<div> We propose option realized semivariances and signed jumps as new “observable quantities” to summarize the asymmetric information contained in the sign of high-frequency option returns. These measures successfully capture the direction of the discontinuities related to both the underlying asset and risk factor, yielding incremental information not contained in the aggregate option realized measures. Using options data on S&P 500 ETF (SPY) and 15 individual equities, we document that the negative (positive) semivariance and signed jump of out-of-the-money call (put) options play a prominent role in predicting future variance, variance risk-premia, and excess monthly returns. Out-of-sample volatility timing strategies based on these measures generate economically significant gains of up to 206 basis points annually for risk-averse investors. </div>

This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects forecasting. To account for this, we propose a periodicity-adjusted HAR model, HARP, where predictors are constructed from the periodicity-filtered data. We demonstrate empirically (using 30 stocks from various business sectors and the SPY for the period 2000–2020) and via Monte Carlo simulations that the HARP models produce significantly better forecasts across all forecasting horizons. We also show that adjusting for periodicity when estimating the variance risk premium improves return predictability.