Analysing Global Fixed Income Markets with Tensors

TL;DR

This paper introduces a tensor-based model for analyzing global fixed income markets, capturing multi-dimensional risk factors across maturities and countries, enabling more precise portfolio management and hedging strategies.

Contribution

It presents a novel tensor-valued approach that decomposes global interest rate risks into maturity and country components, improving upon traditional flat multivariate models.

Findings

Identifies shared global risk factors across eight economies.

Demonstrates tensor model's effectiveness in describing macroeconomic environment.

Shows improved risk decomposition for portfolio strategies.

Abstract

Global fixed income returns span across multiple maturities and economies, that is, they naturally reside on multi-dimensional data structures referred to as tensors. In contrast to standard "flat-view" multivariate models that are agnostic to data structure and only describe linear pairwise relationships, we introduce a tensor-valued approach to model the global risks shared by multiple interest rate curves. In this way, the estimated risk factors can be analytically decomposed into maturity-domain and country-domain constituents, which allows the investor to devise rigorous and tractable global portfolio management and hedging strategies tailored to each risk domain. An empirical analysis confirms the existence of global risk factors shared by eight developed economies, and demonstrates their ability to compactly describe the global macroeconomic environment.