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

scholarly journals The Effect of Exchange Rate Volatility on International Trade and Foreign Direct Investment (FDI) in Developing Countries along “One Belt and One Road”

2018 ◽  
Vol 6 (4) ◽  
pp. 86 ◽  
Author(s):  
Rashid Latief ◽  
Lin Lefen
Keyword(s):  
Developing Countries ◽  
Foreign Direct Investment ◽  
International Trade ◽  
Exchange Rate ◽  
Direct Investment ◽  
Exchange Rate Volatility ◽  
Policy Coordination ◽  
Chinese Government ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

The “One Belt and One Road” (OBOR) project was started by the Chinese government with the aim of achieving sustainable economic development and increasing cooperation with other countries. This project has five major objectives, which include (i) increasing trade flow, (ii) encouraging policy coordination, (iii) improving connectivity, (iv) obtaining financial integration, and (v) fortifying closeness between people. This paper aims to analyze the effect of exchange rate volatility on international trade and foreign direct investment (FDI) in developing countries along “One Belt and One Road”. We selected seven developing countries which are part of this project, namely Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka. We collected panel data for the period 1995 to 2016 from the U.S. Heritage Foundation, International Financial Statistics (IFS) (a database developed by the International Monetary Fund), and World Development Indicators (WDI) (a database developed by the World Bank). We applied Generalized Autoregressive Conditional Heteroscedasticity (GARCH) (1,1) and threshold-Generalized Autoregressive Conditional Heteroscedasticity (TGARCH) (1,1) models to measure the exchange rate volatility. Furthermore, we employed a fixed effect model to analyze the relationship of exchange rate volatility with international trade and FDI. The results of this paper revealed that exchange rate volatility affects both international trade and FDI significantly but negatively in OBOR-related countries, which correlates with the economic theory arguing that exchange rate volatility may hurt international trade and FDI. It can be concluded that exchange rate volatility can adversely affect international trade and FDI inflows in OBOR-related countries.

scholarly journals PENGARUH VOLATILITAS NILAI TUKAR TERHADAP VOLUME EKSPOR BEBERAPA KELOMPOK KOMODITI PERDAGANGAN INDONESIA

2008 ◽  
Vol 8 (2) ◽  
pp. 147-173
Author(s):  
Arindra A. Zainal
Keyword(s):  
Exchange Rate ◽  
Export Performance ◽  
Negative Relationship ◽  
Exchange Rate Volatility ◽  
Cointegration Test ◽  
Bretton Wood System ◽  
Conditional Heteroscedasticity ◽  
Long Run ◽  
The Relationship ◽  
Autoregressive Conditional Heteroscedasticity

The relationship between exchange rate volatility and export performance has been scrutinized by many economists since Bretton Wood System collapsed in 1971. Although most of the results show that there is a negative relationship between exchange rate volatility and export performance, we also find that some studies show a positive one. This study used some Indonesian group of commodities data to find the relationship between exchange rate volatility and export performance.While General Autoregressive Conditional Heteroscedasticity (GARCH) was used to calculate exchange rate volatility, this study used Pesharan & Shin ARDL cointegration test in order to find long run relationship between export performance and exchange rate volatility. Only 2 out of 7 equations tested show a long run relationship between exchange rate volatility an export performance and the signs are positive.

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, Uncertainty and Foreign Direct Investment Inflow in West African Monetary Zone

2018 ◽  
Vol 20 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Perekunah B. Eregha
Keyword(s):  
Foreign Direct Investment ◽  
Exchange Rate ◽  
Direct Investment ◽  
Exchange Rate Volatility ◽  
Commodity Prices ◽  
Fixed Effect Model ◽  
West African ◽  
Effect Model ◽  
Exchange Rate Movements ◽  
Global Shocks

Exchange-rate movements are mostly unpredictable, and this tends to affect both trade and foreign investment flows. This is because foreign investors are unclear on the returns to investment decisions in such cases. Hence, this study examines the effect of exchange rate, its volatility and uncertainty on foreign direct investment (FDI) inflow in West African monetary zone (WAMZ). The study covers the period1980–2014, and the within estimator for the fixed effect model is employed. The study accounts for both exchange rate volatility and uncertainty measures which are anticipated and unanticipated exchange rate innovations measures, respectively. The results show that exchange-rate movements in WAMZ countries are more of unanticipated than anticipated innovations in affecting FDI inflow. Therefore, policies aimed at targeting exchange-rate stability are essential in the WAMZ countries since investors are profit maximizers; hence, investment uncertainties must be kept as low as possible. Also since WAMZ export sectors are primary products based, policies should be geared towards the diversification of the export sectors to combat unanticipated global shocks from commodity prices movement in having an effect on the exchange rate through the foreign exchange reserve channel.

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 ARCH GARCH METHOD OF FORECASTING CONSUMER PRICE INDEX (CPI) IN SEMARANG

2020 ◽  
Vol 1 (1) ◽  
pp. 14-22
Author(s):  
Sri Kustiara ◽  
Indah Manfaati Nur ◽  
Tiani Wahyu Utami
Keyword(s):  
Price Index ◽  
Consumer Price Index ◽  
Garch Model ◽  
Conditional Heteroscedasticity ◽  
Autoregressive Conditional Heteroscedasticity

Indeks Harga Konsumen (IHK) merupakan salah satu indikator ekonomi penting yang dapat memberikan informasi mengenai perkembangan harga barang/jasa yang dibayar oleh konsumen di suatu wilayah. Penghitungan IHK ditujukan untuk mengetahui perubahan harga dari sekelompok tetap barang atau jasa yang umumnya dikonsumsi oleh masyarakat setempat. Dalam metode yang digunakan dalam pemodelan data runtun waktu memiliki syarat khusus yaitu yang  teridentifikasi efek heteroskedastisitas. Tujuan dari penelitian ini adalah untuk mengetahui model terbaik peramalan periode berikutnya serta hasil prediksi periode mendatang. Variabel yang digunakan adalah data Indeks Harga Konsumen dalam bulan. Sehingga untuk mengatasi permasalahan pada data penelitian ini digunakan metode Autoregressive Conditional Heteroscedasticity Generalized Autoregressive Conditional Heteroscedasticity (ARCH GARCH). Hasil dari penelitian ini didapatkan metode ARCH GARCH model terbaik yang digunakan adalah ARIMA (1,1,1)~GARCH (1,0). Dengan prediksi dari volatilitas dengan nilai standar deviasi 0.98283514 diperoleh prediksi volatilitas terendah sebesar 0.9632546 dan prediksi volatilitas tertinggi sebesar 0.9980155.

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 ARCH and GARCH Models: Quasi-Likelihood and Asymptotic Quasi-Likelihood Approaches

2020 ◽  
Author(s):  
Raed Alzghool
Keyword(s):  
Exchange Rate ◽  
Kernel Estimation ◽  
Estimation Procedure ◽  
Conditional Variance ◽  
Estimation Methods ◽  
Garch Models ◽  
Unknown Parameters ◽  
Conditional Heteroscedasticity ◽  
Garch Processes ◽  
Autoregressive Conditional Heteroscedasticity

This chapter considers estimation of autoregressive conditional heteroscedasticity (ARCH) and the generalized autoregressive conditional heteroscedasticity (GARCH) models using quasi-likelihood (QL) and asymptotic quasi-likelihood (AQL) approaches. The QL and AQL estimation methods for the estimation of unknown parameters in ARCH and GARCH models are developed. Distribution assumptions are not required of ARCH and GARCH processes by QL method. Nevertheless, the QL technique assumes knowing the first two moments of the process. However, the AQL estimation procedure is suggested when the conditional variance of process is unknown. The AQL estimation substitutes the variance and covariance by kernel estimation in QL. Reports of simulation outcomes, numerical cases, and applications of the methods to daily exchange rate series and weekly prices’ changes of crude oil are presented.

scholarly journals The Relationship between Stock Returns and Trading Hours: Evidence from Amman Stock Exchange

2018 ◽  
Vol 7 (3.21) ◽  
pp. 89
Author(s):  
Buthiena Kharabsheh ◽  
Mahera Hani Megdadi ◽  
Waheeb Abu-ulbeh
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Garch Model ◽  
Conditional Heteroscedasticity ◽  
Amman Stock Exchange ◽  
The Relationship ◽  
Autoregressive Conditional Heteroscedasticity

This study investigates the relationship between stock returns and trading hours for 22 shares listed on Amman Stock Exchange (ASE). We analyze the hourly trading data for the period Dec.2005 to Dec.2006. The two trading hours in ASE were split into four periods; first half of the first hour (10:00-10:30), second half of the first hour (10:30-11:00), first half of the second hour (11:00-11:30), and second half of the second hour (11:30-12:00). Using the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, our results reveal that the hourly trading time significantly affects stock returns.  

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