2ECONOMICS Question 1 Thediagrammaticrepresentationofthecontinuouslycompoundedreturnforthe AUD/USD can be well drawn with the help of monthly data collected for the trend period analyzed and taken into consideration. The data was taken for a sum of two-year of time period where monthly data has been taken for the purpose of analysis. The trend period that has been taken for the purpose of analysis is the time period in accordance from January 2016 to December 2017. The course of interval for which the data has been undertaken into analysis is a two year analysis (Guirguis 2018). In terms of distribution of the given data it could be well said that the distribution seems right skewed whereby positive distribution in the given set of data outweighs the negative set of data returns. A normal set of distribution shows the various set of returns from a negative scale to a positive set of scales. The distribution is also known as Gaussian distribution whereby the given probability distribution is hyped from the mean figure, reflecting that the data near the mean values are very close to mean (Zhang, Dufour and Galbraith 2016). On the other hand, right skewed data as developed in the given case study reflects that the mode value is greater than median, greater then mean value for the data. The AUD/USD data returns is negatively skewed indicating that the data have long right tails and the same shows right skewed distribution (Jiang and Gu 2016). The distribution feature as noted for the exchange rate is as follows: Histogram BinFrequency -2.85%2 -0.43%10 1.99%5 4.40%4 6.82%1 More1
3ECONOMICS -2.85%-0.43%1.99%4.40%6.82%More 0 2 4 6 8 10 12 Histogram Frequency Polynomial (Frequency) Bin Frequency Question 2 The distribution reflected in the above diagram can be well related to a normal distribution graph. However the graph or distribution shows right skewed distribution for the distribution undertaken (Jan and Gopalaswamy 2019). In this case, the mean is towards left to the peak which intentionally reflects the skewness of the distribution making the trend distribution of the graph skew towards a bit right. The distribution can be well compared with the help of following statistics and distributional forms such as: Kurtosis:The figure of Kurtosis measures or reflects the amount or probability of set of data in the tails of a distribution. The value can be well compared with the Kurtosis of a normal
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4ECONOMICS distribution which has been equal to 3 (Jammazi Lahiani and Nguyen 2015). If the figure determined for Kurtosis is greater than 3, then the data set can be said to have a heavier tails other than a normal distributions. In the case of data evaluated here the value of Kurtosis determined was around 0.21 and the same says that the value is comparatively less in the tails of the distributions drawn. The Kurtosis figure here is very low which shows that the tail risk is comparatively lower for the data set of distributions analyzed. The median and mode figures are comparatively higher in this set of data. Skewness:In the theory of probability it can be well said that the skewness is a measure of asymmetry of probability distribution of a real valued random variable in accordance with the mean value. The value of skewness can be positive, negative and the same even can be undefined.If the level ofskewnessin the set of data undertaken is less than -1 or greater than 1, the distribution analyzed can be said to be highly skewed. If the level ofskewnessis between -1 and -0.5or between0.5and 1, the distribution can be well described as moderately skewed. The data analyzed shows a moderate skewed distribution for the data as the skewness was around 0.54. The results of descriptive set of data analyzed for the exchange rate is as follows: Column1 Mean0.004592858 Standard Error0.006015026 Median-0.006997376 Mode#N/A Standard Deviation0.028847049 Sample Variance0.000832152 Kurtosis0.021888734 Skewness0.543716568 Range0.12091367 Minimum-0.048665001
5ECONOMICS Maximum0.07224867 Sum0.105635731 Count23 Question 3 Volatility in the set of given data can be well explained with the help of movement of various factors that can explain the movement in the trend period analyzed for the exchange rate between 2016-2017. The volatility or the standard deviation could be calculated with the help of the movement that is being observed in the given set of data for the exchange rate of AUD/USD for a sum of two-year of time period. The changes in the currency pair can also be explained with the help of key macro-economic factors like the level of interest rate, inflation rate and growth rate of each of the economy for the trend period analyzed (Plakandaras et al., 2015). The volatility of the data was computed to be around 33.856% that shows the high range of volatility in the given set of data for the trend period. It is important to determine and understand the underlying volatility of an asset as it gives a trader, investors and an entity to evaluate the movement or changes in the currency pair (Ishizaki and Inoue 2018). The higher standard deviation or volatility of the currency is generated from the given set of data, the higher is the amount of volatility the greater is the amount of risk involved in the same. The graphical distribution stating the distributions of the exchange rate can be well reflected as below:
6ECONOMICS 17-Jan 1-Feb 16-Feb 3-Mar 18-Mar 2-Apr 17-Apr 2-May 17-May 1-Jun 16-Jun 1-Jul 16-Jul 31-Jul 15-Aug 30-Aug 14-Sep 29-Sep 14-Oct 29-Oct 13-Nov 28-Nov 13-Dec -6.00% -4.00% -2.00% 0.00% 2.00% 4.00% 6.00% 8.00% AUD/USD Return (%) Question 4 Assessment of the directional movement in the trend period can be analyzed or studies with the help of the previous set of data that can explain the movement in the data due to a numerous macro and as well as micro factors that would have affected the changes in the defined time period. It is most likely assumed that the movement between these set of data would be done accordingly with the help of past trend shown by the company (Lee, Wang and Xie 2018). In terms of continuous compounded return the set of currency pair has reflected about 5.51% changes on a continuous compounded data period for the sum of two-year time period analyzed for the AUD/USD Currency. For the next set of time period it is expected that the currency pair would be moving by around 33.856% that is the actual movement captured through the past data. However, it is equally important that macro-economic factors like the level of interest rate, inflation rate and growth rate of each of the economy can also play a substantial role. On the other hand, micro factors including demand and supply of currency pairs which in turn depends on various macro plus business factors in an economy can also play a substantial role in the overall movement of currency pair in the next course of time period (Beckmann and Czudaj 2017).
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7ECONOMICS Question 5 The volatility assessment of Bitcoin would be done for a period of two-year of time frame whereby the given set of data records has been analyzed for a sum of two-period. The asset class selected for the purpose of analysis is definitely varied as Bitcoin as an asset class is a digital crypto-currency that does not have any underlying asset making the prices of the assets to be more volatile. In the given set of data period analyzed it was found that the values of crypto- currency has varied significantly and the same could be well explained with the pattern formed by the data analyzed (Indexmundi.com 2019). 16-Jan 31-Jan 15-Feb 2-Mar 17-Mar 1-Apr 16-Apr 1-May 16-May 31-May 15-Jun 30-Jun 15-Jul 30-Jul 14-Aug 29-Aug 13-Sep 28-Sep 13-Oct 28-Oct 12-Nov 27-Nov 12-Dec -20.00% -10.00% 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% BTC/USD Bitf inex Data Question 6 The association (correlation evident) between the AUD and the resultant value of Coal Prices can be well compatible and researched based on the two-year data period analyzed. The value of bulk minerals considered here is the prices of Coal that has been considered for the purpose of analysis. The prices of Coal would be matched in accordance with the AUD/USD Return for the trend period that would be analyzed for the company (Investing.com 2019). The
8ECONOMICS return generated by the Coal Commodity has been around 42.78% and the corresponding risk for the commodity has been around 29.26%. The correlation for both the AUD and Coal Prices in the trend period has been positive in the trend period whereby the calculated return has been around 0.515. On a risk return basis the risk structure of the company has been quite significant whereby the coal prices have turned out to follow a volatile path in the trend period. It could be wellsaidthatthecommoditiesissomewhatcorrelatedwiththeAUD/USDReturn (Investing.com 2019). AUD/USD ReturnCoal PricesParticularsReturnRisk DatePriceReturnDatePriceReturnAUD/USD5.51%16.80% 16-Jan0.7085Jan-1649.02Coal Price42.78%29.96% 16-Feb0.71420.80%Feb-1650.272.55% 16-Mar0.76587.22%Mar-1652.213.86%Correlation0.515609 16-Apr0.7603-0.72%Apr-1650.69-2.91% 16-May0.7233-4.87%May-1651.311.22% 16-Jun0.74513.01%Jun-1652.853.00% 16-Jul0.75971.96%Jul-1661.2415.88% 16-Aug0.7518-1.04%Aug-1667.3910.04% 16-Sep0.76641.94%Sep-1672.727.91% 16-Oct0.761-0.70%Oct-1694.229.54% 16-Nov0.7386-2.94%Nov-16103.439.80% 16-Dec0.7216-2.30%Dec-1688.15-14.77% 17-Jan0.75855.11%Jan-1783.73-5.01% 17-Feb0.76580.96%Feb-1779.98-4.48% 17-Mar0.7629-0.38%Mar-1780.91.15% 17-Apr0.7489-1.84%Apr-1783.653.40% 17-May0.743-0.79%May-1774.42-11.03% 17-Jun0.76913.51%Jun-1781.098.96% 17-Jul0.80034.06%Jul-1787.497.89% 17-Aug0.7947-0.70%Aug-1798.5812.68% 17-Sep0.7834-1.42%Sep-1797.82-0.77% 17-Oct0.7657-2.26%Oct-1797.11-0.73%
9ECONOMICS 17-Nov0.7567-1.18%Nov-1796.64-0.48% 17-Dec0.78023.11%Dec-17100.814.31% The commodity currency name is given to some currencies whereby the same moves with the world prices of commodity product as the country is heavily dependent on the export and changes associated with these minerals taken into consideration. Commodities currencies are also seen in developed economies as well like Canada and Australia. Due to the nature and effect of the commodity currencies that are directly correlated to the value of the currency the same can be problematic as well as an advantage to the economy. If the exports falls or rises the same would be resulting to deflation or inflation on a respective basis. The impact of the same can be more high when the value of currency is closely tied with the small set of commodities. A currency that is naturally tied to the country’s major commodities can be beneficial if the global demand for a commodity increases, that would eventually be strengthening the value of the currency. As observed in the below figure, the demand for the Commodity Shifts Out that is higher demand increases the price to ‘P’. The increased demand would be increasing the GDP as the value of export would be increasing gradually:
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11ECONOMICS References Beckmann, J. and Czudaj, R., 2017. Exchange rate expectations since the financial crisis: Performance evaluation and the role of monetary policy and safe haven.Journal of International Money and Finance,74, pp.283-300. Guirguis, M., 2018. Application of a Log Likelihood Object In GARCH with T-distributed errors and EGARCH With Generalised Error Distribution Model of the Spot AUD/USD Exchange Rate Volatility.USD Exchange Rate Volatility.(September 21, 2018). Hamilton, A., 2018. Understanding Exchange Rates and Why They Are Important| Bulletin– December Quarter 2018.Bulletin, (December). Indexmundi.com. (2019).Coal, Australian thermal coal - Monthly Price - Commodity Prices - PriceCharts,Data,andNews-IndexMundi.[online]Availableat: https://www.indexmundi.com/commodities/?commodity=coal-australian&months=60 [Accessed 3 Aug. 2019]. Investing.com.2019.AUDUSDHistoricalData-Investing.com.[online]Availableat: https://in.investing.com/currencies/aud-usd-historical-data [Accessed 2 Aug. 2019]. Investing.com. 2019.Gold Futures Historical Prices - Investing.com. [online] Available at: https://in.investing.com/commodities/gold-historical-data [Accessed 3 Aug. 2019]. Ishizaki, R. and Inoue, M., 2018. Time-series analysis of multiple foreign exchange rates using time-dependent pattern entropy.Physica A: Statistical Mechanics and its Applications,490, pp.967-974.
12ECONOMICS Jammazi, R., Lahiani, A. and Nguyen, D.K., 2015. A wavelet-based nonlinear ARDL model for assessing the exchange rate pass-through to crude oil prices.Journal of International Financial Markets, Institutions and Money,34, pp.173-187. Jan,J.H.andGopalaswamy,A.K.,2019.Identifyingfactorsincurrencyexchangerate estimation: a study on AUD against USD.Journal of Advances in Management Research. Jiang, J. and Gu, R., 2016. Asymmetrical long-run dependence between oil price and US dollar exchange rate—Based on structural oil shocks.Physica A: Statistical Mechanicsand its Applications,456, pp.75-89. Lee, C.F., Wang, C.S. and Xie, A.Y., 2018. Exchange Rate Risk in the US Stock Market: A Pooled Panel Data Regression Approach.Available at SSRN 3407704. Plakandaras, V., Papadimitriou, T., Gogas, P. and Diamantaras, K., 2015. Market sentiment and exchange rate directional forecasting.Algorithmic Finance,4(1-2), pp.69-79. Ramasamy, R. and Abar, S.K., 2015. Influence of macroeconomic variables on exchange rates. Journal of economics, Business and Management,3(2), pp.276-281. Zhang, H.J., Dufour, J.M. and Galbraith, J.W., 2016. Exchange rates and commodity prices: Measuring causality at multiple horizons.Journal of Empirical Finance,36, pp.100-120.