EURO 2025 Leeds
Abstract Submission

2835. Measuring variability of nodes in the yield term structure

Invited abstract in session WB-9: Algotrading and Market strategies, stream OR in Finance and Insurance .

Wednesday, 10:30-12:00
Room: Clarendon SR 2.01

Authors (first author is the speaker)

1. Ilaria Stefani
DISMEQ, University of Milan-Bicocca
2. Edit Rroji
University of Milano-Bicocca
3. Lorenzo Mercuri
University of Milan
4. Andrea Perchiazzo
Department of Economics, Management and Quantitative Methods, University of Milan

Abstract

In this paper, we investigate the yield curve extracted from government bonds. Through a comparative analysis of parsimonious models for yield curve estimation we assess the impact of recent political and market events in the bond market for various countries, including Italy, Germany, France, Denmark, Japan, and United States. Market news is expected to affect the shape of the yield curve. We compute daily the coefficients of the parsimonious models and highlight the most important features observed during important events such as the COVID-19 pandemics, the Russian-Ukraine war, and US election results. We also employ non-parametric measures of variability applied to the series of each node in the term structure. An important contribution of this paper is the introduction of a dependence between policy intervention towards a net zero carbon economy and the nodes in the yield term structure. In particular, we introduce a quantity named time to transition computed as the difference of greenium using german twin bonds for two different maturities. Green bonds provide financial backing for developing low-carbon initiatives and facilitating the transition towards a greener economy. The greenium effect refers to the potential premium bondholders are willing to pay to invest in green securities compared to investments with similar characteristics.In our view, this index provides an intrinsic measure of these instruments and serves as a proxy for the investment risk.

Keywords

Status: accepted


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