July 20, 2024

Focus Program on Commodities, Energy and Environmental Finance

Focus Program Working Seminar
Organizing Committee
Mike Ludkovski (UC Santa Barbara)
, Ronnie Sircar (Princeton)

Time Speaker, Title and Abstract
Thursday, August 8 2:00-2:45PM

Fernando Valvano Cerezetti, BM&FBOVESPA
Managing Risk in Multi-Asset Class, Multimarket Central Counterparties: The CORE Approach

Multi-asset class, multimarket central counterparties (CCPs) are becoming less uncommon as a result of merges between specialized (single-asset class, single market) CCPs and market demands for more capital efficiency. Yet, traditional CCP risk management models often lack the necessary sophistication to estimate potential losses relative to the closeout process of a defaulter's portfolio in a multi-asset class, multimarket environment. As a result, multi-asset class, multimarket CCPs usually rely upon a simplied silo approach for calculating risk that, not only fails to deliver efficiency, but can also increase systemic risk. The CORE (Closeout Risk Evaluation) approach, on the other hand, provides the conceptual and mathematical tools necessary for robust and efficient central counterparty risk evaluation in multi-asset class and multimarket environments, acknowledging the portfolio dynamics involved in the closeout process, as well as important "real life" market frictions.

Tuesday, August 20

Franziska Schulz, Humboldt-Universität zu Berlin
Forecasting generalized quantiles of electricity demand: A functional data approach

Electricity load forecasts are in various ways valuable for the operation of utilities. However, for a sustainable risk management of utility operators not only a forecast
of expected demand, but also knowledge about the uncertainty and dispersion of
future load plays an important role. The aim of our research is to model and forecast generalized quantiles of electricity demand, which, in contrast to forecasts of the conditional mean, yield a whole picture of the distribution of electricity demand. We apply methods from functional data analysis to model dynamics of daily generalized quantile curves. Taking temporal dependence between curves into account allows us to conduct short term forecasts at an intraday resolution using multivariate time-series techniques.

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