scholarly journals Exchange Rate Volatility in the Covid-19 Period: An Analysis Using the Markov-Switching ARCH Model

2021 ◽  
Vol 0 (35) ◽  
pp. 205-220
Author(s):  
Havva Koç
Keyword(s):  
Exchange Rate ◽  
Markov Switching ◽  
Exchange Rate Volatility ◽  
Arch Model

Exchange Rate Volatility in Emerging Markets:The Chilean Case

2007 ◽  
pp. 87-100
Author(s):  
Juan R. Castro
Keyword(s):  
Exchange Rate ◽  
Exchange Rate Regime ◽  
External Debt ◽  
Exchange Rate Volatility ◽  
Arch Model ◽  
Macroeconomic Shocks ◽  
Domestic Debt ◽  
Domestic Credit ◽  
The Face ◽  
The Exchange Rate

The document conducts an empirical investigation on the volatility of the Chilean exchange rate regime, using a model of Objective Zones. Through the use of the ARCH model, the document tests the volatility of the exchange rate in the presence of different levels of international reserves and other macroeconomic shocks. The results show that domestic credit, domestic debt and external debt have the greatest impact on the volatility of the variables studied, especially when compared with other fundamental variables. The variance of the exchange rate is heterosedastic but it is not persistent, which implies that the exchange rate is stable, probably when it oscillates between two bands. The volatility of the exchange rate fluctuates to a greater extent in the face of changes in internal and external debt, than with the other variables used.

Exchange rate volatility and non-oil exports in Nigeria: An empirical investigation

2017 ◽  
Vol 5 (2) ◽  
pp. 5
Author(s):  
Sa’ad Babatunde Akanbi ◽  
Halimah Adedayo Alagbe ◽  
Hammed Agboola Yusuf ◽  
Musibau Hammed Oluwaseyi
Keyword(s):  
Exchange Rate ◽  
Export Performance ◽  
Exchange Rate Volatility ◽  
Flexible Exchange Rate ◽  
Arch Model ◽  
Rate System ◽  
Oil Exports ◽  
Oil Export ◽  
Negative Effect ◽  
The Impact

The adoption of a flexible exchange rate system since 1986 in Nigeria has made the country witnessed varying rate of the naira vis-à-vis the U.S dollar. This paper examines exchange rate volatility with ARCH model and its various extensions (GARCH, TGARCH, and EGARCH) using quarterly exchange rate series from 1986-Q1 to 2014-Q4.The impact of exchange rate volatility on non-oil exports was also examined using Error Correction Model (ECM) with two different measures of volatility. The results obtained confirm the existence of exchange rate volatility and also found a significant negative effect on non-oil export performance in Nigeria. Therefore, the Nigerian government should ensure an appropriate policy mix that not only ensures a stable and realistic exchange rate but also conducive atmosphere for production and exportation.

scholarly journals Exchange Rate Volatility Forecasting by Hybrid Neural Network Markov Switching Beta-t-EGARCH

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 207563-207574
Author(s):  
Ruofan Liao ◽  
Woraphon Yamaka ◽  
Songsak Sriboonchitta
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Markov Switching ◽  
Exchange Rate Volatility ◽  
Volatility Forecasting ◽  
Hybrid Neural Network

scholarly journals Is the South African exchange rate volatile? Application of the arch framework

2015 ◽  
Vol 5 (1) ◽  
pp. 110-122
Author(s):  
Thato Julius Mokoma ◽  
Ntebogang Dinah Moroke
Keyword(s):  
South Africa ◽  
Exchange Rate ◽  
Interest Rates ◽  
South African ◽  
Exchange Rate Volatility ◽  
Arch Model ◽  
High Volatility ◽  
Near Future ◽  
Autoregressive Conditional Heteroscedasticity ◽  
Future Exchange

This study applies the autoregressive conditional heteroscedasticity (ARCH) model to forecast exchange rate volatility in South Africa for the period 1990Q1 to 2014Q2. The ARCH (1) and ARCH (2) models were constructed using four variables; namely, exchange rate, gross domestic product, inflation and interest rates. Upon addressing the issue of stationarity, the models were fitted and the ARCH (1) model was found to be fit. This model revealed a high volatility of exchange rate compared to the ARCH (2) model. Prior to forecasting, the selected model was subjected to a battery of diagnostics tests and was found to be stable and well specified. The forecasts from the ARCH (1) model proved that in the near future, exchange rate will not be highly volatile though SA will experience depreciation in its currency.

scholarly journals Identifying structural changes in the exchange rates of South Africa as a regime-switching process

2020 ◽  
Author(s):  
Katleho Makatjane ◽  
Roscoe van Wyk
Keyword(s):  
Machine Learning ◽  
South Africa ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Regime Switching ◽  
Structural Changes ◽  
Markov Switching ◽  
Exchange Rate Volatility ◽  
Learning Approach ◽  
Machine Learning Approach

Exchange rate volatility is said to exemplify the economic health of a country. Exchange rate break points (known as structural breaks) have a momentous impact on the macroeconomy of a country. Nonetheless, this country study makes use of both unsupervised and supervised machine learning algorithms to classify structural changes as regime shifts in real exchange rates in South Africa. Weekly data for the period January 2003–June 2020 are used. To these data we apply both non-linear principal component analysis and Markov-switching generalized autoregressive conditional heteroscedasticity. The former approach is used to reduce the dimensionality of the data using an orthogonal linear transformation by preserving the statistical variance of the data, with the proviso that a new trait is non-linearly independent, and it identifies the number of regime switches that are to be used in the Markov-switching model. The latter is used to partition the variance in each regime by allowing an estimation of multiple break transitions. The transition breakpoints estimates derived from this machine learning approach produce results that are comparable to other methods on similar system sizes. Application of these methods shows that the machine learning approach can also be employed to identify structural changes as a regime-switching process. During times of financial crisis, the growing concern over exchange rate volatility, including its adverse effects on employment and growth, broadens the debates on exchange rate policies. Our results should help the South African monetary policy committee to anticipate when exchange rates will pick up and be prepared for the effects of periods of high exchange rates.

scholarly journals Modeling Fluctuation of the Price of Crude Oil in Nigeria Using ARCH, ARCH-M Models

2020 ◽  
pp. 16-40
Author(s):  
Titus Eli Monday ◽  
Ahmed Abdulkadir
Keyword(s):  
Exchange Rate ◽  
Crude Oil ◽  
Foreign Exchange ◽  
Exchange Rate Volatility ◽  
Price Volatility ◽  
Oil Prices ◽  
Oil Price ◽  
Arch Model ◽  
Oil Price Volatility ◽  
Exchange Rate Movements

As a mono-product economy, where the main export commodity is crude oil, volatility in oil prices has implications for the Nigerian economy and, in particular, exchange rate movements. The latter is particularly important due to the twin dilemma of being an oil exporting and oil-importing country, a situation that emerged in the last decade. The study examined the effects of oil price volatility, demand for foreign exchange, and external reserves on exchange rate volatility in Nigeria using monthly data over the period from May, 1989 to April 2019. Drawing from the works of Atoi [1] Having realized the potentials of an Autoregressive conditional heteroskedasticity (ARCH) model several studies have use it in modeling financial series. However, when using the ARCH model in determining the optimal lag length of variables the processes are very cumbersome. Therefore, often time users encounter problems of over parameterization. Thus, Rydberg (2016) argued that since large lag values are required in ARCH model therefore there is the need for additional parameters. Sequel to that, this research uses the ARCH-M to solve the challenges. The study reaffirms the direct link of demand for foreign exchange and oil price volatility with exchange rate movements and, therefore, recommends that demand for foreign exchange should be closely monitored and exchange rate should move in tandem with the volatility in crude oil prices bearing in mind that Nigeria remains an oil-dependent economy.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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