English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper studies the joint use of high-frequency and VIX information to model and forecast volatility. Our framework relies on an extension of the realized EGARCH (REGARCH) model, namely the component REGARCH model with VIX (hereafter REGARCH(C)-VIX). The REGARCH(C)-VIX model facilitates exploitation of the high-frequency and VIX information through the inclusion of realized measure and VIX for modelling and forecasting volatility. Moreover, the model features a component volatility structure, which has the ability to capture the long memory volatility. An empirical investigation with the S&amp;P 500 index shows that the REGARCH(C)-VIX model outperforms a variety of competing models in both empirical fit and out-of-sample volatility forecasting. Our findings provide strong evidence for including the high-frequency and VIX information as well as the component volatility structure to model and forecast volatility.

Modelling and forecasting volatility with high-frequency and VIX information: a component realized EGARCH model with VIX