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

Constructing a more effective value at risk (VaR) prediction model has longbeen a goal in financial risk management. In this paper, we propose a novelparametric approach and provide a standard paradigm to demonstrate themodeling. We establish a dynamic conditional score (DCS) model based onhigh-frequency data and a generalized distribution (GD), namely, the GD-DCSmodel, to improve the forecasts of daily VaR. The model assumes that intradayreturns at different moments are independent of each other and obey the samekind of GD, whose dynamic parameters are driven by DCS. By predicting themotion law of the time-varying parameters, the conditional distribution ofintraday returns is determined; then, the bootstrap method is used to simulatedaily returns. An empirical analysis using data from the Chinese stock marketshows that Weibull-Pareto -DCS model incorporating high-frequency data issuperior to traditional benchmark models, such as RGARCH, in the prediction ofVaR at high risk levels, which proves that this approach contributes to theimprovement of risk measurement tools.

A Method for Predicting VaR by Aggregating Generalized Distributions Driven by the Dynamic Conditional Score