Dániel Léber , Mihály Ormos (2024.07.01-09.30)

Abstract: We focus on entropy as a measure of risk and what role it can play in equilibrium asset pricing. Similar to the traditionally used capital asset pricing model (CAPM), the entropy can also be divided into mutual (a measure of the non-diversifiable risk) and conditional (a measure of the comovement with the market portfolio) components. We investigate what is the relationship between these and the conventionally used risk metrics, like standard deviation and Beta. We also propose a better solution to the notorious puzzles of asset pricing. Entropy as a measure of risk has been already described and its advantages in portfolio optimization and risk management are also acknowledged in the economic literature. We use data from the OpenBB database and Kenneth R. French’s data library to calculate daily returns and the various risk measures associated with them. We show the diversification effects of different risk measures and their stability over time. We introduce a new method to separate individual and systemic risks of the assets. We also validate our model using the conventional test of the CAPM model. Our regression-based results are tested both in-sample and out-of-sample. The robustness of our model is evaluated by both cross-validation and the use of the rolling windows over time.