Abstract

http://ssrn.com/abstract=1982463
 
 

References (25)



 
 

Footnotes (6)



 


 



Assessing the Performance of Different Volatility Estimators: A Monte Carlo Analysis


Álvaro Cartea


University of Oxford; University of Oxford - Oxford-Man Institute of Quantitative Finance

Dimitris Karyampas


ICMA, University of Reading

January 10, 2012

Applied Mathematical Finance, Volume 19, Issue 6, 2012, 535-552

Abstract:     
We test the performance of different volatility estimators that have recently been proposed in the literature and which have been designed to deal with problems arising when ultra high-frequency data are employed: microstructure noise and price discontinuities. Our goal is to provide an extensive simulation analysis for different levels of noise and frequency of jumps to compare the performance of the proposed volatility estimators. We conclude that the MLE-F, a two-step parametric volatility estimator proposed by Cartea and Karyampas (2010), outperforms most of the well known high-frequency volatility estimators when different assumptions about the path properties of stock dynamics are used.

Number of Pages in PDF File: 23

Keywords: volatility, high-frequency data, jumps, microstructure noise

JEL Classification: C53, G12, G14, C22


Open PDF in Browser Download This Paper

Date posted: January 10, 2012 ; Last revised: March 11, 2013

Suggested Citation

Cartea, Álvaro and Karyampas, Dimitris, Assessing the Performance of Different Volatility Estimators: A Monte Carlo Analysis (January 10, 2012). Applied Mathematical Finance, Volume 19, Issue 6, 2012, 535-552. Available at SSRN: http://ssrn.com/abstract=1982463

Contact Information

Álvaro Cartea (Contact Author)
University of Oxford ( email )
Mansfield Road
Oxford, Oxfordshire OX1 4AU
United Kingdom
University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )
Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom
Dimitris Karyampas
ICMA, University of Reading ( email )
United Kingdom
Feedback to SSRN


Paper statistics
Abstract Views: 1,225
Downloads: 296
Download Rank: 70,151
References:  25
Footnotes:  6

© 2016 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollobot1 in 0.297 seconds