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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We introduce a new factor model for log volatilities that performsdimensionality reduction and considers contributions globally through themarket, and locally through cluster structure and their interactions. We do notassume a-priori the number of clusters in the data, instead using the DirectedBubble Hierarchical Tree (DBHT) algorithm to fix the number of factors. We usethe factor model and a new integrated non parametric proxy to study howvolatilities contribute to volatility clustering. Globally, only the marketcontributes to the volatility clustering. Locally for some clusters, thecluster itself contributes statistically to volatility clustering. This issignificantly advantageous over other factor models, since the factors can bechosen statistically, whilst also keeping economically relevant factors.Finally, we show that the log volatility factor model explains a similar amountof memory to a Principal Components Analysis (PCA) factor model and anexploratory factor model.

A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering