Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F20%3A00114078" target="_blank" >RIV/00216224:14560/20:00114078 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0169207019302250" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0169207019302250</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ijforecast.2019.08.002" target="_blank" >10.1016/j.ijforecast.2019.08.002</a>
Alternative languages
Result language
angličtina
Original language name
Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?
Original language 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50206 - Finance
Result continuities
Project
<a href="/en/project/GA18-05829S" target="_blank" >GA18-05829S: Forecasting Volatility in Emerging Financial Markets</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Forecasting
ISSN
0169-2070
e-ISSN
1872-8200
Volume of the periodical
36
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
18
Pages from-to
628-645
UT code for WoS article
000527898100025
EID of the result in the Scopus database
2-s2.0-85076251581