ARCH Models and an Application on Exchange Rate Volatility: ARCH and GARCH Models

Author(s):  
Lokman Kantar
Author(s):  
Saminu Umar ◽  
Shehu Sidi Abubakar ◽  
Abdulrashid M Salihu ◽  
Zayyanu Umar

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. 


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.


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