EQUITY RISK: MEASURING RETURN VOLATILITY USING HISTORICAL HIGH-FREQUENCY DATA

Alan Chow and Kyre Lahtinen
Additional contact information
Alan Chow: Mitchell College of Business, University of South Alabama, USA
Kyre Lahtinen: Mitchell College of Business, University of South Alabama, USA

Studies in Business and Economics, 2019, vol. 14, issue 3, 60-71

Abstract: Market Volatility has been investigated at great lengths, but the measure of historical volatility, referred to as the relative volatility, is inconsistent. Using historical return data to calculate the volatility of a stock return provides a measure of the realized volatility. Realized volatility is often measured using some method of calculating a deviation from the mean of the returns for the stock price, the summation of squared returns, or the summation of absolute returns. We look to the stocks that make up the DJIA, using tick-by-tick data from June 2015 - May 2016. This research helps to address the question of what is the better measure of realized volatility? Several measures of volatility are used as proxies and are compared at four estimation time intervals. We review these measures to determine a closer/better fit estimator to the true realized volatility, using MSE, MAD, Diebold-Mariano test, and Pitman Closeness. We find that when using a standard deviation based on transaction level returns, shorter increments of time, while containing some levels of noise, are better estimates of volatility than longer increments.

Keywords: Risk; Volatility; Equity; Return; High-Frequency (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
http://eccsf.ulbsibiu.ro/RePEc/blg/journl/14305chow.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:blg:journl:v:14:y:2019:i:3:p:60-71

Access Statistics for this article

More articles in Studies in Business and Economics from Lucian Blaga University of Sibiu, Faculty of Economic Sciences Lucian Blaga University of Sibiu, Faculty of Economic Sciences Dumbravii Avenue, No 17, postal code 550324, Sibiu, Romania. Contact information at EDIRC.
Bibliographic data for series maintained by Mihaela Herciu ().