scholarly journals FORECASTING EXCHANGE RATE VOLATILITY USING INTEGRATED GARCH MODEL: EVIDENCE FROM GHANA

2018 ◽  
Vol 5 (31) ◽  
pp. 4855-4865
Author(s):  
Zeynep KARAÇOR
Keyword(s):  
Exchange Rate ◽  
Exchange Rate Volatility ◽  
Garch Model ◽  
Model Evidence

scholarly journals Dynamic Relationship between RMB Exchange Rate and Interest Rate Based on VAR-DCC-GARCH Model

2020 ◽  
Vol 214 ◽  
pp. 03018
Author(s):  
Xuhang Zhao
Keyword(s):  
Monetary Policy ◽  
Exchange Rate ◽  
Interest Rate ◽  
Exchange Rate Volatility ◽  
Garch Model ◽  
Nominal Exchange Rate ◽  
Practical Significance ◽  
Causality Test ◽  
Dynamic Relationship ◽  
Interest Rate Volatility

Based on the daily data of Shibor and nominal exchange rate from 2006 to 2019, this paper constructs VAR model and uses Granger causality test and impulse response model to analyze the dynamic relationship between exchange rate and interest rate. Based on the DCC-GARCH model, this paper analyzes the correlation between exchange rate volatility and interest rate volatility, and concludes that there is a weak negative correlation between exchange rate and interest rate. Both exchange rate and monetary policy will have an important impact on China’s economic environment, so it is of great practical significance to study the joint impact of exchange rate and monetary policy.

scholarly journals PENGARUH VOLATILITAS NILAI TUKAR TERHADAP KINERJA EKSPOR UTAMA PERTANIAN INDONESIA

2019 ◽  
Vol 13 (2) ◽  
pp. 163-186
Author(s):  
Eka Dewi Satriana ◽  
Harianto ◽  
Dominicus Savio Priyarsono
Keyword(s):  
Exchange Rate ◽  
Natural Rubber ◽  
Gravity Model ◽  
Palm Oil ◽  
Exchange Rate Volatility ◽  
Garch Model ◽  
Trading Partner ◽  
Easy Access ◽  
Crude Palm Oil ◽  
Agricultural Exports

Abstrak Nilai tukar merupakan salah satu aspek yang memengaruhi daya saing ekspor. Pada tahun 2013 hingga tahun 2015, volatilitas nilai tukar mengalami kenaikan, khususnya pada triwulan akhir tahun 2015 yaitu sebesar 16,90%. Kondisi ekspor utama pertanian Indonesia pada tahun tersebut rata-rata mengalami penurunan. Penelitian ini bertujuan untuk menganalisis pengaruh volatilitas nilai tukar terhadap kinerja ekspor utama pertanian Indonesia ke negara mitra dagang utama dengan menggunakan gravity model. Ekspor utama pertanian yang dianalisis yaitu karet alam, kopi, udang, dan Crude Palm Oil (CPO). Model ARCH-GARCH digunakan untuk mengukur volatilitas nilai tukar. Hasil analisis menunjukkan bahwa volatilitas nilai tukar berpengaruh negatif terhadap ekspor karet alam, kopi, dan udang Indonesia. Artinya, semakin fluktuatif nilai tukar rupiah maka akan menurunkan ekspor karet alam, kopi, dan udang Indonesia ke negara mitra dagang utama. Pengaruh negatif tersebut juga menunjukkan adanya penghindaran risiko yang dilakukan oleh pelaku usaha. Beberapa rekomendasi hasil kajian yang dapat dilakukan Pemerintah Indonesia adalah menjaga stabilitas nilai tukar, kemudahan akses ke lembaga keuangan, penerapan lindung nilai (hedging), kontrak jangka panjang (longterm contracts), dan menjaga pertumbuhan produksi komoditas. Kata Kunci: Volatilitas Nilai Tukar, Ekspor Utama Pertanian, Model ARCH-GARCH   Abstract The exchange rate is one aspect that affects export competitiveness. From 2013 to 2015, exchange rate volatility increased, especially in the final quarter of 2015, which was 16.90%. Indonesia's main agricultural export conditions in the year on average experienced a decline. This paper analyzes the effect of exchange rate volatility on the performance of Indonesia's main agricultural exports to major trading partner countries using the gravity model. The main agricultural exports analyzed were natural rubber, coffee, shrimp, and Crude Palm Oil (CPO). The ARCH-GARCH model is used to measure exchange rate volatility. The analysis shows that exchange rate volatility harms on Indonesia's exports of natural rubber, coffee, and shrimp. This means, the more the rupiah exchange rate fluctuates will reduce Indonesia's natural rubber, coffee and shrimp exports to the main trading partner countries. The negative influence also indicates the existence of risk aversion by business actors. Some recommendations for the Government of Indonesia based on the study findings are maintaining exchange rate stability, easy access to financial institutions, implementing hedging, long-term contracts, and maintaining commodity production growth. Keywords: Exchange Rate Volatility, Main Agricultural Exports, ARCH-GARCH Model JEL Classification: F14, F31, F41, Q17

scholarly journals Modelling Nigeria Naria Exchange Rate against Some Selected Country’s Currencies Volatility: Application of GARCH Model

2019 ◽  
pp. 1-13
Author(s):  
Ajayi Abdulhakeem ◽  
Samuel Olorunfemi Adams ◽  
Rafiu Olayinka Akano
Keyword(s):  
United Kingdom ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Exchange Rate Volatility ◽  
Garch Model ◽  
Leverage Effect ◽  
Type Model ◽  
Log Likelihood ◽  
Us Dollar ◽  
Chinese Yuan

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.

Modeling exchange rate volatility in the gambia using dynamic conditonal correlaton model

2020 ◽  
Vol 7 (1) ◽  
pp. 805-827
Author(s):  
Fatoumata Baboucar Omar Kah ◽  
Abdou Kâ Diongue
Keyword(s):  
Exchange Rate ◽  
Exchange Rate Volatility ◽  
Garch Model ◽  
Multivariate Garch ◽  
Dependence Structure ◽  
Financial Contagion ◽  
Dynamic Features ◽  
Conditional Correlation ◽  
Public Attention ◽  
T Distribution

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.

scholarly journals Exchange rate volatility and global shocks in Russia: an application of GARCH and APARCH models

2016 ◽  
Vol 13 (4) ◽  
pp. 203-211 ◽  
Author(s):  
Adebayo Augustine Kutu ◽  
Harold Ngalawa
Keyword(s):  
Exchange Rate ◽  
Exchange Rate Volatility ◽  
Garch Model ◽  
Jel Classification ◽  
Conditional Heteroscedasticity ◽  
United States Dollar ◽  
Global Shocks ◽  
Student’S T ◽  
T Distribution ◽  
Autoregressive Conditional Heteroscedasticity

This study examines global shocks and the volatility of the Russian rubble/United States dollar exchange rate using the symmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH), and Asymmetric Power Autoregressive Conditional Heteroscedasticity (APARCH) models. The GARCH and APARCH are employed under normal (Normal Gaussian) and non-normal (Student’s t and Generalized Error) distributions. Using monthly exchange rate data covering January 1994 – December 2013, the study finds that the symmetric (GARCH) model has the best fit under the non-normal distribution, which improves the overall estimation for measuring conditional variance. Conversely, the APARCH model does not show asymmetric response in exchange rate volatility and global shocks, resulting in no presence of leverage effect. The GARCH model under the Student’s t distribution produces better fit for estimating exchange rate volatility and global shocks in Russia, compared to the APARCH model. Keywords: exchange rate volatility, global Shocks, GARCH and APARCH models. JEL Classification: F30, F31, P33

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