Winner of the Best Student Paper Award at the 33rd Annual Meeting of the Midwest Econometrics Group, 2023 This paper combines a traditional portfolio construction problem with demand estimation techniques to estimate the demand for green stocks of US institutional investors. The methodology presented innovates along two dimensions with respect to recent influential work on asset demand estimation. First, in our framework investors have heterogeneous portfolios not only through differential beliefs about future returns, but also because they place varying importance on the non-financial characteristics of the portfolios they construct. Second, by using a mixed logit demand specification, we can estimate asset demand that delivers more realistic substitution patterns across assets. Using data on the environmental performance of firms and quarterly stock holdings data from institutional investors, we estimate the demand for stocks accounting for environmental scores and return-related stock characteristics. We find that taste for green stocks fluctuates over time and by investor’s assets under management. In a counterfactual exercise we study the equity price effects of a ban on green investing for pension funds; we find that a portfolio with the top brown stocks is estimated to have capital gains of 1.1% due to the policy, while a portfolio with the top green stocks is estimated to have capital losses of 1.6%.
Winner of the Hiran C. Haney Fellowship Award in Economics, University of Pennsylvania, 2022 This paper shows how to use a hybrid of supervised and unsupervised learning models to go from text from news articles to an FX news index that can be used to enhance traditional models from the FX literature. To do so we rely on Supervised Latent Dirichlet Allocation (sLDA) (Blei and Mcauliffe (2008)) which combines information about a supervising variable with topic extraction over a corpus of text in a single-stage estimation. Although this estimation can be done in two stages, we document with a Monte Carlo simulation that there are efficiency gains from a single-stage approach. The empirical application we suggest is centered around the Monex Market, the main Costa Rican platform for FX trade; accordingly news articles are gathered from the main Costa Rican newspapers. The exchange rate of interest is the Costa Rican Colón (CRC), the local currency, and the United States dollar (USD). Using the CRC/USD exchange rate as the supervising variable we suggest using sLDA to extract the topics from the news article corpus that are relevant as covariates for the exchange rate over short frequencies.
Least squares regression with heteroskedasticity and autocorrelation consistent (HAC) standard errors has proved very useful in cross section environments. However, several major difficulties, which are generally overlooked, must be confronted when transferring the HAC estimation technology to time series environments. First, in plausible time-series environments involving failure of strong exogeneity, OLS parameter estimates can be inconsistent, so that HAC inference fails even asymptotically. Second, most economic time series have strong autocorrelation, which renders HAC regression parameter estimates highly inefficient. Third, strong autocorrelation similarly renders HAC conditional predictions highly inefficient. Finally, the structure of popular HAC estimators is ill-suited for capturing the autoregressive autocorrelation typically present in economic time series, which produces large size distortions and reduced power in HAC based hypothesis testing, in all but the largest samples. We show that all four problems are largely avoided using a simple dynamic regression procedure, which is easily implemented. We demonstrate the advantages of dynamic regression with detailed simulations covering a range of practical issues.
The cornerstone result of the market microstructure literature to FX markets is that the order flow (the difference between buyer-initiated and seller-initiated transactions) is a key determinant of the exchange rate short run dynamics. This paper advocates to study the short term dynamics of the Costa Rican Colón to United States Dollar exchange rate generated in the Monex market by employing market microstructure tools.Using transaction level data for 729 trading days we have gathered evidence that the order flow has explanatory power on the short term dynamics of exchange rate returns, even after accounting for a feedback effect. Additionally we show evidence suggesting that the informational content of the order flow has persistent effects. Finally when characterizing the role of the interventions by the monetary authority on the market, data shows that interventions affect the informational content of the order flow and that the monetary authority acts as a liquidity provider and a market maker in the Monex platform.