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 nonstandard applications
Friday, October 14, 2022 — 4:00 PM EDT
Please Note: This seminar will be given inperson.
Distinguished Lecture Series
Viktor Todorov Kellogg School of Management at Northwestern University
Room: EIT 1015

Recalcitrant Betas: Intraday CrossSectional Distributions of Systematic Risk
Highfrequency 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 highfrequency asset returns, with the size of the crosssection 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 FamaFrench 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 timevarying correlations between the observable risk factors drive a wedge between the timeofday pattern of market betas, estimated with and without control for the other observable risk factors.
Thursday, October 20, 2022 — 4:00 PM EDT
Please Note: This seminar will be given inperson.
Distinguished Lecture
Claudia Klüppelberg Technical University of Munich
Room: M3 3127

Maxlinear Graphical Models for Extreme Risk Modelling
Graphical models can represent multivariate distributions in an intuitive way and, hence, facilitate statistical analysis of highdimensional data. Such models are usually modular so that highdimensional 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 maxlinear 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 maxlinear Bayesian networks, which are maxlinear 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 maxlinear 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 nonnoisy model has a leftsided 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 maxlinear 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