We propose an autoregressive conditional duration (ACD) model with periodic time-varying parameters and multiplicative error form. We name this model periodic autoregressive conditional duration (PACD). First, we study the stability properties and the moment structures of it. Second, we estimate...

We propose an autoregressive conditional duration (ACD) model with periodic time-varying parameters and multiplicative error form. We name this model periodic autoregressive conditional duration (PACD). First, we study the stability properties and the moment structures of it. Second, we estimate...

Using numerous transaction data on the number of stock trades, we conduct a forecasting exercise with INGARCH models, governed by various conditional distributions. The model parameters are estimated with efficient Markov Chain Monte Carlo methods, while forecast evaluation is done by...

This paper proposes a noisy GARCH model with two volatility sequences (an unobserved and an observed one) and a stochastic time-varying conditional kurtosis. The unobserved volatility equation, equipped with random coefficients, is a linear function of the past squared observations and of the...

A class of generalized ARMA models with an identity link function and a conditional beta prime (BP-ARMA) distribution is proposed for modeling positive time series. Sufficient and necessary conditions for the existence of an ergodic stationary BP-ARMA process having finite moments are first...

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 examine if the day-of-the-week effect is present in Bitcoin return series. The model specification in use accounts for conditional heteroscedasticity, which is captured in the form of a stochastic volatility process that allows for periodic time-varying parameters. We find periodicity in...

We propose a new model for transaction data that accounts jointly for the time duration between transactions and for the discreteness of the intraday stock price changes. Duration is assumed to follow a stochastic conditional duration model, while price discreteness is captured by an...

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...

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...