This dissertation studies three topics in financial economics. In the first chapter, "ESG Investing in Emerging Markets: Betting on Firm Fundamentals or Riding Investor Preferences?", we examine the relation between firms' environmental, social, and governance (ESG) practices and the pricing of corporate bonds in emerging markets, which is an important yet understudied market for ESG-related issues. Firms with different ESG scores can have different costs of capital, either because ESG scores help forecast future cash flows -- the "fundamental" channel -- or because investors have non-pecuniary preferences for high-ESG-score assets -- the "preference" channel. We identify the existence of a preference channel with a natural experiment -- the historical opening of the Chinese onshore bond market -- that leads to an increase in the proportion of international investors, who are more ESG-conscious. Consistent with theory, we find that the bond yield of companies with high ESG scores decreases more than that of companies with low ESG scores. By focusing on firms that also have bonds traded in the offshore market, which, as opposed to the onshore market, does not experience any change in regulation, we can control for issuer-time fixed effects in a triple difference design, hence reducing considerably the influence of the fundamental channel.
In the second chapter, "Watch what they do, not what they say: Estimating regulatory costs from revealed preferences", we show that distortion in the size distribution of banks around regulatory thresholds can be used to identify costs of bank regulation. We build a structural model in which banks can strategically bunch their assets below regulatory thresholds to avoid regulations. The resulting distortion in the size distribution of banks reveals the magnitude of regulatory costs. Using U.S. bank data, we estimate the regulatory costs imposed by the Dodd-Frank Act. Although the estimated regulatory costs are substantial, they are significantly lower than those in self-reported estimates by banks.
In the third chapter, "Fuzzy Bunching", we introduce a new fuzzy bunching approach that is robust to noise. The existing bunching approach identifies the extent of bunching from a sharp spike in the probability density function. In many finance settings, however, the sharp spike could be diffused by data noise. The key idea behind our fuzzy bunching estimator is to identify bunching from the area of a bulge in the cumulative distribution function. The fuzzy bunching approach also avoids density estimation, which makes it easy to implement in sparse data. We provide the theoretical foundation of this approach and illustrate the advantages by using simulated and real data.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/sxh7-6874 |
| Date | January 2022 |
| Creators | Alvero, Adrien |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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