Detecting pump-and-dump schemes involving cryptoassets with high-frequency data is challenging due to imbalanced datasets and the early occurrence of unusual trading volumes. To address these issues, we propose constructing synthetic balanced datasets using resampling methods and flagging a...

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...

Interaction terms are often misinterpreted in the empirical economics literature by assuming that the coefficient of interest represents unconditional marginal changes. I present the correct way to estimate conditional marginal changes in a series of non-linear models including (ordered)...

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...

Peek and Rosengren (2005) suggested the mechanism of ``unnatural selection,'' where Japanese banks with impaired capital increase credit to low-quality firms because of their motivation to pursue balance sheet cosmetics. In this study, we reexamine this mechanism in terms of the interaction...

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...

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...

Semiparametric models are useful in econometrics, social sciences and medicine application. In this paper, a new estimator based on least square methods is proposed to estimate the direction of unknown parameters in semi-parametric models. The proposed estimator is consistent and has asymptotic...

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,...