Conditional Betas and NVIX

• Grant number: 23/15780-3
• Support Opportunities: Scholarships abroad - Research
• Start date: July 01, 2024
• End date: January 31, 2025
• Field of knowledge: Applied Social Sciences - Economics
• Principal Investigator: Fernando Daniel Chague
• Grantee: Fernando Daniel Chague
• Host Investigator: Gustavo da Silva Cortes Goncalves
• Host Institution: Escola de Economia de São Paulo (EESP). Fundação Getúlio Vargas (FGV). São Paulo , SP, Brazil
• Institution abroad: University of Florida, Gainesville (UF), United States

Abstract

The proposal is comprised by a main project and a secondary project. The main project relates conditional betas and NVIX. Conditional asset pricing models have been proposed as a solution to cross-sectional asset pricing puzzles. Indeed, there is evidence suggesting that the market beta of high book-to-market stocks increases during economic recessions precisely when the price of market risk is high, which suggests that a time-varying conditional CAPM could resolve the value anomaly. There are surprisingly few analytical models to guide beta dynamics and the key state variables driving conditional betas, however. In this project, we link conditional betas to deep parameters and state variables implied by a rational expectation model. The key insight of the model is that economic uncertainty is the key driver of beta dynamics. To proxy for economic uncertainty, we use an alternative measure of VIX based on news that was proposed by Manela and Moreira (2017), the so-called News Implied Volatility (NVIX). This is a text-based measure of economic uncertainty that has been successfully used in a number of recent papers, such as Cortes and Weidenmier (2019) and Nagel and Xu (2023), and has a long time-series, a crucial feature for our purposes.The secondary project examines how the trading infrastructure of each financial institution results in specialization in the stock markets. In particular, we are interested in finding which are the institutions responsible for gathering and processing information and making stocks prices efficient. The theoretical implications that guide our empirical study come from Garleanu and Pedersen (2018, Journal of Finance) model about efficiently inefficient markets.