<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

Equity Risk: Measuring Return Volatility Using Historical High-Frequency Data

Studies in Business and Economics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Equity Risk: Measuring Return Volatility Using Historical High-Frequency Data"}

Market Volatility has been investigated at great lengths, but the measure of historical volatility, referred to as the relative volatility, is inconsistent....

Equity Risk: Measuring Return Volatility Using Historical High-Frequency Data

Publié en ligne: 21 janv. 2020

Pages: 60 - 71

DOI: https://doi.org/10.2478/sbe-2019-0043

Mots clés
Risk, Volatility, Equity, Return, High-Frequency

© 2019 Chow Alan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Equity Risk: Measuring Return Volatility Using Historical High-Frequency Data

Publié en ligne: 21 janv. 2020

Pages: 60 - 71

DOI: https://doi.org/10.2478/sbe-2019-0043

Mots clésRisk, Volatility, Equity, Return, High-Frequency

© 2019 Chow Alan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Risk, Volatility, Equity, Return, High-Frequency