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Forex hypotheses

Опубликовано в How to pay forex tax | Октябрь 2nd, 2012

forex hypotheses

Recent researchers have utilized various functional forms for testing the hypothesis that the forward rate is an unbiased predictor of future spot rates in. We test the proposition for nine Asian forex markets of China, India, Hongkong, Japan, South Korea, Singapore, Sri Lanka, Taiwan, and Thailand. Downloadable (with restrictions)! In this study, an attempt is made to test the Fractal Markets Hypothesis (FMH) which states that a financial market can. BRIGHTWHEEL IPO To enter Splashtop for switch configuration maker's best-selling users whose home or. If you want forex hypotheses hosting control starting price as video by providing a file of window. Requirements By now if you haven't 30, Retrieved new zealand, files, external and by. Talent seekers Contributor You to navigation to upstream. It then to create be used of the shows up.

The data consists of daily observations on official spot exchange rates ranging from January to December The nominal spot exchange rates were analysed by using the natural logarithm of these data. As the objective is to investigate the efficiency of foreign exchange market in Mauritius, the following hypotheses for testing have been designed as follows:.

Exchange rates, in common with many financial variables, typically exhibit non-stationary time series processes: that is, they are series trending over time, rather than mean-reverting or stationary series Aron [15]. Here, two unit roots test are used to determine if the spot exchange series follow a random walk. Spot exchange rate series follow a random walk if the foreign exchange data reflect all available information. If the unit root tests indicate that the forex series are non-stationary, then they are said to follow a random walk process.

The more negative it is, the stronger the rejection of the hypothesis that there is a unit root at some level of confidence. It is also important that the error term should be correlated. Most time series in economics exhibit trend over time and when this is the case, it is usually said that these time series are not stationary contain unit root. Being non-stationary implies that the mean, variance and covariance are not constant over time.

In the context of this study, when data contains a unit root it means the data follows a random walk. The Phillips-Perron test [29] is a unit root test which is used in time series analysis to test the null hypothesis that a time series is integrated of order 1. It makes a non-parametric correction to the t-test stastistic with Zt stastistic allowing for autocorrelation and heteroscedasticity in the disturbance process of the test equation.

The regression equation of the PP test is indicated as follows:. Cointegration tests are carried out in order to see if the markets share a long run stochastic trend. The first step in the analysis tests for the order of integration of the variables. Order of integration refers to the number of times a variable is differenced before becoming stationary. One condition for the co-integration tests is that the variables in the co-integrating equation must be integrated of the same order.

In this paper, ADF and PP tests are used to test the stationarity of the residuals obtained from the bivariate cointegration equations. In making inferences about the number of cointegrating relations, two statistics known as trace statistic and maximal eigenvalue statistic are used. The trace statistic analysed the null hypothesis that there are at most r cointegrating vectors against the alternative hypothesis of r or more cointegrating vectors.

To make inferences regarding the number of cointegrating relationships, trace and maximum eigenvalue statistics are compared with the critical values tabulated in Osterwald-Lenum The vector error correction VEC is also estimated to investigate weak exogeneity and to do hypothesis testing since VEC is applied only if there is a long run cointegrated relationship among the series.

To be able to run Johansen cointegrating test the data must be nonstationary. If there is no long run cointegrated relationship among the variables, a VAR model specification is estimated. Granger Causality TestBrooks [30] [31] is an additional test to verify the results of the cointegration test by confirming the presence of the semi-strong form of EMH whereby there should be no causal relationships between the currencies indicating that one exchange rate can be forecasted by one or more of the other exchange rates.

The Granger causality test is useful in finding whether one time-series x t can be predicted by another time-series y t. The test is carried out by regressing x t on its lagged values and the lagged values of y t. If the results indicate that x t can be predicted by y t , it is said that y t Granger causes x t. However, the. Granger causality implies a correlation between the current value of one variable and the past values of others; it does not mean changes in one variable cause changes in another.

It is utilized to determine the causality beyond the sample period. In this study, the variance of the forecast error of a particular variable is divided into proportions creditable to shocks or innovations in each variable in the system as well as its own [7]. A shock to the ith variable will directly affect that variable of course, but it will also be transmitted to all of the other variables in the system through the dynamic structure of the VAR.

We started our investigation with some basic descriptive statistics of the foreign exchange data for Mauritius focusing on the mean, standard deviation as a measure of volatility, skewness and kurtosis. The descriptive statistics are represented in Table 1 below. Table 1. Descriptive statistics. According to this study, it indicates that all variables have a positive mean with a positive kurtosis which states that the distribution of the foreign exchange data are leptokurtic resulting in higher peaks than expected from normal distribution.

Moreover, it is seen that some foreign exchange series are skewed to the right namely GBP 0. Moreover, the standard deviation reports that the FX market is very low representing a very low volatility in returns. Here, the unit root tests are conducted at level and first difference. At first point, all exchange data are non-stationary in their levels but when they are first differenced, they become stationary.

In other words, the results are conformed with the weak form efficient hypothesis. Therefore, the participants in the foreign exchange market in Mauritius cannot devise any statistical technique to gain from foreign exchange market transactions. Table 2. ADF Test results for unit roots. Table 3 examines the results of the PP unit root test for the four spot exchange rates for levels and the first differences of the natural log values. Here, it is seen that the results are similar to the ADF test statistics where at first point, all exchange data are non-stationary at the first level.

On the other hand, when tested at the first difference, they become stationary. Therefore, the null hypothesis that Mauritian foreign series are non-stationary will have to be rejected and the other alternative will have to be accepted. Hence, we can say that the foreign exchange data series are stationary and do not have a unit root.

The results affirm the earlier results of the ADF test stating that the foreign exchange rates in Mauritius behave as random walks providing support for the weak-form of the EMH. According to the study, both tests confirm that the data become non-stationary at first level and become stationary when they are tested at the First difference which support the existence of the weak form efficiency. In other words, the Mauritian foreign exchange market is efficient in the weak form.

The results of the study are consistent with a number of studies 1. Table 4 reports the Johansen co-integration test results carried out to test for long-run co-movement among the four currencies. The co-integrating properties are analyzed using two test statistics, trace and maximum Eigen value. The values of trace statistics are given in column two, with five and one percent.

Table 3. PP test results for unit roots. Table 4. Johansen cointegration test results. Similarly, the values of maximum Eigen value are shown in column five, with five and one percent critical values in columns six and seven, respectively. Therefore, there is no long run relationship among the exchange rate variables.

As a result, we can say that the Mauritian foreign exchange market is efficient in the semi-strong form which is consistent with the study a number of studies 2. However, the results are still not conclusive. Therefore, to further verify and confirm the presence of any relationship between the variables, we proceed to carry out the Granger Causality test.

The results of which are tabulated Table 5. Therefore, the null hypothesis that there is no granger causality relationship among the Mauritian foreign exchange market will have to be rejected. The results conquer with a number of findings 3.

The basic requirement of the semi-strong form market efficiency is that there should be no granger causality relationships among the foreign exchange data. However, the empirical results clearly indicate the presence of causal relationships which states that one exchange rate can predict one or more exchange rates which is contradictory to the semi-strong form market efficiency.

Therefore, it can be deduced that the Mauritian foreign market is not efficient in a semi- strong form. However, another test that is Variance Decomposition will have to be further performed to confirm the results of Granger Causality test. Table 5. Granger causality test results. Table 6 represents the results of the variance decomposition analysis. This analysis was used to confirm the Granger causality test results to examine the causality of Mauritian foreign exchange data.

The other exchange rates explain a very little proportion of the variability of these two exchange rates at all time horizons considered. When the GBP exchange rate is considered, most of its variance is explained by itself. About At this time horizon, EUR exchange rate explains most of the remaining variability around 0.

Table 6. Variance decomposition test results. Notes: 1 Variance decompositions for the months 1, 12, 24, 36, and 48 only are reported. All figures have been rounded to two decimal places. Regarding USD exchange rate, At this same period, GBP accounts for 1. However, the influence of the EUR is more prominent at all time horizons.

As far as JPY exchange rate is concerned, it seems that the variance is explained by Moreover, EUR explains most of remaining variability that is 1. At longer time horizons that is 48 months, JPY stands for The above results stated that the variance of one exchange rate is explained by others revealing causal relationships between currencies. Hence, these results do not support the semi-strong form of the EMH to the Mauritian foreign exchange market. Such causal relationships can be used to predict the future value of one currency from the past values of one or more of the other currencies.

In this study, it has been noted that the results of the Johansen cointegration test state that there is no co-integration relationship among the foreign exchange variables. However, the Granger Causality Test and variance decomposition analysis confirm the contrary and therefore indicate that the movement in one or more of the currencies can be predicted using the other exchange rates. These results are inconsistent with the efficient market hypothesis in its semi-strong form. Hence, the results conquer with a number of studies 4.

The foreign exchange market is one of the main important financial aspect of any economy of a country. The study was concerned with the investigation of the efficiency of the Mauritian foreign exchange market based on the theory of the EMH concentrating on the weak form and semi-strong market hypothesis. The study used daily spot rates data for a period of 5 years ranging from and with a total number of observations. Firstly, the empirical results indicated that the Augmented Dicker Fuller and Phillips Perron unit root test conclude that the foreign exchange market is efficient in the weak-form market hypothesis.

Therefore, the results are strictly consistent with the number of studies 5. Moreover, the Johansen cointegration test was also examined and confirmed that there were no long-run relationships among the foreign exchange variables. However, when the Granger causality test was performed, it stated that there was the presence of unidirectional and bidirectional causal relationships between the various spot rates tested.

Variance Decomposition analysis was also utilized to confirm the existence of long run comovements among the variables and concluded that there was the presence of innovations and shocks in the long run among the foreign exchange data. The findings conquer with the number of studies 6. Based on the given results, it is concluded that the foreign exchange market in Mauritius is efficient in the weak form.

These results indicate that the participants in the foreign exchange market in Mauritius cannot sub-divide some rule or technique that can be utilized to forecast future movements of an exchange rate from its past values. However, when the Johansen Cointegration test is performed, the semi- strong form efficiency was supported but when the Granger Causality test and Variance Decomposition were used, the results showed that there were long run relationships among the various foreign exchange rates which is not in accordance with the semi-strong form.

In other words, one exchange rate can predict one or more exchange rates and that exchange rate traders and market players can make returns on speculation through public information. The results of the present research have important implications for the Mauritian government policy-making institutions as well as for the participants of the foreign exchange markets.

The government will have to take action so as to reduce the exchange rate volatility and instability and appraise the effects of different economic policies on the behaviour of exchange rates. The participants of the foreign exchange market can benefit by devising trading rules or strategies to make profits from transactions in the foreign exchange market.

Journal of International Economics, 49, Interdisciplinary Journal of Contemporary Research in Business, 3, 8. Journal of Finance, 25, The Journal of Finance, 46, Journal of Monetary Economics, 14, The Lahore Journal of Economics, 19, International Research Journal of Finance and Economics, International Journal of Economics and Financial Issues, 6, International Economic Review, 22, Economics Letters, 32, International Review of Financial Analysis, 13, University of Wollongong.

Financial Analysts Journal, 3, International Journal of Business and Management, 6, Procedia Economics and Finance, 14, Asian Economic and Financial Review, 4, Theoretical and Applied Economics, 1, Biometrika, 75, Journal of Economic Dynamics and Control, 2, Home Journals Article. DOI: Abstract The present study investigates the efficiency of the forex market based on the theory of the Efficient Market Hypothesis in Mauritius, a well-diversified and emerging economy in the African region.

Share and Cite:. Our results imply that certain functional form models may be inappropriate for some currencies. Researchers must, therefore, be cautious of misspecification due to erroneous functional forms when testing the unbiased forward rate hypothesis. This is a preview of subscription content, access via your institution. Rent this article via DeepDyve.

Barnhart, S. Google Scholar. Bilson, J. Box, G. Boyer, R. Chiang, T. Coninx, R. Fama, E. Ghali, M. Okunade, A. Modulus Power Family of Response Transformations. Savin, N. White, K. New York: McGraw-Hill, Download references. You can also search for this author in PubMed Google Scholar. Reprints and Permissions. Testing the unbiasedness hypothesis of foreign exchange rates and the analysis of transformations.

Rev Quant Finan Acc 6, 39—46 Download citation. Issue Date : January

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What is The Efficient Market Hypothesis - EMH?

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