Measuring AI-Driven Risk with Stock Prices
Susana Campos-Martins (Global Priorities Institute, University of Oxford)
GPI Working Paper No. 31-2024
We propose an empirical approach to identify and measure AI-driven shocks based on the co-movements of relevant financial asset prices. For that purpose, we first calculate the common volatility of the share prices of major US AI-relevant companies. Then we isolate the events that shake this industry only from those that shake all sectors of economic activity at the same time. For the sample analysed, AI shocks are identified when there are announcements about (mergers and) acquisitions in the AI industry, launching of new products, releases of new versions, and AI-related regulations and policies.
Other working papers
Cassandra’s Curse: A second tragedy of the commons – Philippe Colo (ETH Zurich)
This paper studies why scientific forecasts regarding exceptional or rare events generally fail to trigger adequate public response. I consider a game of contribution to a public bad. Prior to the game, I assume contributors receive non-verifiable expert advice regarding uncertain damages. In addition, I assume that the expert cares only about social welfare. Under mild assumptions, I show that no information transmission can happen at equilibrium when the number of contributors…
The cross-sectional implications of the social discount rate – Maya Eden (Brandeis University)
How should policy discount future returns? The standard approach to this normative question is to ask how much society should care about future generations relative to people alive today. This paper establishes an alternative approach, based on the social desirability of redistributing from the current old to the current young. …
Dynamic public good provision under time preference heterogeneity – Philip Trammell (Global Priorities Institute and Department of Economics, University of Oxford)
I explore the implications of time preference heterogeneity for the private funding of public goods. The assumption that players use a common discount rate is knife-edge: relaxing it yields substantially different equilibria, for two reasons. First, time preference heterogeneity motivates intertemporal polarization, analogous to the polarization seen in a static public good game. In the simplest settings, more patient players spend nothing early in time and less patient players spending nothing later. Second…