Dynamic Adaptive Mixture Models with an Application to Volatility and Risk. (14th June 2019)

• Record Type: Journal Article
• Authors: Catania, Leopoldo
• Is Part Of: Journal of financial econometrics. Volume 19:Number 4(2021)
• Page Start: 531
• Page End: 564
• Publication Date: 2019-06-14
• Subjects: adaptive models -- dynamic mixture models -- quantitative risk management -- score-driven models; Capital market -- Law and legislation -- Periodicals; Financial institutions -- Law and legislation -- Periodicals; 338.544205
• DOI: 10.1093/jjfinec/nbz018
• Languages: English
• ISSNs: 1479-8409

Abstract

Abstract: In this paper we propose a new class of dynamic mixture models (DAMMs) being able to sequentially adapt the mixture components as well as the mixture composition using information coming from the data. The information driven nature of the proposed class of models allows to exactly compute the full likelihood and to avoid computer intensive simulation schemes. Specific models for financial data are developed starting from the general specification. These models nest many specifications already available in the literature. The properties of the new class of models are discussed through the paper and a large-scale application in quantitative risk management using U.S. equity data is reported.