Events tagged with Seminar

Wednesday, October 5, 2022 — 2:30 PM EDT

Please Note: This seminar will be given in person.

Statistics and Biostatistics seminar series

Liangliang Wang
Simon Fraser University

Room: M3 3127

Annealed sequential Monte Carlo method with non-standard applications

Friday, October 14, 2022 — 4:00 PM EDT
Viktor Todorov

Please Note: This seminar will be given in-person.

Distinguished Lecture Series

Viktor Todorov 
Kellogg School of Management at Northwestern University

Room: EIT 1015

Recalcitrant Betas: Intraday Cross-Sectional Distributions of Systematic Risk


High-frequency financial data allows for efficient estimation of assets’ exposures to systematic risk, provided these exposures do not vary significantly at high frequencies.  We develop a test for deciding whether this is the case. The test is constructed for a panel of high-frequency asset returns, with the size of the cross-section and the sampling frequency increasing simultaneously. It is based on a comparison of the empirical characteristic functions of estimates of the assets' factor loadings at different parts of the trading day, formed from local blocks of asset returns and the corresponding factor realizations. The limiting behavior of the test statistic is governed by unobservable latent factors in the asset prices. Empirical implementation of the test to stocks in the S&P 500 index and the five Fama-French factors, as well as the momentum factor, reveals different intraday behavior of the factor loadings: assets' exposure to size, market and value risks vary systematically over the trading day while the three remaining factors do not exhibit statistically significant intraday variation. Moreover, we find diverse, and for some factors large, reactions in the assets' factor loadings to major economic or firm specific news releases. Finally, we document that time-varying correlations between the observable risk factors drive a wedge between the time-of-day pattern of market betas, estimated with and without control for the other observable risk factors.


Thursday, October 20, 2022 — 4:00 PM EDT
Claudia Klüppelberg

Please Note: This seminar will be given in-person.

Distinguished Lecture

Claudia Klüppelberg  
Technical University of Munich

Room: M3 3127

Max-linear Graphical Models for Extreme Risk Modelling


Graphical models can represent multivariate distributions in an intuitive way and, hence, facilitate statistical analysis of high-dimensional data. Such models are usually modular so that high-dimensional distributions can be described and handled by careful combination of lower dimensional factors. Furthermore, graphs are natural data structures for algorithmic treatment. Moreover, graphical models can allow for causal interpretation, often provided through a recursive system on a directed acyclic graph (DAG) and the max-linear Bayesian network we introduced in [1] is a specific example. This talk contributes to the recently emerged topic of graphical models for extremes, in particular to max-linear Bayesian networks, which are max-linear graphical models on DAGs. 

In this context, the Latent River Problem has emerged as a flagship problem for causal discovery in extreme value statistics. In [2] we provide a simple and efficient algorithm QTree to solve the Latent River Problem. QTree returns a directed graph and achieves almost perfect recovery on the Upper Danube, the existing benchmark dataset, as well as on new data from the Lower Colorado River in Texas. It can handle missing data, and has an automated parameter tuning procedure. In our paper, we also show that, under a max-linear Bayesian network model for extreme values with propagating noise, the QTree algorithm returns asymptotically a.s. the correct tree. Here we use the fact that the non-noisy model has a left-sided atom for every bivariate marginal distribution, when there is a directed edge between the the nodes.

For linear graphical models, algorithms are often based on Markov properties and conditional independence properties. In [3] we characterise conditional independence properties of max-linear Bayesian networks and in my talk I will present some of these results and exemplify the difference to linear networks. 

Wednesday, October 26, 2022 — 2:30 PM EDT

Please Note: This seminar will be given in person.

Statistics and Biostatistics seminar series

Mireille Schnitzer
University of Montreal

Room: M3 3127

TBA

Wednesday, November 2, 2022 — 2:30 PM EDT

Please Note: This seminar will be given in person.

Statistics and Biostatistics seminar series

Alexandra Schmidt
McGill University

Room: M3 3127

TBA

Wednesday, November 9, 2022 — 2:30 PM EST

Please Note: This seminar will be given in person.

Statistics and Biostatistics seminar series

Justin Slater
University of Toronto

Room: M3 3127

TBA

Wednesday, November 16, 2022 — 2:30 PM EST

Please Note: This seminar will be given in person.

Statistics and Biostatistics seminar series

Patrick Brown
Centre for Global Health Research

Room: M3 3127

TBA

Wednesday, November 30, 2022 — 2:30 PM EST

Please Note: This seminar will be given in person.

Statistics and Biostatistics seminar series

Lucia Petito
Northwestern University

Room: M3 3127

TBA

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