Publication details

Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?

Investor logo
Authors

LYÓCSA Štefan TODOROVA Neda

Year of publication 2020
Type Article in Periodical
Magazine / Source International Journal of Forecasting
MU Faculty or unit

Faculty of Economics and Administration

Citation
Web https://www.sciencedirect.com/science/article/pii/S0169207019302250
Doi http://dx.doi.org/10.1016/j.ijforecast.2019.08.002
Keywords High frequency data; Realized volatility; Overnight volatility; Forecasting; Market risk
Attached files
Description We study the potential merits of using trading and non-trading period market volatilities to model and forecast the stock volatility over the next one to 22 days. We demonstrate the role of overnight volatility information by estimating heterogeneous autoregressive (HAR) model specifications with and without a trading period market risk factor using ten years of high-frequency data for the 431 constituents of the S&P 500 index. The stocks’ own overnight squared returns perform poorly across stocks and forecast horizons, as well as in the asset allocation exercise. In contrast, we find overwhelming evidence that the market-level volatility, proxied by S&P Mini futures, matters significantly for improving the model fit and volatility forecasting accuracy. The greatest model fit and forecast improvements are found for short-term forecast horizons of up to five trading days, and for the non-trading period market-level volatility. The documented increase in forecast accuracy is found to be associated with the stocks’ sensitivity to the market risk factor. Finally, we show that both the trading and non-trading period market realized volatilities are relevant in an asset allocation context, as they increase the average returns, Sharpe ratios and certainty equivalent returns of a mean–variance investor.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.

More info