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Constructing an accurate yield curve is essential for evaluating financialinstruments and analyzing market trends in the bond market. However, in thecase of the Burundian sovereign bond market, the presence of missing data posesa significant challenge to accurately constructing the yield curve. In thispaper, we explore the limitations and data availability constraints specific tothe Burundian sovereign market and propose robust methodologies to effectivelyhandle 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 parametricMissing Value Imputation using Random Forest (miss-Forest) method performs wellfor coupon rates but poorly for bond prices, and the Next value method showsmixed results. Ultimately, the Linear Regression (LR) method is recommended forimputing missing data due to its ability to approximate normality andpredictive capabilities. However, filling missing values with previous valueshas high accuracy, thus, it will be the best choice when we have lessinformation to be able to increase accuracy for LR. This research contributesto the development of financial products, trading strategies, and overallmarket development in Burundi by improving our understanding of the yield curvedynamics.

Handling missing data in Burundian sovereign bond market