The Effect of the Arab Spring Revolution on the Yemeni Economy

2019 ◽  
pp. 134-144
Author(s):  
Maroa Nasser Al Katheri
Keyword(s):  
Quality Of Life ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Exchange Rate ◽  
The Public ◽  
The Us ◽  
Us Dollar ◽  
The Arab Spring ◽  
Poverty Levels

The economic and humanitarian conditions of the Yemeni population have been deteriorating. The variable that affects the Yemeni economy is the foreign exchange rate of the Yemeni currency. In 2014, one US Dollar equaled to 240 Yemeni Ryal. In 2018, one US Dollar equals 700 Yemeni Ryal. The massive leap of the value of the Yemeni Ryal against the US Dollar, paired with the stabilization of the public sector occupation salaries, deeply affected the quality of life of the Yemeni citizen. Furthermore, the leap of the Yemeni Ryal value leads to the increase of the merchandise prices as well as the price of public services. The decrease of the value of the Yemeni Ryal against the US Dollar is one variable that is assisting the levels of poverty in Yemen. However, this chapter believes that economic mismanagement and the foreign exchange rate are essential variables that explain the increase of poverty levels in Yemen.

Optimistic and pessimistic economic sentiments and US Dollar exchange rate

2021 ◽  
Author(s):  
Sonia Kumari ◽  
Suresh Kumar Oad Rajput ◽  
Rana Yassir Hussain ◽  
Jahanzeb Marwat ◽  
Haroon Hussain
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Ordinary Least Squares ◽  
Exchange Market ◽  
Dollar Exchange Rate ◽  
The Us ◽  
Short Run ◽  
Us Dollar ◽  
The Exchange Rate

This study investigates the affiliation of various proxies of economic sentiments and the US Dollar exchange rate, mainly focusing on the real effective exchange rate of USD pairing with three other major currencies (USDEUR, USDGBP, and USDCAD). The study has employed Google Trends data of economy optimistic and pessimistic sentiments index and survey-based economy sentiments data on monthly basis from January 2004 to December 2018. The study engaged Ordinary Least Squares (OLS) and Auto-Regressive Distributed Lag (ARDL) estimation techniques to evaluate the short-run and long-run effects of economy-related sentiments and macroeconomic variables on the exchange rate. The results from the study found that Economy Optimistic Sentiments Index (EOSI) and Economy Pessimistic Sentiments Index (EPSI) appreciate and depreciate the US Dollar exchange rate in the short-run, respectively. Our sentiment measures are robust to survey-based Michigan Consumer Sentiment Index (MSCI), Consumer Confidence Index (CCI), and various macroeconomic factors. The MSCI and CCI sentiments show a long-term impact on the foreign exchange market. This study implies that economic sentiments play a vital role in the foreign exchange market and it is essential to consider behavioral aspects when modeling the exchange rate movements.

Hong Kong and Denmark will keep currency pegs

2015 ◽  
Keyword(s):  
Hong Kong ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Medium Term ◽  
Content Type ◽  
The Us ◽  
Exchange Rate Arrangements ◽  
Exchange Markets ◽  
Us Dollar ◽  
Currency Pegs

Subject The longevity and outlook for currency pegs. Significance The abandonment of the Swiss franc's three-year-old peg to the euro on January 15 put into question the longevity of pegged exchange rate arrangements. It also highlights how unusual such arrangements are today. Impacts The SNB will still have to continue to intervene in foreign-exchange markets to stabilise the Swiss franc. The SNB move will not cause Danish authorities to stop pegging the Danish krone to the euro. The near- and medium-term longevity of the Hong Kong dollar peg to the US dollar will not be questioned.

scholarly journals An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System

2019 ◽  
Vol 9 (15) ◽  
pp. 2980 ◽  
Author(s):  
Muhammad Yasir ◽  
Mehr Yahya Durrani ◽  
Sitara Afzal ◽  
Muazzam Maqsood ◽  
Farhan Aadil ◽  
...  
Keyword(s):  
Deep Learning ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Currency ◽  
Foreign Exchange Rate ◽  
Exchange Rate Forecasting ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Us Dollar ◽  
The Exchange Rate

Financial time series analysis is an important research area that can predict various economic indicators such as the foreign currency exchange rate. In this paper, a deep-learning-based model is proposed to forecast the foreign exchange rate. Since the currency market is volatile and susceptible to ongoing social and political events, the proposed model incorporates event sentiments to accurately predict the exchange rate. Moreover, as the currency market is heavily dependent upon highly volatile factors such as gold and crude oil prices, we considered these sensitive factors for exchange rate forecasting. The validity of the model is tested over three currency exchange rates, which are Pak Rupee to US dollar (PKR/USD), British pound sterling to US dollar (GBP/USD), and Hong Kong Dollar to US dollar (HKD/USD). The study also shows the importance of incorporating investor sentiment of local and foreign macro-level events for accurate forecasting of the exchange rate. We processed approximately 5.9 million tweets to extract major events’ sentiment. The results show that this deep-learning-based model is a better predictor of foreign currency exchange rate in comparison with statistical techniques normally employed for prediction. The results present evidence that the exchange rate of all the three countries is more exposed to events happening in the US.

scholarly journals DAMPAK HANCURNYA GEDUNG WTC AMERIKA SERIKAT TERHADAP NILAI TUKAR MATA UANG BEBERAPA NEGARA ASEAN

2003 ◽  
Vol 2 (1) ◽  
Author(s):  
Mudji Utami ◽  
Heru Suprihhadi
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
September 11 ◽  
Foreign Exchange Rate ◽  
Rate Of Return ◽  
Rate Fluctuation ◽  
Before And After ◽  
Us Dollar ◽  
Asean Countries ◽  
The Given

The falling down of WTC building in US at September 11, 2001 give the seriously impact to the foreign exchange rate fluctuation, especially for the given countries around the world whose has deeply active transaction concern to US Dollar, including the countries in ASEAN region. The change of foreign exchange rate in ASEAN region not always appreciated but some of them turn to depreciated when it's ahead to US Dollar. This situation will influence the discrepancy on rate of return of US dollar in ASEAN countries, before and after the accident of September 11.According to our research, the result significantly explore the differentiation on rate return during seven days before and after the accident but there's no differentiation on rate of return during three days and the day after seven days from the accident.

THE VALUATION OF OPTIONS ON FOREIGN EXCHANGE RATE IN A TARGET ZONE

2016 ◽  
Vol 19 (03) ◽  
pp. 1650020 ◽  
Author(s):  
GUANGLI XU ◽  
SHIYU SONG ◽  
YONGJIN WANG
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Spectral Expansion ◽  
Foreign Exchange Rate ◽  
Target Zone ◽  
Cir Model ◽  
Rate Behavior ◽  
Us Dollar ◽  
Real Market ◽  
The One

This paper derives a simple model to analyze foreign exchange rate behavior under a target zone regime. From the real market data of exchange rate of US Dollar (USD) to Hong Kong Dollar (HKD) (USD/HKD), somewhat surprisingly, we find that some of the observations fall outside the stated range. Consequently, a so-called skew CIR model for this exchange rate which has a probability of exceeding the stated boundary is developed. A spectral expansion approach is used to analyze the model. The valuation of the barrier and the one-touch options for the derivative written on the exchange rate is studied in the end.

scholarly journals Forecasting Malaysian Exchange Rate using Artificial Neural Network

2020 ◽  
Vol 15 (2) ◽  
pp. 136-145
Author(s):  
Ikhwan Muzammil Amran ◽  
Anas Fathul Ariffin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Exchange Rate ◽  
Foreign Exchange ◽  
Global Economy ◽  
Information Criterion ◽  
Foreign Exchange Rate ◽  
Kuala Lumpur ◽  
Us Dollar ◽  
Artificial Neural

In todays fast paced global economy, the accuracy in forecasting the foreign exchange rate or predicting the trend is a critical key for any future business to come. The use of computational intelligence based techniques for forecasting has been proved to be successful for quite some time. This study presents a computational advance for forecasting the Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar. A neural network based model has been used in forecasting the days ahead of exchange rate. The aims of this research are to make a prediction of Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar using artificial neural network and determine practicality of the model. The Alyuda NeuroIntelligence software was utilized to analyze and to predict the data. After the data has been processed and the structural network compared to each other, the network of 2-4-1 has been chosen by outperforming other networks. This network selection criteria are based on Akaike Information Criterion (AIC) value which shows the lowest of them all. The training algorithm that applied is Quasi-Netwon based on the lowest recorded absolute training error. Hence, it is believed that experimental results demonstrate that Artificial Neural Network based model can closely predict the future exchange rate.

An Empirical Analysis of Foreign Exchange Exposure of CNX 100 Companies

GIS Business ◽  
2017 ◽  
Vol 12 (5) ◽  
pp. 1-9 ◽  
Author(s):  
Sriram Mahadevan
Keyword(s):  
Exchange Rate ◽  
Stock Returns ◽  
Foreign Exchange ◽  
Foreign Exchange Rate ◽  
Future Research ◽  
Export Ratio ◽  
Exchange Rate Exposure ◽  
Ratio Size ◽  
Foreign Exchange Exposure ◽  
Exchange Rate Changes

The present study has empirically examined the level of foreign exchange exposure and its determinants of CNX 100 companies. For the purpose of study, the relationship between exchange rate changes and stock returns for a sample of 82 companies was determined for the period April 2011-March 2016. The study finds that 49% of the sample companies had significant positive foreign exchange rate exposure and the found that the companies could be exporters or net importers. To explore factors determining foreign exchange rate exposure, variables such as export ratio, import ratio, size of a company, hedging activities were regressed against the exchange exposure and the study found that none of the factors was influencing the exchange rate exposure. The study concludes that the reasons for insignificant influence of the variables could be the natural hedging practices of companies, offsetting of exports and imports and heterogeneous of the sample size. The study offers few directions for future research in this area.

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