  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 (LR) method is recommended for
imputing missing data due to its ability to approximate normality and
predictive capabilities. However, filling missing values with previous values
has high accuracy, thus, it will be the best choice when we have less
information to be able to increase accuracy for LR. This research contributes
to the development of financial products, trading strategies, and overall
market development in Burundi by improving our understanding of the yield curve
dynamics.
