STATSCALE SEMINARS
CURRENT SEMINARS

12th March 2021 - 3-4pm UK time
Speaker: Sumanta Basu (Cornell University)

Title: Learning Financial Networks with Graphical Models of Time Series Data

 

Abstract: After the 2007-09 financial crisis, there has been a growing interest in measuring systemic risk, broadly defined as the risk of widespread failure of the entire financial system. In a highly interlinked financial market, a large body of recent works have proposed to use network connectivity amongst financial institutions to assess their systemic importance. In this work, we will present some graphical modeling techniques for learning interactions among the  components of a large dynamic system from multivariate time series data, where the core idea is to learn from lead-lag relationships (commonly known as Granger causality) between time series in addition to their co-movements. In the context of modeling networks of interactions amongst financial institutions and measuring systemic risk, we will demonstrate how linear and quantile-based Granger causality analyses using vector autoregressive (VAR) models can provide insight. We will present some non-asymptotic statistical theory for our proposed algorithms, estimate these graphical models using stock returns of large financial institutions in U.S. and India, and demonstrate their usefulness in detecting systemically risky periods and institutions.


 

26th March 2021 - 4-5pm UK time
Speaker: Hao Chen (UC Davis)

Title: A universal event detection framework for neuropixels data.

Abstract to Follow

To register your interest in accessing the StatScale Seminars, contact Dr Hyeyoung Maeng 

PREVIOUS SEMINARS

26th February 2021 - Eric Kolaczyk (Boston University)

How hard is it to work with a network `average'?

12th February 2021 - Matteo Barigozzi (Università di Bologna)
Quasi Maximum Likelihood Estimation and Inference of Large Approximate Dynamic Factor Models via the EM algorithm

29th January 2021 - Holger Dette (Ruhr-Universitaet Bochum)

Testing Relevant Hypotheses in Functional Time Series via Self-Normalization

4th December 2020 - Priyanga Dilini Talagala (University of Moratuwa)

Anomaly Detection in Streaming Time Series Data

 

 

20th November 2020 - Florian Pein (University of Cambridge)

About the loss function for cross-validation in change-point regression

 

 

6th November 2020 - Alex Aue (UC Davis)

Random matrix theory aids statistical inference in high dimensions

 

 

23rd October 2020 - Yoav Zemel (University of Cambridge)

Probabilistic approximations to optimal transport

 

 

9th October 2020 - Solt Kovacs (ETH Zurich)

Optimistic search strategy: change point detection for large-scale data via adaptive logarithmic queries

 

 

17th July 2020 - Tobias Kley (University of Bristol)

A new approach for open-end sequential change point monitoring

 

 

3rd July, 2020 - Claudia Kirch (Otto-von-Guericke University)

Functional change point detection for fMRI data 

 

 

19th June, 2020 - Martin Tveten (Dept. of Mathematics, University of Oslo)

Scalable changepoint and anomaly detection in cross-correlated data

 

 

5th June, 2020 - Yudong Chen (University of Cambridge)

High-dimensional, multiscale online changepoint detection