WORKING PAPERS
WORKING PAPERS
Leverage Change and Stock Returns: the role of intangible capital, with Pedro Vallocci. Paper. [Codes]
Abstract: Firm leverage is known to affect stock returns, but the impact of intangible capital (IK) on this relationship remains understudied. The lower collateral value associated with IK suggests that firms with high IK exhibit a higher probability of default, likely affecting the relationship between leverage and stock returns. In this paper, we examine how this relationship is affected in the presence of IK and find that the stock returns of firms with higher IK are more sensitive to leverage changes. Through portfolio analysis, we demonstrate that stocks with the largest leverage decreases substantially outperform those with increases, particularly for high IK firms. We propose a new “Intangible Delta Leverage (IDL)” factor that is not explained by other commonly used factors and develop a profitable long-short equity strategy based on leverage changes and IK ratios. Our findings highlight the importance of intangible assets in the leverage return dynamic and offer new insights for asset pricing and investment strategies.
Interest Rate Fluctuations and Investment Dynamics. Paper. [Codes]
Abstract: This paper investigates how intangible capital affects firms’ investment responses to interest rate changes. Using Compustat data, I find that firms with high intangible capital concentrations exhibit muted investment responses to interest rate fluctuations. Using local projection techniques, I find that after a 100-basis-point increase in the 2- year Treasury rate, these firms’ cumulative investment increases 4.79%, compared to a decline of 1.93% for all other firms after eight quarters. Exploiting state-level legal changes that reduced collateral seizure uncertainty, I show that firms with tangible assets significantly increased debt issuance, while firms with more intangible assets did not. These findings suggest that limited collateral value from intangible assets weakens monetary policy transmission by constraining firms’ leverage capacity.
Technology greening and productivity, with Galina Hale, Julian di Giovanni, and Victoria Nuguer. Paper. [Codes]
Abstract: This paper examines the relationship between green technologies and productivity through industry-level and establishment-level analyses. Using emissions and energy productivity as proxies for green technology adoption, we find that this relationship varies across specifications. At the industry level, instrumenting with climate change mitigation policies reveals that emissions productivity growth negatively affects industry productivity growth, particularly in environmentally efficient industries. At the establishment level, using Mexican data, we find contrasting results: cross-section analysis shows positive correlations between energy productivity and conventional productivity measures (labor, capital, and TFP), while within establishment analysis indicates temporary negative effects following energy efficiency improvements. Evidence from Mexican climate policy interventions suggests these trade-offs are transitory, with targeted policies ultimately increasing labor productivity in polluting industries.
PUBLISHED PAPERS
Abstract: This paper extends the empirical literature on volatility risk premium (VRP) and future returns by analyzing the predictive ability of commodity currency VRP and commodity VRP. The empirical evidence throughout this paper provides support for a positive relationship of commodity currencies VRP and future commodity returns, but only for the period after the 2008 global financial crisis. This predictability survives the inclusion of control variables like equity VRP and past currency returns. Furthermore, gold VRP also has the ability to predict future commodity returns. However, this predictability is restricted to precious metals when control variables are considered.
Implied volatility term structure and exchange rate predictability, with José Renato Haas Ornelas, International Journal of Forecasting, 2019.
Abstract: This paper provides empirical evidence of the predictive power of the currency implied volatility term structure (IVTS) for the behavior of the exchange rate from both cross-sectional and time series perspectives. Intriguingly, the direction of the prediction is not the same for developed and emerging markets. For developed markets, a high slope means low future returns, while for emerging markets it means high future returns. We analyze predictability from a cross-sectional perspective by building portfolios based on the slope of the term structure, and thus present a new currency trading strategy. For developed (emerging) currencies, we buy (sell) the two currencies with the lowest slopes and sell (buy) the two with the highest slopes. The proposed strategy performs better than common currency strategies – carry trade, risk reversal, and volatility risk premium (VRP) – based on the Sharpe ratio, considering only currency returns, which supports the exchange rate predictability of the IVTS from a cross-sectional perspective.
Abstract: We study the dynamics of the oil sector using a new multivariate stochastic volatility model with a structure of common factors subjected to jumps in mean and conditional variance. This model contributes to the literature allowing the estimation of spillover effects between assets in a multivariate framework through joint jumps (co-jumps), identifying the permanent and transitory effects through a structure defined by Bernoulli processes. The jump structure introduced in the article can be interpreted as a regime-switching model with an endogenous number of states, avoiding the difficulties associated with models with a fixed number of regimes. We apply the model to oil prices and stock prices of integrated oil companies. The jump structure allows dating the relevant events in the oil sector in the period 2000–2019. The period analyzed encompasses important events in the oil market such as the price escalation in 2008 and the falling prices in 2014. We also apply the model to estimate risk management measures and portfolio allocation and perform a comparison with other multivariate models of conditional volatility, showing the good properties of the model in these applications.
Abstract: We introduce a new multivariate stochastic volatility model based on the presence of a latent common factor with random jumps. The common factor is parameterized as a permanent component using a compound binomial process. This model can capture common jumps in the latent volatilities between markets, with particular relevance in the presence of crises and contagion in emerging markets.