Forecasting the South African Real Spot-rate Curve During Covid-19 Stress

Abstract

Purpose: The aim of this study is to investigate the performance of forecasting the South African real spot rate curve using different mathematical term-structure models during the Covid-19 period. This study follows previous studies by Reid (2009) and Mashoene et al., 2021) where dynamic term-structure models performed well compared to static term-structure models over normal market conditions. For high volatility, normality often breaks, which might result in a need for recalibration in the currently used methodology or a total change in the methodology.
 Methodology: This study explores the dynamism of both dynamic term-structure models, which follow the Nelson-Siegel framework, and static term-structure models with the option of recalibration of model parameters to account for a change in macro-economic dynamics brought by the effect of the Covid-19 pandemic. The forecasting exercise was applied to term-structure models following the Nelson-Siegel framework using AR(1) process.
 Findings: The incorporation of macroeconomic factors performed poorly in estimating future model parameters. This could be attributable to the effect of global stress, which resulted in poor linear relationships between macroeconomic and financial factors. Recalibration process on static Nelson-Siegel term-structure models improved the models' performance, especially during the high volatility/period of total economic shutdown due to the Covid-19 pandemic.
 Implications: The application of static Nelson-Siegel term-structure models with recalibrated parameters might improve bond pricing processes and risk management strategies in emerging markets, especially during market stress.