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  • 學位論文

考慮流動性下之利率期間結構預測

Forecasting the Term Structure of Interest Rates with Liquidity

指導教授 : 林蒼祥
共同指導教授 : 蔡蒔銓(Shih-Chuan Tsai)

摘要


由於我國公債市場的交易分配不均,所以流動性不同的債券,流動性溢酬應該也不相同,而這種情形也會反應在殖利率曲線上,影響到殖利率曲線的形狀,所以,在配適殖利率曲線時應將流動性納入考量。本文以Svensson模型為基礎,在模型中額外加入流動性加權函數,配適台灣公債市場的公債殖利率曲線,並利用模型中四個估計因子的時間序列模型進行債券價格之預測,根據我們所配適出的公債殖利率曲線發現,採用流動性目標函數所配適出的公債殖利率曲線確實較符合現實的公債市場。 在預測結果方面,透過流動性加權目標函數所求得的模型因子,經過時間序列模型的診斷後,對於樣本債券的預測結果,在各種平均預測天期下的預測結果皆具有一致性,準確度也較高;另外,皆隨著平均預測期間的增加,對於二年債及五年債的預測準確度下降,波動性也提高,表示預測期間對於預測的準確性也會造成影響。

並列摘要


Because bonds with different liquidities have different premiums, it should be reflect on the yield curve. So, when we estimate the term structure of interest rates, liquidities should be considered. This research decomposes the term structure into four factors by using Svensson model, and attempts to predict the factors themselves. Because the yield curve depends only on the factors, forecasting the yield curve is equivalent to forecasting the factors. Empirical results show that when applied the liquidity weighted objective function, the yield curve is appropriate to explain the situation of Taiwan Government Bond Market and forecasting price errors of sample bonds are also smaller in all kinds of horizons. In addition, the results also find the degree of accuracy decline as forecasting horizon increasing.

參考文獻


3.謝承熹,2000,「以分段三次方指數函數配適台灣公債市場之利率期間結構:線性最適化與非線性最適化之比較」,中國財務學刊,第八卷第二期,第25-47頁。
1.Adams, K. J., and D. R. Deventer (1994), “Fitting Yield Curves and Forward Rate Curves with Maximum Smoothness,” Journal of Fixed Income, Vol. 4, pp. 52-62.
2.Alper, C. E., K. Kazimov, and A. Akdemir (2007), “Forecasting the Term Structure of Interest Rates for Turkey: A Factor Analysis Approach,” Applied Financial Economics, Vol. 17, pp. 77-85.
3.Box, G. E. P., and G. M. Jenkins (1976), Time Series Analysis: Forecasting and Control, San Francisco, CA: Holden-Day.
4.Chambers, D. R., W. T. Carleton, and D.W. Waldman (1984), “A New Approach to Estimation of the Term Structure of Interest Rates,” Journal of Financial and Quantitative Analysis, Vol. 19, pp. 233-252.

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