Template-Type: ReDIF-Paper 1.0
Author-Name: Xavier Gabaix
Author-X-Name-First: Xavier
Author-X-Name-Last: Gabaix 
Author-Name: Ralph S J Koijen
Author-X-Name-First: Ralph S J 
Author-X-Name-Last: Koijen
Author-Name: Robert Richmond
Author-X-Name-First: Robert 
Author-X-Name-Last: Richmond
Author-Name: Motohiro Yogo
Author-X-Name-First: Motohiro 
Author-X-Name-Last: Yogo
Title: Artificial intelligence and big holdings data: Opportunities for central banks
Abstract: Asset demand systems specify the demand of investors for financial assets and the supply of securities by firms. We discuss how realistic models of the asset demand system are essential to assess ex post, and predict ex ante, how central bank policy interventions impact asset prices, the distribution of wealth across households and institutions, and financial stability. Due to the improved availability of big holdings data and advances in modelling techniques, estimating asset demand systems is now a practical reality. We show how demand systems provide improved information for policy decisions (eg in the context of financial contagion, convenience yield or the strength of the dollar) or to design optimal policies (eg in the context of quantitative easing or designing climate stress tests). We discuss how recent AI methods can be used to improve models of the asset demand system by better measuring asset and investor similarity through so-called embeddings. These embeddings can for instance be used for policymaking by central banks to understand the rebalancing channel of asset purchase programs and to measure crowded trades.
Creation-Date: 2024-10
File-URL: https://www.bis.org/publ/work1222.pdf
File-Format: Application/pdf
File-Function: Full PDF document
File-URL: https://www.bis.org/publ/work1222.htm
File-Format: text/html
Number: 1222
Keywords: asset prices, central bank policies, artificial intelligence, embeddings
Classification-JEL: C5, G11, G12
Handle: RePEc:bis:biswps:1222