Estimating the Currency Composition of Foreign Exchange Reserves

TL;DR

This paper introduces a Hidden Markov Model to estimate the currency composition of central banks' foreign exchange reserves, addressing data gaps and revealing insights into China and Singapore's reserve holdings.

Contribution

The paper develops a novel Hidden Markov Model approach to infer reserve currency composition from valuation fluctuations, overcoming reporting limitations.

Findings

China's reserve composition likely resembles the global average.

Singapore probably holds fewer US dollars in reserves.

The model provides new insights into reserve compositions of major economies.

Abstract

Central banks manage about \$12 trillion in foreign exchange reserves, influencing global exchange rates and asset prices. However, some of the largest holders of reserves report minimal information about their currency composition, hindering empirical analysis. I describe a Hidden Markov Model to estimate the composition of a central bank's reserves by relating the fluctuation in the portfolio's valuation to the exchange rates of major reserve currencies. I apply the model to China and Singapore, two countries that collectively hold about \$3.4 trillion in reserves and conceal their composition. I find that both China's reserve composition likely resembles the global average, while Singapore probably holds fewer US dollars.