USD INR PARITY QUANTO MODEL
Financial Modeling using various economic variables for studying Correlation between factors affecting USD/INR parity – A Comparison and Policy Recommendations towards Monitoring of variables and its impact for reduced volatility.
Statement of the problem and Objective of Study
The aim of the research is to compare various forecasting tools used and to suggest a tool which is simple and provides standardized information. Multiple variable correlation to arrive at stable model and to suggest policy initiative towards monitoring of specific variables affecting the USD/INR exchange rates. Recent blocking of imports of Gold to reduce the volatility was an unprecedented move.
Brief Background of The Foreign Exchange Market
Fx Market started in 1978 and thereafter it was liberalized after 1991 crisis. Forex Market has been subjected to FERA, FEMA and is governed by FEDAI and RBI. The Indian foreign exchange market has exhibited significant growth over the last decade, with average daily turnover recording a quantum jump from US$6 billion a year in 2000 to US$60 billion in recent times. The major market participants in the domestic foreign exchange market now include banks, corporates and foreign institutional investors (FIIs). Besides having an active over-the-counter market, India also has an exchange-traded currency futures and options market that has shown reasonable growth since its inception in 2008.
Hypothesis of the study
A simple, standardized and comprehensive model for USD/INR exchange rate to facilitate short/long term policy formulation.
Null Hypothesis Ho : Multiple Variables does not give a positive correlation on exchange rate of USD/INR; Individual Variables Gives a better correlation
Alternate Hypothesis : Multiple Variables gives a positive correlation on exchange rate of USD/INR; Individual Variable are not able to give better correlation with USD/INR exchange Rate
Research Methodology
Multivariate Linear Correlation
Correlation is a statistical technique, which measures and analyses the degree or extent to which two or more variables fluctuate with reference to one another. Correlation thus denotes the inter-dependence amongst variables. The degrees are expressed by a coefficient, which ranges between –1 and +1. The direction of change is indicated by (+) or (-) signs. The former refers to a sympathetic movement in a same direction and the later in the opposite direction.
A coefficient of correlation is a mathematical measure of how much one number (such as a share price) can expected to be influenced by changes in another (such as an index). It is closely related to covariance (see below).
A correlation coefficient of 1 means that the two numbers are perfectly correlated: if one grows so does the other, and the change in one is a multiple of the change in the other.
A correlation coefficient of -1 means that the numbers are perfectly inversely correlated. If one grows the other falls. The growth in one is a negative multiple of the growth in the other.
A correlation coefficient of zero means that the two numbers are not related.
A non-zero correlation coefficient means that the numbers are related, but unless the coefficient is either 1 or -1 there are other influences and the relationship between the two numbers is not fixed.
Multiple Variables are taken to jointly regress and correlate so that model is more dependable, “noise” or abrubt results due weightages are reduced and better results are arrived.
An analytical and exploratory research where three forecasting methods will be compared and analyzed and the parameters common in the three models will be identified. The data will be secondary and published by dependable institutions. Period of the data will be around 10 years.
Scope of study
The scope of research will be limited to modeling while discussing issues pertaining to USD/INR exchange rate movement and relevant scenarios during that period.
Research design or detailed chapterization
Proposed Chapter plan is as under:
- Introduction to Foreign Exchange Market
- Structure of Financial system in India
- Case Studies of other Countries and Institutions using forecasting methods
- Exchange Rate Forecasting - Need and Relevance
- A comparison of various models
- Regulatory of Foreign Exchange Markets
- Multivariate Linear Model – a sustainable way forward
- Various Economic Variables and their relevance
- Stock Market and its relationship with exchange rate movement
- Conclusions
11. Annexures for Data
Rational of the study
The idea behind this research is to explore if there can be a better and comprehensive dependable model Institutions a reference point which is simple and can be standardized by the policy makers and to focus and monitor variables leading to reduced volatility
Contribution to the knowledge or subject, the society and the nation.
Foreign Exchange Market is the largest and affects the profitability, economic well being of India and every Indian in the light of greater globalization and opening up of economy.
The exponential growth of market and OTC products and even Non Deliverable markets in Singapore and Dubai has attracted considerable attention of development practitioners, policy makers, funders, academicians, researchers and even corporate bodies.
Recent movements in the exchange rate and volumes and rate differential in the NDF market has resulted in the need for the policy makers to change the guidelines.
The research aims at developing a model which will aid the policymakers to standardize a model for forecasting USD/INR exchange rate.
Limitation of the study
Despite, the wholehearted effort by the researcher, the research would still be characterized by certain limitations. The shear nature of the market , dynamic economic scenario.
Review of literature
Various studies by Jamal Mecklai, RBI Reports, Investment banks study and International reports and research papers.
Further various central banks have also come out with the studies. Dr Krishna Murari and Dr Rajesh Sharma of MITS University have used 6 variables to conclude that there is very high correlation between 6 economic variables and exchange rate.
Researchers used models like granger causality, GARCH (1, 1), vector autoregressive (VAR), Vector Error Correction Model (VECM), regression, multi-regression for finding out relationship between stock market and foreign exchange market. An early attempt to examine the exchange rate and stock price dynamics was by Franck and Young (1972) who showed that there is no significant interaction between the variables. Soenen and Hennigar (1988) studied the same market but considered a different time period and contrast with prior studies by showing a significant negative relationship between stock prices and exchange rates.
Bibliography
1. Dua, P., & Ranjan, R. (2011). Modeling and Forecasting the Indian Re/US Dollar Exchange Rate. Centre for Development Economics, Working Paper No.197 . University of Delhi.
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3. Johri, D., & Miller, M. (n.d.). Devaluation of the Rupee: Tale of Two Years, 1966 and 1991. Retrieved October 19, 2013, from centre for civil society: http://www.ccs.in/ccsindia/
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8. TOI. (2013, August 15). Journey of Indian rupee since independence. The Times of India
9. International Finance by P G Apte
10. Donald E. Fischer & Ronald J. Jordan (SECURITY ANALYSIS AND PORTFOLIO MANAGENENT)
11. The Management of Foreign Exchange Risk by Ian H. Giddy & Gunter Dufay
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