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Autoren: 
Rossetti, Nara
Nagano, Marcelo Seido
Faria Meirelles, Jorge Luis
Datum: 
2017
Quellenangabe: 
[Journal:] Journal of Economics, Finance and Administrative Science [ISSN:] 2218-0648 [Volume:] 22 [Year:] 2017 [Issue:] 42 [Pages:] 99-128
Zusammenfassung: 
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.
Schlagwörter: 
Volatility
Emerging countries
ARCH-GARCH models
Fixed income
JEL: 
G14
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
https://creativecommons.org/licenses/by/4.0/
Dokumentart: 
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