We propose a multiplicative autoregressive conditional proportion (ARCP) model for (0,1)-valued time series, in the spirit of GARCH (generalized autoregressive conditional heteroscedastic) and ACD (autoregressive conditional duration) models. In particular, our underlying process is defined as...

A common approach to analyze count time series is to fit models based on random sum operators. As an alternative, this paper introduces time series models based on a random multiplication operator, which is simply the multiplication of a variable operand by an integer-valued random coefficient,...

We consider two popular classes of volatility models, the generalized autoregressive conditional heteroscedastic (GARCH) model and the stochastic volatility (SV) model. We compare these two models with two classes of intensity models, the integer-valued GARCH (INGARCH) model and the...

We propose a broad class of count time series models, the mixed Poisson integer-valued stochastic intensity models. The proposed specification encompasses a wide range of conditional distributions of counts. We study its probabilistic structure and design Markov chain Monte Carlo algorithms for...

The paper presents the results of an econometric assessment of probabilistic default models on a sample of medium-sized manufacturing companies in Russia for the period from 2012 to 2020. Characteristics of the macroeconomic environment were included in the models. The inclusion of the real...

This work is devoted to the analysis of the factors influencing the bankruptcy of the Russian manufacturing industry companies for the period from 2012 to 2020. Logistic regression was used as an econometric tool for the modelling the probability of companies’ default. According to the...