Improving stock market volatility forecasts with complete subset linear and quantile HAR models

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving stock market volatility forecasts with complete subset linear and quantile HAR models

Popis výsledku v původním jazyce

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&amp;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.

Název v anglickém jazyce

Improving stock market volatility forecasts with complete subset linear and quantile HAR models

Popis výsledku anglicky

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&amp;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Expert Systems with Applications

ISSN

0957-4174

e-ISSN

1873-6793

Svazek periodika

183

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

1-11

Kód UT WoS článku

000691769900005

EID výsledku v databázi Scopus

2-s2.0-85109394941