Dependence Structure Between Renminbi Movements and Volatility of Foreign Exchange Rate Returns

China Report ◽  
2021 ◽  
Vol 57 (1) ◽  
pp. 57-78
Author(s):  
Wing-Choong Lai ◽  
Kim-Leng Goh

This article investigates the linkages of the movements in Renminbi (RMB) to volatility of exchange rate returns of other currencies before and after the yuan devaluation on 11 August 2015. A comparison between the onshore Chinese yuan (CNY) and the offshore Chinese yuan (CNH) is made. Standard regression methods underestimate the tail dependence between yuan and other exchange rate volatility, as financial data are non-normally distributed, especially when extreme event occurs. We apply Gumbel copulas to capture the presence of tail dependence between RMB returns and the volatility of exchange rate returns for 13 selected currencies, and found dependencies not revealed by the standard ARCH models. The tail dependence has increased after the RMB devaluation, suggesting that RMB depreciation is associated with higher downside risks in these currencies. This is most obvious in the currencies of Asian and ASEAN-5 countries that have strong trade and financial linkages with China. The dependence structure has shifted away from the dominance of onshore CNY rates before the devaluation to the growing importance of more volatile offshore CNH rates after the devaluation. Hence, any large depreciation in CNH will lead to a higher volatility in the other exchange rate returns, and the corresponding downside currency risks are higher than those of the CNY.

2019 ◽  
Vol 22 (01) ◽  
pp. 1950005
Author(s):  
Wing-Choong Lai ◽  
Kim-Leng Goh

This paper investigates the linkages of Chinese yuan to other currencies before and after the yuan devaluation on 11 August 2015. Linear regression analysis shows that only a few of the 14 currencies considered are significantly affected by the devaluation. However, the devaluation of Chinese yuan has been associated with larger fluctuations in these currencies and the occurrence of extreme positive and negative returns. The regression method may under estimate the tail dependence between currencies, as financial data are usually non-normally distributed, especially when extreme event occurs. We apply the Archimedean copulas to capture the presence of lower and upper tail dependence between the exchange rate returns of Chinese yuan and the selected currencies, and found dependencies not revealed by the linear regression analysis. The extreme returns after the Chinese yuan devaluation have resulted in higher dependence with the selected currencies. While the dependence structure was dominated by risks due to unusual currency gains before the devaluation, the market responses to large losses and gains have become more symmetric after the devaluation.


2019 ◽  
Vol 11 (19) ◽  
pp. 5487 ◽  
Author(s):  
Liu ◽  
Wang ◽  
Sriboonchitta

Based on the canonical vine (C-vine) copula approach, this paper examines the interdependence between the exchange rates of the Chinese Yuan (CNY) and the currencies of major Association of Southeast Asian Nations (ASEAN) countries. The differences in the dependence structure and degree between currencies before and after the Belt and Road (B&R) Initiative were compared in order to investigate the changing role of the Renminbi (RMB) in the ASEAN foreign exchange markets. The results indicate a positive dependence between the exchange rate returns of CNY and the currencies of ASEAN countries and show the rising power of RMB in the regional currency markets after the B&R Initiative was launched. Besides this, the Malaysian Ringgit proved to be most relevant to the other ASEAN currencies, thus playing an important role in the stability of regional financial markets. Moreover, evidence of tail dependence was found in the returns of three currency pairs after the B&R Initiative, which implies the presence of asymmetric dependence between exchange rates. The results from time-varying C-vine copulas further confirmed the robustness of the results from the static C-vine copulas.


2009 ◽  
Vol 12 (01) ◽  
pp. 141-158 ◽  
Author(s):  
Yongjian E ◽  
Anthony Yanxiang Gu ◽  
Chau-Chen Yang

The exchange-rate behavior of the Chinese yuan (RMB) and the Malaysian ringgit (MYR) indicates that the real exchange rate volatility of both the pegged currency/the anchor currency (the US dollar), and the pegged currency/the non-anchor currencies (Japanese yen and British pound) are lower under the pegged regime. The dynamic behavior of the pegged currencies' real exchange rates is consistent with the anchor currency as the speed of convergence of the Big Mac real exchange rates of the RMB, MYR, and the dollar against the floating currencies are almost identical during the pegged period. This may be due to similar inflation rate movements in the related economies. These results do not support the opinion that China has manipulated the value of its currency.


1999 ◽  
Vol 6 (11) ◽  
pp. 717-722 ◽  
Author(s):  
Simon Sosvilla-Rivero ◽  
Fernando Fernandez-Rodriguez ◽  
Oscar Bajo-Rubio

Author(s):  
Ajayi Abdulhakeem ◽  
Samuel Olorunfemi Adams ◽  
Rafiu Olayinka Akano

This paper examines the exchange rate volatility with GARCH-type model of the daily exchange rate return series from January 2012 – August 2016 for Naira/Chinese Yuan, Naira/India Rupees, Naira/Spain Euro, Naira/UK Pounds and Naira/US Dollar returns. The studies compare estimates of variants of GARCH (1, 1), EGARCH (1, 1), TGARCH (1,1) and GJR-GARCH (1,1) models. The result from all models indict presence of volatility in the five currencies and equally indicate that most of the asymmetric models rejected the existence of a leverage effect except for models with volatility break. For GARCH (1, 1), GJR-GARCH (1, 1,) EGARCH (1,1) and TGARCH (1, 1), it was observed that India have the best exchange rate with the highest log-likelihood (Log L) and the lowest AIC and BIC followed by USA, China, Spain and United Kingdom respectively. The four models was later compared for the exchange rates of the five countries under consideration i.e. China, India, Spain, UK and USA  to select the best fitted model for each country and it was discovered that GJR-GARCH (1,1) is the best fitted model for all the countries followed by GARCH (1,1), TGARCH (1,1) and EGARCH (1,1) in that order.


2020 ◽  
Vol 7 (1) ◽  
pp. 805-827
Author(s):  
Fatoumata Baboucar Omar Kah ◽  
Abdou Kâ Diongue

The relationship between different international stock markets is of importance for both financial practitioners and academicians in order to manage risks. Especially after the financial crisis, the pronounced financial contagion draws the public attention to look into such associations. However, measuring and modelling dependence structure becomes complicated when asset returns present non-linear, non-Gaussian and dynamic features. This paper examines the time-varying conditional correlations to the weekly exchange rate returns for the USD, EURO and GBP against the Gambian Dalasi (GMD) during the period 2000 to 2017. We use a dynamic conditional correlation (DCC) multivariate GARCH model. This model can be simplified by estimating univariate GARCH models for each return series, and then, using transformed residuals resulting from the first stage, estimating a conditional correlation estimator. DCC-GARCH model was implemented for two different assumptions of the error distribution; assuming Gaussian and Student t-distribution. Empirical results show substantial evidence of significant increase in conditional correlation. It is also clean that, the Student t-distributed errors better forecast the conditional correlation.


2018 ◽  
Vol 7 (4.11) ◽  
pp. 99 ◽  
Author(s):  
Marwan Abdul Hameed Ashour ◽  
Arshad Jamal ◽  
Rabab Alayham Abbas Helmi

This research aims to study and analyze which type of Artificial Neural Network (ANN) is more efficient and suitable in handling non-homogenous variance for financial series. Apart from addressing the behavior and efficiency of ANN, the paper also aims to present an advanced methodology for ANN, as a replacement of GARCH and ARCH models in crisis management decision makers. The application part was applied to the Egyptian exchange market, to study the local currency exchange rate volatility (1/1/2009-4/6/2013) in order to develop a model describing those changes in the exchange rate. The research concludes that the best network type to represent such financial series is the Back Propagation. Moreover, comparing the result with general regression and probabilistic networks rendered the later two inefficient at handling such series. 


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