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

Financial studies require volatility based models which provides usefulinsights on risks related to investments. Stochastic volatility models are oneof the most popular approaches to model volatility in such studies. The assetreturns under study may come in multiple clusters which are not captured wellassuming standard distributions. Mixture distributions are more appropriate insuch situations. In this work, an algorithm is demonstrated which is capable ofstudying finite mixtures but with unknown number of components. This algorithmuses a Birth-Death process to adjust the number of components in the mixturedistribution and the weights are assigned accordingly. This mixturedistribution specification is then used for asset returns and a semi-parametricstochastic volatility model is fitted in a Bayesian framework. A specific caseof Gaussian mixtures is studied. Using appropriate prior specification, Gibbssampling method is used to generate posterior chains and assess modelconvergence. A case study of stock return data for State Bank of India is usedto illustrate the methodology.

Bayesian Analysis of Stochastic Volatility Model using Finite Gaussian Mixtures with Unknown Number of Components