QFinLab – Department of Mathematics
Politecnico di Milano
April, 15 2026
Seminar room VI floor
15.00-17.00
Luca Trapin (Università di Bologna) Critical Peak Pricing or Rebate? An Impact Analysis in a Cold Climate Market
As electricity becomes more important to meet the energy needs of societies, electricity demand management is increasingly being used. Dynamic pricing, in particular, can contribute to lower consumption during peak periods. Critical peak pricing (CPP) and critical peak rebate (CPR) can be used to specifically target potentially problematic periods and reduce load. Using a very large set of data (15-minute consumption of 13,483 consumers over three winters) and a difference-in-difference approach, we show that CPP consumers achieve larger reductions than CPR consumers, and that the extent of the reduction in both cases diminishes over time. Analyzing subgroups of consumers with different average consumption levels, we also find that the size of the reduction to be heterogeneous across groups. In addition, larger CPP consumers significantly reduce their consumption in non-critical peak periods, displaying a spillover effect. Finally, given the need to define a reference consumption level with CPR, larger than necessary average rebates are paid to these consumers.
Discussant: Michele Azzone (Politecnico di Milano)
Andrea Tarelli (Università Cattolica del Sacro Cuore) Structural Pricing of Physical Climate Risk in CDS Markets
This paper develops a structural credit-risk framework for pricing Credit Default Swaps (CDS) under stochastic physical climate risk. Firm asset dynamics incorporate downside jumps triggered by climate-related disaster events with variable intensity. The pricing methodology employs Laplace-transform techniques and a multiscale approximation that reduces the two-dimensional first-passage problem to a tractable one-dimensional formulation, yielding semi-closed-form CDS valuation expressions. We identify market prices of financial and physical risks using U.S. equity returns and media-based climate concern indices. Sequential Monte Carlo estimation on panels of CDS quantifies firm-level exposure to physical climate risk and its impact on creditworthiness.
Discussant: Emilio Barucci (Politecnico di Milano)
This event has been (partially) supported by MUR, Department of Excellence 2023-27