Dynamic Adaptive Mixture Models with an Application to Volatility and Risk

32 Pages Posted: 14 May 2019

See all articles by Leopoldo Catania

Leopoldo Catania

Aarhus University - School of Business and Social Sciences; Aarhus University - CREATES

Date Written: April 9, 2019

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 US equity data is reported.

Keywords: Dynamic Mixture Models, Score--Driven models, Adaptive Models, Quantitative Risk Management

Suggested Citation

Catania, Leopoldo, Dynamic Adaptive Mixture Models with an Application to Volatility and Risk (April 9, 2019). Available at SSRN: https://ssrn.com/abstract=3372151 or http://dx.doi.org/10.2139/ssrn.3372151

Leopoldo Catania (Contact Author)

Aarhus University - School of Business and Social Sciences ( email )

Fuglesangs Allé 4
Aarhus V, DK-8210
Denmark
+4587165536 (Phone)

HOME PAGE: http://pure.au.dk/portal/en/leopoldo.catania@econ.au.dk

Aarhus University - CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

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