Peilun He, Gareth W. Peters, Nino Kordzakhiac, Pavel V. Shevchenko
{"title":"用于多曲线固定收益利差分析和压力测试的状态空间动态函数回归","authors":"Peilun He, Gareth W. Peters, Nino Kordzakhiac, Pavel V. Shevchenko","doi":"arxiv-2409.00348","DOIUrl":null,"url":null,"abstract":"The Nelson-Siegel model is widely used in fixed income markets to produce\nyield curve dynamics. The multiple time-dependent parameter model conveniently\naddresses the level, slope, and curvature dynamics of the yield curves. In this\nstudy, we present a novel state-space functional regression model that\nincorporates a dynamic Nelson-Siegel model and functional regression\nformulations applied to multi-economy setting. This framework offers distinct\nadvantages in explaining the relative spreads in yields between a reference\neconomy and a response economy. To address the inherent challenges of model\ncalibration, a kernel principal component analysis is employed to transform the\nrepresentation of functional regression into a finite-dimensional, tractable\nestimation problem. A comprehensive empirical analysis is conducted to assess\nthe efficacy of the functional regression approach, including an in-sample\nperformance comparison with the dynamic Nelson-Siegel model. We conducted the\nstress testing analysis of yield curves term-structure within a dual economy\nframework. The bond ladder portfolio was examined through a case study focused\non spread modelling using historical data for US Treasury and UK bonds.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"78 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"State-Space Dynamic Functional Regression for Multicurve Fixed Income Spread Analysis and Stress Testing\",\"authors\":\"Peilun He, Gareth W. Peters, Nino Kordzakhiac, Pavel V. Shevchenko\",\"doi\":\"arxiv-2409.00348\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Nelson-Siegel model is widely used in fixed income markets to produce\\nyield curve dynamics. The multiple time-dependent parameter model conveniently\\naddresses the level, slope, and curvature dynamics of the yield curves. In this\\nstudy, we present a novel state-space functional regression model that\\nincorporates a dynamic Nelson-Siegel model and functional regression\\nformulations applied to multi-economy setting. This framework offers distinct\\nadvantages in explaining the relative spreads in yields between a reference\\neconomy and a response economy. To address the inherent challenges of model\\ncalibration, a kernel principal component analysis is employed to transform the\\nrepresentation of functional regression into a finite-dimensional, tractable\\nestimation problem. A comprehensive empirical analysis is conducted to assess\\nthe efficacy of the functional regression approach, including an in-sample\\nperformance comparison with the dynamic Nelson-Siegel model. We conducted the\\nstress testing analysis of yield curves term-structure within a dual economy\\nframework. The bond ladder portfolio was examined through a case study focused\\non spread modelling using historical data for US Treasury and UK bonds.\",\"PeriodicalId\":501139,\"journal\":{\"name\":\"arXiv - QuantFin - Statistical Finance\",\"volume\":\"78 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Statistical Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.00348\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.00348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
State-Space Dynamic Functional Regression for Multicurve Fixed Income Spread Analysis and Stress Testing
The Nelson-Siegel model is widely used in fixed income markets to produce
yield curve dynamics. The multiple time-dependent parameter model conveniently
addresses the level, slope, and curvature dynamics of the yield curves. In this
study, we present a novel state-space functional regression model that
incorporates a dynamic Nelson-Siegel model and functional regression
formulations applied to multi-economy setting. This framework offers distinct
advantages in explaining the relative spreads in yields between a reference
economy and a response economy. To address the inherent challenges of model
calibration, a kernel principal component analysis is employed to transform the
representation of functional regression into a finite-dimensional, tractable
estimation problem. A comprehensive empirical analysis is conducted to assess
the efficacy of the functional regression approach, including an in-sample
performance comparison with the dynamic Nelson-Siegel model. We conducted the
stress testing analysis of yield curves term-structure within a dual economy
framework. The bond ladder portfolio was examined through a case study focused
on spread modelling using historical data for US Treasury and UK bonds.