The purpose of the paper is to propose an autocorrelogram estimation procedure for irregularly spaced data which are modelled as subordinated continuous time series processes. Such processes, also called time deformed stochastic processes, have been discussed in a variety of contexts. Before...

Subordinated stochastic processes, also called time deformed stochastic processes, have been proposed in a variety of contexts to describe asset price behavior. They are used when the movement of prices is tied to the number of market transactions, trading volume or the more illusive concept of...

Globalization of trading in foreign exchange markets is a principal source of the daily and weekly seasonability in market volatility. One way to model such phenomena is to adopt a framework where market volatility is tied to the intensity of (world) trading through a subordinated stochastic...

This paper presents a study of intra-day patterns of stock market activity and introduces duration based activity measures for single stocks and multiple assets. The proposed measures involve weighted durations, i.e. times necessary to sell (buy) a predetermined volume or value of stocks. As...

This paper examines the consequences of estimating a past-dependent (causal) AR model from data generated by a stationary noncausal process with a future-dependent component. We show that the outcomes of that estimation depend on the noncausal persistence. When the noncausal persistence is...

This paper revisits the filtering and prediction in noncausal and mixed autoregressive processes and provides a simple alternative set of methods that are valid for processes with infinite variances. The prediction method provides complete predictive densities and prediction intervals at any...

This paper examines causality between the series of returns and transaction volumes in high frequency data. The dynamics of both series is restricted to transitions between a finite number of states. Depending on the state selection criteria, this approach approximates the dynamics of varying...