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on International Finance |
| By: | Vania Stavrakeva; Jenny Tang |
| Abstract: | This paper presents new stylized facts about exchange rates and their relationship with macroeconomic fundamentals. We show that macroeconomic surprises explain a large majority of the variation in nominal exchange rate changes at a quarterly frequency. Using a novel present value decomposition of exchange rate changes that is disciplined with survey forecast data, we show that macroeconomic surprises are also a very important driver of the currency risk premium component and explain about half of its variation. These surprises have even greater explanatory power during economic downturns and periods of financial uncertainty. |
| Keywords: | exchange rates; exchange rate disconnect; macroeconomic announcements; international finance; professional forecast |
| JEL: | E44 F31 G14 G15 |
| Date: | 2020–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedbwp:89607&r=all |
| By: | Giovanni Caggiano; Efrem Castelnuovo |
| Abstract: | We estimate a novel measure of global financial uncertainty (GFU) with a dynamic factor framework that jointly models global, regional, and country-specific factors. We quantify the impact of GFU shocks on global output with a VAR analysis that achieves set-identification via a combination of narrative, sign, ratio, and correlation restrictions. We find that the world output loss that materialized during the great recession would have been 13% lower in absence of GFU shocks. We also unveil the existence of a global finance uncertainty multiplier: the more global financial conditions deteriorate after GFU shocks, the larger the world output contraction is. |
| Keywords: | Global Financial Uncertainty, dynamic hierarchical factor model, structural VAR, world output loss, global finance uncertainty multiplier |
| JEL: | C32 E32 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8885&r=all |
| By: | Alexis Marchal (EPFL; SFI) |
| Abstract: | I propose a new tool to characterize the resolution of uncertainty around FOMC press conferences. It relies on the construction of a measure capturing the level of discussion complexity between the Fed Chair and reporters during the Q&A sessions. I show that complex discussions are associated with higher equity returns and a drop in realized volatility. The method creates an attention score by quantifying how much the Chair needs to rely on reading internal documents to be able to answer a question. This is accomplished by building a novel dataset of video images of the press conferences and leveraging recent deep learning algorithms from computer vision. This alternative data provides new information on nonverbal communication that cannot be extracted from the widely analyzed FOMC transcripts. This paper can be seen as a proof of concept that certain videos contain valuable information for the study of financial markets. |
| Keywords: | FOMC, Machine learning, Computer vision, Alternative data, Asset pricing, Equity premium. |
| JEL: | C45 C55 C80 E58 G12 G14 |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2118&r=all |