One of the significant task used in analyzing any data is done by characterizing the location and the variability of the data set, which can be done with the help of skewness and kurtosis. With the help of skewness, it can be said whether the data is positively skewed or negatively skewed in nature. The histogram is one of the effective graphical technique which is used for showing both the skewness and the kurtosis of any kind of data sets. Measurement of the symmetry is done with the help of skewness (Alagidede and Ibrahim 2017). The skewness in case of the normal distribution is zero and any data which have a skewness near zero can be stated as the normal distribution.
By taking the historical data of the exchange rate dollars of the Australian Dollar and pairing with the United States dollar from the month of December 2016 till December 2018, the percentage changes in the values had been found.The mean, median, skewness, kurtosis, standard deviation , standard error along with the maximum and minimum values have been found out (Hajilee and Al Nasser 2014).
Normal distribution is a kind of probability distribution which is quite symmetric about the mean and data which is near mean they are more frequent in occurrence than the data present far from the mean. The normal distribution is very important in statistics. The normal distribution is quite significant in nature as a result of the central limit theorem. It can be also calculated with the help of standard normal distribution. The normal distribution is also a continuous distribution. By computing the distribution, the skewness is 0.53 which states that it is moderately skewed. This also suggests that the data set has normal distribution. As the value of skewness have been found to be within 0-1, it can be said that the dataset is normally distributed. The value of kurtosis is found to be -0.7525. In the theory of probability kurtosis measures the tiredness of the probability distribution of a real valued normal variable (Ponomarev, Rey and Radchenko 2018). One of the standard measure of the kurtosis is the Karl Pearson method. Normally in case of normal distribution the value of the kurtosis is known to be 3. When any distribution with the kurtosis less than 3 are found, it is called to be platykurtic which are generally the uniform distribution. When the distribution is more than three, then it is termed as leptokurtic in nature. One of the example of leptokurtic is the Laplace distribution. The histogram above however depicts to be left tailed in nature.
The volatility of currency is also known as the foreign exchange volatility which is the unpredictable movement of the rate of exchange in the global foreign exchange market. The foreign exchange volatility is known to be one of the greatest risk. Volatility is also known as the basic measure for the risk associated with the financial market instruments. It is a kind of statistical measure of the dispersion used for the market index. The higher the volatility, the riskier will be the security. Volatility is the amount of the risk related to the size off changes in the security value. The volatility is usually calculated by using variance and the standard deviation. The currency pair can be stated as the quotation of the two kinds of different countries. The first listed currency of the currency of the currency pair is known as the base currency and the second currency is also known as the quote currency (Alagidede and Ibrahim 2017). . Therefore, the volatility of the currency pairing and the currency pairing trend over the stated interval can be calculated by comparing standard deviation and the mean. From the excel sheet it can be seen that the value of the standard deviation is 0.024623211 and the other hand the value of the mean is 0.00114. As the value of the standard deviation is higher from the value of mean, it can be stated that that the currency pairing in the mentioned interval is volatile in nature.
It is not always possible to depict the data for the next week using moving average methods. By computing the weekly moving average in the sheet 4 of excel, it can be seen that for the past few weeks in November and December the change is positive in nature. Therefore over the next few weeks, it can be said that the exchange rate changes will be increasing and will be also positive in nature
One of the features of the volatility is that it is not directly observable in nature. When comparing the currency pairing of AUD/USD with the Bitcoin currency which is given in BTC/USD it have been found out that the value of mean of the currency pairing of Bitcoin is 0.95 and on the other hand the value of the standard deviation is 0.28. This means that the value of standard deviation is much more than the mean which states that Bitcoin is very volatile in nature (Hajilee and Al Nasser 2014). It have been found out that the value of Bitcoin is many times more volatile than any other assets. Bitcoin is also known to be highly volatile in nature in relative to the U.S dollar. Bitcoin is a kind of Cryptocurrency used in case of electronic cash which have been released as a result in the year 2009. Bitcoins can be created as a reward for the process of mining. Therefore, it can be said that bitcoins are one of the famous Cryptocurrencies used for measuring different types of exchange rates (Ponomarev, Rey and Radchenko 2018).. The unit of the Bitcoin system is generally measured with the help of Bitcoin and is represented by the symbols BTC and XBT. By comparing the volatility it can be said that the Cryptocurrency Bitcoin is one of the most volatile currency. By comparing the standard deviation with the mean of both the AUD/USD currency pairing, the volatility of both the exchange rates can be depicted easily. However, both the currency paring shows that both the currencies are volatile in nature.
The volatility index of the Bitcoin usually examines the standard deviation of the daily returns for the variety of assets which also comprises of the Bitcoin. Bitcoin is therefore said to be one of the most volatile in nature. On the other hand while comparing the currency pairing with AUD/USD it can be stated that in this case also the value of the standard deviation is higher than the value of the mean, therefore the currency pairing of AUD/USD is volatile in nature (Barunik, Krehlik and Vacha 2016). Although while comparing the standard deviation of the AUD/USD with the standard deviation of the bitcoins, the value of the standard deviation for the bitcoins is much higher than the value of the Australian dollars.
A commodity currency are usually those currencies which co moves with the world prices of the commodity products as the country heavily depends on the exports of the raw materials for the income. When the exports rises as a result of high demand, the gross domestic product will also rise as the country depends heavily on the commodity, which also rises to high price resulting to inflation. The share of the export of the earnings of the Australia which are derived from the bulk minerals have risen over the recent years. Australia is also known to be the world’s largest exporter of the bulk commodities, although the share of global production of Australia is much smaller in nature (Calderón and Kubota 2018). The gold prices based on the United States Dollar, and the rate of exchange present in the Australian dollar and the US dollar have a combined effect on the Australian minerals industry. The trend refers the economic sustainability and the growth of the minerals industry and the rates of production of the collective industry. Recently, the prices of the commodities on the spot market have fallen sharply. The market analysis have also revised the expectations in case of the bulk commodity. Commodity currencies are quite popular in the developing countries. There are both positive and negative effects of the commodity currency. Australia had been one of the largest exporters of the bulk commodities in the world.
Reference list
Alagidede, P. and Ibrahim, M., 2017. On the causes and effects of exchange rate volatility on economic growth: Evidence from Ghana. Journal of African Business, 18(2), pp.169-193.
Barunik, J., Krehlik, T. and Vacha, L., 2016. Modeling and forecasting exchange rate volatility in time-frequency domain. European Journal of Operational Research, 251(1), pp.329-340.
Boon, T.H. and Hook, L.S., 2017. Real exchange rate volatility and the Malaysian exports to its major trading partners. In ASEAN in an Interdependent World: Studies in an Interdependent World (pp. 95-117). Routledge.
Calderón, C. and Kubota, M., 2018. Does higher openness cause more real exchange rate volatility?. Journal of International Economics, 110, pp.176-204.
Choudhry, T. and Hassan, S.S., 2015. Exchange rate volatility and UK imports from developing countries: The effect of the global financial crisis. Journal of International Financial Markets, Institutions and Money, 39, pp.89-101.
Della Corte, P., Ramadorai, T. and Sarno, L., 2016. Volatility risk premia and exchange rate predictability. Journal of Financial Economics, 120(1), pp.21-40.
Hajilee, M. and Al Nasser, O.M., 2014. Exchange rate volatility and stock market development in emerging economies. Journal of Post Keynesian Economics, 37(1), pp.163-180.
Khin, A.A., Yee, C.Y., Seng, L.S., Wan, C.M. and Xian, G.Q., 2017. Exchange Rate Volatility on Macroeconom?c Determinants In Malaysia: Vector Error Correction Method (Vecm) Model. Journal of Global Business and Social Entrepreneurship (GBSE) Vol, 3, pp.36-45.
Li, Y., Perron, P. and Xu, J., 2017. Modelling exchange rate volatility with random level shifts. Applied Economics, 49(26), pp.2579-2589.
Ponomarev, Y., Rey, A. and Radchenko, D., 2018. Investigation of the Relationship between the Intensity of International Trade and the Volatility of Paired Exchange Rates of the Russian Federation and its Trading Partners (No. 061823).
Yamamoto, A., 2018. Cryptocurrency Investing: 4 Crypto Books-Includes Pros & Cons of Bitcoin-Bitcoin Hacking-Why Not to Invest in Bitcoin-Cryptocurrency Trading & Investing (Volume 1).
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