Thematische Bibliographien / Foreign exchange rates – Ethiopia / Zeitschriftenartikel
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Zeitschriftenartikel zum Thema „Foreign exchange rates – Ethiopia“

Autor: Grafiati
Veröffentlicht am 30. Juni 2021
Zuletzt aktualisiert am 18. Februar 2022

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1

Rao, Nandeeswara, und TassewDufera Tolcha. „DETERMINANTS OF REAL EXCHANGE RATE IN ETHIOPIA“. International Journal of Research -GRANTHAALAYAH 4, Nr. 6 (30.06.2016): 183–210. http://dx.doi.org/10.29121/granthaalayah.v4.i6.2016.2652.

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Real exchange rate has direct effects on trade particularly on international trade and has indirect effects on productions and employments, so it is crucial to understand the factors which determine its variations. This study analyses the main determinants of the real exchange rate and the dynamic adjustment of the real exchange rate following shocks to those determinants using yearly Ethiopian time series data covering the period 1971 to 2010. It begins with a review of literatures on Exchange rate, real exchange rate, determinants of the real exchange rate and provides an updated background on the exchange rate system in Ethiopia. An empirical model linking the real exchange rate to its theoretical determinants is then specified. This study had employed the cointegration and vector autoregression (VAR) analysis with impulse response and variance decomposition analyses to provide robust long run effects and short run dynamic effects on the real exchange rate. Share of investment, foreign exchange reserve, capital inflow and government consumption of non-tradable goods were the variable that have been found to have a long run relationship with the real exchange rate. The estimate of the speed of adjustment coefficient found in this study indicates that about a third of the variation in the real exchange rate from its equilibrium level is corrected within a year. The regression result of VECM reveals that terms of trade, nominal exchange rate, and one period lag of capital flow were the variables significantly affects the real exchange rate in the short run. However, the impulse response and variance decomposition analysis shows a better picture of the short run dynamics. The their analysis provided evidence that the Shocks to terms of trade, nominal exchange rate, capital inflow and share of investment have persistent effects on the real exchange rate in the short run. In general the regression results of both long run and short run models mostly suggest that the fluctuations of real exchange rates are predominantly responses to monetary policies shocks rather than fiscal policy shocks.
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2

Dube, Ahmed, Burhan Ozkan und Ramu Govindasamy. „Analyzing the Export Performance of the Horticultural Sub-Sector in Ethiopia: ARDL Bound Test Cointegration Analysis“. Horticulturae 4, Nr. 4 (16.10.2018): 34. http://dx.doi.org/10.3390/horticulturae4040034.

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High dependency on traditional primary agricultural commodities and recurrent world market price fluctuations had exposed Ethiopia to foreign earnings instability. To reduce the high dependence on primary agricultural commodities and the associated vulnerability of negative price declines, diversification of trade from primary agricultural commodities into high-value horticultural commodities has attracted the attention of policy makers. The developments made in this area have brought the sector to the position of fifth largest foreign revenue generator for the country. However, given the comparative advantage in marketing and the potential to achieve trade gains that the country possesses, the benefit from the horticultural sub-sector is far below its potential. In this regard, knowledge of the determinants of the industry’s development is very important. So far, no attempt was made to examine factors influencing the export performance of the sector, taking the long period performance of the sector into consideration. Consequently, this study was proposed to examine the factors that have influenced the horticultural export performance of Ethiopia for the period from 1985–2016. Secondary data collected from National Bank of Ethiopia, Ethiopia Horticulture Producer Exporter Association, Ministry of Agriculture of Ethiopia, FAOSTAT, UNCTAD, and the World Bank were used in this study. The short-run and long-run relationships among the series were investigated using the autoregressive-distributed lag (ARDL) bound test cointegration approach. The model result of the Error Correction Model (ECM (-1)) was revealed as negative and significant, whereby it confirmed the existence of cointegration among the series. Its coefficient value was 0.472, which showed 47% of the adjustment will be made in the first year and it will return to its long-run equilibrium after 2.12 years. The model results also showed that the real effective exchange rate, the real GDP of Ethiopia, foreign direct investment (FDI), prices, and the structural break had significantly influenced the horticultural export performance both in the short-run and the long-run. Foreign GDP and real interest rates were revealed significant only in the long-run. Finally, important policy measures deemed to improve the horticultural export performance of Ethiopia were recommended.
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3

Havenner, Arthur, und Bagher Modjtahedi. „Foreign exchange rates“. Journal of Econometrics 37, Nr. 2 (Februar 1988): 251–64. http://dx.doi.org/10.1016/0304-4076(88)90005-x.

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4

Nathani, Navita, Jaspreet Kaur und Pooja Shrivas. „Dynamics of Foreign Exchange Rates“. Prestige International Journal of Management & IT - Sanchayan 04, Nr. 02 (15.12.2015): 35–58. http://dx.doi.org/10.37922/pijmit.2015.v04i02.002.

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5

van de Gucht, Linda M., Marnik G. Dekimpe und Chuck C. Y. Kwok. „Persistence in foreign exchange rates“. Journal of International Money and Finance 15, Nr. 2 (April 1996): 191–220. http://dx.doi.org/10.1016/0261-5606(96)00001-0.

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6

JÜTTNER, D. JOHANNES, und BERND P. LUEDECKE. „Interest Rates, Exchange Rates and Foreign Debt“. Economic Record 67, Nr. 2 (Juni 1991): 139–46. http://dx.doi.org/10.1111/j.1475-4932.1991.tb02537.x.

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7

Sideris, Dimitrios A. „Foreign exchange intervention and equilibrium real exchange rates“. Journal of International Financial Markets, Institutions and Money 18, Nr. 4 (Oktober 2008): 344–57. http://dx.doi.org/10.1016/j.intfin.2007.04.001.

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8

LOWE, PHILIP, und ALISON TARDITI. „Interest Rates, Exchange Rates and Foreign Debt: Comment*“. Economic Record 69, Nr. 1 (März 1993): 77–79. http://dx.doi.org/10.1111/j.1475-4932.1993.tb01800.x.

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9

JÜTTNER, D. JOHANNES. „Interest Rates, Exchange Rates and Foreign Debt: Rejoinder“. Economic Record 69, Nr. 1 (März 1993): 80–81. http://dx.doi.org/10.1111/j.1475-4932.1993.tb01801.x.

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10

Cheung, Yin-Wong. „Long Memory in Foreign-Exchange Rates“. Journal of Business & Economic Statistics 11, Nr. 1 (Januar 1993): 93. http://dx.doi.org/10.2307/1391309.

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11

Mahajan, Arvind, und Andrew J. Wagner. „Nonlinear dynamics in foreign exchange rates“. Global Finance Journal 10, Nr. 1 (März 1999): 1–23. http://dx.doi.org/10.1016/s1044-0283(99)00002-2.

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12

Stevens, Guy V. G. „Exchange Rates and Foreign Direct Investment“. Journal of Policy Modeling 20, Nr. 3 (Juni 1998): 393–401. http://dx.doi.org/10.1016/s0161-8938(97)00007-0.

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13

Cheung, Yin-Wong. „Long Memory in Foreign-Exchange Rates“. Journal of Business & Economic Statistics 11, Nr. 1 (Januar 1993): 93–101. http://dx.doi.org/10.1080/07350015.1993.10509935.

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14

Bollen, Nicolas P. B., Stephen F. Gray und Robert E. Whaley. „Regime switching in foreign exchange rates:“. Journal of Econometrics 94, Nr. 1-2 (Januar 2000): 239–76. http://dx.doi.org/10.1016/s0304-4076(99)00022-6.

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15

HUANG, GUOBO, und CLEMENT YUK-PANG WONG. „UNIFICATION OF CHINA'S FOREIGN EXCHANGE RATES“. Contemporary Economic Policy 14, Nr. 4 (Oktober 1996): 42–57. http://dx.doi.org/10.1111/j.1465-7287.1996.tb00632.x.

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16

Strauch, Bruce, und Katina Strauch. „Foreign exchange rates and journal pricing“. Library Acquisitions: Practice & Theory 13, Nr. 4 (Januar 1989): 417–22. http://dx.doi.org/10.1016/0364-6408(89)90052-5.

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17

Wolff, Christian C. P. „Forward foreign exchange rates and expected future spot rates“. Applied Financial Economics 10, Nr. 4 (August 2000): 371–77. http://dx.doi.org/10.1080/09603100050031499.

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18

Rahim, Muhammad Abdur, und Zahangin Alam. „Foreign Exchange Reserves“. International Journal of Finance & Banking Studies (2147-4486) 2, Nr. 4 (21.10.2013): 1–12. http://dx.doi.org/10.20525/ijfbs.v2i4.159.

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This study is about foreign exchange reserves of Bangladesh. The main purpose of this study is to the influence of exchange rateson foreign exchange reserves to the Bangladesh context. Both the primary and secondary data has been used in this study. The primary data has been collected through a structured questionnaire from 50 respondents. The secondary data, namely Bangladesh foreign exchange reserves (FER), Bangladesh current account balance (CAB), Bangladesh capital and financial account balance (CFAB), and BDT/USD exchange rates (ER). This study covers yearly data from July 01, 1996 to June 30, 2005 and quarterly data from July 01, 2005 to June 30, 2012. Findings of this study shows that out of the selected 16 factors affecting foreign exchange reserves, exchange rates occupy the first position, weighted average score (WAS) being 4.56. Foreign exchange reserves (FER) and current account balance (CAB) have increased by 502.9087% and 1451.218%, whereas capital and financial account (CFAB) has decreased by -649.024% on June 30, 2012 compared to June 30, 1997. The influence of other factors held constant, as ER changes by 285.6894 units due to one unit change in FER, on average in the same direction which represents that ER has positive effect on the FER and this relationship is statistically significant. 62.1526 percent of the variation in FER is explained by ER. The outcomes of Breusch-Godfrey test (LM test), ARCH test, and the Normality test are that there is a serial correlation among residuals, the variance of residuals is not constant, and the residuals are not normally distributed.
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19

Hsieh, David A. „Modeling Heteroscedasticity in Daily Foreign-Exchange Rates“. Journal of Business & Economic Statistics 7, Nr. 3 (Juli 1989): 307. http://dx.doi.org/10.2307/1391528.

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20

Gonçalves, Fernando M. „Accumulating Foreign Reserves Under Floating Exchange Rates“. IMF Working Papers 08, Nr. 96 (2008): 1. http://dx.doi.org/10.5089/9781451869576.001.

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21

Brenner, Menachem, Young Ho Eom und Yoram Landskroner. „Implied foreign exchange rates using options prices“. International Review of Financial Analysis 5, Nr. 3 (1996): 171–83. http://dx.doi.org/10.1016/s1057-5219(96)90012-5.

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22

Hsieh, David A. „Modeling Heteroscedasticity in Daily Foreign-Exchange Rates“. Journal of Business & Economic Statistics 7, Nr. 3 (Juli 1989): 307–17. http://dx.doi.org/10.1080/07350015.1989.10509740.

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23

Bhawnani, Vijay, und K. Rao Kadiyala. „Forecasting foreign exchange rates in developing economies“. Applied Economics 29, Nr. 1 (Januar 1997): 51–62. http://dx.doi.org/10.1080/000368497327399.

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24

Suzuki, Tomoya, Tohru Ikeguchi und Masuo Suzuki. „Multivariable nonlinear analysis of foreign exchange rates“. Physica A: Statistical Mechanics and its Applications 323 (Mai 2003): 591–600. http://dx.doi.org/10.1016/s0378-4371(03)00052-9.

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25

Guerard, John B. „Composite model building for foreign exchange rates“. Journal of Forecasting 8, Nr. 3 (Juli 1989): 315–29. http://dx.doi.org/10.1002/for.3980080313.

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26

MacDermott, Raymond. „Linking Exchange Rates to Foreign Direct Investment“. International Trade Journal 22, Nr. 1 (12.02.2008): 3–16. http://dx.doi.org/10.1080/08853900701784045.

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27

So, Jacky C. „The Behavior of Foreign Exchange Rates – Comment“. Journal of International Business Studies 17, Nr. 3 (September 1986): 165–75. http://dx.doi.org/10.1057/palgrave.jibs.8490806.

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28

Calderón-Rossell, Jorge R., und Moshe Ben-Horim. „The Behavior of Foreign Exchange Rates – Reply“. Journal of International Business Studies 17, Nr. 3 (September 1986): 177–80. http://dx.doi.org/10.1057/palgrave.jibs.8490807.

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29

Aczel, Amir D., und Norman H. Josephy. „The Chaotic Behavior of Foreign Exchange Rates“. American Economist 35, Nr. 2 (Oktober 1991): 16–24. http://dx.doi.org/10.1177/056943459103500203.

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30

Vandewalle, N., und M. Ausloos. „Sparseness and Roughness of Foreign Exchange Rates“. International Journal of Modern Physics C 09, Nr. 05 (Juli 1998): 711–19. http://dx.doi.org/10.1142/s0129183198000613.

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An accurate multiaffine analysis of 23 foreign currency exchange rates has been performed. The roughness exponent H1 which characterizes the excursion of the exchange rate has been numerically measured. The degree of intermittency C1 has been also estimated. In the (H1,C1) phase diagram, the currency exchange rates are dispersed in a wide region around the Brownian motion value (H1=0.5,C1=0) and have a significantly intermittent component (C1≠0).
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31

Guo, Hui, und Robert Savickas. „Forecasting foreign exchange rates using idiosyncratic volatility“. Journal of Banking & Finance 32, Nr. 7 (Juli 2008): 1322–32. http://dx.doi.org/10.1016/j.jbankfin.2007.11.006.

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32

Das, Atin, und Pritha Das. „Chaotic analysis of the foreign exchange rates“. Applied Mathematics and Computation 185, Nr. 1 (Februar 2007): 388–96. http://dx.doi.org/10.1016/j.amc.2006.06.106.

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33

Sewell, Martin, und John Shawe-Taylor. „Forecasting foreign exchange rates using kernel methods“. Expert Systems with Applications 39, Nr. 9 (Juli 2012): 7652–62. http://dx.doi.org/10.1016/j.eswa.2012.01.026.

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34

Baek, In-Mee, und Tamami Okawa. „Foreign exchange rates and Japanese foreign direct investment in Asia“. Journal of Economics and Business 53, Nr. 1 (Januar 2001): 69–84. http://dx.doi.org/10.1016/s0148-6195(00)00038-2.

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35

Pinto, Brian. „Black markets for foreign exchange, real exchange rates and inflation“. Journal of International Economics 30, Nr. 1-2 (Februar 1991): 121–35. http://dx.doi.org/10.1016/0022-1996(91)90008-t.

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36

EVANS, Martin D. D. „Exchange Rates and Liquidity Risk“. Journal of Advanced Studies in Finance 11, Nr. 2 (23.12.2020): 159. http://dx.doi.org/10.14505//jasf.v11.2(22).08.

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I use Forex trading data to study how risks associated with the lack of liquidity contribute to the dynamics of 17 spot exchange rates through their time-varying contributions to risk premia. I find that liquidity risk matters. All the foreign exchange risk premia compensate investors for exposure to liquidity risk; and, for many currencies, exposure to liquidity risk appears to be more important than exposure to the traditional carry and momentum risk factors. I also find that variations in the price of liquidity risk make economically important contributions to the behavior of individual foreign currency returns: they account for approximately 34%, on average, of the variability in currency returns compared to the contribution of approximately 8% from the prices of carry and momentum risk.
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37

Baek, H. Young, und Chuck C. Y. Kwok. „Foreign exchange rates and the corporate choice of foreign entry mode“. International Review of Economics & Finance 11, Nr. 2 (Mai 2002): 207–27. http://dx.doi.org/10.1016/s1059-0560(01)00106-x.

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38

Pozo, Catalina Amuedo-Dorantes, Susan. „FOREIGN EXCHANGE RATES AND FOREIGN DIRECT INVESTMENT IN THE UNITED STATES“. International Trade Journal 15, Nr. 3 (Juli 2001): 323–43. http://dx.doi.org/10.1080/088539001753228018.

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39

Tucker, Alan L. „Foreign exchange option prices as predictors of equilibrium forward exchange rates“. Journal of International Money and Finance 6, Nr. 3 (September 1987): 283–94. http://dx.doi.org/10.1016/0261-5606(87)90003-9.

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40

Hilgers, Angela, und Christian Beck. „Turbulent Behavior of Stock Exchange Indices and Foreign Currency Exchange Rates“. International Journal of Bifurcation and Chaos 07, Nr. 08 (August 1997): 1855–59. http://dx.doi.org/10.1142/s0218127497001424.

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The time evolution of stock exchange indices and foreign currency exchange rates has many similarities with turbulent flows. In particular, the probability densities of price changes are non-Gaussian and develop stretched exponential tails, quite similar to the densities of velocity differences measured in fully developed hydrodynamical turbulence. We show that a simple cascade model, based on a self-similar, hierarchical dynamics of price changes, describes the observed probability densities of the financial indices in a quantitatively correct way.
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41

Stevens, Guy V. G. „Exchange Rates and Foreign Direct Investment : A Note“. International Finance Discussion Paper 1993, Nr. 444 (1993): 1–13. http://dx.doi.org/10.17016/ifdp.1993.444.

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42

Xing, Yuqing, und Laixun Zhao. „Reverse Imports, Foreign Direct Investment and Exchange Rates“. International Economy 2004, Nr. 55 (2004): 207. http://dx.doi.org/10.5652/kokusaikeizai.2004.207.

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43

Beran, Jan, und Dirk Ocker. „SEMIFAR forecasts, with applications to foreign exchange rates“. Journal of Statistical Planning and Inference 80, Nr. 1-2 (August 1999): 137–53. http://dx.doi.org/10.1016/s0378-3758(98)00247-x.

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44

Huang, Hongxuan, und Zhengjun Zhang. „Virtual Standard Currency for Approximating Foreign Exchange Rates“. International Journal of Electronic Commerce 23, Nr. 1 (02.01.2019): 33–62. http://dx.doi.org/10.1080/10864415.2018.1512273.

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45

Inoue, Jun-Ichi, und Naoya Sazuka. „Queueing theoretical analysis of foreign currency exchange rates“. Quantitative Finance 10, Nr. 2 (25.06.2009): 121–30. http://dx.doi.org/10.1080/14697680802665859.

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46

Tenti, Paolo. „Forecasting foreign exchange rates using recurrent neural networks“. Applied Artificial Intelligence 10, Nr. 6 (Dezember 1996): 567–82. http://dx.doi.org/10.1080/088395196118434.

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47

ZIMMERMANN, GEORG, RALPH NEUNEIER und RALPH GROTHMANN. „MULTI-AGENT MARKET MODELING OF FOREIGN EXCHANGE RATES“. Advances in Complex Systems 04, Nr. 01 (März 2001): 29–43. http://dx.doi.org/10.1142/s021952590100005x.

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A market mechanism is basically driven by a superposition of decisions of many agents optimizing their profit. The macroeconomic price dynamic is a consequence of the cumulated excess demand/supply created on this micro level. The behavior analysis of a small number of agents is well understood through the game theory. In case of a large number of agents one may use the limiting case that an individual agent does not have an influence on the market, which allows the aggregation of agents by statistic methods. In contrast to this restriction, we can omit the assumption of an atomic market structure, if we model the market through a multi-agent approach. The contribution of the mathematical theory of neural networks to the market price formation is mostly seen on the econometric side: neural networks allow the fitting of high dimensional nonlinear dynamic models. Furthermore, in our opinion, there is a close relationship between economics and the modeling ability of neural networks because a neuron can be interpreted as a simple model of decision making. With this in mind, a neural network models the interaction of many decisions and, hence, can be interpreted as the price formation mechanism of a market.
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48

Roberts, John. „Liberalizing Foreign-Exchange Rates in Sub-Saharan Africa“. Development Policy Review 7, Nr. 2 (Juni 1989): 115–42. http://dx.doi.org/10.1111/j.1467-7679.1989.tb00123.x.

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49

Blair, Andrew R., Robert Nachtmann, Josephine E. Olson und Thomas L. Saaty. „Forecasting foreign exchange rates: an expert judgment approach“. Socio-Economic Planning Sciences 21, Nr. 6 (Januar 1987): 363–69. http://dx.doi.org/10.1016/0038-0121(87)90010-3.

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50

Bleaney, M., und M. Tian. „Net foreign assets and real exchange rates revisited“. Oxford Economic Papers 66, Nr. 4 (13.03.2014): 1145–58. http://dx.doi.org/10.1093/oep/gpu014.

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