly improved after noise is removed using the wavelet method.

AB - Financial time series are known to carry noise. Hence, techniques to de-noise such data deserve great attention. Wavelet analysis is widely used in science and engineering to de-noise data. In this paper we show, through the use of Monte Carlo simulations, the power of the wavelet method in the de-noising of option price data. We also find that the estimation of risk-neutral density functions and out-of-sample price forecasting is significantly improved after noise is removed using the wavelet method.

KW - De-noise

KW - Monte Carlo simulation

KW - Option pricing

KW - Wavelet analysis

UR - http://www.scopus.com/inward/record.url?scp=84861828980&partnerID=8YFLogxK

U2 - 10.1016/j.ejor.2012.04.020

DO - 10.1016/j.ejor.2012.04.020

M3 - Article

AN - SCOPUS:84861828980

SN - 0377-2217

VL - 222

SP - 104

EP - 112

JO - European Journal of Operational Research

JF - European Journal of Operational Research

IS - 1

ER -

De-noising option prices with the wavelet method

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this