High-Dimensional Financial Time Series
Category: International Society for Business and Industrial Statistics (ISBIS)
Title: High-Dimensional Financial Time Series
The increasing of data available in finance has substantially grows over the years, this large amount of data bring new challenges in how to model and forecasting financial time series. This session is devoted to discuss some advances in high-dimensional time series data and its application in finance as well as encourage other researchers to dive, propose and develop new procedures to deal with high-dimensional financial time series. The session will be divided into five presentations of 20 minutes each one (including Q&A). This IPS will be of great interest to all ISI WSC 2023 attendees interested in time series, business statistics, and computational statistics. Members of the ISBIS, IASC, and the ISI SIG on Data Science will be particularly interested in the talks and discussion of the IPS. [This session if co-organized by Carlos Trucíos and Paulo Canas Rodrigues.]
The title and speaker of the five presentations are detailed below:
- Robust Estimation of High-Dimensional cDCC Models. Luiz Hotta (University of Campinas, Brazil).
- Modelling and Forecasting Covariance Matrices: A Simple Model with Stochastic Volatility Latent Factors. Roxana Halbleib (University of Freiburg, Germany).
- Modeling Multivariate Positive-Valued Time Series with Financial Applications. Nalini Ravishanker (University of Connecticut, USA).
- Tail risk measures for ESG and nonESG companies. Alessandra Amendola (University of Salermo, Italy).
- Forecasting Value-at-Risk and Expected Shortfall in Large Panels: A Robust General Dynamic Factor Approach. Carlos Trucios (University of Campinas, Brazil).
- Adaptively weighted combinations of tail-risk forecasts
- Forecasting Value-at-Risk and Expected Shortfall in Large Panels: A Robust General Dynamic Factor Approach.
- Modeling Multivariate Positive-Valued Time Series with Financial Applications
- Modelling and Forecasting Covariance Matrices: A Simple Model with Stochastic Volatility Latent Factors
- Robust Estimation of High-Dimensional cDCC Model