Authors: Aashutosh , Abhay Dubey, Abhay Kumar, Dr./ Prof. Nishtha Dwivedi
DOI Link: https://doi.org/10.22214/ijraset.2022.42555
Certificate: View Certificate
I. INTRODUCTION
The foreign exchange market determines the relative of different currencies. The primary purpose of the foreign exchange is to assist international trade and investment, by allowing business to convert one currency to another currency. For example, if permits a US business to impart British goods and pay pound sterling, even though the business’s income is in dollars.
It also supports direct speculation in the value of currencies, and the carry trade, speculation on the change in interest rate in two currencies. In a typical foreign exchange transaction, a party purchases a quantity of one currency by paying a quantity of another currency.
The modern foreign exchange market began forming during the 1970s after three decades of government restriction on foreign exchange transaction (the Bretton Woods system of monetary management established the rules for commercial and financial relation among the world’s major industrial states after World War II), When countries gradually switched to floating exchange rates from the previous exchange rate regime, Which remained fixed as per the Bretton Woods system.
A. Currency Trading
Currency trading is the act of buying and selling international currencies. Generally banks and financial trading institutions engage in the act of currency trading. Individual investors can also engage in currency trading, attempting to benefit from variations in the exchange rate of the currencies.
B. Currency Markets
The currency trading (FOREX) market is the biggest and the fastest growing market in the world economy. Its daily turnover is more than 2.5 trillion dollars, which is 100 times greater than the NASDAQ daily turnover. Markets are places to trade goods.
Any currency can be traded on the international level. However, on the Multi Commodity Exchange only 4 major currencies are traded against the Indian Rupee.
USD, EURO, GBP, JPY
C. Key Factors That Affect Foreign Exchange Rates
Foreign Exchange rate (Forex rate) is one of the most important means through which a country’s relative level of economic health is determined.
A country's foreign exchange rate provides a window to its economic stability, which is why it is constantly watched and analyzed. It deals with the sending or receiving money from overseas on the currency exchange rates.
The exchange rate is defined as "the rate at which one country's currency may be converted into another." It may fluctuate daily with the changing market forces of supply and demand of currencies from one country to another. For these reasons; when sending or receiving money internationally, it is important to understand what determines exchange rates.
Some of the leading factors that influence the variations and fluctuations in exchange rates and explain the reasons behind their volatility are explained below,
3. Country’s Current Account / Balance of Payments: A country’s current account reflects balance of trade and earnings on foreign investment. It consists of total number of transactions including its exports, imports, debt, etc. A deficit in current account due to spending more of its currency on importing products than it is earning through sale of exports causes depreciation. Balance of payment fluctuates exchange rate of its domestic currency.
4. Government Debt: Government debt is public debt or national debt owned by the central government. A country with government debt is less likely to acquire foreign capital, leading to inflation. Foreign investors will sell their bonds in the open market if the market predicts government debt within a certain country. As a result, a decrease in the value of its exchange rate will follow.
5. Terms of Trade: Related to current accounts and balance of payments, the terms of trade is the ratio of export prices to import prices. A country's terms of trade improves if its exports prices rise at a greater rate than its imports prices. This results in higher revenue, which causes a higher demand for the country's currency and an increase in its currency's value. This results in an appreciation of exchange.
6. Political Stability & Performance: A country's political state and economic performance can affect its currency strength. A country with less risk for political turmoil is more attractive to foreign investors, as a result, drawing investment away from other countries with more political and economic stability. Increase in foreign capital, in turn, leads to an appreciation in the value of its domestic currency.
7. Recession: When a country experiences a recession, its interest rates are likely to fall, decreasing its chances to acquire foreign capital. As a result, its currency weakens in comparison to that of other countries, therefore lowering the exchange rate.
8. Speculation: If a country's currency value is expected to rise, investors will demand more of that currency in order to make a profit in the near future. As a result, the value of the currency will rise due to the increase in demand. With this increase in currency value comes a rise in the exchange rate as well.
D. Why Currency Derivatives?
E. Basics of Currency Trading Forward Contract
Future Contracts
a. These are agreements to buy or sell an asset for a certain price at a future time.
b. Exchange traded and standardized contracts
c. No counter party risk as settlement is guaranteed by the exchange.
Option contracts
Traders can also buy Currency option contracts.
Here he commits for a future exchange of currency with an agreement that the contract will be valid only if the price is favorable. Pays a premium for this.
Buy USDINR Call options if one is bullish on the Dollar.
Sell USDINR Put options if one is bearish on the Dollar.
F. Objectives Of The Study
G. Scope Of The Study
H. Limitations of the Study
It helps the investor in making the investment decision but not every investment is entirely depending on the analysis done.
The tools used for analysis is subject to inherent limitations.
The study is done by using tools like EViews.
I. Research Methodology
II. REVIEW OF THE LITERATURE
The literature on the effect of intervention in foreign exchange markets is extensive. This section reviews some of the main contributions – for comprehensive surveys, refer to Edison (1993), ALMEKINDERS (1995) lmekinders (1995), Schwartz (2000) and Sarno and Taylor (2001).
In the 1980s and early 1990s, attention focused on the effect of sterilised intervention on the level of the exchange rate and on the channels through which it works. The results on the effectiveness of intervention are mixed and depend on which exchange rate is analysed, what sample period is studied and the intervention strategy that was used. In an influential paper, Dominguez and Frankel (1993) use daily and weekly official and press report data on intervention directed at the yen/dollar and mark/dollar exchange rates between 1984 and 1990. The authors find that intervention had a significant impact on the exchange rate, especially when it was publicly announced and coordinated.
Later studies have not provided a unanimous confirmation of Dominguez and Frankel’s finding that intervention has an impact on exchange rate levels. Using a case study approach for the yen/dollar and mark/dollar exchange rates during the period 1985–91, Catte et al. (1994) confirm that intervention influences exchange rates particularly for coordinated interventions. Fatum (2000) and Fatum and Hutchinson (2002, 2003) argue in favour of an event study approach to examine the effect of intervention on exchange rate changes, as methods relying on time series data do not capture the sporadic occurrence of intervention. Fatum (2000) uses a nonparametric estimation technique to show that during the months following the Plaza agreement, intervention by the Federal Reserve and the Bundesbank was effective, especially when it was coordinated. Using similar techniques, Fatum and Hutchison (2002, 2003) find evidence supporting the effectiveness of intervention in the mark/dollar and yen/dollar markets. Ito (2003) presents evidence based on Japanese Ministry of Finance data that intervention in the yen/dollar market in the second half of the 1990s was effective. Dominguez (2003a) concludes that recent G3 intervention was often successful with regard to both short and longerterm exchange rate movements. However, other papers do not support the conclusion that intervention is effective. Humpage (1988), for example, concludes that intervention was unable to influence the dollars’ level.
Baillie and Osterberg (1997) find that over the period August 1985 to March 1990, Federal Reserve intervention did not influence the mark/dollar or yen/dollar exchange rates.
In terms of transmission channels, there is now general consensus in the literature that intervention does not affect exchange rates through the portfolio channel, i.e. by changing the relative outstanding supply of domestic and foreign assets and thereby of the expected relative returns on these assets.
There is some, but not conclusive, evidence that intervention mainly works through the signaling channel, i.e. by the central bank conveying a signal to market participants about information on future fundamentals. Recent work on the microstructure of foreign exchange markets has highlighted the role of imperfect information as a channel through which intervention might influence exchange rates Eur and Resnick (1988) tried to develop ex ante portfolio selection strategies to realize potential gains from international diversification under flexible exchange rates. For the empirical analysis the Morgan Stanley Capital international Perspective daily stock index values for the United States and the other six countries were adopted. The stock indices of United States, Canada, France, Germany, Japan, Switzerland, and the U.K. were value weighted and it was a representative of a domestic stock index fund. The data series were provided in both the United States and the local currencies for the period from December 3 1, 1979, through December 10, 1985. Methods such as correlation, variance and covariance have also been employed to know the changes in stock market across the countries. The analysis reveals that exchange rate uncertainty is a largely non diversifiable factor adversely affecting the performance of international portfolios. The authors have suggested two methods such as multicurrency diversification and hedging via forward exchange contracts for reducing the exchange rate risks.
Ma and Kao (1990) eExamined the stock price reactions to the exchange rate changes. The authors have studied the case of six developed countries namely United Kingdom, Canada, France, West Germany, Italy and Japan. Monthly stock indices and monthly exchange rates arc gathered from the Exchange Rates and Interest Rates Tape Provided by the Federal Reserve.
The sample period was from January 1973 to December 1983, and a two factor model was adopted for the empirical analysis. The paper demonstrates two possible impacts of changes in a country's currency value on stock price movements. Firstly, the financial effects of exchange rate changes on the transaction exposure. Secondly, the economic effect from exchange rate changes suggests that, for an exportdominant country, the currency appreciation reduces the competitiveness of export markets and has a negative effect on the domestic stock market. On the other hand for an import dominated country, the currency appreciation will lower import costs and generate a positive impact on the stock market.
Jorion (1991) examined the pricing of exchange rate risk in the United States (US) stock market, by using twofactor and multifactor arbitrage pricing models. For the purpose of empirical analysis, monthly data are collected for a period ranging from January 1971 to December 1987. The data on the tradeweighted exchange rate is derived from the weights in the Multilateral Exchange Rata Model (MERM) computed by the International Monetary Fund (IMF). Monthly data on the Stock market return are collected from the University of Chicago's Centre for Research in Security Prices (CRSP) database.
An ordinary least squares (OLS) regression method was employed for examining the objective. Bartov and Bodnar (1994) reexamined the anticipated changes in the dollar and equity value. The period of study ranges from the fiscal year 1978 and runs through the fiscal year 1989. The authors have used the COMPUSTAT MergedExpanded Annual Industrial File and Full Coverage File for firms that reported significant foreign currency gains or losses on their annual financial statements. The data on stock prices were collected either the Centre for Research in Security Prices (CRSP)
New York Stock Exchange (NYSE) American Stock Exchange (AMEX) Daily Return File or the
National Association of Security Dealers Automated Quotation (NASDAQ) Daily or Master Files. The results of the study show that contemporaneous changes in the dollar have little power in explaining abnormal stock return. This finding is consistent with the failure of prior research to document a contemporaneous relation between dollar fluctuations and firm value and suggests that problems with sample selection technique are not a complete explanation for their failure.
Choi and Prasad (1995) estimated a model of firm valuation to examine the exchange risk sensitivity of firm value. For the empirical analysis monthly timeseries of stock returns were obtained from the University of Chicago Centre for Research in Security Prices (CRSP) tapes and COMPUSTAT database. The period of study was from January 1978 to December 1989. The nominal exchange rate variable was the United States (US) dollar value of one unit of foreign currency, where foreign currency was the multilateral trade weighted basket of ten major currencies as published in the Federal Reserve Bulletin. The least squares (OLS) and the generalized least squares (GLS) methods were employed for examining the objective of the study.
Chamberlain eta1 (1997) examined the foreign exchange exposure of a sample of United States (US) and Japanese banking firms. In constructing the United States (US) sample, both daily and monthly stock returns of thirty bank holding companies that were traded over the entire sample period from 1986 to 1992 on the New York Stock Exchange (NYSE) or the American Stock Exchange (AMEX) were selected from the Centre for Research in Security Prices (CRSP). For Japanese bank samples, monthly observations of the largest 110 Japanese bank returns were collected from World scope data, and daily bank retunes were considered from excel Research data. The authors estimated the sensitivity of returns to the exchange rate in the context of an augmented market model.
Friberg and Nydahl (1999) examined the exchange rate exposure of national stock markets. The authors have investigated the relationship between the valuation of the stock market and a trade weighted exchange rate index for 11 industrialized countries. For the analysis, monthly data for the period 19731996 were considered. The MorganStanley stock market indexes and world market index were from MorganStanley. Data on nominal exchange rates and local stock market data were collected from the Ecowin database. The analysis is carried out using an ordinary least squares (OLS) regression.
Amain and Hook (2000) investigated the relationship between the exchange rate of Malaysian ringgit in tens of United States dollar and stock prices in Kuala Lumpur Stock Exchange (KLES) using the singleindex and multiindex models. The authors have used 256 weekly closing stock price indices and the Malaysian Ringgitr United States Dollar (RM/US$) exchange rate spanning from September 1993 to July 1998. The data were collected from the Daily Diary published by Kuala Lumpur Stock
Exchange (KLSE). The study period was divided as a cycle of a strong ringgit (September, 93 January, 97) and a cycle covering a weak ringgit (July, 97 July, 98). Besides an ordinary least square (OLS) method was employed to identify the relationship between stock prices and exchange rate.
Koch and Saporoschenko (2001) examined the sensitivity of individual and portfolio stock returns for Japanese horizontal Keiretsu financial firms to unanticipated changes in market returns, bond returns, exchange rate changes and nominal interest rate spread changes. For the empirical analysis, weekly stock returns from January 14, 1986 through December 29, 1992 were collected. Weekly traded weighted yen exchange rate return innovation was estimated using data from the JP Morgan economic department for the same period. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model has been employed to examine the stock return sensitivity of Japanese horizontal Keiretsu financial firms to exchange rates. The results indicate that Kirsten financial firms have insignificant exposure to exchange rate changes.
Bailey eta1 (2003) tested the impact of switching over among silver, gold and paper money standards on stock returns from seven small open economics. The sample was collected for a period from December 1872 to November 1941. End of month stock prices were collected from principal national, colonial or metropolitan news papers. The authors have translated all stock prices into pounds using endofmonth exchange rate, and then its log differences were also computed. To perform three diagnostic tests Engle and Ng Generalized Autoregressive Conditional Heteroskedasticity (GARCH) specification was used.
Shamsuddin and Kim (2007) attempted to study the extent of stock market integration between Australia and its two leading trading partners, the United States and Japan. In addition, this study determines whether the extent and nature of stock market integration in the period of the post Asian crisis differs from that of the preAsian crisis. The data used in this study are the end of week closing stock price indexes for Australia, Japan and the United States, and the Australian dollar value of the Japanese yen and United States dollar. The national stock indexes used were the Standard and Poors
500 composite index for the United States, the Tokyo Stock Price Index (TOPIX) for Japan and the All Ordinaries Index (AOI) for Australia. The pre Asian crisis period covers two subperiods i.e. from January 1991 to December 1993 and from January 1994 to July 1997. The postAsian crisis period covers from January 1998 to May 2001. All data were collected from Data stream.
Augmented Dickey Fuller (ADF) test for unit root was adopted to test the stationary property of each variable. Cointegration technique was employed to examine longrun comovement of stock prices for Australia, Japan and the US, and the Australian dollar value of the Japanese yen and US dollar. To understand the dynamic linkages among national stock prices as well as the interaction between stock prices and exchange rates, a Vector Error Correction Model (VECM) was employed for two sub periods, and Vector Autoregressive (VAR) model in first difference was employed for the post Asian crisis period.
Cheneta1 (2009)investigated the firm value sensitivity to exchange rate fluctuation by focusing mainly on individual firms and also looked at the differing rate of sensitivity between currencies. For the empirical analysis a sample of 161 New Zealand Stock Exchange (NZSE) listed firms were considered. Monthly share return indexes were obtained from the Global Data stream database for the period from January 1993 to December 2000. The New Zealand (NZ) dollar, trade  weighted Index (TWI), the exchange rates for US dollar and Australian dollar were obtained from the web page of Reserve Bank of New Zealand. The trade  weighted Index (TWI) is calculated using the rates of the five currencies of New Zealand's five main trading partners (Australian Dollar  38 per cent, Japanese Yen  24 per cent, United States Dollar  22 per cent, Great Britain Pound Sterling per cent and Euro  6 per cent). Test was conducted using a residual regression model. The cross sectional analysis was done by employing the multivariate regression model.
Dash and Madhava (2010) a study conducted by analyzed the impact of appreciation of Indian rupee that took place in 2007 on Indian IT sector. The study was restricted just to know the impact of rupee appreciation and that too only on IT sector. Whereas the present study relates to overall management of currency exposure of different categories of business enterprises.
Jain, Yadav, and Rastogi (2012)examine and compare the policies of foreign exchange risk and interest rate risk management followed by public Sector, private sector business houses and foreign controlled firms in India. The study was limited to know the awareness of Indian firms about the foreign exchange risk and interest rate risk but study fails to discuss in detail the management of these risks. But the present study is comprehensive survey relating to almost all aspects of currency exposure management.
Prof. Pareshkumar J. Patel and Dr.Ashok R. Patel (2014): Graph of Currency trading has increased dramatically in last few years in India, so the need for more effective ways for better analysis of movements in currency has been arise. Currency is highly uncertain and unpredictable instrument. There are ample of of factors affecting movement of currency. People have started using
Currency futures as an investment option and they can trade various currencies as per the current economical condition of the country. Before investment it is important to identify effect of various factors on index value of currency. The purpose of this paper is to indicate main factors which are influencing currency rates, focusing on economical formulas based on the economics theory to check health of the currency and useful prediction models for currency exchange rate.
A. Foreign Exchange Market
Compared to other financial markets, FX markets have unique features. It has wide structure, composition; effects of change in technology and in regulations then draw out implications for their functioning.
Foreign exchange market is described as an OTC (Over the counter) market as there is no physical place where the participants meet to execute their deals. It is more an informal arrangement among the banks and brokers operating in a financing centre purchasing and selling currencies, connected to each other by telecommunications like telex, telephone and a satellite communication network, SWIFT. The term foreign exchange market is used to refer to the wholesale a segment of the market, where the dealings take place among the banks. The retail segment refers to the dealings take place between banks and their customers. The retail segment refers to the dealings take place between banks and their customers. The retail segment is situated at a large number of places. They can be considered not as foreign exchange markets, but as the counters of such markets.
The leading foreign exchange market in India is Mumbai, Calcutta, Chennai and Delhi is other centers accounting for bulk of the exchange dealings in India. The policy of Reserve Bank has been to decentralize exchanges operations and develop broader based exchange markets. As a result of the efforts of Reserve Bank Cochin, Bangalore, Ahmadabad and Goa have emerged as new centre of foreign exchange market.
B. Aspects of the Indian Foreign Exchange Market
The percentage of intervention to interbank turnover fell from 13.4 in 200102 to 0.9 in 200607, but it was still large compared to mature economies. The Bank of Japan intervened successfully in 2011 even with a percentage of 0.2. This is the annual intervention percentage. The CB share can be much higher for daily intervention, which tends to be concentrated on a few days. Since the interbank market remains a large size of the total, the interbank share is not much higher than the percentage of CB intervention to total turnover. CB intervention, however, affects only domestic markets. Even so, the derivative segment of the FX market also evolved. Cross currency derivatives with the rupee as one leg were introduced, with some restrictions, in April 10 1997. Rupeeforeign exchange options were allowed in July 2003. Exchange traded currency futures were started in 2008. The most widely used derivative instruments were the forwards and foreign exchange swaps (rupeedollar), but there was user demand for liquid and transparent exchange traded hedging products, which are easier to regulate.
C. Company Profile IIFL LTD.,
IIFL is a financial services conglomerate which was started by a group of passionate entrepreneurs in 1995. The genesis of IIFL lies in the power of dreaming big and believing in your dreams.
IIFL was the pioneer in the retail broking industry with its launch of 5paisa trading platform which offered the lowest brokerage in the industry and the freedom from traditional ways of transacting. Our strength has been to continuously innovate and reinvent ourselves. IIFL’s evolution from an entrepreneurial startup in 1995 to a full range diversified financial services group is a story of steady growth by adapting to the dynamic business environment, without losing focus on our core domain of financial services.
Today, IIFL Holdings Limited (Bloomberg Code: IIFL IN, NSE: IIFL, BSE: 532636) is India’s leading integrated financial services group with diverse operating businesses, mainly, Non Banking and Housing Finance, Wealth and Asset Management, Financial Advisory and Broking, Mutual Funds and Financial
Product Distribution, Investment Banking, Institutional Equities, Realty Broking and Advisory Services.
IIFL serves more than 4 million satisfied customers across various business segments and is continuously building on its strengths to deliver excellent service to its expanding customer base.
D. Origin
India Infoline Ltd., was founded in 1995 by a group of professional with impeccable educational qualifications and professional credentials. Its institutional investors include Intel Capital leading Technology Company, CDC (promoted by UK government), ICICI, TDA and Reeshanar. India Infoline group offers the entire gamut of investment products including stock broking, Commodities broking, Mutual Funds, Fixed Deposits, GOI Relief bonds, Post office savings and life Insurance. India Infoline is the leading corporate agent of ICICI Prudential Life Insurance Co. Ltd., which is India' No. 1 Private sector life insurance company.
Www.indiainfoline.com has been the only India Website to have been listed by none other than
Forbes in it’s “Best of the Web” survey of global website, not just once but three times in a row and counting... “A must read for investors in south Asia” is how they choose to describe India Infoline. It has been rated as No.l the category of Business News in Asia by Alexia rating.
Stock and Commodities broking is offered under the trade name 5paisa. India Infoline Commodities pvt Ltd., a wholly owned subsidiary of India Infoline Ltd., holds membership of MCX and NCDEX.
E. Main Objects of the Company
Main objects as contained in its Memorandum or Association are:
Products: the India Infoline ltd offers the following products A.
a. Ebroking.
b. EBroking
It refers to Electronic Broking of Equities, Derivatives and Commodities under the brand name of
5paisa
c. Distribution
d. Insurance
F. The Corporate Structure
The India Infoline group comprises the holding company, India Infoline Ltd, which has 5 wholly owned subsidiaries, engaged in distinct yet complementary businesses which together offer a whole bouquet of products and services to make your money grow.
The corporate structure has evolved to comply with oddities of the regulatory framework but still beautifully help attain synergy and allow flexibility to adapt to dynamics of different businesses. The parent company, India Infoline Ltd owns and manages the web properties www.Indiainfoline.com and www.5paisa.com. It also undertakes research Customized and offtheshelf.
Indian Infoline Securities Pvt. Ltd. is a member of BSE, NSE and DP with NSDL. Its business encompasses securities broking Portfolio Management services. India Infoline.com Distribution Co. Ltd., Mobilizes Mutual Funds and other personal investment products such as bonds, fixed deposits, etc. India Infoline Insurance Services Ltd. is the corporate agent of ICICI Prudential Life Insurance, engaged in selling Life Insurance, General Insurance and Health Insurance products.
India Infoline Commodities Pvt. Ltd. is a registered commodities broker MCX and offers futures trading in commodities.
India Infoline Investment Services Pvt. Ltd., is proving margin funding and NBFC services to the customers of India Infoline Ltd.,
G. Mission 2020
From an entrepreneurial startup in 1995, we have steadily grown to emerge as one of India’s leading financial services group. Ever since our inception, our strategy has been to align our capabilities and market insights to the country’s rapidly changing business environment. Our growth trajectory has only served to reinforce our focus on our domain of financial services.
H. Doubling
Revenue  2X /Net Profit  2.5X Over FY16  FY20
FY16 to FY20  Doubling of revenue and 2.5x profit and target to raise ROE from 17.3% to 24% Adequately capitalized to sustain volume growth Margin improvement to be driven by rating upgrade to help lower cost of funds
I. Durability
Reducing volatility and cyclicality of earnings in all businesses
NBFC  Retail Lending, Digital Delivery
Wealth  Focus on advisory mandate for customer stickiness
Broking  Online retail. Research driven Institutional
J. DeRisking
Diversifying revenue sources with focus on financial services
Diversified asset mix, geographically well spread
Broadening service offerings
Bestinclass risk management framework
Scale & digitization to bring costs down
K. Corporate Structure
Chart depicts only key businesses and subsidiaries of IIFL Holdings Limited and not all the businesses and subsidiaries.
L. Management
Name 
Designation 
A K Purwar 
Independent Director 
Anand Mathur 
President – HR 
Aniruddha Dange 
Chief Strategy Officer 
Apoorva Tiwari 
Chief Operating Officer 
Arun Malkani 
Chief Marketing Officer 
Ashok Mittal 
Group Head 
Chandran Ratnaswami 
Non Executive Director 
Gajendra Thakur 
Co. Secretary & Compl. Officer 
Gajendra Thakur 
Secretary 
Geeta Mathur 
Independent Director 
H Nemkumar 
President 
Kranti Sinha 
Independent Director 
Narendra Jain 
President 
Nilesh Vikamsey 
Independent Director 
Nipun Goel 
President 
Nirmal Jain 
Chairman 
Prabodh Agrawal 
Chief Financial Officer 
R Mohan 
Chief Compliance Officer 
R Venkataraman 
CEO 
R Venkataraman 
Managing Director 
S Narayan 
Independent Director 
S Venu 
Chief Administrative Officer 
Shubhalakshmi Panse 
Independent Director 
Subhash Kelkar 
Chief Technology Officer 
Vasudev Jagannath 
President 
IV. DATA ANALYSIS
A. EViews
EViews stands for econometric views. EViews can be used for general statistical analysis and econometric analyses, such as crosssection and panel data analysis and time series estimation and forecasting. EViews combines spreadsheet and relational database technology with the traditional tasks found in statistical software, and uses a Windows GUI. This is combined with a programming language which displays limited object orientation.
EViews organizes data, graphs, output, and so forth, as objects. Each of these objects can be copied, saved, cutandpasted into other Windows programs, or used for further analysis. A collection of objects can be saved together in a work file. Since EViews creates new objects with everything you do, it makes sense to delete unimportant intermediate results to avoid a messy work file.
Unit root test defines whether the data is stationary or not. In case if its not stationary then we have to make the data stationary using difference at level. We can find whether the data is stationary using P value. If P value is less than 0.05 then it is said to be at stationary. If P value is greater than 0.05 then it is said to be non stationary.
2. Ordinary Least Squares
Ordinary Least Squares defines whether the independent variable influence the dependent variable or not. If P value is less than 0.05 then it is said to be independent variable influence the dependent variable. If P value is greater than 0.05 then it is said to be independent variable does not influence the dependent variable.
In case if it influence the dependent variable then we have to check residuals. Whether there is heteroskedasticity or not. If there is no heteroskedasticity then we can conclude that there is no arch(autoregressive conditional heteroskedasticity) effect. If there is heteroskedasticity than there is an arch effect. We can able to find whether there is heteroskedasticity or not using null hypothesis guideline. If the P value is less than 0.05 than reject null hypothesis and accept alternate hypothesis. If the P value is greater than 0.05 than accept null hypothesis and reject alternate hypothesis.
3. Autoregressive Conditional Heterodasticity
If there is heteroskedasticity than there is an arch effect. If the P value is greater than 0.05 than accept null hypothesis and reject alternate hypothesis.
Here we have many models which is said to be as arch family. Now we are going to see what are all the models involved in arch family:
From all the three model to fit the best model we should follow the guideline which AIC and SIC is stating the lowest is said to be the best model. and then find the residuals of that model.
4. Cointegration Test
Cointegration test is defined using the stationary test. Only when the data describes stationary level at first difference I(1)and resid describes stationary at I(0) then only we can conclude that we can use cointegration test.
If there are three or more variables then we can use Johansen Cointegration Test. Only when there is cointegration then we have to use VECM (Vector Error Correction Estimates).
5. Vector Error Correction Estimates
Vector Error Correction Estimates defines the factors which are all influencing the dependent variables.
And also it states long run variables using P value.
B. Future and Spot Rate of USD/INR
For the study four currencies are taken for analysis. The first currency is USD/INR. Before doing any analysis it is mandatory to check for stationary. The stationarity can be tested with unit root test. For unit root test ADF test is used.
Table 4.1: Unit Root Test for Future Rate of USD/INR
Null Hypothesis: LUFUT has a unit root 


Exogenous: None 




Lag Length: 0 (Automatic  based on SIC, maxlag=20) 





tStatistic 

Prob.* 

Augmented DickeyFuller test statistic 
1.052041 

0.9238 

Test critical values: 
1% level 

2.567792 




5% level 

1.941211 




10% level 

1.616439 



*MacKinnon (1996) onesided pvalues. 



Augmented DickeyFuller Test Equation 



Dependent Variable: D(LUFUT) 




Method: Least Squares 




Variable 
Coefficient 
Std. Error 
tStati stic 
Prob. 

LUFUT(1) 
2.73E05 
2.59E05 
1.0520 41 
0.2931 

Rsquared 
0.000032 
Mean dependent var 
0.000115 

Adjusted Rsquared 
0.000032 
S.D. dependent var 
0.003100 

S.E. of regression 
0.003101 
Akaike info criterion 
8.713282 

Sum squared resid 
0.007854 
Schwarz criterion 
8.707528 

Log likelihood 
3564.733 
HannanQuinn criter . 
8.711074 

DurbinWatson stat 
2.042371 




From the above output, we have derived unit root test for the Future Rate of USDINR. The PValue of unit root test is 0.9238 which is greater than 0.05. Hence, the above output derives the result Non Stationary at level I(0).
Since it results in Non Stationary, the next step is to make the variable stationary. In order to make it stationary we have to generate difference for future rate of USDINR. Equation generated to test difference at first level is dlufut=d(lufut)
Table 4.2: Unit Root Test for Future Rate of USD/INR at difference
Null Hypothesis: D(LUFUT) has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=20) 





tStatistic 
Prob.* 
Augmented DickeyFuller test statistic 
29.14973 
0.0000 

Test critical values: 
1% level 

2.567796 


5% level 

1.941211 


10% level 

1.616439 

*MacKinnon (1996) onesided pvalues. 


Augmented DickeyFuller Test Equation 


Dependent Variable: D(LUFUT,2) 


Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(LUFUT(1)) 
1.021527 
0.035044 
29.14973 
0.0000 
Rsquared 
0.510116 
Mean dependent var 
8.09E06 

Adjusted Rsquared 
0.510116 
S.D. dependent var 
0.004434 

S.E. of regression 
0.003103 
Akaike info criterion 
8.711563 

Sum squared resid 
0.007858 
Schwarz criterion 
8.705803 

Log likelihood 
3559.673 
HannanQuinn . 
8.709352 

DurbinWatson stat 
2.000690 



From the above test it is clear that the difference for future rate of USDINR is stationary as PValue for this unit root test is 0.0000 which is less than 0.05. Hence the unit root test has became stationary at first difference I(1).
Table 4.3: Unit Root Test for Spot Rate of USD/INR
Null Hypothesis: LUSPOT has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=20) 




tStatistic 
Prob.* 
Augmented DickeyFuller test statistic 
0.298706 
0.7720 

Test critical values: 
1% level 

2.567792 


5% level 

1.941211 


10% leve 

1.616439 

*MacKinnon (1996) onesided pvalues. 


Augmented DickeyFuller Test Equation 


Dependent Variable: D(LUSPOT) 



Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
LUSPOT(1) 
7.43E06 
2.49E05 
0.298706 
0.7652 
Rsquared 
0.000010 
Mean dependent var 
3.25E05 

Adjusted Rsquared 
0.000010 
S.D. dependent var 
0.002977 

S.E. of regression 
0.002977 
Akaike info criterion 
8.794327 

Sum squared resid 
0.007243 
Schwarz criterion 
8.788573 

Log likelihood 
3597.880 
HannanQuinn criter. 
8.792119 

DurbinWatson stat 
1.998725 



From the above output, we have derived unit root test for the Spot Rate of USDINR. The P Value of unit root test is 0.7720 which is greater than 0.05. Hence, the above output derives the result Non Stationary at level I (0).
Since it results in Non Stationary, the next step is to make the variable stationary. In order to make it stationary we have to generate difference for spot rate of USDINR. Equation generated to test difference at first level is dluspot=d(luspot)
Table 4.4: Unit Root Test for Spot Rate of USD/INR at difference
Null Hypothesis: D(LUSPOT) has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=20) 




tStatistic 
Prob.* 
Augmented DickeyFuller test statistic 
28.63769 
0.0000 

Test critical values: 
1% level 

2.567796 


5% level 

1.941211 


10% level 

1.616439 

*MacKinnon (1996) onesided pvalues. 


Augmented DickeyFuller Test Equation 


Dependent Variable: D(LUSPOT,2) 


Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(LUSPOT(1)) 
1.001018 
0.034955 
28.63769 
0.0000 
Rsquared 
0.501257 
Mean dependent var 
7.69E06 

Adjusted R squared 
0.501257 
S.D. dependent var 
0.004212 

S.E. of regression 
0.002974 
Akaike info criterion 
8.796265 

Sum squared resid 
0.007220 
Schwarz criterion 
8.790506 

Log likelihood 
3594.274 
HannanQuinn r. 
8.794055 

DurbinWatson 
2.002045 



From the above test it is clear that the difference for spot rate of USDINR is stationary as PValue for this unit root test is 0.0000 which is less than 0.05. Hence the unit root test has became stationary at first difference I(1).
Table 4.5: Ordinary Least Squares of USD/INR
Dependent Variable: DLUSPOT 




Method: Least Squares 




Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
C 
2.28E05 
0.000104 
0.219773 
0.8261 
DLUFUT 
0.083726 
0.033489 
2.500106 
0.0126 
Rsquared 
0.007602 
Mean dependent var 
3.25E05 

Adjusted R squared 
0.006386 
S.D. dependent var 
0.002977 

S.E. of regression 
0.002968 
Akaike info criterion 
8.799523 

Sum squared resid 
0.007187 
Schwarz criterion 
8.788014 

Log likelihood 
3601.005 
HannanQuinn criter. 
8.795106 

Fstatistic 
6.250528 
DurbinWatson stat 
2.000604 

Prob(Fstatistic) 
0.012611 



The output stated above defines PValue of DLUFUT as 0.0000 which is less than 0.05. Hence, OLS model clearly states that future rate of USDINR influence spot rate of USDINR.
DLUSPOT= 0.083726(DLUFUT)+2.28E05
2. Residual Checking for Ordinary Least Squares Model: Next step is to check Heteroskedasticity for the residuals to ascertain the whether there is uniform variance in the error by using Breusch Pagan Godfry test.
Table 4.6: Residual Checking for Ordinary Least Squares Model of USD/INR
Heteroskedasticity Test: BreuschPaganGodfrey 


Fstatistic 
0.046748 
Prob. F(1,816) 
0.8289 

Obs*Rsquared 
0.046860 
Prob. ChiSquare(1) 
0.8286 

Scaled explained SS 
0.071045 
Prob. ChiSquare(1) 
0.7898 

Dependent Variable: RESID^2 



Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
C 
8.78E06 
5.37E07 
16.34552 
0.0000 
DLUFUT 
3.75E05 
0.000173 
0.216212 
0.8289 
Rsquared 
0.000057 
Mean dependent var 
8.79E06 

Adjusted Rsquared 
0.001168 
S.D. dependent var 
1.53E05 

S.E. of regression 
1.54E05 
Akaike info criterion 
19.32768 

Sum squared resid 
1.92E07 
Schwarz criterion 
19.31617 

Log likelihood 
7907.019 
HannanQuinn criter. 
19.32326 

Fstatistic 
0.046748 
DurbinWatson stat 
1.665081 

Prob(Fstatistic) 
0.828876 



From the derived output, PValue for residual checking on OLS model is 0.8286 which is greater than 0.05. And so it defines, accept Null Hypothesis and reject Alternate Hypothesis. Here, Null Hypothesis states that there is no Heteroskedasticity. Hence, if there is no Heteroskedasticity then there is no ARCH effect.
C. Future and Spot Rate of EUR/INR
Stationarity test with unit root test using ADF test for EURINR.
Table 4.7: Unit Root Test for Future Rate of EUR/INR
Null Hypothesis: LEFUT has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=20) 




tStatistic 
Prob.* 
Augmented DickeyFuller test statistic 
0.073073 
0.7058 

Test critical values: 
1% level 

2.567654 


5% level 

1.941192 


10% level 

1.616452 

*MacKinnon (1996) onesided pvalues. 


Augmented DickeyFuller Test Equation 


Dependent Variable: D(LEFUT) 



Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
LEFUT(1) 
3.47E06 
4.74E05 
0.073073 
0.9418 
Rsquared 
0.000004 
Mean dependent var 
1.88E05 

Adjusted R squared 
0.000004 
S.D. dependent var 
0.005989 

S.E. of regression 
0.005989 
Akaike info criterion 
7.396730 

Sum squared resid 
0.030807 
Schwarz criterion 
7.391199 

Log likelihood 
3181.594 
HannanQuinn criter. 
7.394613 

DurbinWatson stat 
1.991186 



From the above output, we have derived unit root test for the Future Rate of EURINR. The P Value of unit root test is 0.7058 which is greater than 0.05. Hence, the above output derives the result Non Stationary at level I(0).
Since it results in Non Stationary, the next step is to make the variable stationary. In order to make it stationary we have to generate difference for future rate of EURINR. Equation generated to test difference at first level is dlefut=d(lefut)
Table 4.8: Unit Root Test for Future Rate of EUR/INR at difference
Null Hypothesis: D(LEFUT) has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=20) 





tStatistic 
Prob.* 
Augmented Di ckeyFuller test statistic 
29.18073 
0.0000 

Test critical values: 
1% level 

2.567657 


5% level 

1.941192 


10% level 

1.616451 

*MacKinnon (1996 
) onesided pv 
alues. 


Augmented Dickey 
 Fuller Test Eq 
uation 


Dependent Variable: 
D(LEFUT,2) 



Method: Least Squ 
ares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(LEFUT(1)) 
0.995946 
0.034130 
29.18073 
0.0000 










Rsquared 
0.498103 
Mean dep 
endent var 
3.04E06 
Adjusted R squared 
0.498103 
S.D. dep 
endent var 
0.008455 
S.E. of regression 
0.005990 
Akaike in 
fo criterion 
7.396198 
Sum squared resid 
0.030788 
Schwarz 
criterion 
7.390661 
Log likelihood 
3177.667 
HannanQ 
uinn criter. 
7.394078 
DurbinWatsont 
1.999947 



From the above test it is clear that the difference for future rate of EURINR is stationary as P Value for this unit root test is 0.0000 which is less than 0.05. Hence the unit root test has became stationary at first difference I(1).
Table 4.9: Unit Root Test for Spot Rate of EUR/INR
Null Hypothesis: LESPOT has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=21) 





tStatistic 
Prob.* 
Augmented Dic keyFuller test st atistic 
0.138805 
0.7260 

Test critical values: 
1% level 

2.567263 


5% level 

1.941138 


10% level 

1.616488 

*MacKinnon (1996) one sided pvalu es. 


Augmented DickeyFuller Test Equati on 


Dependent Variable: D(LESPOT) 



Method: Least Squares 




Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 










LESPOT(1) 
6.04E06 
4.35E05 
0.138805 
0.8896 










Rsquared 
0.000006 
Mean dep 
endent var 
2.97E05 
Adjusted Rsquared 
0.000006 
S.D. dep 
endent var 
0.005936 
S.E. of regression 
0.005936 
Akaike in 
fo criterion 
7.414525 
Sum squared resid 
0.035414 
Schwarz 
criterion 
7.409641 
Log likelihood 
3730.506 
HannanQ 
uinn criter. 
7.412669 
DurbinWatson stat 
2.096015 



From the above output, we have derived unit root test for the Spot Rate of EURINR. The P Value of unit root test is 0.7260 which is greater than 0.05. Hence, the above output derives the result Non Stationary at level I (0).
Since it results in Non Stationary, the next step is to make the variable stationary. In order to make it stationary we have to construct difference for spot rate of EURINR. Equation generated to test difference at first level is dlespot=d(lespot)
Table 4.10: Unit Root Test for Spot Rate of EUR/INR at difference
Null Hypothesis: D(LESPOT) has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=21) 





tStatistic 
Prob.* 
Augmented DickeyFuller test statistic 
33.33385 
0.0000 

Test critical values: 
1% level 

2.567265 


5% level 

1.941139 


10% level 

1.616487 

*MacKinnon (1996) onesided pvalues. 


Augmented DickeyFuller Test Equation 


Dependent Variable: D(LESPOT,2) 


Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(LESPOT(1)) 
1.050493 
0.031514 
33.33385 
0.0000 
Rsquared 
0.525326 
Mean dependent var 
1.83E05 

Adjusted R squared 
0.525326 
S.D. dependent var 
0.008598 

S.E. of regression 
0.005924 
Akaike info criterion 
7.418628 

Sum squared resid 
0.035234 
Schwarz criterion 
7.413740 

Log likelihood 
3728.861 
HannanQuinn criter. 
7.416771 

DurbinWatson stat 
1.997566 



From the above test it is clear that the difference for spot rate of EURINR is stationary as P Value for this unit root test is 0.0000 which is less than 0.05. Hence the unit root test has become stationary at first difference I (1).
Table 4.11: Ordinary Least Squares of EUR/INR
Dependent Variable: DLESPOT 




Method: Least Squares 




Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
C 
6.07E06 
0.000209 
0.029061 
0.9768 
DLEFUT 
0.011320 
0.034919 
0.324182 
0.7459 
Rsquared 
0.000122 
Mean dependent var 
6.29E06 

Adjusted R squared 
0.001043 
S.D. dependent var 
0.006126 

S.E. of regression 
0.006129 
Akaike info criterion 
7.349251 

Sum squared resid 
0.032230 
Schwarz criterion 
7.338188 

Log likelihood 
3162.178 
HannanQuinn criter. 
7.345016 

Fstatistic 
0.105094 
DurbinWatson stat 
2.073262 

Prob(Fstatistic) 
0.745879 



The output stated above defines PValue of DLEFUT as 0.7459 which is greater than 0.05. Hence, OLS model clearly states that future rate of EURINR does not influence spot rate of EURINR
D. Future And Spot Rate of GBP/INR
Stationarity test with unit root test using ADF test for GBPINR.
Table 4.12: Unit Root Test for Future Rate of GBP/INR
Null Hypothesis: LGFUT has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=20) 





tStatistic 
Prob.* 
Augmented DickeyFuller test statistic 
0.250973 
0.5957 

Test critical values: 
1% level 

2.567789 


5% level 

1.941210 


10% level 

1.616439 

*MacKinnon (1996) onesided pvalues. 


Augmented DickeyFuller Test Equation 


Dependent Variable: D(LGFUT) 



Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
LGFUT(1) 
1.27E05 
5.06E05 
0.250973 
0.8019 
Rsquared 
0.000013 
Mean dependent var 
5.24E05 

Adjusted R squared 
0.000013 
S.D. dependent var 
0.006531 

S.E. of regression 
0.006531 
Akaike info criterion 
7.223234 

Sum squared resid 
0.034893 
Schwarz criterion 
7.217485 

Log likelihood 
2958.914 
HannanQuinn criter. 
7.221028 

DurbinWatson stat 
1.904900 



From the above output, we have derived unit root test for the Future Rate of GBPINR. The P Value of unit root test is 0.5957 which is greater than 0.05. Hence, the above output derives the result Non Stationary at level I (0).
Since it results in Non Stationary, the next step is to make the variable stationary. In order to make it stationary we have to generate difference for future rate of GBPINR. Equation generated to test difference at first level is dlgfut=d(lgfut)
Table 4.13: Unit Root Test for Future Rate of GBP/INR at difference
Null Hypothesis: D(LGFUT) has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=20) 





tStatistic 
Prob.* 
Augmented Di ckeyFuller test s tatistic 
27.25656 
0.0000 

Test critical values: 
1% level 

2.567792 


5% level 

1.941211 


10% level 

1.616439 

*MacKinnon (1996) one sided pvalu es. 


Augmented DickeyFuller Test on Equati 


Dependent Variable: D(LGFUT,2) 


Method: Least Squares 




Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 










D(LGFUT(1)) 
0.952531 
0.034947 
27.25656 
0.0000 










Rsquared 
0.476255 
Mean dep 
endent var 
3.79E07 
Adjusted Rsquared 
0.476255 
S.D. dep 
endent var 
0.009020 
S.E. of regression 
0.006528 
Akaike in 
fo criterion 
7.224318 
Sum squared resid 
0.034813 
Schwarz 
criterion 
7.218564 
Log likelihood 
2955.746 
HannanQ 
uinn criter. 
7.222110 
DurbinWatson stat 
1.996928 



From the above test it is clear that the difference for future rate of GBPINR is stationary as PValue for this unit root test is 0.0000 which is less than 0.05. Hence the unit root test has became stationary at first difference I(1).
Table 4.14: Unit Root Test for Spot Rate of GBPINR
Null Hypothesis: LGSPOT has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=20) 




tStatistic 
Prob.* 
Augmented Di 
ckeyFuller test 
statistic 
0.213085 
0.6095 
Test critical values: 
1% level 

2.567481 


5% level 

1.941168 


10% level 

1.616467 

*MacKinnon (1996 
) onesided pv 
alues. 


Augmented Dickey 
 Fuller Test Eq 
uation 


Dependent Variable: 
D(LGSPOT) 



Method: Least Squ 
ares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
LGSPOT(1) 
9.79E06 
4.59E05 
0.213085 
0.8313 










Rsquared 
0.000010 
Mean dependent var 
3.96E05 

Adjusted R squared 
0.000010 
S.D. dep endent var 
0.006277 

S.E. of regression 
0.006277 
Akaike info criterion 
7.302869 

Sum squared resid 
0.036166 
Schwarz criterion 
7.297621 

Log likelihood 
3356.668 
HannanQuinn criter. 
7.300866 

DurbinWatson stat 
2.090274 



From the above output, we have derived unit root test for the Spot Rate of GBPINR. The PValue of unit root test is 0.6095 which is greater than 0.05. Hence, the above output derives the result Non Stationary at level I(0).
Since it results in Non Stationary, the next step is to make the variable stationary. In order to make it stationary we have to generate difference for spot rate of GBPINR. Equation generated to test difference at first level is dlgspot=d(lgspot)
Table 4.15: Unit Root Test for Spot Rate of GBPINR at difference
Null Hypothesis: D(LGSPOT) has a unit root 


Exogenous: None 



Lag Length: 1 (Automatic  based on SIC, maxlag=20) 




tStatistic 
Prob.* 
Augmented DickeyFuller test statistic 
19.81579 
0.0000 

Test critical values: 
1% level 

2.567486 


5% level 

1.941169 


10% level 

1.616467 

*MacKinnon (1996) onesided pvalues. 


Augmented DickeyFuller Test Equation 


Dependent Variable: D(LGSPOT,2) 


Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(LGSPOT(1)) 
0.942241 
0.047550 
19.81579 
0.0000 
D(LGSPOT(1),2) 
0.097980 
0.032866 
2.981170 
0.0029 
Rsquared 
0.527175 
Mean dependent var 
1.01E05 

Adjusted R squared 
0.526658 
S.D. dependent var 
0.009074 

S.E. of regression 
0.006243 
Akaike info criterion 
7.312510 

Sum squared resid 
0.035663 
Schwarz criterion 
7.301995 

Log likelihood 
3354.786 
HannanQuinn criter. 
7.308497 

DurbinWatson stat 
1.992737 



From the above test it is clear that the difference for spot rate of GBPINR is stationary as P Value for this unit root test is 0.0000 which is less than 0.05. Hence the unit root test has become stationary at first difference I (1).
Table 4.16: Ordinary Least Squares of GBP/INR
Dependent Variable: DLGSPOT 




Method: Least Squares 




Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
C 
3.99E05 
0.000226 
0.176552 
0.8599 
DLGFUT 
0.029160 
0.034584 
0.843155 
0.3994 
Rsquared 
0.000869 
Mean dependent var 
3.83E05 

Adjusted R squared 
0.000354 
S.D. dependent var 
0.006459 

S.E. of regression 
0.006460 
Akaike info criterion 
7.243874 

Sum squared resid 
0.034097 
Schwarz criterion 
7.232377 

Log likelihood 
2968.366 
HannanQuinn criter. 
7.239462 

Fstatistic 
0.710910 
DurbinWatson stat 
2.084250 

Prob(Fstatistic) 
0.399388 



The output stated above defines PValue of DLGFUT as 0.3994 which is greater than 0.05. Hence, OLS model clearly states that future rate of GBPINR does not influence spot rate of GBPINR.
E. Future and Spot Rate of JPY/INR
Stationarity test with unit root test by using ADF test for JPYINR.
Table 4.17: Unit Root Test for Future Rate of JPY/INR
Null Hypothesis: LJFUT has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=20) 




tStatistic 
Prob.* 
Augmented DickeyFuller test statistic 
1.040458 
0.2690 

Test critical values: 
1% level 

2.567578 


5% level 

1.941181 


10% level 

1.616459 

*MacKinnon (1996) onesided pvalues. 


Augmented DickeyFuller Test Equation 


Dependent Variable: D(LJFUT) 



Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
LJFUT(1) 
0.000429 
0.000413 
1.040458 
0.2984 
Rsquared 
0.000432 
Mean dependent var 
0.000190 

Adjusted R squared 
0.000432 
S.D. dependent var 
0.006748 

S.E. of regression 
0.006747 
Akaike info criterion 
7.158373 

Sum squared resid 
0.040239 
Schwarz criterion 
7.152965 

Log likelihood 
3168.580 
HannanQuinn criter. 
7.156305 

DurbinWatson stat 
2.038042 



From the above output, we have derived unit root test for the Future Rate of JPYINR. The PValue of unit root test is 0.2690 which is greater than 0.05. Hence, the above output derives the result Non Stationary at level I(0).
Since it results in Non Stationary, the next step is to make the variable stationary. In order to make it stationary we have to generate difference for future rate of JPYINR. Equation generated to test difference at first level is dljfut=d(ljfut)
Table 4.18: Unit Root Test for Future Rate of JPY/INR at difference
Null Hypothesis: D(LJFUT) has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=20) 




tStatistic 
Prob.* 
Augmented DickeyFuller test statistic 
30.26156 
0.0000 

Test critical values: 
1% level 

2.567581 


5% level 

1.941182 


10% level 

1.616458 

*MacKinnon (1996) onesided pvalues. 


Augmented DickeyFuller Test Equation 


Dependent Variable: D(LJFUT,2) 


Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(LJFUT(1)) 
1.018260 
0.033649 
30.26156 
0.0000 
Rsquared 
0.509107 
Mean dependent var 
2.19E06 

Adjusted R squared 
0.509107 
S.D. dependent var 
0.009639 

S.E. of regression 
0.006754 
Akaike info criterion 
7.156349 

Sum squared resid 
0.040275 
Schwarz criterion 
7.150937 

Log likelihood 
3164.106 
HannanQuinn criter. 
7.154280 

DurbinWatson stat 
2.000132 



From the above test it is clear that the difference for future rate of JPYINR is stationary as PValue for this unit root test is 0.0000 which is less than 0.05. Hence the unit root test has became stationary at first difference I(1).
Table 4.19: Unit Root Test for Spot Rate of JPY/INR
Null Hypothesis: LJSPOT has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=21) 





tStatistic 
Prob.* 
Augmented Di ckeyFuller test statistic 
0.943312 
0.3081 

Test critical values: 
1% level 

2.567324 


5% level 

1.941147 


10% level 

1.616482 

*MacKinnon (1996) onesided pval ues. 


Augmented Dickey Fuller Test Equation 


Dependent D(LJSPOT) Variable: 



Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 










LJSPOT(1) 
0.000353 
0.000374 
0.943312 
0.3458 
Rsquared 
0.000359 
Mean dep 
endent var 
0.000150 
Adjusted R squared 
0.000359 
S.D. dep 
endent var 
0.006392 
S.E. of regression 
0.006391 
Akaike in 
fo criterion 
7.266850 
Sum squared resid 
0.039986 
Schwarz 
criterion 
7.261862 
Log likelihood 
3561.756 
HannanQ 
uinn criter. 
7.264952 
DurbinWatson stat 
2.044412 



From the above output, we have derived unit root test for the Spot Rate of JPYINR. The PValue of unit root test is 0.3081 which is greater than 0.05. Hence, the above output derives the result Non Stationary at level I(0).
Since it results in Non Stationary, the next step is to make the variable stationary. In order to make it stationary we have to generate difference for spot rate of JPYINR. Equation generated to test difference at first level is dljspot=d(ljspot)
Table 4.20: Unit Root Test for Spot Rate of JPY/INR at difference
Null Hypothesis: D(LJSPOT) has a unit root 


Exogenous: None 



Lag Length: 0 (Automatic  based on SIC, maxlag=21) 





tStatistic 
Prob.* 
Augmented DickeyFuller test statistic 
31.99025 
0.0000 

Test critical values: 
1% level 

2.567326 


5% level 

1.941147 


10% level 

1.616482 

*MacKinnon (1996) onesided pvalues. 


Augmented DickeyFuller Test Equation 


Dependent Variable: D(LJSPOT,2) 


Method: Least Squares 



Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
D(LJSPOT(1)) 
1.024596 
0.032028 
31.99025 
0.0000 
Rsquared 
0.511336 
Mean dependent var 
7.89E06 

Adjusted R squared 
0.511336 
S.D. dependent var 
0.009144 

S.E. of regression 
0.006392 
Akaike info criterion 
7.266445 

Sum squared resid 
0.039962 
Schwarz criterion 
7.261453 

Log likelihood 
3557.925 
HannanQuinn criter. 
7.264546 

DurbinWatson stat 
1.997560 



From the above test it is clear that the difference for spot rate of JPYINR is stationary as PValue for this unit root test is 0.0000 which is less than 0.05. Hence the unit root test has become stationary at first difference I (1).
Table 4.21: Ordinary Least Squares of JPY/INR
Dependent Variable: DLJSPOT 




Method: Least Squares 




Variable 
Coefficient 
Std. Error 
tStatistic 
Prob. 
C 
0.000118 
0.000220 
0.537078 
0.5913 
DLJFUT 
0.054795 
0.032535 
1.684209 
0.0925 
Rsquared 
0.003202 
Mean dependent var 
0.000128 

Adjusted R squared 
0.002073 
S.D. dependent var 
0.006535 

S.E. of regression 
0.006528 
Akaike info criterion 
7.223256 

Sum squared resid 
0.037626 
Schwarz criterion 
7.212441 

Log likelihood 
3198.291 
HannanQuinn criter. 
7.219121 

Fstatistic 
2.836561 
DurbinWatson stat 
2.038600 

Prob(Fstatistic) 
0.092495 



The output stated above defines PValue of DLJFUT as 0.0925 which is greater than 0.05. Hence, OLS model clearly states that future rate of JPYINR does not influence spot rate of JPYINR.
F. Long Term Relationship Among Futures
As the all currency futures are stationary at first difference, there may a chance to have long term relationship among future. The long term relationship is tested with cointegration. As there are multiple variables Johansen cointegration is used.
Table 4.22: Johansen Cointegration Model of Future Rate for all four Currencies
Trend assumption: Linear deterministic trend 


Series: DLUFUT DLJFUT DLGFUT DLEFUT 



Lags interval (in first differences): 1 to 4 


Unrestricted Cointegration Rank Test (Trace) 


Hypothesized 

Trace 
0.05 

No. of CE(s) 
Eigenvalu e 
Statistic 
Critical Value 
Prob.** 
None * 
0.203575 
623.1100 
47.85613 
0.0001 
At most 1 * 
0.195521 
438.0530 
29.79707 
0.0001 
At most 2 * 
0.155538 
261.1760 
15.49471 
0.0001 
At most 3 * 
0.141179 
123.7343 
3.841466 
0.0000 
The above output results Johansen Cointegration Test which says about the number of cointegration equation in Unrestricted Cointegration Rank Test (Trace), there are four equations are possible since probability of At most 3 * is 0.000 which is less than 0.05. Since there are four equations are there, it means variables are cointegrated. For estimating the relationship we have to perform VECM.
Table 4.23: Vector Error Correction Estimates of Future Rate for all four Currencies
Vector Error Correction Estimates 



Date: 08/23/18 Time: 00:32 



Sample (adjusted): 5 819 



Included observations: 815 after adjustments 


Standard errors in ( ) & tstatistics in [ ] 


Cointegrating Eq: 
CointEq1 



DLUFUT(1) 
1.000000 



DLJFUT(1) 
0.198177 




(0.03843) 




[5.15677] 



DLGFUT(1) 
0.526566 




(0.03479) 




[15.1345] 



DLEFUT(1) 
0.093722 




(0.04187) 




[ 2.23835] 



C 
0.000101 



Error Correction: 
D(DLUFUT 
D(DLJFUT 
D(DLGFUT 
D(DLEFUT 
CointEq1 
0.487755 
0.301523 
1.302059 
0.022716 

(0.04707) 
(0.10884) 
(0.09370) 
(0.08446) 

[10.3624] 
[ 2.77046] 
[ 13.8966] 
[ 0.26894] 
D(DLUFUT(1)) 
0.318710 
0.274025 
0.521960 
0.048482 

(0.04513) 
(0.10434) 
(0.08983) 
(0.08098) 

[7.06257] 
[2.62622] 
[5.81065] 
[ 0.59871] 
D(DLUFUT(2)) 
0.199250 
0.119080 
0.322731 
0.080166 

(0.03543) 
(0.08191) 
(0.07052) 
(0.06357) 

[5.62448] 
[1.45378] 
[4.57661] 
[ 1.26108] 
D(DLJFUT( 1)) 
0.094413 
0.643382 
0.135390 
0.406287 

(0.01595) 
(0.03689) 
(0.03176) 
(0.02863) 

[5.91793] 
[17.4413] 
[ 4.26328] 
[ 14.1919] 
D(DLJFUT( 2)) 
0.038163 
0.374049 
0.070771 
0.194502 

(0.01638) 
(0.03787) 
(0.03260) 
(0.02939) 

[2.33003] 
[9.87682] 
[ 2.17065] 
[ 6.61775] 
D(DLGFUT(1)) 
0.191454 
0.065820 
0.146102 
0.014172 

(0.02195) 
(0.05076) 
(0.04370) 
(0.03940) 

[8.72063] 
[ 1.29662] 
[3.34320] 
[ 0.35973] 
D(DLGFUT(2)) 
0.127120 
0.014896 
0.069159 
0.007605 

(0.01657) 
(0.03832) 
(0.03299) 
(0.02974) 

[7.67107] 
[ 0.38876] 
[2.09658] 
[ 0.25574] 
D(DLEFUT(1)) 
0.008266 
0.043381 
0.124321 
0.625320 

(0.01865) 
(0.04313) 
(0.03713) 
(0.03347) 

[0.44310] 
[1.00573] 
[3.34792] 
[18.6803] 
D(DLEFUT(2)) 
0.017421 
0.108191 
0.065785 
0.241931 

(0.01655) 
(0.03826) 
(0.03294) 
(0.02969) 

[1.05275] 
[2.82763] 
[1.99713] 
[8.14740] 
C 
9.93E06 
1.85E05 
2.61E06 
8.71E06 

(0.00012) 
(0.00027) 
(0.00024) 
(0.00021) 

[ 0.08356] 
[ 0.06726] 
[ 0.01104] 
[ 0.04086] 
Rsquared 
0.422366 
0.363412 
0.447570 
0.495999 
Adj. Rsquared 
0.415908 
0.356295 
0.441394 
0.490365 
Sum sq. resids 
0.009260 
0.049509 
0.036694 
0.029819 
S.E. equation 
0.003392 
0.007842 
0.006751 
0.006086 
Fstatistic 
65.40173 
51.06161 
72.46644 
88.02451 
Log likelihood 
3483.031 
2799.897 
2921.964 
3006.504 
Mean dependent 
4.95E06 
8.10E06 
3.37E06 
6.67E07 
S.D. dependent 
0.004438 
0.009775 
0.009033 
0.008525 
Determinant resid covariance (dof adj.) 
1.15E18 



Determinant resid covariance 
1.10E18 



Log likelihood 
12225.97 


Above outputs defines the coefficient, standard error and tstatistics so in order to get Pvalue we have to estimate variables.
Table 4.24: Estimation of Variables of all the four currency futures
Estimation Method: Least Squares 



Coefficient 
Std. Error 
tStatistic 
Prob. 
C(1) 
0.487755 
0.047070 
10.36236 
0.0000 
C(2) 
0.318710 
0.045127 
7.062568 
0.0000 
C(3) 
0.199250 
0.035426 
5.624484 
0.0000 
C(4) 
0.094413 
0.015954 
5.917926 
0.0000 
C(5) 
0.038163 
0.016379 
2.330026 
0.0199 
C(6) 
0.191454 
0.021954 
8.720633 
0.0000 
C(7) 
0.127120 
0.016571 
7.671072 
0.0000 
C(8) 
0.008266 
0.018655 
0.443098 
0.6577 
C(9) 
0.017421 
0.016548 
1.052749 
0.2925 
C(10) 
9.93E06 
0.000119 
0.083561 
0.9334 
C(11) 
0.301523 
0.108835 
2.770460 
0.0056 
C(12) 
0.274025 
0.104342 
2.626222 
0.0087 
C(13) 
0.119080 
0.081911 
1.453776 
0.1461 
C(14) 
0.643382 
0.036888 
17.44134 
0.0000 
C(15) 
0.374049 
0.037871 
9.876820 
0.0000 
C(16) 
0.065820 
0.050762 
1.296625 
0.1949 
C(17) 
0.014896 
0.038316 
0.388756 
0.6975 
C(18) 
0.043381 
0.043134 
1.005725 
0.3146 
C(19) 
0.108191 
0.038262 
2.827633 
0.0047 
C(20) 
1.85E05 
0.000275 
0.067259 
0.9464 
C(21) 
1.302059 
0.093696 
13.89659 
0.0000 
C(22) 
0.521960 
0.089828 
5.810652 
0.0000 
C(23) 
0.322731 
0.070517 
4.576611 
0.0000 
C(24) 
0.135390 
0.031757 
4.263279 
0.0000 
C(25) 
0.070771 
0.032604 
2.170647 
0.0300 
C(26) 
0.146102 
0.043701 
3.343196 
0.0008 
C(27) 
0.069159 
0.032987 
2.096584 
0.0361 
C(28) 
0.124321 
0.037134 
3.347917 
0.0008 
C(29) 
0.065785 
0.032940 
1.997131 
0.0459 
C(30) 
2.61E06 
0.000236 
0.011038 
0.9912 
C(31) 
0.022716 
0.084464 
0.268936 
0.7880 
C(32) 
0.048482 
0.080977 
0.598708 
0.5494 
C(33) 
0.080166 
0.063569 
1.261077 
0.2074 
C(34) 
0.406287 
0.028628 
14.19188 
0.0000 
C(35) 
0.194502 
0.029391 
6.617751 
0.0000 
C(36) 
0.014172 
0.039395 
0.359727 
0.7191 
C(37) 
0.007605 
0.029736 
0.255738 
0.7982 
C(38) 
0.625320 
0.033475 
18.68028 
0.0000 
C(39) 
0.241931 
0.029694 
8.147398 
0.0000 
C(40) 
8.71E06 
0.000213 
0.040863 
0.9674 
Determinant residual covariance 
1.10E18 



Equation: D(DLUFUT) = C(1)*( DLUFUT(1)  0.198177114215*DLJFUT(1) 

 0.526565670821*DLGFUT(1) + 0.0937217369791*DLEFUT(1)  
0.00010109077115 ) + C(2)*D(DLUFUT(1)) + C(3)*D(DLUFUT(2)) + 

C(4)*D(DLJFUT(1)) + C(5)*D(DLJFUT(2)) + C(6)*D(DLGFUT(1)) + 

C(7)*D(DLGFUT(2)) + C(8)*D(DLEFUT(1)) + C(9)*D(DLEFUT(2)) + 

C(10) 




Observations: 815 



Rsquared 
0.422366 
Mean dependent var 
4.95E06 

Adjusted R squared 
0.415908 
S.D. dependent var 
0.004438 

S.E. of regression 
0.003392 
Sum squared resid 
0.009260 

DurbinWatson stat 
2.160228 



Equation: D(DLJFUT) = C(11)*( DLUFUT(1)  0.198177114215*DLJFUT( 

1)  0.526565670821*DLGFUT(1) + 0.0937217369791*DLEFUT(1)  

0.00010109077115 ) + C(12)*D(DLUFUT(1)) + C(13)*D(DLUFUT(2)) 

+ C(14)*D(DLJFUT(1)) + C(15)*D(DLJFUT(2)) + C(16)*D(DLGFUT( 

1)) + C(17)*D(DLGFUT(2)) + C(18)*D(DLEFUT(1)) + C(19) 

*D(DLEFUT(2)) + C(20) 



Observations: 815 



Rsquared 
0.363412 
Mean dependent var 
8.10E06 

Adjusted R squared 
0.356295 
S.D. dependent var 
0.009775 

S.E. of regression 
0.007842 
Sum squared resid 
0.049509 

DurbinWatson stat 
2.144383 



Equation: D(DLGFUT) = C(21)*( DLUFUT(1)  0.198177114215*DLJFUT( 

1)  0.526565670821*DLGFUT(1) + 0.0937217369791*DLEFUT(1)  

0.00010109077115 ) + C(22)*D(DLUFUT(1)) + C(23)*D(DLUFUT(2)) 

+ C(24)*D(DLJFUT(1)) + C(25)*D(DLJFUT(2)) + C(26)*D(DLGFUT( 

1)) + C(27)*D(DLGFUT(2)) + C(28)*D(DLEFUT(1)) + C(29) 

*D(DLEFUT(2)) + C(30) 



Observations: 815 



Rsquared 
0.447570 
Mean dependent var 
3.37E06 

Adjusted R squared 
0.441394 
S.D. dependent var 
0.009033 

S.E. of regression 
0.006751 
Sum squared resid 
0.036694 

DurbinWatson stat 
2.061050 



Equation: D(DLEFUT) = C(31)*( DLUFUT(1)  0.198177114215*DLJFUT( 

1)  0.526565670821*DLGFUT(1) + 0.0937217369791*DLEFUT(1)  

0.00010109077115 ) + C(32)*D(DLUFUT(1)) + C(33)*D(DLUFUT(2)) 

+ C(34)*D(DLJFUT(1)) + C(35)*D(DLJFUT(2)) + C(36)*D(DLGFUT( 

1)) + C(37)*D(DLGFUT(2)) + C(38)*D(DLEFUT(1)) + C(39) 

*D(DLEFUT(2)) + C(40) 



Observations: 815 



Rsquared 
0.495999 
Mean dependent var 
6.67E07 

Adjusted R squared 
0.490365 
S.D. dependent var 
0.008525 

S.E. of regression 
0.006086 
Sum squared resid 
0.029819 

DurbinWatson stat 
2.187088 



Equation: D(DLUFUT) = C(1)*( DLUFUT(1)  0.198177114215*DLJFUT(1) 

 0.526565670821*DLGFUT(1) + 0.0937217369791*DLEFUT(1)  

0.00010109077115 ) + C(2)*D(DLUFUT(1)) + C(3)*D(DLUFUT(2)) + 

C(4)*D(DLJFUT(1)) + C(5)*D(DLJFUT(2)) + C(6)*D(DLGFUT(1)) + 

C(7)*D(DLGFUT(2)) + C(8)*D(DLEFUT(1)) + C(9)*D(DLEFUT(2)) + 

C(10) 


Here, only C1, C2, C3, C4, C5, C7 are showing the PValue which is less than 0.05, thus they only influence USDINR.
2. Equation for JPY variable
Equation: D(DLJFUT) = C(11)*( DLUFUT(1)  0.198177114215*DLJFUT(
1)  0.526565670821*DLGFUT(1) + 0.0937217369791*DLEFUT(1)  

0.00010109077115 ) + C(12)*D(DLUFUT(1)) + C(13)*D(DLUFUT(2)) 

+ C(14)*D(DLJFUT(1)) + C(15)*D(DLJFUT(2)) + C(16)*D(DLGFUT( 

1)) + C(17)*D(DLGFUT(2)) + C(18)*D(DLEFUT(1)) + C(19) 

*D(DLEFUT( 2 C(20) 


Here, only C11, C12, C14, C15 and C19 are showing the Pvalue which is less than 0.05 thus they influence JPYINR.
3. Equation for GBP variable
Equation: D(DLGFUT) = C(21)*( DLUFUT(1)  0.198177114215*DLJFUT( 

1)  0.526565670821*DLGFUT(1) + 0.0937217369791*DLEFUT(1)  

0.00010109077115 ) + C(22)*D(DLUFUT(1)) + C(23)*D(DLUFUT(2)) 

+ C(24)*D(DLJFUT(1)) + C(25)*D(DLJFUT(2)) + C(26)*D(DLGFUT( 

1)) + C(27)*D(DLGFUT(2)) + C(28)*D(DLEFUT(1)) + C(29) 

*D(DLEFUT(2)) + C(30) 


Here, only C21, C22, C23, C24, C25, C26, C27, C28 and C29 are showing Pvalue which is less than
0.05 thus they influence GBPINR.
4. Equation for EUR variable
Equation: D(DLEFUT) = C(31)*( DLUFUT(1)  0.198177114215*DLJFUT( 

1)  0.526565670821*DLGFUT(1) + 0.0937217369791*DLEFUT(1)  

0.00010109077115 ) + C(32)*D(DLUFUT(1)) + C(33)*D(DLUFUT(2)) 

+ C(34)*D(DLJFUT(1)) + C(35)*D(DLJFUT(2)) + C(36)*D(DLGFUT( 

1)) + C(37)*D(DLGFUT(2)) + C(38)*D(DLEFUT(1)) + C(39) 

*D(DLEFUT(2)) + C(40) 

Here, only C34, C35, C38 and C39 are showing Pvalue which is less than 0.05 thus they influence EURINR.
G. Long Term Relationship Among Spots
As the all currency spots are stationary at first difference, there may a chance to have long term relationship among spots. The long term relationship is tested with cointegration. As there are multiple variables Johansen cointegration is used.
Table 4.25: Johansen Cointegration Model of Spot Rate for all four Currencies
Trend assumption: Linear deterministic trend 


Series: DLUSPOT DLJSPOT DLGSPOT DLESPOT 


Lags interval (in first differences): 1 to 4 


Unrestricted Cointegration Rank Test (Trace) 


Hypothesize 

Trace 
0.05 

No. of CE(s 
Eigenvalue 
Statistic 
Critical Valu 
Prob.** 










None * 
0.200198 
633.1563 
47.85613 
0.0001 
At most 1 * 
0.181024 
451.5395 
29.79707 
0.0001 
At most 2 * 
0.174964 
289.1831 
15.49471 
0.0001 
At most 3 * 
0.150724 
132.8204 
3.841466 
0.0000 
The above output results Johansen Cointegration Test which says about the number of cointegration equation in Unrestricted Cointegration Rank Test (Trace), there are four equations are possible since probability of At most 3 * is 0.000 which is less than 0.05. Since there are four equations are there, it means variables are cointegrated. For estimating the relationship we have to perform VECM.
Table 4.26: Vector Error Correction Estimates of Spot Rate for all four Currencies
Vector Error Correction Estimates 



Standard errors in ( ) & tstatistics in [ ] 


Cointegrating Eq: 
CointEq1 



DLUSPOT(1) 
1.000000 



DLJSPOT(1) 
0.001112 




(0.02857) 




[0.03890] 



DLGSPOT(1) 
0.181136 




(0.02883) 




[6.28287] 



DLESPOT(1) 
0.278807 




(0.03087) 




[ 9.03130] 



C 
5.23E05 



Error Correction: 
D(DLUSPO ) 
D(DLJSPOT 
D(DLGSPO ) 
D(DLESPO ) 
CointEq1 
0.810580 
0.488048 
0.356048 
1.015304 

(0.05547) 
(0.13827) 
(0.13080) 
(0.12396) 

[14.6121] 
[3.52977] 
[ 2.72213] 
[8.19066] 
D(DLUSPOT(1)) 
0.088306 
0.440742 
0.255791 
0.763760 

(0.04762) 
(0.11869) 
(0.11228) 
(0.10641) 

[1.85448] 
[ 3.71351] 
[2.27825] 
[ 7.17785] 
D(DLUSPOT(2)) 
0.079780 
0.158683 
0.125136 
0.497998 

(0.03649) 
(0.09095) 
(0.08604) 
(0.08154) 

[2.18640] 
[ 1.74476] 
[1.45447] 
[ 6.10761] 
D(DLJSPOT(1)) 
0.011818 
0.671714 
0.030026 
0.007966 

(0.01437) 
(0.03581) 
(0.03388) 
(0.03211) 

[0.82250] 
[18.7557] 
[ 0.88626] 
[0.24811] 
D(DLJSPOT(2)) 
0.012863 
0.337028 
0.056535 
0.000304 

(0.01441) 
(0.03591) 
(0.03397) 
(0.03219) 

[0.89289] 
[9.38598] 
[1.66437] 
[0.00944] 
D(DLGSPOT(1)) 
0.095122 
0.032317 
0.668854 
0.141260 

(0.01619) 
(0.04035) 
(0.03817) 
(0.03617) 

[5.87591] 
[0.80093] 
[17.5231] 
[3.90500] 
D(DLGSPOT(2)) 
0.030367 
0.006068 
0.242557 
0.087390 

(0.01483) 
(0.03697) 
(0.03497) 
(0.03314) 

[2.04732] 
[0.16413] 
[6.93555] 
[2.63664] 
D(DLESPOT(1)) 
0.130801 
0.044031 
0.072150 
0.482526 

(0.01807) 
(0.04504) 
(0.04261) 
(0.04038) 

[ 7.23879] 
[ 0.97765] 
[1.69345] 
[11.9504] 
D(DLESPOT(2)) 
0.064950 
0.006555 
0.063069 
0.212528 

(0.01538) 
(0.03833) 
(0.03626) 
(0.03436) 

[ 4.22352] 
[0.17102] 
[1.73940] 
[6.18472] 
C 
2.96E06 
7.55E06 
1.14E05 
2.87E06 

(0.00011) 
(0.00027) 
(0.00026) 
(0.00024) 

[0.02706] 
[0.02774] 
[ 0.04410] 
[ 0.01174] 
Rsquared 
0.457246 
0.339052 
0.382729 
0.394899 
Adj. Rsquared 
0.451178 
0.331663 
0.375827 
0.388134 
Sum sq. resids 
0.007833 
0.048661 
0.043546 
0.039112 
S.E. equation 
0.003119 
0.007775 
0.007355 
0.006970 
Fstatistic 
75.35296 
45.88314 
55.45851 
58.37305 
Log likelihood 
3551.257 
2806.935 
2852.192 
2895.958 
Akaike AIC 
8.690200 
6.863645 
6.974705 
7.082105 
Schwarz SC 
8.632492 
6.805937 
6.916997 
7.024398 
Mean dependent 
3.16E06 
9.65E06 
8.37E06 
5.81E06 
S.D. dependent 
0.004211 
0.009510 
0.009309 
0.008911 
Determinant resid covariance (dof adj.) 
1.29E18 



Determinant resid covariance 
1.22E18 



Log likelihood 
12181.32 


Above output defines the coefficient, standard error and tstatistics and so in order to get Pvalue we have to estimate variables.
Table 4.27: Estimation of Variables of all the currency spots
Estimation Method: Least Squares 



Coefficient 
Std. Error 
tStatistic 
Prob. 
C(1) 
0.810580 
0.055473 
14.61206 
0.0000 
C(2) 
0.088306 
0.047618 
1.854483 
0.0638 
C(3) 
0.079780 
0.036489 
2.186396 
0.0289 
C(4) 
0.011818 
0.014369 
0.822501 
0.4109 
C(5) 
0.012863 
0.014406 
0.892889 
0.3720 
C(6) 
0.095122 
0.016188 
5.875906 
0.0000 
C(7) 
0.030367 
0.014833 
2.047322 
0.0407 
C(8) 
0.130801 
0.018069 
7.238790 
0.0000 
C(9) 
0.064950 
0.015378 
4.223517 
0.0000 
C(10) 
2.96E06 
0.000109 
0.027065 
0.9784 
C(11) 
0.487890 
0.138189 
3.530592 
0.0004 
C(12) 
0.440385 
0.118616 
3.712689 
0.0002 
C(13) 
0.158181 
0.090886 
1.740431 
0.0819 
C(14) 
0.671447 
0.035785 
18.76311 
0.0000 
C(15) 
0.336359 
0.035833 
9.386792 
0.0000 
C(16) 
0.032779 
0.040304 
0.813288 
0.4161 
C(17) 
0.006147 
0.036949 
0.166372 
0.8679 
C(18) 
0.043999 
0.045013 
0.977465 
0.3284 
C(19) 
0.006557 
0.038308 
0.171162 
0.8641 
C(20) 
1.08E05 
0.000272 
0.039612 
0.9684 
C(21) 
0.356887 
0.131008 
2.724168 
0.0065 
C(22) 
0.257689 
0.112452 
2.291554 
0.0220 
C(23) 
0.127804 
0.086163 
1.483287 
0.1381 
C(24) 
0.031446 
0.033926 
0.926894 
0.3541 
C(25) 
0.052979 
0.033971 
1.559540 
0.1190 
C(26) 
0.671309 
0.038210 
17.56916 
0.0000 
C(27) 
0.242980 
0.035029 
6.936587 
0.0000 
C(28) 
0.072322 
0.042674 
1.694769 
0.0902 
C(29) 
0.063078 
0.036318 
1.736855 
0.0825 
C(30) 
5.76E06 
0.000258 
0.022334 
0.9822 
C(31) 
1.014889 
0.123957 
8.187436 
0.0000 
C(32) 
0.762820 
0.106400 
7.169376 
0.0000 
C(33) 
0.496676 
0.081526 
6.092278 
0.0000 
C(34) 
0.007263 
0.032100 
0.226273 
0.8210 
C(35) 
0.001458 
0.032143 
0.045347 
0.9638 
C(36) 
0.142476 
0.036153 
3.940908 
0.0001 
C(37) 
0.087599 
0.033144 
2.643029 
0.0083 
C(38) 
0.482611 
0.040377 
11.95263 
0.0000 
C(39) 
0.212532 
0.034363 
6.184921 
0.0000 
C(40) 
5.61E06 
0.000244 
0.022998 
0.9817 
Determinant residual covariance 
1.23E18 



Equation: D(DLUSPOT) = C(1)*( DLUSPOT(1)  0.00111152295798 

*DLJSPOT(1)  0.181135923358*DLGSPOT(1) + 0.278807137474 
*DLESPOT(1)  5.23468209954E05 ) + C(2)*D(DLUSPOT( 1)) + C(3) 

*D(DLUSPOT(2)) + C(4)*D(DLJSPOT(1)) + C(5)*D(DLJSPOT(2)) + 

C(6)*D(DLGSPOT(1)) + C(7)*D(DLGSPOT(2)) + C(8)*D(DLESPOT( 

1)) + C(9)*D(DLESPOT(2)) + C(10) 


Observations: 815 



Rsquared 
0.457246 
Mean dependent var 
3.16E06 

Adjusted R squared 
0.451178 
S.D. dependent var 
0.004211 

S.E. of regression 
0.003119 
Sum squared resid 
0.007833 

DurbinWatson stat 
2.074218 



Equation: D(DLJSPOT) = C(11)*( DLUSPOT(1)  0.00111152295798 

*DLJSPOT(1)  0.181135923358*DLGSPOT(1) + 0.278807137474 

*DLESPOT(1)  5.23468209954E05 ) + C(12)*D(DLUSPOT( 1)) + 

C(13)*D(DLUSPOT(2)) + C(14)*D(DLJSPOT(1)) + C(15)*D(DLJSPOT( 

2)) + C(16)*D(DLGSPOT(1)) + C(17)*D(DLGSPOT(2)) + C(18) 

*D(DLESPOT(1)) + C(19)*D(DLESPOT(2)) + C(20) 

Observations: 816 



Rsquared 
0.338960 
Mean dependent var 
9.22E06 

Adjusted R squared 
0.331578 
S.D. dependent var 
0.009505 

S.E. of regression 
0.007771 
Sum squared resid 
0.048668 

DurbinWatson stat 
2.159165 



Equation: D(DLGSPOT) = C(21)*( DLUSPOT(1)  0.00111152295798 

*DLJSPOT(1)  0.181135923358*DLGSPOT(1) + 0.278807137474 

*DLESPOT(1)  5.23468209954E05 ) + C(22)*D(DLUSPOT( 1)) + 

C(23)*D(DLUSPOT(2)) + C(24)*D(DLJSPOT(1)) + C(25)*D(DLJSPOT( 

2)) + C(26)*D(DLGSPOT(1)) + C(27)*D(DLGSPOT(2)) + C(28) 

*D(DLESPOT(1)) + C(29)*D(DLESPOT(2)) + C(30) 

Observations: 816 



Rsquared 
0.383314 
Mean dependent 
1.56E05 



var 


Adjusted R squared 
0.376428 
S.D. dependent var 
0.009329 

S.E. of regression 
0.007367 
Sum squared resid 
0.043741 

DurbinWatson stat 
2.123636 



Equation: D(DLESPOT) = C(31)*( DLUSPOT(1)  0.00111152295798 

*DLJSPOT(1)  0.181135923358*DLGSPOT(1) + 0.278807137474 

*DLESPOT(1)  5.23468209954E05 ) + C(32)*D(DLUSPOT( 1)) + 

C(33)*D(DLUSPOT(2)) + C(34)*D(DLJSPOT(1)) + C(35)*D(DLJSPOT( 

2)) + C(36)*D(DLGSPOT(1)) + C(37)*D(DLGSPOT(2)) + C(38) 

*D(DLESPOT(1)) + C(39)*D(DLESPOT(2)) + C(40) 

Observations: 816 



Rsquared 
0.394594 
Mean dependent var 
2.53E06 

Adjusted R squared 
0.387834 
S.D. dependent var 
0.008909 

S.E. of regression 
0.006970 
Sum squared resid 
0.039159 
Equation: D(DLUSPOT) = C(1)*( DLUSPOT(1)  0.00111152295798 

*DLJSPOT(1)  0.181135923358*DLGSPOT(1) + 0.278807137474 

*DLESPOT(1)  5.23468209954E05 ) + C(2)*D(DLUSPOT(1)) + C(3) 

*D(DLUSPOT(2)) + C(4)*D(DLJSPOT(1)) + C(5)*D(DLJSPOT(2)) + 

C(6)*D(DLGSPOT(1)) + C(7)*D(DLGSPOT(2)) + C(8)*D(DLESPOT( 

1)) + C(9)*D(DLESPOT(2)) + C(10) 

Here, only C1, C3, C6, C7, C8, and C9 are showing the Pvalue which is less than 0.05 thus th influence USDINR.
2. Equation for JPY variable
Equation: D(DLJSPOT) = C(11)*( DLUSPOT(1)  0.00111152295798 
*DLJSPOT(1)  0.181135923358*DLGSPOT(1) + 0.278807137474 
*DLESPOT(1)  5.23468209954E05 ) + C(12)*D(DLUSPOT(1)) + 
C(13)*D(DLUSPOT(2)) + C(14)*D(DLJSPOT(1)) + C(15)*D(DLJSPOT( 
2)) + C(16)*D(DLGSPOT(1)) + C(17)*D(DLGSPOT(2)) + C(18) 
*D(DLESPOT(1)) + C(19)*D(DLESPOT(2)) + C(20) 
Here, only C11, C12, C14 and C15 are showing Pvalue which is less than 0.05 thus they influence JPYINR.
3. Equation for GBP variable
Equation: D(DLGSPOT) = C(21)*( DLUSPOT(1)  0.00111152295798 
*DLJSPOT(1)  0.181135923358*DLGSPOT(1) + 0.2788071374 
*DLESPOT(1)  5.23468209954E05 ) + C(22)*D(DLUSPOT(1)) 
C(23)*D(DLUSPOT(2)) + C(24)*D(DLJSPOT(1)) + C(25)*D(DLJSPOT( 
2)) + C(26)*D(DLGSPOT(1)) + C(27)*D(DLGSPOT(2)) + C(28) 
*D(DLESPOT(1)) + C(29)*D(DLESPOT(2)) + C(30) 
Here, only C21, C22, C26 and C27 are showing Pvalue which is less than 0.05 thus influence GBPINR.
4. Equation for EUR variable
Equation: D(DLESPOT) = C(31)*( DLUSPOT(1)  0.00111152295798 
*DLJSPOT(1)  0.181135923358*DLGSPOT(1) + 0.278807137474 
*DLESPOT(1)  5.23468209954E05 ) + C(32)*D(DLUSPOT(1)) + 
C(33)*D(DLUSPOT(2)) + C(34)*D(DLJSPOT(1)) + C(35)*D(DLJSPOT( 
2)) + C(36)*D(DLGSPOT(1)) + C(37)*D(DLGSPOT(2)) + C(38) 
*D(DLESPOT(1)) + C(39)*D(DLESPOT(2)) + C(40) 
Here, C31, C32, C33, C36, C37, C38 and C39 are showing Pvalue which is less than thus they influence EURINR.
V. FINDINGS, SUGGESTION AND CONCLUSION
A. Findings
And also we had find what are all the cointegrated equation using Johansen Cointegration test.
Johansen Cointegration Test which says about the number of cointegration equation in Unrestricted Cointegration Rank Test (Trace), (Maximum Eigenvalue), (normalized by b'*S11*b=I), (alpha) from the PValue.
At last, we had also noted what are all the factors influencing the currencies using Vector Error Correction Estimates;
a. Only C1, C2, C3, C4, C6 and C7 influence the USDINR future rate.
b. Only C11, C12, C14, C15 and C19 influence the JPYINR future rate.
c. Only C21, C22, C23, C24, C25, C26, 27, C28 and C29 influence the GBPINR future rate.
d. Only C34, C35, C38, and C39 influence the EURINR future rate.
e. Only C1, C3, C6, C7, C8 and C9 influence the USDINR spot rate.
f. Only C11, C12, C14 and C15 influence the JPYINR spot rate.
g. Only C21, C22, C26, and C27 influence the GBPINR spot rate.
h. Only C31, C32, C33, C36, C37, C38 and C39 influence the EURINR spot rate.
These are all the findings estimated from analysis using EVIEWS.
B. Suggestion
Foreign Exchange market is highly risk oriented. If we don’t have thorough knowledge of the technical analysis then we might lose the money. A return of the investment in the currencies depends on the volatility of the market. Foreign exchange market is highly volatile. Volatility gives itself an ‘opportunity’ as well as ‘risk’ whichever way one may look at it, we can’t wish it away. Some traders lost their money in the Foreign Exchange market due the fluctuation of the currency value and only remaining few traders are earning money. At the every point of trade investors were asked to maintain STOP LOSS order to minimize the loss. So the aim is “Don’t focus on making money; focus on protecting what you have.” If investors follow this strategy for a period of time then they can earn plenty of money in the Foreign Exchange market. The aim must be to ascertain the target of the market operators. Market is always up & down so based on this prevailing situation the investors either buy or sell the currencies.
[1] www.nseindia.com [2] www.bseindia.com www.moneycontrol.com www.eviews.com www.investing.com [3] www.hossainacademy.com www.iifl.com
Copyright © 2022 Aashutosh , Abhay Dubey, Abhay Kumar, Dr./ Prof. Nishtha Dwivedi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Authors : Aashutosh
Paper Id : IJRASET42555
Publish Date : 20220512
ISSN : 23219653
Publisher Name : IJRASET
DOI Link : Click Here