Studies on Paramaeter Indentification of Factor mode for Bonds

债券因子模式参数辨识研究

• 批准号: 17560402
• 负责人: AIHARA Shinichi
• 金额: $ 2.18万
• 依托单位: Tokyo University of Science, Suwa
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2005
• 资助国家: 日本
• 起止时间: 2005 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-17560402/
• 关键词: US-treasury bond; forward rate process; Parabolic type stochastic DPE; Maximum likelihood; Particle filter; 放物型確率微分方程式; リスク中立測度

During the first two years, the new factor model for bond process has been proposed. After checking the various types of the arbitragy free or empirical models, we met with the conclusion that the stochastic Parabolic partial differential equation is the most adequate model. To apply the proposed model to the practical situation, we established the on-line parameter estimation algorithm from the obtained yield curve data. The idea to construct the on-line estimator is to use the particle filter algorithm. Despite the success of particle filter, there are two factors which cause difficulties in its implementation. The first one is the choice of importance functions commonly used in the literature which are far from being optimal. The second one is the combined state and parameter estimation problem. In the final year, we are able to circumvent both these problems. There was an additional difficulty of proper discretization because of the inherently continuous nature of financial model. Numerical results which are presented in the published papers listed below show the effectiveness of the proposed algorithms.

在前两年，提出了新的键合过程因子模型。在对各种无套利模型或经验模型进行检验后，我们得出结论：随机抛物型偏微分方程是最合适的模型。为了将所提出的模型应用于实际情况，我们根据获得的收益率曲线数据建立了在线参数估计算法。构造在线估计器的思想是使用粒子滤波算法。尽管粒子滤波取得了成功，但存在两个因素导致其实施困难。首先是文献中常用的重要函数的选择，这些重要函数的选择远非最优。第二个问题是组合状态和参数估计问题。在最后一年，我们能够规避这两个问题。由于财务模型固有的连续性质，适当的离散化还有一个额外的困难。本文所发表的论文的数值结果表明了所提出算法的有效性。

项目成果

Recursive Parameter Identification for Infinite-dimensional Factor Model by using Particle Filter-Application to US-Treasury Bonds-

使用粒子滤波器的无限维因子模型的递归参数识别-在美国国债中的应用-

• 发表时间: 2008
• 期刊: International Journal of Innovative Computing, Information & Control 4
• 作者: Maina;J.;Fujinami;Ki.;Matsui;K.;Inoue;T.;相原 伸一
• 通讯作者: 相原 伸一

Filtering and identification of stochastic volatility for Parabolic type factor modles

抛物型因子模型随机波动性的过滤与识别

• 发表时间: 2006
• 期刊: International Journal of Innovative Computing, Information and Control 2
• 作者: J.Xin;A.Sano;相原 伸一
• 通讯作者: 相原 伸一

Filtering and Identification of Stochastic Volatility for Parabolic Type Factor Models

抛物型因子模型随机波动率的过滤和识别

• 发表时间: 2006
• 期刊: Int. J. Innovation, Computing Information & Control vol.2
• 作者: Fujinami;K.;Maina;J.W.;Inoue;T.;Matsui;K.;Kikuta;Y.;S. AIHARA
• 通讯作者: S. AIHARA

Filtering and Identification of Interest Rate Model with Stochastic Volatility

随机波动利率模型的过滤与辨识

• 发表时间: 2005
• 期刊: Proc.of 44th IEEE CDC and ECC'05
• 作者: Maina;J.W.;Higashi;S.;Kikuta;Y.;Matsui;K.;相原 伸一;相原 伸一;相原 伸一;相原 伸一
• 通讯作者: 相原 伸一

Filtering and Identification of Parabolic Type Factor Model with Stochastic Volatlity

随机波动抛物型因子模型的滤波与辨识

• 发表时间: 2006
• 期刊: Proc. of the 37th International Symposium on Stochastic Systems Theory and Its Applications
• 作者: Maina;J.W.;Higashi;S.;Kikuta;Y.;Matsui;K.;相原 伸一
• 通讯作者: 相原 伸一