[ABE-L] Workshops FGV EMAp em Novembro

[Rodrigo Targino]

Lembrete: amanhã (segunda) teremos a realização do Workshop em Modelagem
Estocástica de Eventos Climáticos e na quarta-feira o Workshop em
Estatística, Séries Temporais e Finanças Quantitativas na FGV EMAp.

Workshop em Modelagem Estocástica de Eventos Climáticos
13h30 – 14h15 – Klaus Boesch (IFSul)
14h15 – 15h00 – Leonardo Voltarelli (IMPA)
15h00 – 15h30 – Intervalo
15h30 – 16h15 – Livia Cereja (FGV EMAp)
16h15 – 17h30 – Reinaldo Marques (IRB-Re)

Workshop em Estatística, Séries Temporais e Finanças Quantitativas
13h30 – 14h15 – Eduardo Horta (UFRGS)
14h15 – 15h00 – Eduardo Mendes (FGV EESP)
15h00 – 15h30 – Intervalo
15h30 – 16h15 – Flavio Ziegelmann (UFRGS)
16h15 – 17h30 – Rodrigo Targino (FGV EMAp)

Aproveito a oportunidade para informar que o primeiro Workshop conta também
com o apoio da FAPERGS.

Mais informações sobre esses e outros eventos organizados pela FGV EMAp
podem ser encontradas no site https://emap.fgv.br/eventos. Dúvidas e
pedidos de inscrição devem ser enviados para eventos.emap em fgv.br.

Atenciosamente,
Rodrigo Targino

On Thu, 30 Oct 2025, 12:05 Rodrigo Targino, <targinorj em gmail.com> wrote:

> Prezados colegas,
> Gostaria de convidá-los para dois eventos que serão realizados na Escola
> de Matemática Aplicada (EMAp) da Fundação Getulio Vargas (FGV), no Rio de
> Janeiro.
>
> Dia 03/nov/25 (segunda-feira): Workshop em Modelagem Estocástica de
> Eventos Climáticos
> Dia 05/nov/25 (quarta-feira): Workshop em Estatística, Séries Temporais e
> Finanças Quantitativas
> Local (dos dois eventos): Fundação Getulio Vargas, Praia de Botafogo, 190,
> 5o andar, Auditório 537
> Horário (dos dois eventos): 13.30 - 18.00h
>
> Os eventos ocorrerão de forma presencial (sem transmissão online ou
> gravação) e estão sendo organizados pela FGV EMAp e o PPG de Estatística do
> IME-UFRGS, com o apoio dos dois departamentos e do CNPq através do Projeto
> Universal “Modelagem Econométrica de Problemas Complexos: Séries Temporais
> Funcionais, Modelos Não Lineares, Aprendizado Estatístico e Modelos de Alta
> Dimensão”.
>
> Os interessados devem enviar um email para eventos.emap em fgv.br para
> realizar sua inscrição.
>
> Os títulos e resumos dos trabalhos estão disponíveis abaixo.
>
> Atenciosamente,
> Rodrigo Targino
>
> ======================================================================
>
> Workshop em Modelagem Estocástica de Eventos Climáticos (3/nov)
>
> Leonardo Voltarelli (IMPA)
>
> Precipitation nowcasting of satellite data using physically-aligned neural
> networks
>
> Accurate short-term precipitation forecasts remain concentrated in
> radar-rich regions, limiting operational value in places most exposed to
> climate extremes. We present TUPANN (Transferable and Universal
> Physics-Aligned Nowcasting Network), a satellite-only model trained on
> GOES-16 RRQPE. Unlike most deep learning models for nowcasting, TUPANN
> separates the forecast into physically meaningful components: a variational
> encoder–decoder infers motion and intensity fields from recent imagery
> under optical-flow supervision, a lead-time-conditioned MaxViT evolves the
> latent state, and a differentiable advection operator reconstructs future
> frames. We investigate TUPANN's performance across four distinct climates
> (Rio de Janeiro, Manaus, Miami, La Paz) at 30–180-min lead times using
> CSI/HSS over 4–64 mm h^{-1} thresholds and both IMERG and GOES-16 data. Our
> model is benchmarked against leading models based on optical flow
> (PySTEPS), deep learning (Earthformer and CasCast) and a combination of the
> two (NowcastNet). Overall, TUPANN delivers the best or second-best skill in
> most settings, with pronounced gains at higher thresholds; training on
> multiple cities further improves performance, while cross-city tests show
> limited degradation and occasional gains for rare heavy-rain regimes.
> GOES-16's near 5-min latency supports our model's real-time use. Beyond
> skill, TUPANN exposes smooth motion fields that align with numerical
> optical flow, improving interpretability for forecasters. These results
> indicate that physically aligned learning can provide nowcasts that are
> skillful, transferable and global.
>
>
> —--------------------------------------------------------------------------------------------------------
>
> Klaus Boesch (IFSul)
>
> Modelling and Forecasting the Dynamics of Spatial Surfaces via Dynamic
> FPCA: Application to Daily Temperature Fields in Southern Brazil
>
> This work introduces a framework for modeling and forecasting spatial
> surface time series using a dynamic extension of Functional Principal
> Component Analysis (FPCA). We generalize the Bathia-Yao-Ziegelmann Dynamic
> FPCA to cases where observations are spatial surfaces. Simulation studies
> confirm the method's ability to accurately recover underlying dynamic
> structures and identify the true latent dimension. An empirical analysis of
> daily mean, maximum, and minimum temperature surfaces in Rio Grande do Sul,
> Brazil, demonstrates the practical utility of the framework. Our findings
> show that the optimal predictive dimensionality varies with the forecast
> horizon and the specific temperature variable. The analysis yields
> interpretable spatial patterns, captures their temporal dynamics, and
> provides reliable forecasts, offering a valuable tool for climate science,
> environmental studies, and other fields such as quantitative finance.
>
>
> —--------------------------------------------------------------------------------------------------------
>
> Reinaldo Marques (IRB-Re)
> Spatial Extreme Events: a climate risk assessment in Brazil
>
> Extreme weather events have become a major challenge for insurers' pricing
> and catastrophe areas. In this study, we employ daily data (1961–2024) on
> 0.1°×0.1° grids for seven climate variables, from which more than 50
> climate indices were computed. Spatial clusters were delineated within
> Brazilian river basins using Machine Learning clustering algorithm. For
> each cluster, representative extreme climate indices of Heat Waves (WSDI),
> Cold Waves (CSDI), Heavy Rainfall (RX1D), and Droughts (CDD) were modeled
> using the Generalized Extreme Value (GEV) distribution, enabling
> characterization of distribution, variability, and frequency, as well as
> estimation of return levels for 2-, 5-, 10-, and 50-year periods under
> future scenarios. Additionally, extremes were spatially quantified using
> the area-perimeter ratio based on excursion sets, assessing their extent
> and displacement across the national territory. This study highlights the
> high climate variability resulting from Brazil’s extensive territorial
> heterogeneity, providing essential support for preventive planning in the
> insurance sector and for the formulation of policies and products adapted
> to different levels of exposure to climate-related disasters.
>
>
> —--------------------------------------------------------------------------------------------------------
>
> Livia Cereja (FGV EMAp)
>
> Automatic Climate Events Categorization with Masked Siamese Networks
>
> Understanding and representing complex climate variability is essential
> for both scientific analysis and predictive modeling. However, identifying
> meaningful climate regimes from raw variables is challenging, as they
> exhibit high noise and nonlinear dependencies. In this work, we explore the
> use of Masked Siamese Networks to discretize climate time series into
> semantically rich clusters. Focusing on daily minimum and maximum
> temperature, we show that the resulting representations: (i) yield clusters
> that reflect meaningful climate states under our modeling assumptions,
> offering a simplified representation for downstream use; (ii) enable
> sampling and analysis of specific climate scenarios; and (iii) exhibit
> statistical associations with El Niño events, underscoring their scientific
> relevance. Our findings highlight the potential of self-supervised
> discretization as a tool for climate data analysis and open avenues for
> incorporating richer climate indicators in future work.
>
> ======================================================================
>
> Workshop em Estatística, Séries Temporais e Finanças Quantitativas (05/Nov)
>
> Eduardo Horta (UFRGS)
>
> Product Disintegrations of Markov Chains: EVT and an application to
> climate data
>
> For a given sequence of random variables, Borsato et al. (2024, Statistics
> & Probability Letters - DOI: https://doi.org/10.1016/j.spl.2024.110056)
> introduce the concept of a product disintegration, which is a latent
> sequence of random probability measures upon which conditioning makes the
> original sequence independent, and such that a fixed point property holds
> for the conditioning operator. Drawing from these authors, we show
> constructively that any discrete-time Markov chain on a countable state
> space admits, under mild conditions, a non-trivial product disintegration
> which is also a Markov chain. In an EVT framework, we derive the “quenched”
> limit for the conditional distribution of the maximum of such chains, and
> obtain bounds for the “annealed” limit. Finally, we present a brief
> application to precipitation data.
>
>
> —--------------------------------------------------------------------------------------------------------
>
> Flavio Ziegelmann (UFRGS)
>
> Improving Copula-GARCH Risk Forecasting Learning from Factor Functional
> Time Series
>
> In modern days, the accurate prediction and forecasting of risk measures,
> such as Value at Risk and Expected Shortfall, is an essential task for
> asset market managers. When calculating risk measures, an essential step,
> for most approaches, is to estimate the probability density function of
> asset returns. A daily sequence of intraday return densities of p assets,
> denoted by Y_t, t=1,…,n, can be seen as a p-dimensional functional time
> series. If p is large (Y_t is high dimensional), then one has to perform a
> two-way dimension reduction: in the high dimensional vector and in the
> infinite dimensional curves. Here we propose combining a Functional Factor
> Model with a univariate Dynamic Functional Principal Components Analysis as
> a two way dimension reduction approach, which allied to a copula model
> feeds the error term of a high-frequency ARMA-GARCH model aiming to
> forecast future daily risk measures.
>
>
> —--------------------------------------------------------------------------------------------------------
>
> Eduardo Mendes (FGV EESP)
>
> Estimation Risk in Conditional Expectiles
>
> We establish the consistency and asymptotic normality of a two-step
> estimator of conditional expectiles in the context of location-scale
> models. We first estimate the parameters of the conditional mean and
> variance by quasi-maximum likelihood and then compute the unconditional
> expectile of the innovations using the empirical quantiles of the
> standardized residuals. We show how replacing true innovations with
> standardized residuals affects the asymptotic variance of the expectile
> estimator. In addition, we also obtain asymptotic-valid bootstrap-based
> confidence intervals. Finally, our empirical analysis reveals that
> conditional expectiles are very interesting alternatives to assess tail
> risk in cryptomarkets, relative to traditional quantile-based risk
> measures, such as value at risk and expected shortfall.
>
>
> —--------------------------------------------------------------------------------------------------------
>
> Rodrigo Targino (FGV EMAp)
>
> Risk-Budgeted Mean Variance Portfolios
>
> We introduce the Risk-Budgeted Mean-Variance (RBMV) portfolio, a novel
> framework that connects the classical Markowitz mean-variance problem and
> the risk budgeting approach. By modifying the risk budgeting optimization
> problem to include constraints on expected returns and volatility, RBMV
> offers a disciplined way to manage the trade-off between risk concentration
> and return maximization. The investor gains a lever to adjust how close the
> portfolio sits to either framework, depending on her preferences. We show
> that the optimization problem that defines the RBMV portfolio is convex,
> efficiently computable, and typically delivers competitive returns with
> reduced risk concentration in the context of long-only portfolios. We
> illustrate our methodology using daily equity returns from the U.S. and
> show that our methodology efficiently controls the volatility of returns
> while also delivering Sharpe ratios that are consistently higher than the
> traditional mean-variance approach.
>
>
>
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