Modeling and Forecasting the Yield Curve by an Extended Nelson‐Siegel Class of Models: A Quantile Autoregression Approach

This paper compares the in‐sample fitting and the out‐of‐sample forecasting performances of four distinct Nelson–Siegel class models: Nelson–Siegel, Bliss, Svensson, and a five‐factor model we propose in order to enhance the fitting flexibility. The introduction of the fifth factor resulted in superior adjustment to the data. For the forecasting exercise the paper contrasts the performances of the term structure models in association with the following econometric methods: quantile autoregression evaluated at the median, VAR, AR, and a random walk. As a pattern, the quantile procedure delivered the best results for longer forecasting horizons. Copyright © 2011 John Wiley & Sons, Ltd.     

Siegel定理的一个新应用

利用特征和的估计及其Ｓｉｅｓｅｌ定理给出ＤｉｒｉｃｈｌｅｔＬ－函数的一种新型的均值公式．     

Forecasting the Yield Curve in a Data‐Rich Environment Using the Factor‐Augmented Nelson–Siegel Model

This paper compares various ways of extracting macroeconomic information from a data‐rich environment for forecasting the yield curve using the Nelson–Siegel model. Five issues in extracting factors from a large panel of macro variables are addressed; namely, selection of a subset of the available information, incorporation of the forecast objective in constructing factors, specification of a multivariate forecast objective, data grouping before constructing factors, and selection of the number of factors in a data‐driven way. Our empirical results show that each of these features helps to improve forecast accuracy, especially for the shortest and longest maturities. Factor‐augmented methods perform well in relatively volatile periods, including the crisis period in 2008–9, when simpler models do not suffice. The macroeconomic information is exploited best by partial least squares methods, with principal component methods ranking second best. Reductions of mean squared prediction errors of 20–30% are attained, compared to the Nelson–Siegel model without macro factors. Copyright © 2011 John Wiley & Sons, Ltd.     

Nelson–Siegel,Affine and Quadratic Yield Curve Specifications: Which One is Better at Forecasting?

In this paper we compare the in‐sample fit and out‐of‐sample forecasting performance of no‐arbitrage quadratic, essentially affine and dynamic Nelson–Siegel term structure models. In total, 11 model variants are evaluated, comprising five quadratic, four affine and two Nelson–Siegel models. Recursive re‐estimation and out‐of‐sample 1‐, 6‐ and 12‐month‐ahead forecasts are generated and evaluated using monthly US data for yields observed at maturities of 1, 6, 12, 24, 60 and 120 months. Our results indicate that quadratic models provide the best in‐sample fit, while the best out‐of‐sample performance is generated by three‐factor affine models and the dynamic Nelson–Siegel model variants. Statistical tests fail to identify one single best forecasting model class. Copyright © 2011 John Wiley & Sons, Ltd.     

一类非自共轭锥上的特殊函数

给出了一类非自共轭锥上的Ｇａｍｍａ函数，计算了其上的Ｓｉｅｇｅｌ积分并给出其应用．     

合理性的问题与出路

赛格尔论证了劳丹工具合理性的失败。赛格尔认为,合理性的本质在于证据和科学方法。在变化的科学方法中可以刻画出科学方法的惟一本质特征,那就是对证据的赞同。赛格尔的方法论不变性论题揭示了工具合理性的重大缺陷。但是,他把科学合理性仅仅归于方法论,也仍然是狭隘的,没有脱离传统逻辑合理性的窠臼。劳丹合理性理论中对目标的关注并不是什么缺点,可惜劳丹对这些具体目标的研究尚不成熟。科学合理性的最好出路是协调合理性。     

Parsimonious modeling and forecasting of corporate yield curve

This paper investigates the sensitivity of out‐of‐sample forecasting performance over a span of different parameters of l in the dynamic Nelson–Siegel three‐factor AR(1) model. First, we find that the ad hoc selection of l is not optimal. Second, we find a substantial difference in factor dynamics between investment‐grade and speculative‐grade corporate bonds from 1994:12 to 2006: 4. Third, we suggest that the three‐factor model is sufficient to explain the main variations of corporate yield changes. Finally, the parsimonious Nelson–Siegel three‐factor AR(1) model remains competitive in the out‐of‐sample forecasting of corporate yields. Copyright © 2008 John Wiley & Sons, Ltd.     

齐性Siegel域的自同构群

令Ｄ表示有界齐性Ｓｉｅｇｅｌ域，Ｇ（Ｄ）是Ｄ的自同构群，ｇ（Ｄ）是关于Ｇ（Ｄ）的李代数，则对ｇ（Ｄ），Ｓ．Ｍｕｒａｋａｍｉ得到下列直和：ｇ（Ｄ）＝ｇ－１＋ｇ－１／２十ｇ０十ｇ１／２＋ｇ１其中ｇ－１，ｇ－１／２和ｇ０是大家熟知的，本文我们给出ｇ１／２和ｇ１的构造．即在非常弱的条件下，我们证明了ｇ１／２＝｛０｝和ｇ１＝｛∑Ｐ２０Ｋ｝．同时，我们给出一些Ｓｉｅｇｅｌ域的例子，它们的自同构群可以显式给出．