Indian Rupee: Is it Really Unpredictable?

FIN 3560: Financial Markets and Instruments

“I pledge my honor that I neither received nor provided any

unauthorized assistance during the completion of this work.”

“The authors of this paper hereby give permission to Professor Michael

Goldstein to distribute this paper by hard copy, to put it on reserve at

Horn Library at Babson College, or to post a PDF version of this paper

on the Internet.”

Stephanie Boenawan

Connor Boyen

Aydarbek Kurbansho

Mirela Tadic

(Section 04)

December 4, 2013

1

Table of Contents

Executive Summary ……………………………………………………………………………. 2

Background ………………………………………………………………………………..…… 3

Regression Analysis ……………………………………………………………………………. 4

Variable Analysis ………………………………………………………………………………. 8

Exchange Rate Calculations …………………………………………………………………... 15

Conclusion ………………………………………………………………….…………………... 16

References ………………………………………………………………………………...……. 17

Exhibits ………………………………………………………………………...……………….. 19

2

Executive Summary

The paper opens with a history on the volatility of the Indian Rupee that was majorly driven by

inflation in 1966. Since 1966, the Indian Rupee has gone through some major policy changes that affect the

intrinsic value of the currency. The Indian Rupee was fixed to the US Dollar from 1966 until the crisis of

1991, and India was implementing strict protectionist policies with a huge current account deficit1. These

factors causes the Indian Rupee to be highly overvalued as the spread between nominal exchange rate and

real exchange rate is around 10.5 Rupee to a Dollar. The Indian Rupee devalued against the US Dollar from

4.79 Rupee to a Dollar in 1958 to 18.52 Rupee to a Dollar in 1991 and further depreciated to 62.39 Rupee to

a Dollar today2. After 1991, India changed into a floating exchange rate regime. This change has caused a

volatility problem to the exchange rate of the Indian Rupee. The source of the volatility problem is deeply

analyzed through a regression model that encompasses these variables from 1992 to 2012; total Gross

Domestic Product or GDP (in US$ bln.), public debt to GDP, inflation, nominal interest rate, current account,

and terms of trade.

Our regression model examined the correlation between the exchange rate of the Indian Rupee and

each of these six independent variables. The purpose of the model is to see if each variable has a statistically

significant effect towards the Indian Rupee. Also it eliminates such variables that might intuitively seem

contradictive such as inflation rate, which were perceived as the main problem to the fluctuation of the

Indian Rupee. In addition, the regression analysis provides a best subset of variables that gives the highest R2

or coefficient of determination value out of a 100% for predicting the changes in the Indian Rupee to the

Dollar.

The paper concludes with a result that shows a strong relationship exists between the exchange rate

of the Indian Rupee and GDP, public debt to GDP, nominal interest rate, current account deficit, and terms of

trade. Since changes in these five variables are statistically proven to be highly correlated to the changes in

the exchange rate of the Indian Rupee, it is recommended if policy makers in India would carefully consider

these five variables to maintain a more stable Rupee.

1 Jyoti P. "Indian Rupee, seen as Overvalued Against U.S. Dollar, Likely to Fall." Asian Wall Street Journal: 24. May

09 1997. ProQuest. Web. 4 Dec. 2013 . 2 Merchant, Minhaz. "Rupee Is Undervalued by 25%; Fair Value Would Be Rs 40/dollar." The Economic Times. N.p.,

n.d. Web. 28 Nov. 2013. <http://articles.economictimes.indiatimes.com/2012-03-10/news/31143096_1_convertibility-

Rupee-finance-minister>.

3

Background

History

The Rupee is one of the oldest forms of currency still used in the modern day world. However, it was

not until 1947 when India broke free of British control and began introducing their monetary policy. It took

ten years after the induction of the Rupee for a decimalization system to be put into place. After 1957, the

paisa was inducted into the system as one hundredth of a Rupee. Over the past 65 years the Rupee has

significantly depreciated in value compared to the US dollar. Originally, the ratio was 1:1 for the Indian

Rupee to the US dollar. After less than one year the ratio quickly changed to 1 US dollar to every 4.79

Rupees. There have been two major economic crises in India’s recent past that have helped create the

downward spiral of the Rupee’s value.

In 1966, India’s inflation caused its own goods to become more expensive than foreign goods and

therefore the amount of imports increased while deports decreased. The depletion of India’s foreign currency

reserves finally blocked foreign aid and subsequently the Rupee was devalued. The 1991 devaluation of the

Rupee was primarily because of India’s economic reform. During the eighties, made worse by the Gulf War,

India’s oil import bill grew, thus causing the country to have a balance of payments problem. The

government’s deficit rose in 1981 from 9 percent of the country’s GDP to 12.7 percent in early 1991. At

this point, the value of the Rupee declines drastically; the government began expanding the international

reserves and by the end of 1991, the Indian government depleted its foreign reserves and had to allow the

currency to sharply decrease in value.3

The 1991 devaluation was different than 1966 because the Indian government was trying to increase

trade with foreign powers which effectively devalued the Rupee. Similar to 1966, high inflation lowered the

amount of imports which caused trade deficits. India’s primary focus was to stabilize the value of the Rupee.

Indian currency is susceptible to economic altercations in other areas such as Bangladesh, Pakistan and

Nepal. One of the major reasons is because these countries have adopted the Rupee as currency for their

nations. Currently, one American dollar is worth roughly 62 Rupees. 4

Gold

India is known to be one of the biggest gold importers in the world. There are two major reasons for

this huge amount of gold import. The first one is the jewelry industry that is extremely profitable in India,

allowing for an existence of a $1.2 billion industry.5 The second cause is the unpredictable performance of

3 World Bank, 23 Aug. 1991. Web.

4 "Money Studies in India." N.p., n.d. Web. 1 Dec. 2013.<http://www.ccs.in/ccs india/policy/money/studies/wp0

028.pdf>. 5 Shivom, Seth. "Gold Exports in June Slump 70% in India." Gold News. Mineweb, 19 July 2013. Web. 3 Dec. 2013.

<http://www.mineweb.com/mineweb/content/en/mineweb-gold-news?oid=198286>.

4

the Indian currency. Gold is known to be a safe haven for investors and is one of the best choices to look into

during a currency unstable run.

However, increased imports hurt India’s current account deficit, putting more pressure on the

Rupee6. Even though it is great that the ability to possess gold will allow numerous investors to keep their

money safe away from the corrupted government issues, sudden spikes in the currency movement and other

“cataclysmic” factors, the high amount of gold imports keeps the Rupee weaker and the economic

development of India is slowing down.

Regression Analysis

The type of data collected for our regression was time-series. Please see Exhibit 1 for the regression

output from Minitab. We chose six factors which we believe affect the Rupee Exchange Rate and explain the

recent currency troubles India has been facing. All observations are collected over a time period from 1992-

2012. Time is an important dimension in a time series data, and our regressions on the six factors which

affect the Rupee are dependent across time. Number of observations in our regression is 20, and the

significance level is 5% (1.96 = critical value).

The six factors chosen and the data indicators used are as follows:

1. Political Stability & Economic Performance : Gross Domestic Product (GDP)

2. Public Debt: Public Debt/GDP

3. Inflation: Consumer Price Index

4. Interest Rates: Nominal Interest Rates

5. Current Account Deficit: Trade of Imports and Exports

6. Terms of Trade: Exports Prices/Import Prices

Our hypothesis for the regression is as follows:

Ho: Political Stability & Economic Performance, Public Debt, Inflation, Interest Rates, Current

Account Deficit, and Terms of Trade have no effect on the exchange rate of the Indian Rupee

H1: Political Stability & Economic Performance, Public Debt, Inflation, Interest Rates, Current

Account Deficit, and Terms of Trade do have an effect on the exchange rate of the Indian Rupee.

In determining whether or not the six factors affect the Indian Rupee Exchange Rate value, we will analyze

the critical value test and p-value test to test our assumptions and hypothesis. We will also analyze the

collinearity test and best subset test.

6 Canavan, Greg. "Why India Is Buying Gold." The Daily Reckoning Australia. Port Phillip Publishing, 28 June 2012.

Web. 03 Dec. 2013. <http://www.dailyreckoning.com.au/why-india-is-buying-gold/2012/06/28/>.

5

R2 & Adjusted R

2

The coefficient of determination (R2) presents how the data values of the model are organized. A

high R2

value shows that all the variables presented in the regression model fit a single straight line at a

higher succession rate7. Our model’s R

2 is equal to 92.9%. The high percentage means that the majority of

movements of a security are explained by movements in the index.

The adjusted coefficient of determination accounts all the variables added to the model. Otherwise, it

serves the same role as the regular coefficient of determination. Our model’s adjusted R2 is 89.6%, making

our model fairly precise.

We also looked at the number of variables that is appropriate to give us a regression model that has a

justifiable high R2 value by looking at the difference between R

2 and R

2 adjusted. The difference between R

2

and R2 adjusted is 3.3%, which is larger than the normal 2% difference according to goodness of the fit

hypothesis8 The difference of 3.3% tells us that we should not add more variables to the model.

T-Statistic

The T- statistic is a reference to the relationship between a single variable and a single predictor. It is

a statistical examination of two population means.9 The absolute value of the T-statistic is used to reject the

null hypothesis for the regression model if the absolute value of the T- statistic is higher than the critical

value. The absolute value is used due to the two-tailed nature of the statistical analysis.

|t| > 1.96* significant

*The T-Statistic will be analyzed for all six chosen factors following the same statistic rules as above

P-Value

The P value serves as an estimated probability to reject the null-hypothesis for the regression model.

The lower the P value, the lower the relevance of the null hypothesis of the variable in the regression

model10

. With a 5% significance level, we determined whether or not the p-value for the regression is

significant by using the following statistics rule:

P-value < Significance Level

0.000 < 0.05* significant

7 "R-Squared." Investopedia. Investopedia US, n.d. Web. 30 Nov. 2013. <http://www.investopedia.com/terms/r/r-

squared.asp>. 8 Berenson, Mark L., David M. Levine, and Timothy C. Khrebiel. Basic Business Statistics: Concepts and Applications.

Upper Saddle River, NJ: Prentice-Hall, 1999. Print. 9 "T-Test." Investopedia. Investopedia US, n.d. Web. 30 Nov. 2013. <http://www.investopedia.com/terms/t/t-test.asp>.

10 "P Values." Statistical Help. Statsdirect.com, n.d. Web. 30 Nov. 2013.

<http://www.statsdirect.com/help/default.htm>.

6

The regression’s p-value of 0.000 indicates that the overall regression we ran with the six factors is

significant. The majority of our P-values for the chosen variables are close enough to zero to allow us to

consider these variables to be more statistically relevant. The inflation variable, that doesn’t look statistically

variable, might be affected by the nature of this particular variable. (This will be analyzed further)

*The P-Value will be analyzed for all six chosen factors in the same format as above

Collinearity Test

It is very important to test collinearity problem in our regression model, because an independent

variable that is highly correlated to other independent variables in the model would cloud the result in

exchange rate of Indian Rupee or our dependent variable. To test this problem, we looked at the VIF value of

each independent variable in our model that is higher than the appropriate 5. A VIF higher than 5 means that

there is a collinearity problem according to VIF test11

.

The result above shows that GDP ($bln.) variable and Current Account Deficit variable have the

highest VIF in the model. This means that there is a high correlation between GDP ($bln.) and Public

Debt/GDP, Inflation, Nominal Interest Rate, and Current Account Deficit. Also it means that Current

Account Deficit is highly correlated with GDP ($bln.), Public Debt/GDP, Inflation, and Nominal Interest

Rate.

Best Subset (Please see exhibit 1b)

Best subset regression tells us the exact variables and number of variables that would give the best

R2, R

2 adjusted value, and standard error of the estimate (S) value. The best amount of variables that gives

the highest R2 and R

2 adjusted value is five variables, which are GDP ($bln.), Public Debt/GDP, Nominal

Interest Rate, Current Account Deficit, and Terms of Trade. These variables also give the lowest S value of

2.2548, which means that the variability of the data around the regression line is 2.2548 points away. In

addition, the rule for coefficient of variation says that the closer the formula

to 0 means the less volatility

or variation in the data to the regression line of the exchange rate of Indian Rupee12

.

11

Berenson, Mark L., David M. Levine, and Timothy C. Khrebiel. Basic Business Statistics: Concepts and

Applications. Upper Saddle River, NJ: Prentice-Hall, 1999. Print. 12

"Coefficient Of Variation." Investopedia. N.p., n.d. Web. 22 Nov. 2013. <http://www.investopedia.com /terms/c/

coefficientofvariation.asp>.

7

Changes to the Model

After considering the collinearity problem in the regression model, we decided to run a regression

without the GDP ($bln.) and current account deficit variable. The result shows that there is very little

collinearity in the model, which means that each independent variable is not too correlated that it clouds the

data.

Predictor Coef SE Coef T P VIF

Constant 104.82 14.40 7.28 0.000

Public Debt/GDP -0.3127 0.1657 -1.89 0.079 1.227

Inflation -0.1511 0.2934 -0.52 0.614 1.428

Nominal Interest Rate -2.7897 0.3528 -7.91 0.000 1.331

ToT -0.00000000 0.00000000 -1.90 0.076 1.597

S = 3.27203 R-Sq = 83.9% R-Sq(adj) = 79.6%

However, the result shows a poorer R2 and R

2 adjacent difference of 4.3% as well as a poorer R

2

value of 83.9% compared to our original 92.9%. This led us to do another change to the model according to

our t-test and p-value result by eliminating insignificant independent variables such as inflation from the

model. The result shows an improvement in our R2 adjusted with a value of 90.3%, which makes the

difference between R2

and R2 adjusted smaller with a value of 2.6%. This means that the model is a better

predictor of changes in the exchange rate of Indian Rupee given the variables listed below.

Predictor Coef SE Coef T P VIF

Constant 107.88 11.35 9.51 0.000

GDP ($bln.) 0.019522 0.004583 4.26 0.001 19.207

Public Debt/GDP -0.6309 0.1451 -4.35 0.001 1.983

Nominal Interest Rate -1.9537 0.3341 -5.85 0.000 2.514

Current Acc.Deficit 0.31275 0.07745 4.04 0.001 16.265

ToT -0.00000000 0.00000000 -3.71 0.002 1.325

S = 2.25477 R-Sq = 92.9% R-Sq(adj) = 90.3%

Residual Plots Assumptions

The normal probability plot graph further shows a supporting point for the high R2 value that the data

points of the exchange rate of Indian Rupee lie closely to the regression line assuming a straight line

relationship as seen on exhibit 1c. Since, we ran a regression model instead of a time series, the Versus Fits

graph show no pattern for the data points to show that the data is not cyclical. The Versus Fits graph

indicates that the residuals in the models are independent of each other, which means that the assumption of

independence of errors is valid13

. In addition, the histogram on exhibit 1c shows a peak in the middle at 0 on

13

Berenson, Mark L., David M. Levine, and Timothy C. Khrebiel. Basic Business Statistics: Concepts and

Applications. Upper Saddle River, NJ: Prentice-Hall, 1999. Print.

8

the x-axis and two tails at each end of the graph. The histogram does not show a perfectly shaped normal

distribution pattern, but it does portray a closer picture to a bell-curved shape, which justify our reasoning to

do a two-tailed test type for our t-test. Lastly, the Versus Order graph assumes an equal variance14

for the

regression model by looking at the data points that lie inside the -2 and 2 bands on the y-axis. The graph

shows that the equal variance assumption is valid due to all data points lie inside the -2 and 2 bands on the y-

axis.

Variable Analysis

Political and Economic Stability

In the last 20 years, the Indian economy has been one of the most cherishing economies in the world.

Thanks to their service industry that is mostly based off IT-support for a lot of western companies, the

development rates of the Indian economy have been somewhat stable. A slow decline in the economic

development as well as the Rupee exchange rate was witnessed in 2012 due to global economic issues,

mostly related to the economical downfall in several countries of the European Union, as well as the slow

decision making from the Indian parliament when it comes down to laws15

.

To determine the political and economic situation in India, we decided to use the main factor that

reflects changes in both of the factors – Gross Domestic Product. The stability of GDP without any radical

changes shows that the economic performance (which is based off the government actions).

Our hypothesis for GDP is as follows:

Ho: GDP has no effect on the exchange rate of the Indian Rupee

H1: A higher GDP increases the value of the Indian Rupee.

Our regression indicated a T-statistic of 4.03 for the GDP variable

|4.03| > 1.96

The absolute value of 4.03 is 4.03, which is larger than the critical value of 1.96. Therefore, we are rejecting

the null hypothesis and determine that according to the t-statistic, a higher GDP value increases the value of

the Indian Rupee.

14

Equal variance means that there is no major differences in the variability of the residuals for different Xi values

(Berenson 541). 15

Potia, Zeenat, and Tarun Khanna. "Behind India’s Economic and Political Woes." HBS Working Knowledge.

Harvard Business School, n.d. Web. 22 Nov. 2013. <http://hbswk.hbs.edu/item/7320.html>.

9

Our regression indicated a P-value of 0.001 for the GDP variable

0.001 < 0.5

The p-value of the GDP variable is 0.001, which is smaller than the significance level of 0.5. Therefore, we

are rejecting the null hypothesis and determine that according to the p-value test, a higher GDP increases the

value of the Indian Rupee.

After examining the GDP variable for India, we have determined that economic and political

stability is significant, and does affect the Indian Rupee Exchange Rate. The higher the GDP in India, the

higher the exchange rate value will be. However it is important to note an usual observation on year 2007 in

GDP that caused the value of Indian Rupee to be 4.096 points away given its actual value of 41.350 Rupee

per Dollar and its predicted value of 45.446 Rupee per Dollar according to our regression model16

. Since the

GDP changes relate to the currency rate, we can make a connection with the politic and economic stability.

Public Debt

Public or government debt is important factor, when it comes down to the foreign exchange rates

fluctuation due to its ability to predict the stability of country’s economic performance for the foreign

investors. Depending on how much the government borrowed and how capable it is to pay the debts back,

the investors make the final decision to invest in the currency. Therefore, the strength of the currency is

dependent on the demand from the investors. 1718

Our hypothesis for Public Debt is as follows:

Ho: Public Debt has no effect on the exchange rate of the Indian Rupee

H1: A higher Public Debt increases the value of the Indian Rupee.

Our regression indicated a T-statistic of -4.08 for the Public Debt variable

|-4.08| > 1.96

The absolute value of -4.08 is 4.08, which is larger than the critical value of 1.96. Therefore, we are

rejecting the null hypothesis and determine that according to the t-statistic, a higher Public Debt/GDP value

decreases the value of the Indian Rupee.

16

Another important note is that the unusual observation for our regression model is only 4.76% of our total data points. 17

Bergen, Jason Van. "6 Factors That Influence Exchange Rates." Investopedia. Investopedia US, n.d. Web. 22 Nov.

2013. <http://www.investopedia.com/articles/basics/04/050704.asp>. 18

"A Walk on the Wild Side." The Economist. The Economist Newspaper Ltd., 23 Feb. 2013. Web. 23 Nov. 2013.

<http://www.economist.com/news/asia/21572224-government-borrowing-generates-inflation-widens-external-deficit-

and-crowds-out-much-needed>.

10

Our regression indicated a P-value of 0.001 for the Public Debt variable

0.001 < 0.5

The p-value of the Public Debt/GDP variable is 0.001, which is smaller than the significance level of 0.5.

Therefore, we are rejecting the null hypothesis and determine that according to the p-value test, a higher

Public Debt/GDP decreases the value of the Indian Rupee.

After examining the Public Debt variable for India, we have determined that Public Debt is

significant, and does affect the Indian Rupee Exchange Rate. The higher the Public Debt in India, the lower

the exchange rate value will be.

Inflation

WPI (Wholesale Price Index) is the most common inflationary measured used by policy makers in

India. WPI ‘represents the price of goods at a wholesale stage’19

. On the other hand, CPI (Consumer Price

Index) measures ‘the weighted average of prices of a basket of consumer goods and services’20

. However,

India’s economy is moving in reaction towards changes in consumer-price inflation. This is because more

than 800 million people in India are living on less than $2 per day21

. This is a major reason that caused us to

use CPI as inflation measure rather than WPI, because CPI will provide a better grasp of the volatility in

price change and its impact towards the Indian Rupee.

Inflation rate or changes in price of goods and services will impact the exchange rate of the Indian

Rupee. An increase in inflation rate means price of goods and services have become higher or more

expensive due to lower purchasing power of Indian Rupee. This means that consumers in India will be less

willing and able to purchase goods and services. On the other hand, domestic goods and services or India’s

export will be demanded less as price becomes more expensive.

Our hypothesis for Inflation (CPI) is as follows:

Ho: Inflation rate has no effect on the exchange rate of the Indian Rupee

H1: An increase in the inflation rate will cause a decrease in the value of the Rupee

19

"Wholesale Price Index." The Economic Times. N.p., n.d. Web. 28 Nov. 2013.

<http://economictimes.indiatimes.com/definition/wholesale-price-index>. 20

"Consumer Price Index - CPI." Investopedia. N.p., n.d. Web. 22 Nov. 2013.

<http://www.investopedia.com/terms/c/consumerpriceindex.asp>. 21

Goyal, Kartik. "Rajan Spurs Surge in India's Reserves to Support Rupee: Economy." Bloomberg.com. Bloomberg, 12

Nov. 2013. Web. 22 Nov. 2013. <http://www.bloomberg.com/news/2013-11-11/rajan-spurs-india-reserve-surge-to-

support-Rupee-as-taper-looms.html>.

11

Our regression indicated a T-statistic of -0.34 for the Inflation variable

|-0.34| > 1.96

The absolute value of -0.34 is 0.34, which is smaller than the critical value of 1.96. Therefore, we accept the

null hypothesis and determined that inflation rate has no effect on the exchange rate of the Indian Rupee.

Our regression indicated a P-value of 0.740 for the Inflation variable

0.740 < 0.05

The p-value of the inflation variable is 0.740, which is bigger than the significance level of 0.05. Therefore,

we accept the null hypothesis and determine that inflation rate has no effect on the exchange rate of the

Indian Rupee.

Inflation rate in India is not a very significant driver in the exchange rate of Indian Rupee according

to the t-test performed on the regression model. This is largely due to the volatility of the inflation variable

that causes too much movement on data points for the model that it becomes statistically insignificant.

However, this does not mean that inflation rate is not a key indicator for policy makers in India that would

inevitably affect the Indian Rupee indirectly.

Interest Rate

Nominal interest rate that is used in the regression model is the lending interest rate for ‘short and

medium-term financing needs of the private sector’22

. Nominal Interest rates signals borrowers and lenders

on the rate of borrowing and lending money, which will affect spending and investment by firms and the

public. As interest rates rises for lenders, firms and public will be less willing to borrow money as borrowing

cost becomes more expensive. This will slow down India’s growth, but it will also mean that foreign

investors will be more willing to invest in India as rate rises. As demand for Rupee rises, so will its value

compared to other currencies.

Our hypothesis for Interest Rates is as follows:

Ho: Nominal interest rates have no effect on the exchange rate of the Indian Rupee

H1: An increase in the nominal interest rate will cause an increase in the value of the Indian Rupee

Our regression indicated a T-statistic of -4.82 for the Interest Rate variable

|-4.82| > 1.96

22

"Deposit Interest Rate (%)." Data. N.p., n.d. Web. 22 Nov. 2013.

<http://data.worldbank.org/indicator/FR.INR.DPST>.

12

The absolute value of -4.82 is 4.82, which is greater than the critical value of 1.96. Therefore, we reject the

null hypothesis and determine that an increase in nominal interest rate will cause an increase in the value of

Indian Rupee.

Our regression indicated a P-value of 0.000 for the Interest Rate variable

0.000 < 0.05

The p-value for nominal interest rate variable is smaller than the significance level of 0.05. Therefore, we

reject the null hypothesis and determined that an increase in nominal interest rate will cause an increase in

the value of Indian Rupee.

According to our t-test, nominal interest rate is significant as a key driver to the exchange rate of

Indian Rupee. Since the relationship between nominal interest rate and exchange rate of Indian Rupee is

negatively correlated, an increase in nominal interest rate will caused an increase in the value of Indian

Rupee. Therefore when making a policy that will affect the Rupee, policy makers should be aware of the

change in nominal interest rate.

Current Account Deficit

The current account deals with the trade of goods and services between two countries. The monetary

value of exports from a country and imports into a country are measured in the current account. If the value

of a country’s exports exceeds the values of the goods and services it imports, then that country has a trade

surplus.23

Our hypothesis for the Current Account Deficit is as follows:

Ho: The current account deficit has no effect on the exchange rate of the Indian Rupee

H1: A higher current account deficit will cause a decrease in the value of the Indian Rupee

Our regression indicated a T-statistic of 3.56 for the Current Account Deficit variable

|3.56| > 1.96

The absolute value of 3.56 is 3.56, which is larger than the critical value of 1.96. Therefore, we are rejecting

the null hypothesis and determine that according to the t-statistic, a higher current account deficit value

decreases the value of the Indian Rupee.

23

"Current Account Deficit." Investopedia. N.p., n.d. Web. 04 Dec. 2013. <http://www.investopedia.com/terms/c/cu

rrentaccountdeficit.asp>

13

Our regression indicated a P-value of 0.004 for the Current Account Deficit variable

0.004 < 0.05

The p-value of the Terms of Trade variable is 0.004, which is smaller than the significance level of 0.5.

Therefore, we are rejecting the null hypothesis and determine that according to the p-value test, a higher

current account deficit decreases the value of the Indian Rupee.

After examining the Current Account Deficit Variable for India, we have determined that the Current

Account is significant, and does affect the Indian Rupee Exchange Rate. The higher the deficit in India, the

lower the exchange rate value will be. In recent months, India’s imports have fallen, while its exports

climbed 13%, causing the trade deficit no fall from 20.1 billion in May, to $10.9 billion in August.24

The Reserve Bank of India recently came out with a statement saying that “the current account

deficit in 2013-14 will be USD 56 billion” and that due to this number being lower than projected earlier;

there is no reason for the Rupee, India’s currency, to depreciate. This figure is less than three percent of

India’s GDP and 32 Billion dollars less than last year’s figure, which is a positive movement. Governor

Raghuram Rajan said that although this is a good movement some of it can be explained by “our strong

measures to curb gold import”. It is fortunate that this figure fell because the CAD reached an all-time high

in India from 2012-2013 at 88.2 Billion dollars and 4.8 percent of GDP. Since 2008, India’s CAD was steady

between 2008 and 2010, but then grew significantly and has been fluctuating ever since. 25

Terms of Trade

In India, the terms of trade effect corresponds to the ratio of price of exportable goods to the price of

importable goods. 26

Our hypothesis for Terms of Trade is as follows:

Ho: Terms of Trade has no effect on the exchange rate of the Indian Rupee

H1: A higher terms of trade will cause an increase in the value of the Indian Rupee

Our regression indicated a T-statistic of -2.95 for the Terms of Trade variable

|-2.95| > 1.96

24

Raza, Syed. "Effects of Terms and Trade." N.p., 3 Apr. 2012. Web. 3 Dec. 2013. <http://mpra.ub.uni-muenchen.de/

38998/1/MPRA_paper_38998.pdf-first quote>. 25

"India Terms of Trade." TRADING ECONOMICS. N.p., n.d. Web. 04 Dec. 2013. <http://www.tradingeconomics.com

/india/terms-of-trade>. 26

Raza, Syed. "Effects of Terms and Trade." N.p., 3 Apr. 2012. Web. 3 Dec. 2013. <http://mpra.ub.uni-muenchen.

de/38998/1/MPRA_paper_38998.pdf-first quote>.

14

The absolute value of -2.95 is 2.95, which is larger than the critical value of 1.96. Therefore, we are

rejecting the null hypothesis and determine that according to the t-statistic, a higher Terms of Trade value

increases the value of the Indian Rupee.

Our regression indicated a P-value of 0.011 for the Terms of Trade variable

0.011 < 0.05

The p-value of the Terms of Trade variable is 0.011, which is smaller than the significance level of 0.5.

Therefore, we are rejecting the null hypothesis and determine that according to the p-value test, a higher

Terms of Trade increases the value of the Indian Rupee.

After examining the Terms of Trade Variable for India, we have determined that Terms of Trade is

significant, and does affect the Indian Rupee Exchange Rate. The higher the Terms of Trade in India, the

higher the exchange rate value will be. When a nation’s Terms of Trade improves, thus making the Rupee

exchange rate higher, the country can buy more imports for any given level of exports. A higher value in the

currency lowers the prices of its imports.

The terms of trade in India are reported by the Reserve Bank of India. In the last few decades the

Terms of Trade situation in India has been improving. “In the 1980’s the average terms of trade was 84, in

1990’s it increased to 105 and in the decade of 2000 the average terms of trade marginally improved and

became 107.” Similarly, during the period of time, as the Terms of Trade was improving India’s GDP was

also improving, and it has been shown that there is a connection between the two factors. Between the years

2000 and 2011, India hit its lowest point in term of trade of 77 Index Points in the year 2007. It is forecasted

to continue improving just slightly in the coming years and there has been an upward trend since its lowest

point almost seven years ago. This means that India is continuously exporting more that it is importing at an

increasing rate, which causes capital to flow into the country, which is a very positive indicator for India’s

continued growth. 27

27

"Ideas for India." Exchange Rate Movements and Indian Firms' Exports. N.p., n.d. Web. 04 Dec. 2013.

<http://ideasforindia.in/article.aspx?article_id=211>.

15

Exchange Rate Calculations28

Purchasing Power Parity

A difference in inflation rates between countries such as India and the US can affect the exchange

rate. The purchasing power parity takes into consideration the differences in inflation rates between the US

and India. It is important to note the inaccuracy of the purchasing power parity in this exchange. The

inaccuracy comes from the assumption that there are other factors affecting the exchange rate such as interest

rates and GDP. The original equation is derived from the assumption that each country’s real interest rates

are the same and therefore can be set equal to one. Eliminating this variable from the equation allows for the

inflation rate of the US minus the inflation rate of India to equal the change in the spot price divided by the

current spot price. In theory, a higher inflation rate will create a lower value for the currency.

US Inflation Rate - Indian Inflation Rate = Change in the spot exchange rate/ Current Spot Exchange Rate

IPus-IPI= (ΔSus/I)/(Sus/I)

The purchasing power theory describes that in the long run exchange rates will theoretically move

towards rates that would equalize the price of an identical basket of goods in two different countries.

Essentially, the purchasing power theory states that a good such as a cheeseburger should cost the same in

two separate countries once the currency is converted using the exchange rate. In Exhibit 2c, the US inflation

rate is compared to the Indian inflation rate on a monthly basis since the middle of 1994. Finding the

difference in the inflation rates (per month) allows for the change in the spot price to be calculated. It is

important to note that the inflation rates pertain to the entire month while the spot rates are from the

beginning of the month. Using these variables allows for the calculation of the next months predicted spot

rate. Comparing the predicted spot rate to the actual spot rate of the month allows for the percentage

difference to be calculated. Exhibit 2a shows the graph of the difference between the predicted and the

actual. There are several time periods where sharp spikes occur that prove the purchasing power parity

inaccuracy. Exhibit 2b is a graph of the 10 year time period in the middle where the difference in predicted

and actual is relatively evenly balanced. The spike to negative .07 at the 18th observation in Exhibit 2b

happens to fall on September 2001. Clearly, the September 11th attack on the United States had a quick and

dramatic effect on the spot rate. This proves the assumption that there are more factors than just inflation that

28

24 US Department of the Treasury, n.d. Web. <http://www.treasury.gov/resource-center/data-chart-center/interest-

rates/Pages/TextView.aspx?data=yieldYear&year=2013>. 25

"India Interest Rate." TRADING ECONOMICS. N.p., n.d. Web. 04 Dec. 2013. <http://www.tradingeconomics

.com/india/interest-rate>. 26

"United States | Economic Indicators." United States | Economic Indicators. N.p., n.d. Web. 04 Dec. 2013.

<http://www.tradingeconomics.com/united-states/indicators>. 27

Reserve Bank of India, n.d. Web. <http://dbie.rbi.org.in/DBIE/dbie.rbi?site=home>. 28

"TRADING ECONOMICS | 300.00 INDICATORS | 196 COUNTRIES." TRADING ECONOMICS | 300.00

INDICATORS | 196 COUNTRIES. N.p., n.d. Web. 04 Dec. 2013. <http://www.tradingeconomics.com/india/inflation>.

16

affect the exchange rate. If the theory were exactly correct then the difference should be zero and therefore

the graph would not fluctuate as it does. Exhibit 2c shows the regression analysis of the difference between

the actual and predicted spot rates. The sample size is 234 observations which include each month over the

past 19 years. The t stat of .91701433974977 is lower than the significance level which leads to the

conclusion that inflation is not directly correlated to the change in spot price. There are other factors that can

affect the rate. Exhibit 2b proves that there are other factors

The value of the rupee has dropped lower than expectations based on the higher rate of inflation in

India.

Uncovered Interest Rate Parity

The expected spot price, E(S), for the Indian Rupee is the rate at which a bank believes the value of

the foreign exchange will be. The price of the expected spot rate is based off of the current spot rate. The

price is also adjusted with the cost of carrying the currency. The spot exchange rate and the difference in the

two countries interest rates are the key variable to using the interest rate parity. Under the uncovered interest

rate parity, the expected spot rate is going to equal the US interest rate divided by the current spot rate

multiplied by the interest rate of India. The interest rates used to calculate the E(S) are 3 month Treasury bill

yields. Using 3 month T-bills will theoretically predict the spot rate for 3 months in advance using the current

month’s interest rates as shown in Exhibit 3b.

The interest rates for India are substantially higher than that of the US and therefore the value of the

rupee will be decrease. Similar to the purchasing power parity, the uncovered interest parity proves that

interest rates are not the only determinant of the exchange rate.

Conclusion

The purpose of this paper is to understand the underlying variables behind the volatility of the

exchange rate of the Indian Rupee through a multivariate regression analysis. The analysis concludes with a

result that says the GDP, public debt to GDP, nominal interest rate, current account deficit, and terms of

trade have a statistically significant effect towards the Indian Rupee. This means that as each of these

variables changes, so does the Indian Rupee according to each positive or negative relationship between the

Rupee and each variable. The most statistically significant variable in the model after removing the inflation

rate variable is nominal interest rate. Nominal interest rate of the United States and India are also a factor in

determining the forward exchange rate of the Indian Rupee. From our analysis based on the interest rate

parity, we concluded that the difference between the 3-month Treasury bills in the US and the 3-month

Treasury bills in India is statistically significant with the difference between the forward exchange rate of the

Indian Rupee and the spot exchange rate of the Indian Rupee. This result allows us to predict for the forward

exchange rate of the Indian Rupee using the interest rate parity theory more accurately on a monthly basis for

the next two years.

17

References

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<http://www.ccs.in/ccsindia/policy/money/studies/wp0028.pdf>.

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Shivom, Seth. "Gold Exports in June Slump 70% in India." Gold News. Mineweb, 19 July 2013.

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news?oid=198286>.

Canavan, Greg. "Why India Is Buying Gold." The Daily Reckoning Australia. Port Phillip Publishing, 28

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erms/r/r-squared.asp>.

Berenson, Mark L., David M. Levine, and Timothy C. Khrebiel. Basic Business Statistics: Concepts and

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<http://www.investopedia.com/terms/t/t-test.asp>.

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<http://www.statsdirect.com/help/default.htm>.

Berenson, Mark L., David M. Levine, and Timothy C. Khrebiel. Basic Business Statistics: Concepts and

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/terms/c/ coefficientofvariation.asp>.

Potia, Zeenat, and Tarun Khanna. "Behind India’s Economic and Political Woes." HBS Working

Knowledge. Harvard Business School, n.d. Web. 22 Nov. 2013.

<http://hbswk.hbs.edu/item/7320.html>.

Bergen, Jason Van. "6 Factors That Influence Exchange Rates." Investopedia. Investopedia US, n.d. Web.

22 Nov. 2013. <http://www.investopedia.com/articles/basics/04/050704.asp>.

"A Walk on the Wild Side." The Economist. The Economist Newspaper Ltd., 23 Feb. 2013. Web. 23

Nov. 2013. <http://www.economist.com/news/asia/21572224-government-borrowing-generates-

inflation-widens-external-deficit-and-crowds-out-much-needed>.

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<http://economictimes.indiatimes.com/definition/wholesale-price-index>.

18

"Consumer Price Index - CPI." Investopedia. N.p., n.d. Web. 22 Nov. 2013.

<http://www.investopedia.com/terms/c/consumerpriceindex.asp>.

Goyal, Kartik. "Rajan Spurs Surge in India's Reserves to Support Rupee: Economy." Bloomberg.com.

Bloomberg, 12 Nov. 2013. Web. 22 Nov. 2013. <http://www.bloomberg.com/news/2013-11-

11/rajan-spurs-india-reserve-surge-to-support-Rupee-as-taper-looms.html>.

"Deposit Interest Rate (%)." Data. N.p., n.d. Web. 22 Nov. 2013.

<http://data.worldbank.org/indicator/FR.INR.DPST>.

"Current Account Deficit." Investopedia. N.p., n.d. Web. 04 Dec. 2013.

<http://www.investopedia.com/terms/c/currentaccountdeficit.asp>1

Raza, Syed. "Effects of Terms and Trade." N.p., 3 Apr. 2012. Web. 3 Dec. 2013. <http://mpra.ub.uni

muenchen.de/38998/1/MPRA_paper_38998.pdf-first quote>.

"India Terms of Trade." TRADING ECONOMICS. N.p., n.d. Web. 04 Dec. 2013.

"Ideas for India." Exchange Rate Movements and Indian Firms' Exports. N.p., n.d. Web. 04 Dec. 2013.

<http://ideasforindia.in/article.aspx?article_id=211>.

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center/interest-rates/Pages/TextView.aspx?data=yieldYear&year=2013>.

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<http://www.tradingeconomics.com/india/interest-rate>.1

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300.00 INDICATORS | 196 COUNTRIES. N.p., n.d. Web. 04 Dec. 2013.

<http://www.tradingeconomics.com/india/inflation>.

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<http://www.tradingeconomics.com/india/inflation-cpi>.

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"India Treasury Bill Yield." TRADING ECONOMICS. N.p., n.d. Web. 04 Dec. 2013.

<http://www.tradingeconomics.com/india/interbank-rate>."Selected Interest Rates (Daily) -

H.15." FRB: H.15 Release--Selected Interest Rates--Historical Data. Federal Reserve Bank, n.d.

Web. 02 Dec. 2013. <http://www.federalreserve.gov/releases/h15/data.htm>.

19

Exhibits

Exhibit 1: Regression Output from Minitab

Regression Analysis: Exch.rate versus GDP ($bln.), GDP/Capita PPP, ... The regression equation is

Exch.rate = 118 + 0.0318 GDP ($bln.) - 0.0078 GDP/Capita PPP

- 0.638 Public Debt/GDP - 0.069 Inflation

- 2.12 Nominal Interest Rate + 0.366 Current Acc.Deficit

- 0.000000 ToT

20 cases used, 1 cases contain missing values

Predictor Coef SE Coef T P

Constant 118.35 25.70 4.60 0.001

GDP ($bln.) 0.03183 0.02515 1.27 0.230

GDP/Capita PPP -0.00778 0.01530 -0.51 0.620

Public Debt/GDP -0.6379 0.1600 -3.99 0.002

Inflation -0.0687 0.2711 -0.25 0.804

Nominal Interest Rate -2.1211 0.6068 -3.50 0.004

Current Acc.Deficit 0.3660 0.1519 2.41 0.033

ToT -0.00000000 0.00000000 -2.82 0.015

S = 2.39903 R-Sq = 93.1% R-Sq(adj) = 89.0%

Analysis of Variance

Source DF SS MS F P

Regression 7 926.66 132.38 23.00 0.000

Residual Error 12 69.06 5.76

Total 19 995.73

Source DF Seq SS

GDP ($bln.) 1 428.01

GDP/Capita PPP 1 309.92

Public Debt/GDP 1 73.70

Inflation 1 36.08

Nominal Interest Rate 1 23.86

Current Acc.Deficit 1 9.31

ToT 1 45.78

Unusual Observations

GDP

Obs ($bln.) Exch.rate Fit SE Fit Residual St Resid

16 949 41.350 44.860 1.644 -3.510 -2.01R

18 1224 48.410 43.931 1.453 4.479 2.35R

R denotes an observation with a large standardized residual.

20

Exhibit 1a continued: Regression Output from Minitab

Regression Analysis: Exch.rate versus GDP ($bln.), Public Debt/GDP, ... The regression equation is

Exch.rate = 107 + 0.0193 GDP ($bln.) - 0.622 Public Debt/GDP

- 1.89 Nominal Interest Rate + 0.303 Current Acc.Deficit

- 0.000000 ToT - 0.088 Inflation

20 cases used, 1 cases contain missing values

Predictor Coef SE Coef T P VIF

Constant 106.91 12.07 8.86 0.000

GDP ($bln.) 0.019293 0.004783 4.03 0.001 19.599

Public Debt/GDP -0.6219 0.1523 -4.08 0.001 2.045

Nominal Interest Rate -1.8907 0.3920 -4.82 0.000 3.242

Current Acc.Deficit 0.30291 0.08513 3.56 0.004 18.409

ToT -0.00000000 0.00000000 -2.95 0.011 1.755

Inflation -0.0883 0.2606 -0.34 0.740 2.223

S = 2.32961 R-Sq = 92.9% R-Sq(adj) = 89.6%

Analysis of Variance

Source DF SS MS F P

Regression 6 925.18 154.20 28.41 0.000

Residual Error 13 70.55 5.43

Total 19 995.73

There are no replicates.

Minitab cannot do the lack of fit test based on pure error.

Source DF Seq SS

GDP ($bln.) 1 428.01

Public Debt/GDP 1 0.85

Nominal Interest Rate 1 352.51

Current Acc.Deficit 1 73.38

ToT 1 69.81

Inflation 1 0.62

Unusual Observations

GDP

Obs ($bln.) Exch.rate Fit SE Fit Residual St Resid

16 949 41.350 45.446 1.139 -4.096 -2.02R

R denotes an observation with a large standardized residual.

21

Exhibit 1b: Best Subset

Best Subsets Regression: Exch.rate versus GDP ($bln.), Public Debt/, ...

Response is Exch.rate

20 cases used, 1 cases contain missing values

N

o

m C

i u

n r

a r

P l e

u n

b I t

l n

G i t A

D c e c

P I r c

D n e .

( e f s D

$ b l t e

b t a f

l / t R i

n G i a c T

Mallows . D o t i o

Vars R-Sq R-Sq(adj) Cp S ) P n e t T

1 76.7 75.4 26.7 3.5880 X

1 43.0 39.8 88.6 5.6160 X

2 80.0 77.6 22.7 3.4250 X X

2 78.0 75.4 26.4 3.5922 X X

3 83.6 80.5 18.1 3.1960 X X X

3 81.7 78.2 21.6 3.3777 X X X

4 85.8 82.1 16.0 3.0657 X X X X

4 84.5 80.4 18.4 3.2049 X X X X

5 92.9 90.3 5.1 2.2548 X X X X X

5 88.2 84.0 13.7 2.8999 X X X X X

6 92.9 89.6 7.0 2.3296 X X X X X X

22

Exhibit 1c: Residual Plots Graph from Minitab

210-1-2

99

90

50

10

1

Standardized Residual

Pe

rce

nt

5448423630

2

1

0

-1

-2

Fitted Value

Sta

nd

ard

ize

d R

esid

ua

l

210-1-2

8

6

4

2

0

Standardized Residual

Fre

qu

en

cy

2018161412108642

2

1

0

-1

-2

Observation Order

Sta

nd

ard

ize

d R

esid

ua

l

Normal Probability Plot Versus Fits

Histogram Versus Order

Residual Plots for Exch.rate

23

Exhibit 2: Purchasing Power Parity29

US Inflation Rate – Indian Inflation Rate = Change in the Spot Exchange Rate/Current Spot Exchange Rate

IPus-IPI= (ΔSus/I)/(Sus/I)

Exhibit 2a

Exhibit 2b

29

"India Inflation Rate." TRADING ECONOMICS. N.p., n.d. Web. 04 Dec. 2013. <http://www.tradingeconomics.

com/india/inflation-cpi>.

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1

10

19

28

37

46

55

64

73

82

91

10

0

10

9

11

8

12

7

13

6

14

5

15

4

16

3

17

2

18

1

19

0

19

9

20

8

21

7

22

6

Series1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79

Series1

24

Exhibit 2c

Regression Analysis

T-Stat 0.91701433974977

Average 0.00160559483121

St. Deviation 0.02678352677939

Standard Error 0.00175089391911

Square Root (234) 15.29705854077840

Exhibit 2d

New Spot Rate Actual Old Spot Rate - New Spot Rate

Jun-94

Jul-94 31.77367739 31.3675 0.012948988

Aug-94 31.62930831 31.39 0.007623712

Sep-94 31.7585756 31.375 0.012225517

Oct-94 31.41690925 31.36 0.001814708

Nov-94 31.50629434 31.405 0.003225421

May-13 54.72578356 53.806 0.017094442

Jun-13 54.19943143 56.581 -0.042091313

Jul-13 57.21724689 59.533 -0.038898646

Aug-13 60.46102891 60.635 -0.002869153

Sep-13 61.03792062 66.595 -0.083445895

Oct-13 66.82253294 62.585 0.067708444

25

Exhibit 3: Interest Rate Parity3031

The India interest rates are the 3 month treasury bill yield

The US interest rates are 3 month treasury bills on a monthly frequency

Exhibit 3a

Uncovered Interest Rate Parity

(1+ius)= S rupees/$ *(1+iI)*(1$/E(S)

rupees)

E(S)= S*((1+id)/(1+if))

F= forward exchange rate

S= current spot price

id= interest rate of domestic (US)

if= interest rate foreign (India)

Exhibit 3b

E(Spot

Price)

(3 month

prediction)

Spot

Rate

(Predicted SP-Actual

SP)/ Actual SP

Dec-03 45.82

Jan-04 45.55

Feb-04 45.32

Mar-04 0.021645011 46.2000237 45.32 0.019417999

Apr-04 0.021770341 45.93405258 43.4 0.058388308

May-04 0.021880578 45.70263088 44.53 0.026333503

Jun-04 0.02188356 45.69640445 45.42 0.006085523

Mar-13 0.018056173 55.38272202 54.145 0.022859397

Apr-13 0.017906528 55.84555453 54.285 0.028747435

May-13 0.018405297 54.33218375 53.806 0.009779277

Jun-13 0.018104883 55.23371928 56.581 -0.02381154

Jul-13 0.018061744 55.36563868 59.533 -0.070000862

Aug-13 0.018242178 54.81801656 60.635 -0.095934418

Sep-13 0.017358679 57.60807138 66.595 -0.134948999

Oct-13 0.016490321 60.64163173 62.585 -0.031051662

30

"Selected Interest Rates (Daily) - H.15." FRB: H.15 Release--Selected Interest Rates--Historical Data. Federal

Reserve Bank, n.d. Web. 02 Dec. 2013. <http://www.federalreserve.gov/releases/h15/data.htm>. 31

"India Treasury Bill Yield." TRADING ECONOMICS. N.p., n.d. Web. 04 Dec. 2013.

<http://www.tradingeconomics.com/india/interbank-rate>.

26

Exhibit 4: Data used in Regression

Year

Exchange

Rate:

Rupee/US

Dollar

Inflation

(CPI)

Nominal

Interest

Rate

Current

Account

Deficit

(USD in

Bln)

Public Debt

(Public

Debt/GDP)

Terms of Trade (in

constant Rupee)

Political

Stability &

Economic

Performance

(GDP)

1992 25.92 11.8 18.9 -3.526 76.351 125,178,321,800.22 1205.28

1993 30.49 6.4 16.3 -1.158 76.787 345,418,259,528.26 1246.87

1994 31.37 10.2 14.8 -3.369 76.939 485,764,733,727.22 1281.5

1995 32.43 10.2 15.5 -5.911 74.109 394,725,008,164.72 1341.57

1996 35.43 9 16 -4.619 70.365 207,998,107,331.00 1416.99

1997 36.31 7.2 13.8 -5.499 68.711 549,450,552,310.18 1496.8

1998 41.26 13.2 13.5 -4.038 67.623 710,462,678,301.72 1530.2

1999 43.06 4.7 12.5 -4.698 67.818 460,404,304,403.43 1596.96

2000 44.94 4 12.3 -2.666 70.122 414,090,285,569.64 1702.93

2001 47.19 3.7 12.1 3.4 72.731 381,962,182,034.26 1741.32

2002 48.61 4.4 11.9 6.345 77.849 125,634,787,737.82 1797.68

2003 46.58 3.8 11.5 14.083 82.199 419,907,033,367.77 1838.08

2004 45.32 3.8 10.9 -2.47 84.3 - 1953.11

2005 44.10 4.2 10.8 -9.902 84.063 90,716,563,175.35 2074.47

2006 45.31 6.1 11.2 -9.565 81.764 132,920,373,567.44 2233.86

2007 41.35 6.4 13 -15.736 78.49 138,927,975,725.48 2406.34

2008 43.51 8.4 13.3 -27.913 75.44 742,039,208,631.76 2606.16

2009 48.41 10.9 12.2 -38.182 74.724 529,797,577,678.63 2671.68

2010 45.73 12 8.3 -45.946 74.973 931,591,036,361.65 2860.55

2011 46.67 8.9 10.2 -78.154 69.427 988,707,611,989.04 3121.62

2012 53.44 9.3 10.6 -88.163 68.053 41,359,071,322.05 3277.01