Dynamic Adaptive Mixture Models with an Application to Volatility and Risk

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.

Dynamic Clustering of Multivariate Panel Data

“…Ayadi and Groen (2015) and ECB (2016). Also, in contrast to the application used by for instance Catania (2019), our banking data are observed over only a moderate number of time points T , while the number of units N and the number of firm characteristics D are high. Given present but infrequent transitions, the properties of bank business models are unlikely to be constant throughout the periods of market turbulence and shifts in bank regulations in our sample.…”

Score-Driven Time Series Models

“…The dynamic adaptive mixture model (DAMM) of Catania (2019) has the probability of being in a given regime changing over time. The dynamics are modeled using the scores of the regime probabilities in the conditional distribution.…”

Mixture ratio modeling of dynamic systems

“…The common use of the condition‐independent weights of dynamic components goes against the expansion logic and surely violate universal approximation property. Rare exceptions that considered condition‐dependent weight 30–32 indicate how much is lost when using constant component weights.…”