Apr. 30: Bridging Factor and Sparse Models

I blogged before on the Fan-Masini-Medeiros "Bridging Factor and Sparse Models" paper, here.  Now we can see it presented live on April 30 (16:00 Amsterdam time), together with the equally fascinating Pettenuzzo-Sabbatucci-Timmermann paper on firm behavior during the pandemic (15:00).  See you there!

You will have to register for the webinars in advance, at: https://uva-live.zoom.us/meeting/register/tZMldO2orT8uE9PVj6fY4aWmvoIuH_Fud_N5

On behalf of the organisers Bart Keijsers and Sander Barendse:

We are happy to invite you to the UvA Financial Econometrics Workshop, which consists of two presentations around the topic of financial econometrics. It will be held online on

Friday 30 April, from 15:00 to 17:00.

Below is the list of the speakers, the schedule, and link to register for the workshop. The information is also available on the website: http://bit.ly/uvaqews

List of speakers

• Riccardo Sabbatucci (Stockholm School of Economics)
  Title: Dividend Suspensions and Cash Flows During the COVID-19 Pandemic: A Dynamic Econometric Model

Abstract:

Firms suspended dividend payments in unprecedented numbers and at unparalleled speed in response to the outbreak of the COVID-19 pandemic. We develop a dynamic econometric model that allows dividend suspensions to affect the conditional mean, volatility, and jump probability of growth in daily dividends and demonstrate how the parameters of this model can be estimated using Bayesian Gibbs sampling methods. We find that dividend suspensions had a sharp and immediate impact on the conditional mean and volatility of daily dividend growth. Information embedded in daily dividend suspensions proves valuable in monitoring and predicting the trajectory of broader measures of economic activity during the pandemic.

Coauthors: Davide Pettenuzzo (Brandeis) and Allan Timmermann (UCSD)

Personal website | SSRN link

 Marcelo Medeiros (Pontifical Catholic University of Rio de Janeiro)

• Title: Bridging factor and sparse models
  Abstract:
  Factor and sparse models are two widely used methods to impose a low-dimensional structure in high-dimension. They are seemingly mutually exclusive. In this paper, we propose a simple lifting method that combines the merits of these two models in a supervised learning methodology that allows to efficiently explore all the information in high-dimensional datasets. The method is based on a flexible model for panel data, called factor-augmented regression model with both observable, latent common factors, as well as idiosyncratic components as high-dimensional covariate variables. This model not only includes both factor regression and sparse regression as specific models but also significantly weakens the cross-sectional dependence and hence facilitates model selection and interpretability. The methodology consists of three steps. At each step, the remaining cross-section dependence can be inferred by a novel test for covariance structure in high-dimensions. We developed asymptotic theory for the factor-augmented sparse regression model and demonstrated the validity of the multiplier bootstrap for testing high-dimensional covariance structure. This is further extended to testing high-dimensional partial covariance structures. The theory and methods are further supported by an extensive simulation study and applications to the construction of a partial covariance network of the financial returns and a prediction exercise for a large panel of macroeconomic time series from FRED-MD database.

Coauthors: Jianqing Fan (Princeton) and Ricardo Masini (Princeton)

Personal website | SSRN link

Schedule

15:00 - 16:00 Webinar Riccardo Sabbatucci (45 min talk + 15 min questions)

16:00 - 17:00 Webinar Marcelo Medeiros (45 min talk + 15 min questions)

 Registration

You will have to register for the webinars in advance, via the following link: https://uva-live.zoom.us/meeting/register/tZMldO2orT8uE9PVj6fY4aWmvoIuH_Fud_N5

Feel free to share this with your colleagues, and please let us know if you have any questions.

Kind regards,

Bart Keijsers (b.j.l.keijsers@uva.nl) and Sander Barendse (s.c.barendse@uva.nl)

 

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