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Working Paper
Investing in the Batteries and Vehicles of the Future: A View Through the Stock Market
A large number of companies operating in the EV and battery supply chain have listed on a major U.S. stock exchange in recent years. This paper investigates 1) how these companies’ stock returns are related to systematic risk factors that can explain movements in the stock market and 2) how these companies’ idiosyncratic returns are related to one another. To do so, I compile a unique data set of intradaily stock returns that spans the supply chain, including companies focused on the mining of battery and EV-related critical minerals, advanced battery technology, lithium-ion battery ...
Working Paper
Investing in the Batteries and Vehicles of the Future: A View Through the Stock Market
A large number of companies operating in the EV and battery supply chain have listed on a U.S. stock exchange in recent years. I compile a unique data set of high-frequency stock returns for those companies and investigate the extent to which an “industry” factor specific to the EV and battery supply chain (an “EV” factor) can explain their returns. Those returns are decomposed into systematic and idiosyncratic components, with the former given by a set of latent factors extracted from a large panel of stock returns using high-frequency principal components. It is found that a market ...
Working Paper
Specification and Estimation of Bayesian Dynamic Factor Models: A Monte Carlo Analysis with an Application to Global House Price Comovement
We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single- factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state space approach of Kim and Nelson ...
Report
Revisiting useful approaches to data-rich macroeconomic forecasting
This paper analyzes the properties of a number of data-rich methods that are widely used in macroeconomic forecasting, in particular principal components (PC) and Bayesian regressions, as well as a lesser-known alternative, partial least squares (PLS) regression. In the latter method, linear, orthogonal combinations of a large number of predictor variables are constructed such that the covariance between a target variable and these common components is maximized. Existing studies have focused on modelling the target variable as a function of a finite set of unobserved common factors that ...