The classical asset pricing frameworks is critical for asset valuation, they are all dominated by the expected utility models. Recently, many researchers find this idealization to be overly restrictive feeling that the expected utility model should be generalized. Collecting more and more data, and witnessing the shift in the behaviour of agents, new theoretical approaches need to be developed.

One of the very recent routes in generalizing the concept is towards the change of classical preferences to quantile preferences. Growing empirical evidence that supports nontrivial deviations from expected utility-based model predictions calls for more general theoretical asset pricing. Specifically, it is tempting to develop a dynamic quantile asset pricing approaches that will better explain the behavior of and will provide a closer match with data we collect. In this line of research, our departement contributed with several advancements:

• Panel quantile regressions for estimating and predicting the value‐at‐risk of commodities. F. Cech, J. Barunik. Journal of Futures Markets. 39 (9), 2019, p. 1167-1189.
• Semi-parametric Conditional Quantile Models for Financial Returns and Realized Volatility. F., Žikeš, J. Baruník. Journal of Financial Econometrics. 14 (1), 2016, p. 185-226.