We would like to cordially invite you to two talks in the Vienna Seminar in Mathematical Finance and Probability (jointly organized with TU Wien and University of Vienna), taking place at TU Wien next Thursday

• Natalie Packham (Berlin School of Economics and Law)
  Jump risk premia in the presence of clustered jumps
  Thursday, March 27, 2025, 16:00, TU Wien, "Freihaus" building, yellow section, 7th floor, seminar room DB gelb 07
  
  Abstract:
  This paper presents an option pricing model that incorporates clusters of jumps using a bivariate Hawkes process with exponential decay memory kernels. The Hawkes process captures self- and cross-excitement of positive and negative jumps, allowing the model to effectively capture the volatile price dynamics observed in cryptocurrencies such as Bitcoin (BTC), while also fitting the implied volatility surface. The model can fit the dynamics of implied volatilities with changing preferences for the skewness risk. As an example, we use BTC dynamics where the skewness can change from negative (stronger demand for puts) to positive (stronger demand for calls). We derive positive and negative jump risk premia, defined as the discrepancies in jump measures between the objective measure and the risk-neutral measure. Our findings reveal that these jump risk premia: (i) provide insights on how the BTC options market reacts to major events, such as the COVID-19 outbreak and the FTX scandal; (ii) possess significant predictive power for delta-hedged option returns; and (iii) are indicators in explaining the volatile cost-of-carry implied from BTC futures prices.
  
  Joint work with Francis Liu and Artur Sepp.
   

• Claudio Fontana (Department of Mathematics "Tullio Levi-Civita", University of Padova)
  Data-driven Heath-Jarrow-Morton models
  Thursday, March 27, 2025, 17:00, TU Wien, "Freihaus" building, yellow section, 7th floor, seminar room DB gelb 07
  
  Abstract:
  We develop a data-driven version of Heath-Jarrow-Morton models in the context of interest rate modeling. We consider models driven by a linear functional of the yield curve, such as a family of representative forward rates, possibly augmented by a set of economic factors. The volatility is parameterized by a neural network, the parameters of which are learned by calibration to past market yield curves. This results in a data-driven arbitrage-free model for the prediction of yield curves. Our setup allows for the possibility of scheduled jumps, which can arise from monetary policy decisions. We illustrate our deep learning procedure by reconstructing and forecasting the Euro area yield curves.
  
  Based on joint work with Christa Cuchiero (University of Vienna) and Alessandro Gnoatto (University of Verona).

For further information and the seminar schedule, please visit:
https://fam.tuwien.ac.at/events/vs-mfp/