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A no-arbitrage vector autoregression of term struture dynamics with macroecnomic and latent variables
基于无套利向量自回归模型带有宏观变量和潜在 变量的动态利率期限结构
---Andrew Ang,Monika Piazzesi
Form future long-term interest rates level（R）
The empirical
Stata Descriptive statistics ADF Test
Short term interest rate dynamic estimation model、No arbitrage pricing model
macro variables
latent variables
No-arbitrage VAR model
account for term struture dynamics features
Influence principle
Macro variables（CPI、PI )
Expected short rate（r）level
latent factor intact、Slope factor are attribute to the macro factor
Conclusion
cross-equation restriction no arbitrage macro factors model out-sampleforecast function better
Welcome to our presentation
• In my presentation there are three parts: • First: Introduction • Second:Paper anlysing • Third:Conclusion
Introduction
Paper anlysing
Abstract
• We describe the joint dynamics of bond yields and macroeconomic variables in a Vector Autoregression, where identifying restrictions are based on the absence of arbitrage. Using a term structure model with inflation and economic growth factors, together with latent variables, we investigate how macro variables affect bond prices and the dynamics of the yield curve. We find that the forecasting performance of a VAR improves when no-arbitrage restrictions are imposed and that models with macro factors forecast better than models with only unobservable factors. Variance decompositions show that macro factors explain up to 85% of the variation in bond yields. Macro factors primarily explain movements at the short end and middle of the yield curve while unobservable factors still account for most of the movement at the long end of the yield curve.
Gains
The empirical Model
百度文库
Analysis way
The effect of inflation shocks are strongest at the short end of the yield curve
Conclusion
Advantage
Classical model
Precedent Research
Disadvantage
The theoretical part is not sufficient
• Term struture （利率期限结构） • 是指在某个固定时点上，不同期限的资金收益率
与到期期限的关系。 • Latent variables（潜在变量） • 不可观测的影响收益率变化的其他因子：通过主 成分分析通常情况下用收益率曲线的： Level(水平)、Slope(斜率)、Curvature （曲率）因子来表示。
Estimation results
Parameter estimates
Impulse responses
Variance decomposit ions
Forecasts
Compariso n of factors
macro factors explain up to 85%movements in the short and middle parts ofand 40% in the long end of the yield curve
基于无套利向量自回归模型带有宏观变量和潜在 变量的动态利率期限结构
---Andrew Ang,Monika Piazzesi
Form future long-term interest rates level（R）
The empirical
Stata Descriptive statistics ADF Test
Short term interest rate dynamic estimation model、No arbitrage pricing model
macro variables
latent variables
No-arbitrage VAR model
account for term struture dynamics features
Influence principle
Macro variables（CPI、PI )
Expected short rate（r）level
latent factor intact、Slope factor are attribute to the macro factor
Conclusion
cross-equation restriction no arbitrage macro factors model out-sampleforecast function better
Welcome to our presentation
• In my presentation there are three parts: • First: Introduction • Second:Paper anlysing • Third:Conclusion
Introduction
Paper anlysing
Abstract
• We describe the joint dynamics of bond yields and macroeconomic variables in a Vector Autoregression, where identifying restrictions are based on the absence of arbitrage. Using a term structure model with inflation and economic growth factors, together with latent variables, we investigate how macro variables affect bond prices and the dynamics of the yield curve. We find that the forecasting performance of a VAR improves when no-arbitrage restrictions are imposed and that models with macro factors forecast better than models with only unobservable factors. Variance decompositions show that macro factors explain up to 85% of the variation in bond yields. Macro factors primarily explain movements at the short end and middle of the yield curve while unobservable factors still account for most of the movement at the long end of the yield curve.
Gains
The empirical Model
百度文库
Analysis way
The effect of inflation shocks are strongest at the short end of the yield curve
Conclusion
Advantage
Classical model
Precedent Research
Disadvantage
The theoretical part is not sufficient
• Term struture （利率期限结构） • 是指在某个固定时点上，不同期限的资金收益率
与到期期限的关系。 • Latent variables（潜在变量） • 不可观测的影响收益率变化的其他因子：通过主 成分分析通常情况下用收益率曲线的： Level(水平)、Slope(斜率)、Curvature （曲率）因子来表示。
Estimation results
Parameter estimates
Impulse responses
Variance decomposit ions
Forecasts
Compariso n of factors
macro factors explain up to 85%movements in the short and middle parts ofand 40% in the long end of the yield curve