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The past half-century has seen major shifts in inflation expectations, how inflation comoves with the business cycle, and how stocks comove with Treasury bonds. Against this backdrop, we review the economic channels and empirical evidence on how inflation is priced in financial markets. Not all inflation episodes are created equal. Using a New Keynesian model, we show how “good” inflation can be linked to demand shocks and “bad” inflation to cost-push shocks driving the economy. We then discuss asset pricing implications of “good” and “bad” inflation. We conclude by providing an outlook for inflation risk premia in the world of newly rising inflation.
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Beliefs, Shocks, and the Emergence of Roles in Asset Markets: An Agent-Based Modeling Approach
Although predictive AI models have grown to dominate computational finance, they are often limited in their applications when it comes to studying interventions and explaining behavioral outcomes. Financial economics, on the other hand, has a rich history of analytical approaches to asset-pricing theory, often requiring sweeping assumptions. In this paper, we construct an agent-based model of asset markets that is able to dispense with onerous restrictions on agent behaviors and beliefs, while having analytical validity and providing insights into the functioning of asset markets. In particular, we evaluate our models with respect to several traditional financial economic theories like Tobin’s separation theorem and the capital asset pricing model (CAPM). We devise a network representing trades to show the emergence of different roles played by the agents. We study interventions, such as shocks, and explain the outcomes using our model. Finally, we investigate the effects of noise trading and show that noisy agents converge to different equilibrium points due to their differences in beliefs. Put together, this paper presents an agent-based model that can be used to study the effects of heterogeneous beliefs and risks of the agents and shocks to assets at a systemic level, thereby connecting localized agent and asset characteristics to global or collective outcomes.
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- Award ID(s):
- 1910203
- PAR ID:
- 10557097
- Publisher / Repository:
- Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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