Author: Biao LuPublisher:
ISBN:
Category :
Languages : en
Pages : 152
Book Description
Essays in Term Structure Models and GMM Estimation with Incomplete Knowledge
Author: Biao LuEssays On The Theory And Estimation Of Term Structure Models
Author: Pedro Santa-ClaraAmerican Doctoral Dissertations
Author: Publisher:
ISBN:
Category : Dissertation abstracts
Languages : en
Pages : 848
Book Description
Two Essays on Estimation and Inference of Affine Term Structure Models
Author: Qian WangPublisher:
ISBN:
Category :
Languages : en
Pages : 147
Book Description
Affine term structure models (ATSMs) are one set of popular models for yield curve modeling. Given that the models forecast yields based on the speed of mean reversion, under what circumstances can we distinguish one ATSM from another? The objective of my dissertation is to quantify the benefit of knowing the “true” model as well as the cost of being wrong when choosing between ATSMs. In particular, I detail the power of out-of-sample forecasts to statistically distinguish one ATSM from another given that we only know the data are generated from an ATSM and are observed without errors. My study analyzes the power and size of affine term structure models (ATSMs) by evaluating their relative out-of-sample performance. Essay one focuses on the study of the one-factor ATSMs. I find that the model’s predictive ability is closely related to the bias of mean reversion estimates no matter what the true model is. The smaller the bias of the estimate of the mean reversion speed, the better the out-of-sample forecasts. In addition, my finding shows that the models' forecasting accuracy can be improved, in contrast, the power to distinguish between. different ATSMs will be reduced if the data are simulated from a high mean reversion process with a large sample size and with a high sampling frequency. In the second essay, I extend the question of interest to the multi-factor ATSMs. My finding shows that adding more factors in the ATSMs does not improve models' predictive ability. But it increases the models' power to distinguish between each other. The multi-factor ATSMs with larger sample size and longer time span will have more predictive ability and stronger power to differentiate between models.
Essays on Dynamic Term Structure Models
Author: Thomas Mogensbjerg JensenPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
GMM Estimation of Affine Term Structure Models
Author: Jaroslava HlouskovaPublisher:
ISBN:
Category :
Languages : en
Pages : 34
Book Description
This article investigates parameter estimation of affine term structure models by means of the generalized method of moments. Exact moments of the affine latent process as well as of the yields are obtained by using results derived for p-polynomial processes. Then the generalized method of moments, combined with Quasi-Bayesian methods, is used to get reliable parameter estimates and to perform inference. After a simulation study, the estimation procedure is applied to empirical interest rate data.
Essays on Affine Term Structure Models
Author: Bovorn VichiansinPublisher:
ISBN:
Category : Interest rates
Languages : en
Pages : 116
Book Description
Essays on Dynamic Term Structure Models
Author: Jorge W. HansenPublisher:
ISBN: 9788793943049
Category :
Languages : en
Pages :
Book Description
Term-Structure Estimation in Markets with Infrequent Trading
Author: Gonzalo CortazarPublisher:
ISBN:
Category :
Languages : en
Pages :
Book Description
There are two issues that are of central importance in term-structure analysis. One is the modelling and estimation of the current term structure of spot rates. The second is the modelling and estimation of the dynamics of the term structure. These two issues have been addressed independently in the literature. The methods that have been proposed assume a sufficiently complete price data set and are generally implemented separately. However, there are serious problems when these methods are applied to markets with sparse bond prices.We develop a method for jointly estimating the current term-structure and its dynamics for markets with infrequent trading. We propose solving both issues by using a dynamic term-structure model estimated from incomplete panel-data. To achieve this, we modify the standard Kalman filter approach to deal with the missing-observation problem. In this way, we can use historic price data in a dynamic model to estimate the current term structure. With this approach we are able to obtain an estimate of the current term structure even for days with an arbitrary low number of price observations.The proposed methodology can be applied to a broad class of continuous-time term-structure models with any number of stochastic factors. To show the implementation of the approach, we estimate a three-factor generalized-Vasicek model using Chilean government bond price data. The approach, however, may be used in any market with infrequent trading, a common characteristic of many emerging markets.
Author: Elvezio Ronchetti