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Mastering R for Quantitative Finance

You're reading from   Mastering R for Quantitative Finance Use R to optimize your trading strategy and build up your own risk management system

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Product type Paperback
Published in Mar 2015
Publisher
ISBN-13 9781783552078
Length 362 pages
Edition 1st Edition
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Toc

Table of Contents (15) Chapters Close

Preface 1. Time Series Analysis 2. Factor Models FREE CHAPTER 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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