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 The The Impact of the Exchange Rate Volatility on Stock Markets Dynamics in Tunisia and Turkey: An Artificial Neural Network Analysis

2021 ◽  
pp. 1-21
Author(s):  
Nesrine Mechri ◽  
Christian De Peretti ◽  
Salah BEN HAMAD
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Stock Market ◽  
Multiple Regression ◽  
Stock Markets ◽  
Exchange Rate Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
The Impact

The present research provides an overview of links between exchange rate volatility and the dynamics of stock market returns in order to identify the influence of several macroeconomic variables on the volatility of stock markets, useful for political decision makers as well as investors to better control the portfolio risk level. More precisely, this research aims to identify the impact of exchange rate volatility on the fluctuations of stock market returns, considering two countries that belong to the Middle East and North Africa (MENA) zone: Tunisia and Turkey. Previous works in the literature used very specified and short periods of study, many important variables were neglected, and most of the earlier research was concentrated on the developed countries. In this research, we integrate several control variables of stock market returns that have not been simultaneously studied before. In addition, we spread out our research period up to 15 years including many events and dynamics. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and multiple regression models are first employed. Then, an Artificial Neural Network (ANN) is used and compared with the results of the multiple regression. Hence, the results show that for both Tunisia and Turkey, exchange rate volatility has a significant effect on stock market fluctuations.

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.

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.

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