Increasing the information content of realized volatility forecasts*

Razvan Pascalau and Ryan Poirier

Journal of Financial Econometrics, 2023, vol. 21, issue 4, 1064-1098

Abstract: Assuming N available calendar days, each with M intraday returns, the realized volatility literature suggests creating N end-of-day estimators by summing the M squared returns from each particular date. Instead of this “Calendar” [realized variance (RV)] approach, we propose a “Rolling” [rolling RV (RRV)] approach that simply sums trailing M returns at each timestamp, regardless if all M returns belong to the same calendar date. When estimating an out-of-sample 1-day realized volatility model, the former results in an ordinary least squares (OLS) regression with N−1 datapoints while the latter incorporates M(N−2)+1 datapoints, effectively lowering the standard errors, and potentially resulting in more accurate forecasts. We compare both models for the S&P 500 and 26 Dow Jones Industrial Average stocks; our results generally suggest that the Rolling approach yields both statistically and economically significant superior out-of-sample performance over the traditional Calendar approach.

Keywords: realized variance; rolling window; volatility forecasting (search for similar items in EconPapers)
JEL-codes: C53 C83 G17 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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