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Information in daily data volatility measurements
Author(s) -
Kawakatsu Hiroyuki
Publication year - 2021
Publication title -
international journal of finance and economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.505
H-Index - 39
eISSN - 1099-1158
pISSN - 1076-9307
DOI - 10.1002/ijfe.1868
Subject(s) - volatility (finance) , econometrics , economics , forward volatility , regression , realized variance , statistics , index (typography) , data set , mathematics , implied volatility , computer science , world wide web
Abstract This paper evaluates the information content in daily volatility measures that utilize OHLC (Open‐High‐Low‐Close) price data. An encompassing regression framework is used to evaluate the absolute and relative information contain in such measures. 2‐step GMM (generalized method of moments) estimates using two sets of instruments are used to address potential bias from measurement errors. The evidence using S&P 500 index data suggest that volatility measures that use OHLC data encompass those based only on close‐to‐close returns or high‐minus‐low ranges. However, the proposed instruments do not all pass statistical tests of instrument validity and the identification robust confidence set can be quite large.