Crypto Listens: Asymmetric Reactions to Text-based Signals in Central Bank Communications, joint with Efstathios Polyzos and David Tercero Lucas.
The growing influence of cryptocurrencies in global financial markets has raised questions about the impact of central bank communications on their price dynamics. This paper investigates how central bank communication affects the behaviour of cryptocurrency markets. Using a dataset of over 6,000 central bank speeches and a broad panel of crypto assets, we quantify sentiment, uncertainty, and fear tone through natural language processing and assess their impact using local projection methods. Our results show that positive tone initially depresses returns while raising volatility, whereas uncertainty and fear produce mixed return responses and amplify price fluctuations in the short run. Heterogeneity across asset types reveals stronger responses among emerging, high-performing, and non-stablecoin cryptocurrencies. The findings highlight the informational role of central bank narratives in shaping outcomes in speculative and decentralised markets, with important implications for communication policy and financial stability monitoring.
Machine Learning and Asset Pricing: an Application for Option Pricing across Markets
Machine learning provides a framework for modeling in empirical asset pricing. Advances in computational techniques and availability of big data have made possible that commoditized machines can learn to operate as investment managers and financial analysts. In this paper, a framework is provided within which machine learning may be used for finance, with specific application to option and futures pricing. Different algorithms such as Neural Networks (NN) and Support Vector Machines (SVM) are resorted to in order to reproduce the Black & Scholes (1973) option pricing formula. These techniques are applied to a range of instruments across different markets, with focus on India, Brazil and the US. Results are compared both to the theoretical formula and across markets to check if the accuracy of the ML methods differs according to the Exchange where the instruments are traded.