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Mastering R for Quantitative Finance

You're reading from  Mastering R for Quantitative Finance

Product type Book
Published in Mar 2015
Publisher
ISBN-13 9781783552078
Pages 362 pages
Edition 1st Edition
Languages

Table of Contents (20) Chapters

Mastering R for Quantitative Finance
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
1. Time Series Analysis 2. Factor Models 3. Forecasting Volume 4. Big Data – Advanced Analytics 5. FX Derivatives 6. Interest Rate Derivatives and Models 7. Exotic Options 8. Optimal Hedging 9. Fundamental Analysis 10. Technical Analysis, Neural Networks, and Logoptimal Portfolios 11. Asset and Liability Management 12. Capital Adequacy 13. Systemic Risks Index

Parameter estimation of interest rate models


When using the interest rate models for pricing or simulation purposes, it is important to calibrate their parameters to real data properly. Here, we present a possible method to estimate the parameters. This method was developed by Chan et al, 1992, and is often referred to as the CKLS method. The procedure was elaborated to estimate the parameters of the following interest rate model with the help of the econometric procedure called Generalized Method of Moments (GMM; see Hansen, 1982, for more details):

It is easy to see that this process gives the Vasicek model when γ=0, and the CIR model when γ =0.5. As the first step of the parameter estimation, we discretize this equation with the Euler approximation (see Atkinson, 1989):

Here, δt is the time interval between two observations of the interest rate and et is independent, standard normal random variables. The parameters are estimated with the following null hypothesis:

Let Θ be the vector of...

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