On June 18, 2026, I will present joint work with Joachim Grammig and Johannes Schief in the Brown Bag Seminar Finance. The talk is based on the current working-paper draft Expected-Shortfall Envelopes and Hansen–Jagannathan Kernels from Regularized Conditional Quantiles. The presentation slides are available here: Regularized Conditional Quantile Methods for Portfolio Choice and Stochastic Discount Factor Estimation.

The paper asks how to recover a stable, interpretable, state-dependent stochastic discount factor when returns and conditioning information are high-dimensional. The starting point is a regularized conditional quantile portfolio program. It estimates one portfolio together with a conditional quantile surface, using elastic-net penalties to stabilize both the portfolio weights and the state-dependent quantile coefficients.

The central distinction in the talk is between three objects. The quantile portfolio is a return-side object. The raw expected-shortfall envelope uses residual ranks to identify tail states, but it is not automatically a pricing kernel. Only after projecting that tail-focused object onto the no-arbitrage pricing set do we obtain an admissible stochastic discount factor.

Empirically, we apply the workflow to monthly Fama–French portfolios and U.S. macro-financial states. The main message is diagnostic rather than structural: macro-state patterns, projection failures, and boundary cases reveal where tail emphasis and pricing feasibility align, and where the data put stress on the candidate pricing kernel.