Improving stock market volatility forecasts with complete subset linear and quantile HAR models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F21%3A00122736" target="_blank" >RIV/00216224:14560/21:00122736 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/journal/expert-systems-with-applications" target="_blank" >https://www.sciencedirect.com/journal/expert-systems-with-applications</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.eswa.2021.115416" target="_blank" >10.1016/j.eswa.2021.115416</a>
Alternative languages
Result language
angličtina
Original language name
Improving stock market volatility forecasts with complete subset linear and quantile HAR models
Original language description
Volatility forecasting plays an integral role in risk management, investments and security valuation for all assets with uncertain future payoffs. We enrich the literature by presenting computationally intensive variations of the heterogeneous autoregressive (HAR) volatility model: the complete subset linear/quantile regression HAR models, HAR-CSLR and HAR-CSQR. Predictions of 1-to 22-day-ahead volatility of four major market indices (NIKKEI 225, S&P 500, SSEC and STOXX 50) show that both models tend to outperform several benchmark HAR models. Forecasting accuracy improvements tend to stabilize for longer forecasting horizons: e.g., fiveday-ahead improvements range from 6.57% (SSEC) to 35.62% (NIKKEI 225) and from 3.99% (STOXX) to 9.54% for mean square error (MSE) and QLIKE loss functions. In terms of MSE, the HAR-CSQR model outperforms several standard benchmark HAR models across all market indices and forecast horizons.
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
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Expert Systems with Applications
ISSN
0957-4174
e-ISSN
1873-6793
Volume of the periodical
183
Issue of the periodical within the volume
November
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
11
Pages from-to
1-11
UT code for WoS article
000691769900005
EID of the result in the Scopus database
2-s2.0-85109394941