Paper: arXiv 2309.17379

Authors: Irène Irakoze, Rédempteur Ntawiratsa, David Niyukuri

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 (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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 5.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper provides a practical empirical comparison of standard missing-data imputation techniques applied to frontier sovereign bond market data. While the mathematical formulations are straightforward standard statistical methods, the focus on the Burundian fixed-income market offers unique regional value despite limited methodological novelty.

Research Flowchart

  flowchart TD
    A[Goal: Handle Missing Data in Burundian Sovereign Bond Market] --> B[Data Input: Coupon Rates & Bond Prices]
    B --> C[Evaluate Imputation Methodologies]
    C --> D[Computational Models: LR, Previous Value, missForest, Next Value]
    D --> E[Error Distribution & Accuracy Analysis]
    E --> F[Key Outcome: LR recommended for normality & predictive power]
    E --> G[Key Outcome: Previous Value best alternative for low-information scenarios]