The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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A Bayesian approach for dynamic Nelson-Siegel yield curve modeling on SOFR term rate data

SOFR 기간 데이터에 대한 동적 넬슨-시겔 이자율 곡선의 베이지안 접근법

  • Seong Ho Im (Department of Applied Statistics, Chung-Ang University) ;
  • Beom Seuk Hwang (Department of Applied Statistics, Chung-Ang University)
  • 임성호 (중앙대학교 응용통계학과) ;
  • 황범석 (중앙대학교 응용통계학과)
  • Received : 2023.01.26
  • Accepted : 2023.03.03
  • Published : 2023.08.31
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Abstract

Dynamic Nelson-Siegel model is widely used in modeling term structure of interest rates for financial products. In this study, we explain dynamic Nelson-Siegel model from the perspective of the state space model and explore Bayesian approaches that can be applied to that model. By applying SOFR term rate data to the Bayesian dynamic Nelson-Siegel model, we confirm the performance and compare it with other competing models such as Vasicek model, dynamic Nelson-Siegel model based on the frequentist approach, and the two-factor Bayesian dynamic Nelson-Siegel model. We also confirm that the Bayesian dynamic Nelson-Siegel model outperformed its competitors on SOFR term rate data based on RMSE.

동적 넬슨-시겔 모형은 채권과 같은 기간 구조를 갖고 있는 금융상품의 이자율 곡선모형에서 널리 사용되고 있다. 본 연구에서는 동적 넬슨-시겔 모형을 상태 공간 모형의 관점에서 설명하고 해당 모형에 적용할 수 있는 베이지안 접근법에 대해 알아보고자 한다. 그리고 SOFR 기간 데이터를 베이지안 동적 넬슨-시겔 모형에 적용하여 그 성능을 확인하고 바시첵 모형, 빈도주의 접근법을 활용한 동적 넬슨-시겔 모형, 2요인 베이지안 동적 넬슨-시겔 모형과 같은 다른 경쟁 모형들과 성능을 비교해보고자 한다. 우리는 베이지안 동적 넬슨-시겔 모형이 SOFR 기간 데이터에 대해서 다른 모형들보다 우수한 성능을 보여준다는 것을 확인할 수 있었다.

Keywords

References

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