Term Structure Estimation with Missing Data
Author | : Krisztina Nagy |
Publisher | : |
Total Pages | : 132 |
Release | : 2011 |
Genre | : Interest rates |
ISBN | : |
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Author | : Krisztina Nagy |
Publisher | : |
Total Pages | : 132 |
Release | : 2011 |
Genre | : Interest rates |
ISBN | : |
Author | : Gonzalo Cortazar |
Publisher | : |
Total Pages | : 38 |
Release | : 2004 |
Genre | : |
ISBN | : |
There are two issues that are of central importance in term structure analysis. One is the modeling and estimation of the current term structure of spot rates. The second is the modeling 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, when the methods are applied to markets with sparse bond price, results are unsatisfactory.We develop a method for jointly estimating the current term structure and its dynamics for markets with low-frequency transactions. 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 low-frequency transactions, a common characteristic of many emerging markets.
Author | : David M. Drukker |
Publisher | : Emerald Group Publishing |
Total Pages | : 262 |
Release | : 2011-11-30 |
Genre | : Business & Economics |
ISBN | : 1780525273 |
Part of the "Advances in Econometrics" series, this title contains chapters covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; and, Consistent Estimation and Orthogonality.
Author | : Gonzalo Cortazar |
Publisher | : |
Total Pages | : |
Release | : 2013 |
Genre | : |
ISBN | : |
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 | : David M. Drukker |
Publisher | : Emerald Group Publishing |
Total Pages | : 262 |
Release | : 2011-11-30 |
Genre | : Business & Economics |
ISBN | : 1780525265 |
Part of the "Advances in Econometrics" series, this title contains chapters covering topics such as: Missing-Data Imputation in Nonstationary Panel Data Models; Markov Switching Models in Empirical Finance; Bayesian Analysis of Multivariate Sample Selection Models Using Gaussian Copulas; and, Consistent Estimation and Orthogonality.
Author | : L. Krippner |
Publisher | : Springer |
Total Pages | : 436 |
Release | : 2015-01-05 |
Genre | : Business & Economics |
ISBN | : 1137401826 |
Nominal yields on government debt in several countries have fallen very near their zero lower bound (ZLB), causing a liquidity trap and limiting the capacity to stimulate economic growth. This book provides a comprehensive reference to ZLB structure modeling in an applied setting.
Author | : Stef van Buuren |
Publisher | : CRC Press |
Total Pages | : 444 |
Release | : 2018-07-17 |
Genre | : Mathematics |
ISBN | : 0429960352 |
Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.
Author | : Michael O'Kelly |
Publisher | : John Wiley & Sons |
Total Pages | : 472 |
Release | : 2014-02-14 |
Genre | : Medical |
ISBN | : 1118762533 |
This book provides practical guidance for statisticians, clinicians, and researchers involved in clinical trials in the biopharmaceutical industry, medical and public health organisations. Academics and students needing an introduction to handling missing data will also find this book invaluable. The authors describe how missing data can affect the outcome and credibility of a clinical trial, show by examples how a clinical team can work to prevent missing data, and present the reader with approaches to address missing data effectively. The book is illustrated throughout with realistic case studies and worked examples, and presents clear and concise guidelines to enable good planning for missing data. The authors show how to handle missing data in a way that is transparent and easy to understand for clinicians, regulators and patients. New developments are presented to improve the choice and implementation of primary and sensitivity analyses for missing data. Many SAS code examples are included – the reader is given a toolbox for implementing analyses under a variety of assumptions.