Publications in Peer Reviewed Journals
Santi, Caterina, and Zwinkels, Remco C.J., 2023. Exploring style herding by mutual funds. Journal of International Financial Markets, Institutions and Money 101762. [Link]
Santi, Caterina, 2023. Investor Climate Sentiment and Financial Markets. International Review of Financial Analysis, Volume 86, 102490. [Link]
Data on Investors Climate Sentiment available here.
He, Xuezhong, Kai Li, Caterina Santi, and Shi, Lei, 2022. Social Interaction, Stochastic Volatility, and Momentum. Journal of Economic Behavior and Organization, 203, 125-149. [Link]
Chan, Joshua C.C., and Santi, Caterina, 2021. Speculative Bubbles in Present-Value Models: A Bayesian Markov-Switching State Space Approach. Journal of Economic Dynamics and Control. Volume 127, 104101. [Link]
Moretti, Angelo, and Santi, Caterina, 2020 Commentary to "Klingwort, J., and Schnell, R. (2020). Critical Limitations of Digital Epidemiology: Why COVID-19 Apps Are Useless." Survey Research Methods, 14(2), 95-101. [Link]
Santi, Caterina, and Santoleri, Pietro, 2017. Exploring the link between Innovation and Growth in Chilean firms. Small Business Economics 49 (2): 445-467. [Link]
Carbon Risk Premium and Worries about Climate Change [Link]
with Angelo Moretti. Under review.
Abstract. This paper sheds light on the impact of investor worries about climate change on the pricing of emission (carbon-intensive) and clean (low-emission) stocks. We estimate the carbon risk premium in a cross-section of over 4,800 firms in 21 countries. We do not find evidence of a carbon risk premium when investor worries about climate change are low. Moreover, the carbon premium is significant for medium-high quantiles of the return distribution when investors’ worries are high. Overall, our results are consistent with an interpretation that non-worried investors are not demanding compensation for their exposure to carbon emission risk.
Global Risk and Ambiguity in the determination of CDS Spreads
with Amirhossein Sadoghi.
Abstract. We construct a daily time series of several types of global risks and related ambiguity using news about risk published by major international newspapers. We propose a novel approach to measure ambiguity. First, we perform topic modelling on risk-related news. Then, we compute the ambiguity of a given topic as the expected volatility of probabilities based on the latent Dirichlet allocation (LDA) algorithm. Next, we empirically test the prediction that global risks and related ambiguity have explanatory power of credit default swap (CDSs) spreads. We find that an increase in ambiguity leads to a decrease in CDS spreads of up to 10 bps. Conversely, an increase in risk is associated with an increase in CDS spreads.