응용통계연구 (The Korean Journal of Applied Statistics)

  • 제21권1호
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  • Pages.159-168
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  • 2008
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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가중 최소제곱 서포트벡터기계의 혼합모형을 이용한 수익률 기간구조 추정

Estimating the Term Structure of Interest Rates Using Mixture of Weighted Least Squares Support Vector Machines

  • 노성균 (단국대학교 정보컴퓨터과학부) ;
  • 심주용 (대구가톨릭대학교 응용통계학과) ;
  • 황창하 (단국대학교 정보컴퓨터과학부)
  • Nau, Sung-Kyun (Division of Information and Computer Science, Dankook University) ;
  • Shim, Joo-Yong (Dept. of Applied Statistics, Catholic University of Daegu) ;
  • Hwang, Chang-Ha (Division of Information and Computer Science, Dankook University)
  • 발행 : 2008.02.29
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⟨ 이전 논문 다음 논문 ⟩

초록

수익률 기간구조(term structure of interest rates, 이하 수익률곡선)는 자료의 성격이 경시적(longitudinal)이므로 만기까지 기간과 시간을 동시에 입력변수로 고려해야만 유용하고 효율적인 함수추정이 가능하다. 고러나 이러한 방법은 다루어야 하는 자료가 대용량이기 때문에 대용량 자료에 적합하고 실행속도가 빠른 추정기법을 개발하는 것이 필요하다. 한편 자료에 내재하는 자기상관성 구조 때문에 과대 적합된 추정 결과를 얻기 쉽다. 따라서 본 논문에서는 이러한 문제를 해결하기 위해서 가중 LS-SVM(least squares support vector machine, 최소제곱 서포트벡터기계)의 혼합모형을 제안한다. 미국 재무부 채권에 대한 사례연구를 통해서 추정 결과가 증권시장 붕괴 같은 이례적 사건의 현상을 잘 반영하고 있음을 확인할 수 있었다.

Since the term structure of interest rates (TSIR) has longitudinal data, we should consider as input variables both time left to maturity and time simultaneously to get a more useful and more efficient function estimation. However, since the resulting data set becomes very large, we need to develop a fast and reliable estimation method for large data set. Furthermore, it tends to overestimate TSIR because data are correlated. To solve these problems we propose a mixture of weighted least squares support vector machines. We recognize that the estimate is well smoothed and well explains effects of the third stock market crash in USA through applying the proposed method to the US Treasury bonds data.

키워드

참고문헌

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