Abstract
Purpose This paper aims to analyse the volatility of the fixed income market from 11 countries (Brazil, Russia, India, China, South Africa, Argentina, Chile, Mexico, USA, Germany and Japan) from January 2000 to December 2011 by examining the interbank interest rates from each market. Design/methodology/approach To the volatility of interest rates returns, the study used models of auto-regressive conditional heteroscedasticity, autoregressive conditional heteroscedasticity (ARCH), generalized autoregressive conditional heteroscedasticity (GARCH), exponential generalized autoregressive conditional heteroscedasticity (EGARCH), threshold generalized autoregressive conditional heteroscedasticity (TGARCH) and periodic generalized autoregressive conditional heteroscedasticity (PGARCH), and a combination of these with autoregressive integrated moving average (ARIMA) models, checking which of these processes were more efficient in capturing volatility of interest rates of each of the sample countries. Findings The results suggest that for most markets, studied volatility is best modelled by asymmetric GARCH processes – in this case the EGARCH – demonstrating that bad news leads to a higher increase in the volatility of these markets than good news. In addition, the causes of increased volatility seem to be more associated with events occurring internally in each country, as changes in macroeconomic policies, than the overall external events. Originality/value It is expected that this study has contributed to a better understanding of the volatility of interest rates and the main factors affecting this market.
Highlights
The main goal of most macroeconomic policies is the continuous growth of the gross domestic product in conjunction with low inflation, so the maintenance of price stability has a significant role in the economic growth rate (Nguyen, 2015)
To select the most efficient autoregressive conditional heteroscedasticity (ARCH)-generalized autoregressive conditional heteroscedasticity (GARCH) models, the following criteria were used: heteroscedasticity correction presented by autoregressive integrated moving average (ARIMA) models (p, d, q) by means of the ARCH – Lagrange multiplier (ARCH-LM) test; the majority of the parameters must be significant at the 1, 5 and 10 per cent levels; and exhibiting low information criteria values (BIC, Hanna–Quinn information criterion (HQ) and Akaike information criterion (AIC)) and standard errors
Of nine series tested, seven exhibited better results when estimated by exponential generalized autoregressive conditional heteroscedasticity (EGARCH) models, and the other
Summary
Published in Journal of Economics, Finance and Administrative Science. Journal of Economics, Finance and Administrative Science Vol 22 No 42, 2017 pp. Propósito – Este estudio analiza la volatilidad del mercado de renta fija de once países (Brasil, Rusia, India, China, Sudáfrica, Argentina, Chile, México, Estados Unidos, Alemania y Japón) de enero de 2000 a diciembre de 2011, mediante el examen de las tasas de interés interbancarias de cada mercado. Diseño/metodología/enfoque – Para la volatilidad de los retornos de las tasas de interés, se utilizaron modelos de heteroscedasticidad condicional autorregresiva: ARCH, GARCH, EGARCH, TGARCH y PGARCH, y una combinación de estos con modelos ARIMA, comprobando cuáles de los procesos eran más eficientes para capturar la volatilidad de interés de cada uno de los países de la muestra. Palabras clave – Ingreso fijo, Volatilidad, Países emergentes, Modelos ARCH-GARCH Tipo de artículo – Artículo de investigación
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