scholarly journals Modeling the Exchange Rate Volatility Using the BRICS GARCH-type Models

2021 ◽  
Vol 12 (5) ◽  
pp. 166
Author(s):  
Lebotsa Daniel Metsileng ◽  
Ntebogang Dinah Moroke ◽  
Johannes Tshepiso Tsoku
Keyword(s):  
Financial Markets ◽  
Exchange Rates ◽  
Exchange Rate Volatility ◽  
Garch Models ◽  
Time Series Models ◽  
Univariate Time Series ◽  
Student T Distribution ◽  
Different Types ◽  
Exchange Rates Volatility ◽  
T Distribution

The paper models the performance of GARCH-type models on BRICS exchange rates volatility. The levels of interdependence and dynamic connection among the BRICS financial markets using appropriate univariate time series models were evaluated for the period January 2008 to January 2018. The results revealed the presence of ARCH effects in the BRICS exchange rates. The univariate GARCH models for the BRICS exchange rates were fitted to the data using Student t-distribution. The GARCH (1,1) model found the unconditional volatility for each of the BRICS exchange rates series while EGARCH (1,1) and TGARCH (1,1) models presented the leverage effect. Moreover, the EGARCH (1,1) model illustrated that the asymmetric effects dominate the symmetric effects except for South Africa. The TGARCH (1,1) model on the other hand revealed contrary findings. The paper recommends a study be considered to draw comparison on the different types of GARCH models on the time varying integrated data other than the ones used in the paper.

A hybrid approach to exchange rates

2016 ◽  
Vol 33 (1) ◽  
pp. 50-68 ◽  
Author(s):  
Guangfeng Zhang ◽  
Ian Marsh ◽  
Ronald MacDonald
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Real Time ◽  
Private Information ◽  
Hybrid Approach ◽  
Exchange Rate Volatility ◽  
Content Type ◽  
Macro News ◽  
The Impact ◽  
Exchange Rates Volatility

Purpose – This study aims to investigate the impact of information, both public macro news and private information, on exchange rate volatility in an integrated framework. Design/methodology/approach – The authors apply real-time data of macro announcements and high-frequency trading data (German Deutsche Mark to US dollar, DEM/USD, from 1 May to 31August 1996) to GARCH models and examine various model specifications. Findings – Data analysis demonstrates real-time macro news and market makers’ private information both have a significant impact on exchange rate volatility, but there is no interaction between macro and micro information in the information transmission process. Originality/value – This study contributes to empirical hybrid studies of examining exchange rates volatility, which is in line with literature that combine both macro and micro fundamentals in examining exchange rates variation. Particularly, a key element of this study is to use a microstructure fundamental variable, namely, order flow, to capture private information in an exchange rate volatility study.

scholarly journals Forecasting of the Nigeria Stock Returns Volatility Using GARCH Models with Structural Breaks

2020 ◽  
pp. 39-50
Author(s):  
Yakubu Musa ◽  
Ibrahim Adamu ◽  
Nasiru Sani Dauran
Keyword(s):  
Stock Returns ◽  
Structural Breaks ◽  
Structural Changes ◽  
Mean Reversion ◽  
Garch Models ◽  
Reversion Rate ◽  
Continuous Increase ◽  
Dummy Variable ◽  
Student T Distribution ◽  
T Distribution

This study examines the stock returns series using Symmetric and Asymmetric GARCH models with structural breaks in the presence of some varying distribution assumptions. Volatility models of Symmetric GARCH (1,1), Asymmetric Power GARCH (1,1) and GJR-GARCH(1,1) models were considered in estimating and measuring shock persistence,  leverage effects and mean reversion rate with structural breaks considering dummy variable  for these structural changes and varying distributions . The skewed student-t distribution is considered best distribution for the models; moreover findings showed the high persistence of shock in returns series for the estimated models. However, when structural breaks were incorporated in the estimated models by including dummy variable in the conditional variance equations of all the models, there was significant reduction of shock persistence parameter and mean reversion rate.  The study found the GJR-GARCH (1,1) with skewed student-t distribution best fit the series. The volatility was forecasted for 12 months period using GJR-GARCH (1,1) model and the values are compared with the actual values and the results indicates a continuous increase in unconditional variance.

scholarly journals Interest Rate Policy and Exchange Rates Volatility Lessons from Indonesia

2020 ◽  
Vol 9 (2) ◽  
pp. 19-42
Author(s):  
Haryo Kuncoro
Keyword(s):  
Exchange Rate ◽  
Interest Rate ◽  
Exchange Rates ◽  
Inflation Targeting ◽  
Exchange Rate Regime ◽  
Exchange Rate Volatility ◽  
Flexible Exchange Rate ◽  
Interest Rate Policy ◽  
Rate Policy ◽  
Exchange Rates Volatility

AbstractWhether or not inflation targeting adoption leads to increased volatility of exchange rates is controversial. The volatility increases with inflation targeting as a result of the flexible exchange rate regime. Others argue that inflation targeting delivers the best outcomes in terms of lower exchange rate volatility. The purpose of this paper is to investigate whether interest rate policy in inflation targeting frameworks – that is subjected to control inflation rate – may reduce the volatility of exchange rates. To test the hypothesis, we use monthly data in the case of Indonesia over the period 2005(7)-2016(7). Several control variables are introduced in the regressions. The result of the autoregressive distributed lag model proves the interest rate policy and foreign exchange intervention fail to reduce the exchange rates volatility. It seems inflation targeting in Indonesia puts too much emphasis on stabilizing the domestic currency thus leading to benign neglect of stabilizing its external value, ultimately resulting in increased exchange rate volatility. These findings suggest that central bank credibility plays an important role in conducting inflation targeting policy which operates primarily through a signalling effect.

scholarly journals THE RESPONSE OF ZIMBABWE TOBACCO EXPORTS TO REAL EXCHANGE RATES VOLATILITY

2020 ◽  
Vol 56 (2) ◽  
Author(s):  
Knowledge Mutodi ◽  
Tinashe Chuchu ◽  
Eugine Tafadzwa Maziriri
Keyword(s):  
Exchange Rate ◽  
Exchange Rates ◽  
Real Exchange Rate ◽  
Real Exchange Rates ◽  
Exchange Rate Volatility ◽  
Response Model ◽  
The Real ◽  
Long Run ◽  
Exchange Rates Volatility ◽  
Real Exchange Rate Volatility

The focus of this study was on investigating the response of tobacco exports to real exchange rates and real exchange rate volatility and other factors in Zimbabwe using secondary data spanning from 1980 to 2019. Bilateral nominal exchange rates and time-variant weights of Zimbabwe’s 10 major trading partners were calculated and used to compute the real exchange rate index. The time-dependent weighting system was used to better represent the evolution of trade patterns in the index. The arithmetic method was employed for computing the index. Generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional heteroscedasticity (ARCH) models were used to generate the real exchange rate volatility index. The export response function was adopted as the tobacco exports response model. The variables in the tobacco exports response model were the realworld Gross Domestic Product (GDP), real exchange rate, terms of trade, real exchange rate volatility and dollarization. A vector error correction model (VECM) was used to estimate the response of tobacco exports to real exchange rate, real exchange rate volatility and other factors. The VECM results indicated that real world GDP was insignificant in both the short and long run. In the long run, the real exchange rate appreciation had a negative impact on tobacco exports. Conversely, in the short run, the depreciation of real exchange rate had a positive impact on tobacco exports. Hence, the government has to adopt other mechanisms that reduce uncertain movements of exchange rates.

scholarly journals MEASURING VALUE AT RISK USING GARCH MODEL - EVIDENCE FROM THE CRYPTOCURRENCY MARKET

2021 ◽  
Vol 9 (2) ◽  
pp. 63-84
Author(s):  
Cosmos Obeng
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Garch Models ◽  
Information Communication ◽  
Managerial Decision ◽  
Fund Managers ◽  
Daily Data ◽  
Student T Distribution ◽  
Data Frequency ◽  
T Distribution

There is a growing interest in the activities of the crypto market by various stakeholders. These stakeholders generally include investors, entrepreneurs, governments, fund managers, climate activists, institutional managers, employees with surplus funds, and crypto miners. This study aims to investigate the accuracy of the GARCH models for measuring and estimating Value-at-risk (VaR) using the Cryptocurrency index for future investment and managerial decision making. Because of this, the present study uses the top 30 Cryptocurrencies index in terms of Market capitalization excluding stable coins to determine the best GARCH models. Many entrepreneurs, institutional managers, fund managers, and other stakeholders have recently included cryptocurrency in their investment portfolio because of the increase in transactions and high returns growth in the global financial market with its associated high returns and volatility. Information communication technology has paved the way for such activities in the global markets. The daily data frequency was applied because of the availability of the data. The empirical analysis has been carried out for the period from January 2017 to December 2020 for a total of 1461observation. The returns volatility is estimated using SGARCH and EGARCH models. The findings evidenced that, using both normal distribution and Student t distribution, EGARCH provides a better measure and estimate than SGARCH concerning high persistence and volatility. Against this background, the present study also examined Backtesting to estimate Value at Risk. Interestingly, the findings of the available study would provide industry players, practitioners, entrepreneurs, and investors the maximum edge on how to use or measure such variables against others to make investment decisions. Also, the findings would subsequently contribute more insight into academia on the study area.

Exchange rate volatility in West African countries: is there a shred of Spillover?

2018 ◽  
Vol 13 (6) ◽  
pp. 1457-1474 ◽  
Author(s):  
Kalu Onwukwe Emenike
Keyword(s):  
Exchange Rate ◽  
West Africa ◽  
Exchange Rates ◽  
Exchange Rate Volatility ◽  
West African ◽  
Volatility Spillover ◽  
African Countries ◽  
Content Type ◽  
Exchange Rates Volatility ◽  
Cfa Franc

Purpose The purpose of this paper is to evaluate selected West African currencies/US dollar exchange rates for the evidence of volatility spillover. Specifically, the paper examines West African CFA franc, Gambian dalasi and Nigerian naira exchange rates in relation to the USD, for any evidence of shock and volatility spillover. Design/methodology/approach The author employs multivariate GARCH (1,1)–BEKK model which enables the evaluation of the interaction within the volatility of two or more series because of its capability to detect volatility spillover among time series observations, as well as the persistence of volatility within each series. Findings The major findings of this study are as follows: there is evidence of volatility clustering in West African CFA franc, Gambian dalasi and Nigerian naira exchange rates in relation to the USD. There is evidence of bi-directional shock and volatility spillover between the Nigerian naira and West African CFA franc/USD exchange rates, and uni-directional shock spillover from the Gambian dalasi to the West African CFA franc/USD exchange rates. There is, however, no evidence of exchange rate shock and volatility spillover between Nigerian naira and Gambian dalasi. Originality/value Although considerable literature exists on the volatility of exchange rate in West Africa and comparative analysis of exchange rates volatility in few countries of West Africa, there is absence of empirical studies on exchange rate volatility spillover among countries in the region. Since containing exchange rate volatility is one of the major objectives of monetary policy, understanding the nature and direction of exchange rate volatility spillover would propel formulation exchange rate policies that would minimise exchange rate uncertainty and entrench sustainable development. In addition, the nature of exchange rate volatility spillover between West African countries would provide basis for international traders and foreign portfolio investors to develop effective strategies for hedging against exchange rate shocks that are propagated across countries by designing appropriate risk management techniques.

scholarly journals Does Incomplete Spanning in International Financial Markets Help to Explain Exchange Rates?

2019 ◽  
Vol 109 (6) ◽  
pp. 2208-2244 ◽  
Author(s):  
Hanno Lustig ◽  
Adrien Verdelhan
Keyword(s):  
Exchange Rate ◽  
Financial Markets ◽  
Exchange Rates ◽  
Exchange Rate Volatility ◽  
Risk Premia ◽  
International Financial Markets ◽  
The Exchange Rate ◽  
Free Environment ◽  
Exchange Rate Predictability

We assume that domestic (foreign) agents, when investing abroad, can only trade in the foreign (domestic) risk-free rates. In a preference-free environment, we derive the exchange rate volatility and risk premia in any such incomplete spanning model, as well as a measure of exchange rate cyclicality. We find that incomplete spanning lowers the volatility of exchange rate, increases the risk premia but only by creating exchange rate predictability, and does not affect the exchange rate cyclicality. (JEL E32, F31, F44, G15)

Application of Artificial Neural Network (Ann) in Modeling Foreign Currency Exchange Rates

2018 ◽  
Vol 6 (10) ◽  
Author(s):  
George Kiplagat Kipruto ◽  
Dr. Joseph Kyalo Mung’atu ◽  
Prof. George Otieno Orwa ◽  
Nancy Wairimu Gathimba
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exchange Rate ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Performance Metrics ◽  
Exchange Rate Volatility ◽  
Foreign Currencies ◽  
Exchange Rates Volatility

Investors, Policy makers, Governments etc. are all consumers of exchange rates data and thus exchange rate volatility is of great interest to them. Modeling foreign exchange (FOREX) rates is one of the most challenging research areas in modern time series prediction. Neural Network (NNs) are an alternative powerful data modeling  tool that has ability to capture and represent complex input/output relationships. This study describes application of neural networks in modeling of the Kenyan currency (KES) exchange rates volatility against four foreign currencies namely; USA dollar (USD), European currency (EUR), Great Britain Pound (GBP) and Japanese Yen (JPY) foreign currencies. The general objective of the proposed study is to model the Kenyan exchange rate volatility and confirm applicability of neural network model in the forecasting of foreign exchange rates volatility. In our case the Multilayer Perceptron (MLP) neural networks with back-propagation learning algorithm will be employed. The specific objectives of the study is to build the neural network for the Kenyan exchange rate volatility and examine the properties of the network, finally to forecast the volatility against four other major currencies. The proposed study will use secondary data of the mean daily exchange rates between the major currencies quoted against the Kenyan shilling. The data will be acquired from the central bank of Kenya's (CBK) website collected for ten years of trading period between the years 2005 to 2017. The data will be analyzed using both descriptive and inferential statistics, with the aid of R's neuralnet package. A number of performance metrics will be employed to evaluate the model. Conclusion and recommendations will be made at the end of the study.

A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

2020 ◽  
Vol 5 (1) ◽  
pp. 374
Author(s):  
Pauline Jin Wee Mah ◽  
Nur Nadhirah Nanyan
Keyword(s):  
Time Series ◽  
Palm Oil ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Peninsular Malaysia ◽  
Time Series Models ◽  
Crude Palm Oil ◽  
Univariate Time Series ◽  
Import And Export ◽  
Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA  while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA  appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA  emerged the best model based on the RMSE value.  When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

((((((, , ) (Forecasting of Global Prices in Financial Markets (Commodities, Share Prices, Exchange Rates))

2015 ◽  
Author(s):  
Yakov Mirkin ◽  
Tatyana Zhukova ◽  
Karina Bakhtaraeva ◽  
Anna Levchenko
Keyword(s):  
Financial Markets ◽  
Exchange Rates ◽  
Share Prices
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