Handling missing data in Burundian sovereign bond market
Ir\`ene Irakoze, R\'edempteur Ntawiratsa, David Niyukuri

TL;DR
This paper evaluates methods for handling missing data in the Burundian sovereign bond market, recommending Linear Regression for its accuracy and normality approximation, to improve yield curve construction and market analysis.
Contribution
It introduces robust methodologies tailored to the Burundian market's data constraints, enhancing yield curve accuracy and supporting financial market development.
Findings
Linear Regression performs best for missing data imputation.
Previous value method offers high accuracy with limited information.
miss-Forest method is effective for coupon rates but less so for bond prices.
Abstract
Constructing an accurate yield curve is essential for evaluating financial instruments and analyzing market trends in the bond market. However, in the case of the Burundian sovereign bond market, the presence of missing data poses a significant challenge to accurately constructing the yield curve. In this paper, we explore the limitations and data availability constraints specific to the Burundian sovereign market and propose robust methodologies to effectively handle missing data. The results indicate that the Linear Regression method, and the Previous value method perform consistently well across variables, approximating a normal distribution for the error values. The non parametric Missing Value Imputation using Random Forest (miss-Forest) method performs well for coupon rates but poorly for bond prices, and the Next value method shows mixed results. Ultimately, the Linear Regression…
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Taxonomy
TopicsStock Market Forecasting Methods · Financial Markets and Investment Strategies · Financial Risk and Volatility Modeling
MethodsNetwork On Network · Linear Regression
