Analysis of Global Currencies Fluctuation Against USD

IE 6200 Term Project ANALYSIS OF MAJOR WORLD CURRENCIES AGAINST USD Guide: Dr. Rajesh Jugulum Aman Shah Manan Nagda Tanay Parikh Yash Savaliya Abhinav Korni Hrishikesh Dhokare December 5, 2022

Index Serial Numbe r Table of Contents Page Number 1 Abstract 1 2 Methodology 1 3 P – Diagram 2 4 Data 3 5 Time Series 5 6 Boxplot 7 7 ANOVA 9 8 Weighted Average VS Actual Rates 10 9 Regression Analysis 12 10 Results & Discussion 14 11 Scope of Work 15 12 References 15

Abstract In a typical foreign exchange transaction, a party purchases some quantity of one currency by paying with some quantity of another currency. Conversion rates of currency always fluctuate across the world. Considering this we have focused on conversion of US dollars to different currencies like INR, CNY, RUB, EUR, JPY of the past 5 years. Our focus is to check if the currencies fluctuate in harmony with one another or not using ANOVA. We have used P- diagram to depict the control factors and the noise factors that affect currency conversion rates. This analysis helps to understand the variation of currency rates in the last 5 years. Which we have analyze and study the trends of currency rates using statistical tools like boxplot, time series curve, ANOVA. Methodology The exploration of data started with collecting data on country forex reserves and commodities. A large data set was obtained from the website investing.com. The study was carried out taking the data of USD versus other currencies of the years 2018, 2019, 2020, 2021 and 2022. We created a P-diagram prior our analysis. It is used to analyze Outputs considering Inputs, Noise Factors and Control Factors. Here, the P-diagram is used to identify the various sources of variations in inflation rates, bank interest rates, government policies/relations, terms of trade, conversion rates, Country Forex Reserves and Forex and Commodities. By using a time-series plot, we can view the fluctuation in currency rates of 5 countries with USD over a period of 5 years. A time series is a set of data on a variable measured over successive periods of time. The data collected in this study fits the definition of time series. Time series analysis comprises a method to discover patterns in the historical data and then predict the pattern into the future. Time series includes components like trend, cyclical, seasonal and irregular. The ANOVA has been used to measure any significant variations in the mean of change on all the currencies In our analysis, H 0 :µ 1 =µ 2 =µ 3 =µ 4 =µ 5 Ha: at least one mean is different. Results are summarized in the form of tables. Graphs and suitable charts were used to present the data graphically so that interpretation could be better.

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P – Diagram [1,2]

Data [3] Date (mm-dd-yyyy) INR CNY RUB EUR JPY 1/1/18 63.54 6.2888 56.2075 0.805 109.17 1/2/18 65.2 6.3311 56.3291 0.8199 106.67 1/3/18 65.11 6.2753 57.1444 0.8114 106.26 1/4/18 66.45 6.3332 62.9408 0.8278 109.33 1/5/18 67.42 6.4103 62.403 0.8551 108.81 1/6/18 68.45 6.621 62.7338 0.8557 110.66 1/7/18 68.45 6.8127 62.5078 0.8553 111.86 1/8/18 71 6.8315 67.5458 0.8617 111.02 1/9/18 72.5 6.8689 65.5503 0.8613 113.68 1/10/18 73.95 6.9756 65.8789 0.8839 112.93 1/11/18 69.64 6.9605 66.951 0.8835 113.46 1/12/18 69.56 6.8785 69.8319 0.8717 109.56 1/1/19 70.95 6.7008 65.4103 0.8735 108.87 1/2/19 70.83 6.6942 65.9151 0.8794 111.37 1/3/19 69.18 6.712 65.634 0.8913 110.84 1/4/19 69.636 6.7349 64.6373 0.8916 111.41 1/5/19 69.57 6.905 65.4306 0.894 108.26 1/6/19 68.94 6.8668 63.2305 0.8794 107.88 1/7/19 68.86 6.8841 63.6284 0.9027 108.74 1/8/19 71.451 7.1568 66.7657 0.9097 106.29 1/9/19 70.64 7.1484 64.8569 0.9173 108.06 1/10/19 70.978 7.0395 64.1328 0.8965 108.02 1/11/19 71.746 7.0324 64.3195 0.9075 109.51 1/12/19 71.35 6.9632 61.9863 0.8917 108.61 1/1/20 71.54 6.9367 63.9203 0.9013 108.38 1/2/20 72.534 6.9919 66.8806 0.9068 108.07 1/3/20 75.333 7.0825 78.4426 0.9064 107.53 1/4/20 75.077 7.0622 74.3813 0.9125 107.17 1/5/20 75.59 7.1372 70.1445 0.9009 107.77 1/6/20 75.54 7.0655 71.1734 0.8901 107.92 1/7/20 74.916 6.9752 74.4114 0.8488 105.88 1/8/20 73.254 6.8484 74.0718 0.8377 105.89 1/9/20 73.56 6.7908 77.6319 0.8531 105.45 1/10/20 74.554 6.6927 79.5257 0.8583 104.64 1/11/20 73.99 6.5789 76.4033 0.8381 104.27 1/12/20 73.036 6.5267 74.4121 0.8185 103.24 1/1/21 72.877 6.4277 75.7404 0.8239 104.68 1/2/21 73.92 6.4751 74.6196 0.828 106.58 1/3/21 73.137 6.5526 75.6987 0.8524 110.7 1/4/21 74.05 6.4735 75.2073 0.8318 109.27 1/5/21 72.511 6.37 73.4341 0.8177 109.54 1/6/21 74.36 6.4572 73.1522 0.8433 111.1 1/7/21 74.337 6.4614 73.1409 0.8423 109.7 1/8/21 72.947 6.4607 73.2274 0.8467 110.02 1/9/21 74.164 6.4466 72.7514 0.8632 111.27 1/10/21 74.915 6.4058 70.9464 0.8647 114 1/11/21 75.09 6.3643 74.0838 0.8819 113.13 1/12/21 74.467 6.3561 74.6539 0.8794 115.08 1/1/22 74.529 6.361 77.3792 0.8899 115.1 1/2/22 75.493 6.3093 94.6025 0.891 114.99 1/3/22 75.901 6.34 83.2 0.9034 121.66 1/4/22 76.52 6.6085 70.96 0.9483 129.83 1/5/22 77.569 6.672 61.5 0.9314 128.68 1/6/22 78.95 6.6993 51.45 0.9537 135.73 1/7/22 79.336 6.7442 61.62 0.9783 133.19 1/8/22 79.491 6.8904 60.23 0.9939 138.96 1/9/22 81.509 7.1135 58.45 1.0201 144.75 1/10/22 82.77 7.3015 61.4775 1.0114 148.71 1/11/22 81.091 7.0445 60.4 0.9658 139.16

INR CNY RUB EUR JPY 1.66 0.0423 0.1216 0.0149 -2.5 -0.09 -0.0558 0.8153 -0.0085 -0.41 1.34 0.0579 5.7964 0.0164 3.07 0.97 0.0771 -0.5378 0.0273 -0.52 1.03 0.2107 0.3308 0.0006 1.85 0 0.1917 -0.226 -0.0004 1.2 2.55 0.0188 5.038 0.0064 -0.84 1.5 0.0374 -1.9955 -0.0004 2.66 1.45 0.1067 0.3286 0.0226 -0.75 -4.31 -0.0151 1.0721 -0.0004 0.53 -0.08 -0.082 2.8809 -0.0118 -3.9 1.39 -0.1777 -4.4216 0.0018 -0.69 -0.12 -0.0066 0.5048 0.0059 2.5 -1.65 0.0178 -0.2811 0.0119 -0.53 0.456 0.0229 -0.9967 0.0003 0.57 -0.066 0.1701 0.7933 0.0024 -3.15 -0.63 -0.0382 -2.2001 -0.0146 -0.38 -0.08 0.0173 0.3979 0.0233 0.86 2.591 0.2727 3.1373 0.007 -2.45 -0.811 -0.0084 -1.9088 0.0076 1.77 0.338 -0.1089 -0.7241 -0.0208 -0.04 0.768 -0.0071 0.1867 0.011 1.49 -0.396 -0.0692 -2.3332 -0.0158 -0.9 0.19 -0.0265 1.934 0.0096 -0.23 0.994 0.0552 2.9603 0.0055 -0.31 2.799 0.0906 11.562 -0.0004 -0.54 -0.256 -0.0203 -4.0613 0.0061 -0.36 0.513 0.075 -4.2368 -0.0116 0.6 -0.05 -0.0717 1.0289 -0.0108 0.15 -0.624 -0.0903 3.238 -0.0413 -2.04 -1.662 -0.1268 -0.3396 -0.0111 0.01 0.306 -0.0576 3.5601 0.0154 -0.44 0.994 -0.0981 1.8938 0.0052 -0.81 -0.564 -0.1138 -3.1224 -0.0202 -0.37 -0.954 -0.0522 -1.9912 -0.0196 -1.03 -0.159 -0.099 1.3283 0.0054 1.44 1.043 0.0474 -1.1208 0.0041 1.9 -0.783 0.0775 1.0791 0.0244 4.12 0.913 -0.0791 -0.4914 -0.0206 -1.43 -1.539 -0.1035 -1.7732 -0.0141 0.27 1.849 0.0872 -0.2819 0.0256 1.56 -0.023 0.0042 -0.0113 -0.001 -1.4 -1.39 -0.0007 0.0865 0.0044 0.32 1.217 -0.0141 -0.476 0.0165 1.25 0.751 -0.0408 -1.805 0.0015 2.73 0.175 -0.0415 3.1374 0.0172 -0.87 -0.623 -0.0082 0.5701 -0.0025 1.95 0.062 0.0049 2.7253 0.0105 0.02 0.964 -0.0517 17.2233 0.0011 -0.11 0.408 0.0307 -11.4025 0.0124 6.67 0.619 0.2685 -12.24 0.0449 8.17 1.049 0.0635 -9.46 -0.0169 -1.15 1.381 0.0273 -10.05 0.0223 7.05 0.386 0.0449 10.17 0.0246 -2.54 0.155 0.1462 -1.39 0.0156 5.77 2.018 0.2231 -1.78 0.0262 5.79 1.261 0.188 3.0275 -0.0087 3.96 -1.679 -0.257 -1.0775 -0.0456 -9.55

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Time Series Below (Fig 1-5) we see Time Series graphs from 2018-2022 on x-axis which plots the change of average prices of currencies on y-axis within a 3-σ of mean line and the outliers can be seen below which can demonstrate major movements of a currency in 1 month period. Fig 1 Fig 2 Fig. 3

Fig 4 Fig 5

Boxplots Below (Fig 6-10) charts the movement of currencies in a calendar year within 1 box, the bigger the box more the fluctuations. The Box plots show the mean, median, maximum and minimum, upper quartile, lower quartile. This gives us a summary understanding of the data. Fig 6 Fig 7

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Fig 8 Fig 9 Fig 10

ANOVA: Single Factor

SUMMARY Groups Count Sum Average Variance INR 58 17.551 0.302603448 1.486732419 CNY 58 0.7557 0.01302931 0.011179361 RUB 58 4.1925 0.072284483 22.03801204 EUR 58 0.1608 0.002772414 0.000280234 JPY 58 29.99 0.517068966 7.930624592 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 11.57253787 4 2.893134467 0.459711797 0.765274422 2.403320028 Within Groups 1793.609233 285 6.293365729 Total 1805.181771 289 ANOVA

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Weighted Average VS Actual Rates The below charts depict the movement of currency against its 3-month weighted average in ratio of (70:20:10). This has been done to predict the Exchange rate as of 2 nd dec. 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 60 65 70 75 80 85 Weighted Average VS Actual Rates USD vs INR Fig 11 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 6.2 6.4 6.6 6.8 7 7.2 7.4 Weighted Average VS Actual Rates USD vs CNY Fig 12

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 50 55 60 65 70 75 80 85 90 95 100 Weighted Average VS Actual Rates USD vs RUB Fig 13 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 0.8 0.85 0.9 0.95 1 1.05 Weighted Average VS Actual Rates USD vs EUR Fig 14 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 100 105 110 115 120 125 130 135 140 145 150 Weighted Average VS Actual Rates USD vs JPY Fig 15

Regression Analysis The Regression analysis has been performed on all currencies to calculate the value as of 2 nd dec using regression analysis tool on Excel with a confidence interval of 90% Fig 16 Fig 17

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Fig 18 Fig 19 Fig 20

Results and Discussion ANOVA was conducted to evaluate if the movement between currencies is similar or not by calculating means of change in currencies over a 5-year period. In our analysis, H 0 :µ 1 =µ 2 =µ 3 =µ 4 =µ 5 Ha: at least one movement in currency is different. In our result we found that p-value=0.7652>α=0.05, hence we fail to reject the null hypothesis indicating that the currencies fluctuate at the same rate of 5-year. In addition, we forecasted the value of all currencies using 3-month weighted average and Regression analysis for 1 st December and below are the % change from the actual. INR Value %Change from actual 3 Mon 81.4686 -0.038% Regression 81.2939 0.177% Actual 81.438 CNY Value %Change from actual 3 Mon 7.1028 -1.11% Regression 7.0929 -0.97% Actual 7.0246 RUB Value %Change from actual 3 Mon 60.4205 2.55% Regression 60.5483 2.34% Actual 62 EUR Value %Change from actual 3 Mon 0.98035 6.99% Regression 0.9671 8.24% Actual 1.054 JPY Value %Change from actual 3 Mon 141.629 -5.46% Regression 139.528 5 -3.89% Actual 134.3

Scope of work Like we did analysis on currencies above in the report, in future we can predict currency rates accurately by sourcing quality data from government or verified sites and correlate them with the control factors as mentioned in p-diagram. But in the end, we would never be able to predict the exact rate as there are many noise factors that are not at all in our hands. References 1. https://alpari.com/en/beginner/articles/7-factors-influence-exchange-rates 2. https://www.investopedia.com/trading/factors-influence-exchange-rates 3. https://www.investing.com/currencies/

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PROJECT REPORT IE 6200

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