FOR 5583: Asset allocation and asset pricing in regulated markets and institutions

Basic data for this project

Description

The Research Unit (RU) explores how regulatory uncertainty, a specific form of economic policy uncertainty (Baker et al. (2016)), affects asset allocation and asset pricing in regulated markets. To highlight the heterogeneity and application-specificity of the determinants, characteristics, and consequences of regulatory uncertainty, we rely on three key areas of application: a) specific sectors, b) climate, and c) taxation. Our focus will be on scrutinizing the impact of changes in the legal and regulatory framework on asset allocation and pricing within these key application areas. As such, they call for novel enhancements of existing models of economic policy uncertainty (e.g., Pástor and Veronesi (2012, 2013)), as well as a need for new models and methodologies to capture yet neglected features that are likely to affect asset allocation and asset pricing. From a methodological perspective, we contribute across three layers: 1) creating novel quantitative models for regulatory uncertainty, 2) deriving innovative measures to assess regulatory uncertainty, and 3) exploring the implications associated with regulatory uncertainty. More specifically, our modeling efforts emphasize the significant impacts of both risk and uncertainty on decision-making within financial markets and the real economy. Within our framework, regulatory uncertainty is identified as a crucial factor that influences underlying data-generating processes and regulatory constraints. This uncertainty can be effectively modeled using parameter uncertainty or regime-switching models. By examining regulatory uncertainty through the perspectives of model ambiguity and regime-switching dynamics, we highlight its critical importance for asset allocation and pricing. In addition to these theoretical modelling contributions, we are also breaking new ground in developing novel measurement approaches for regulatory uncertainty. In particular, we exploit advances in topic-based machine learning, text mining and high-dimensional statistical techniques as well as information extracted from novel derivative instruments. We combine theoretical advances with empirical measurement approaches and provide an in-depth analysis of the implications of regulatory uncertainty in our three areas of application. Overall, the RU contributes to the understanding of and coping with regulatory uncertainty by providing approaches for modelling and measuring regulatory uncertainty and by studying its implications. Our models and analyses add to a toolbox of enhanced model building for other applications and generalizations.