In Search of the Best Zero Coupon Yield Curve for Nairobi Securities Exchange: Interpolation Methods vs. Parametric Models

Abstract

We seek to determine which yield curve construction method produces the best zero coupon yield curve (ZCYC) for Nairobi Securities Exchange (NSE). The ZCYC should be differentiable at all points and at the same time, should produce a continuous and positive forward curve at all knot points. A decreasing discount curve is also expected from the resulting ZCYC, as an indication of monotonicity. For the interpolation method, we will use an improvement of monotone preserving interpolation method on ( ) r t t , while the Nelson and Siegel [1] model is the parametric model of choice. This is because compared to other interpolation methods, the improvement of monotone preserving interpolation method on ( ) r t t produces curves with the desirable trait of differentiability, while the Nelson-Siegel [1] model is shown to produce the best-fit results for Kenyan bond data. We compare the models' performance in terms of accuracy in pricing back the fixed-income securities. For this study, we use bond data from Central Bank of Kenya (CBK). The better of the two methods will be used for the Kenyan securities market and, consequently, the East African Securities markets.

Optimal Time Varying Parameters in Yield Curve Modeling and Forecasting: A Simulation Study on BRICS Countries

“…A proper calibration of and makes possible an effective replication of a wide variety of yield curve shapes. Parameters estimation is the result of a two-step procedure, where grid search methods (Nelson & Siegel, 1987;Muthoni et al, 2015) identify the value of that maximizes the medium-term component, varying the maturity. For each the vector of parameters β is then estimated through an OLS regression choosing in the end the values * and β * associated to the highest coefficient of determination.…”

A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling

“…The choice of the fitting models has various motivations: they make a parsimonious use of parameters, being, therefore, easy to handle; in addition, working with fixedincome assets, these models showed a notable ability to replicate the term structure dynamics [12,[14][15][16][17][18][19][20][21][22][23][24][25]. Therefore, provided the similarity with the NG futures market in terms of the varying maturity of the data structure, we tested to what extent the above models can be reliable on the energy markets too.…”