Time Series for QFFE: Special Issue of the Journal of Time Series Analysis

Abstract

QFFE stands for Quantitative Finance and Financial Econometrics conference, an event organized by Sébastien Laurent in Marseille every year since 2018. Each year there are two keynote speakers and two guest speakers, and around 60 selected papers are presented. The program for next year and previous years can be found here. The conference is preceded by a spring school, which offers doctoral students, post-doc, and young academics the opportunity to attend doctoral-level courses.

The QFFE conference is part of the ANR-funded project MLEforRisk (ANR-21-CE26-0007), which stands for Machine Learning and Econometrics for Risk Measurement in Finance. The project seeks to enhance our understanding of the advantages and limitations of integrating econometric methods with machine learning for measuring financial risks. This multidisciplinary initiative bridges the fields of finance and financial econometrics, bringing together a team of junior and senior researchers with expertise in management, economics, applied mathematics, and data science. The project aims to advance both theoretical insights and practical applications, fostering innovation at the intersection of these disciplines.

Since financial data such as stock prices, interest rates, and exchange rates are observed over time, time series analysis is crucial in finance. Finance professionals and academics often rely on fundamental time series models, such as ARMA, as well as essential time series techniques such as spectral analysis. Financial researchers are therefore naturally attracted to any new developments in time series. Econometricians have also developed new time series models and methods to capture the specificities of financial data. Contributions of econometricians include cointegration and error correction models, GARCH and stochastic volatility models, score-driven models, VAR models, Markov switching models, non-causal models, simulation-based inference, state space models, and Kalman filters, realized volatility measures, the Black–Scholes model, and factor models. The field of application of all these time series models and techniques is obviously not limited to finance. The aim of this special issue is to present some recent examples of the interface between time series analysis and finance.

We are very grateful to these authors. We would also like to thank the anonymous reviewers for their valuable review and feedback, which helped to improve the quality of this special issue. Special thanks go to Robert Taylor, Editor-in-Chief of the Journal of Time Series Analysis, for supporting this project, as well as to Priscilla Goldby for her invaluable help.