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Robust term structure estimation in developed and emerging markets

Title	Robust term structure estimation in developed and emerging markets
Author	Ahi, Emrah, Akgiray, V., Şener, Emrah
Publication Date:	2018-01
Publication Place	- Springer Nature
Subject	Term structure, Nelson–Siegel–Svensson, Particle swarm optimization, Robust estimation
Type	Periodical
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	0254-5330
Record ID	342b0932-1c6a-4f1a-8cdd-f0995b1b8a0e
Library Location	Business Administration
Date	2018-01
Sample Text	Despite powerful advances in interest rate curve modeling for data-rich countries in the last 30 years, comparatively little attention has been paid to the key practical problem of estimation of the term structure of interest rates for emerging markets. This may be partly due to limited data availability. However, emerging bond markets are becoming increasingly important and liquid. It is, therefore, important to be understand whether conclusions drawn from developed countries carry over to emerging markets. We estimate model parameters of fully flexible Nelson–Siegel–Svensson term structures model which has become one of the most popular term structure model among academics, practitioners, and central bankers. We investigate four sets of bond data: U.S. Treasuries, and three major emerging market government bond data-sets (Brazil, Mexico and Turkey). By including both the very dense U.S. data and the comparatively sparse emerging market data, we ensure that are results are not specific to a particular data-set. We find that gradient and direct search methods perform poorly in estimating term structures of interest rates, while global optimization methods, particularly the hybrid particle swarm optimization introduced in this paper, do well. Our results are consistent across four countries, both in- and out-of-sample, and for perturbations in prices and starting values. For academics and practitioners interested in optimization methods, this study provides clear evidence of the practical importance of choice of optimization method and validates a method that works well for the NSS model.
DOI	10.1007/s10479-016-2282-5
Cilt	260

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Özyeğin University

Robust term structure estimation in developed and emerging markets

Author Ahi, Emrah, Akgiray, V., Şener, Emrah

Publication Date 2018-01

Publication Place - Springer Nature

Subject Term structure, Nelson–Siegel–Svensson, Particle swarm optimization, Robust estimation

Type Periodical

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 0254-5330

Record ID 342b0932-1c6a-4f1a-8cdd-f0995b1b8a0e

Library Location Business Administration

Date 2018-01

Sample Text Despite powerful advances in interest rate curve modeling for data-rich countries in the last 30 years, comparatively little attention has been paid to the key practical problem of estimation of the term structure of interest rates for emerging markets. This may be partly due to limited data availability. However, emerging bond markets are becoming increasingly important and liquid. It is, therefore, important to be understand whether conclusions drawn from developed countries carry over to emerging markets. We estimate model parameters of fully flexible Nelson–Siegel–Svensson term structures model which has become one of the most popular term structure model among academics, practitioners, and central bankers. We investigate four sets of bond data: U.S. Treasuries, and three major emerging market government bond data-sets (Brazil, Mexico and Turkey). By including both the very dense U.S. data and the comparatively sparse emerging market data, we ensure that are results are not specific to a particular data-set. We find that gradient and direct search methods perform poorly in estimating term structures of interest rates, while global optimization methods, particularly the hybrid particle swarm optimization introduced in this paper, do well. Our results are consistent across four countries, both in- and out-of-sample, and for perturbations in prices and starting values. For academics and practitioners interested in optimization methods, this study provides clear evidence of the practical importance of choice of optimization method and validates a method that works well for the NSS model.

DOI 10.1007/s10479-016-2282-5

Cilt 260