We examine high-frequency market reactions to an intraday stock-specific news flow. Using
unique pre-processed data from an automated news analytics tool based on linguistic …

The explosion of algorithmic trading has been one of the most pro-minent recent trends in
the financial industry. Algorithmic trading consists of automated trading strategies that …

In recent years, tourism demand forecasting has attracted more interests not only in tourism
area but in data science field. In this study, we follow the previous relevant data science …

Financial time series analysis has focused on data related to market trading activity. Next to
the modeling of the conditional variance of returns within the GARCH family of models …

Financial time series are often non‐negative‐valued (volumes, trades, durations, realized
volatility, daily range) and exhibit clustering. When joint dynamics is of interest, the vector …

We propose and illustrate a Markov-switching multifractal duration (MSMD) model for
analysis of inter-trade durations in financial markets. We establish several of its key …

We propose a novel approach to model serially dependent positive-valued variables which
realize a non-trivial proportion of zero outcomes. This is a typical phenomenon in financial …

We study the impact of the arrival of macroeconomic news on the informational and noise-
driven components in high-frequency quote processes and their conditional variances. We …

Realized volatilities observed across several assets show a common secular trend and
some idiosyncratic pattern which we accommodate by extending the class of Multiplicative …

We model the dynamics of ask and bid curves in a limit order book market using a dynamic
semiparametric factor model. The shape of the curves is captured by a factor structure which …