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1

T., Lakshmanasamy. "Relationship Between Exchange Rate and Stock Market Volatilities in India." International Journal of Finance Research 2, no. 4 (2021): 244–59. http://dx.doi.org/10.47747/ijfr.v2i4.443.

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With increasing globalisation and integration of national stock exchanges, for the global investor, the portfolio risk increases not only from the local stock market volatility but also in the exchange rate risk. This paper examines the exchange rate volatility effect on volatility in stock market return from India’s perspective for the period January 2010 to December 2015, applying ARCH and GARCH estimation. The daily data of the BSE SENSEX returns, exchange rates of US dollar/rupee, British pound/rupee, Euros/rupee are used. It is estimated that the Euro/rupee exchange rate volatility has a
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2

Kukreja, Mansi. "IMPACT OF SINKING RUPEE ON INDIAS FOREIGN TRADE(THREATS TO EXCHANGE RATE FLUCTUATIONS)." International Journal of Advanced Research 10, no. 08 (2022): 883–90. http://dx.doi.org/10.21474/ijar01/15247.

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This study investigates the impact of rupee-dollar variations on the Indian economy. The economic conditions brought about by the rupees decline against the dollar demonstrate that there has been a significant detrimental impact of this price fluctuation on several industries. Any countrys export competitiveness and the value of its local currencies in terms of other currencies have a complex relationship. If the exported goods rely heavily on imported resources, this relationship will become more complicated. The Indian rupee has lost value numerous times during the past year, reaching a high
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Rizvi, Bilal Hasan, and Amit Kumar Sinha. "IMPACT OF FED RATE ON US DOLLAR - INDIAN RUPEE EXCHANGE RATE." INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMMERCE, MANAGEMENT & SOCIAL SCIENCE 07, no. 02(II) (2024): 01–6. http://dx.doi.org/10.62823/7.2(ii).6508.

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Through this study, we attempt to understand the dynamics of Indian Rupee fluctuations against US Dollar that have been caused by the fluctuations in the Fed Rate. We tried to understand how the Fed Rate influence the Indian rupee - US Dollar exchange rate movements. After research work done via secondary method, we have observed that factors like differential interest rate, differential inflation rate, differential money supply in both the markets, differential output growth rate of both the countries, among others, are important factors which impact the fluctuation in the fed rate that accou
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4

Bhat, Aparna Prasad. "The economic determinants of the implied volatility function for currency options." International Journal of Emerging Markets 13, no. 6 (2018): 1798–819. http://dx.doi.org/10.1108/ijoem-08-2017-0308.

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Purpose The purpose of this paper is to ascertain the pattern of the implied volatility function for currency options traded on the National Stock Exchange of India (NSE), identify its potential determinants and to investigate any seasonality in the pattern. Design/methodology/approach The paper examines four different specifications for the implied volatility smile of exchange-traded dollar-rupee options. These specifications are tested by running Ordinary Least Squares (OLS) regressions on a daily basis for all options over the entire sample period. Seven potential determinants for the shape
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5

Bhanja, Niyati, Arif Dar, and Aviral Tiwari. "Exchange rate and monetary fundamentals: Long run relationship revisited." Panoeconomicus 62, no. 1 (2015): 33–54. http://dx.doi.org/10.2298/pan1501033b.

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This study re-examines the long run validity of the monetary approach to exchange rate determination for India. In particular, the long run association of bilateral nominal exchange rate of Indian rupee vis-?-vis USD, Pound-sterling, Yen and Euro against the corresponding monetary fundamentals that the model underlines has been tested using Johansen-Juselius maximum likelihood framework and Gregory-Hansen co-integration approach. Irrespective of the exchange rates the study finds a co-integrating relationship among the variables using Johansen-Juselius maximum likelihood approach. The Gregory-
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6

Haider Ali Shah Bukhari, Syed Adnan, Muhammad Shahbaz Akmal, and Mohammad Sabihuddin Butt. "Impact of Exchange Market Forces on Pak-Rupee Exchange Rates during Globalization Period: An Empirical Analysis." LAHORE JOURNAL OF ECONOMICS 11, no. 1 (2006): 121–39. http://dx.doi.org/10.35536/lje.2006.v11.i1.a7.

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This paper analyzes the impact of exchange market forces on Pak-Rupee/US dollar exchange rates during the 1965-1971 globalization period. The main findings are that a) the behavior of Pakistan’s fundamentals relative to those of the USA help to explain exchange market forces against the Pak-Rupee; b) during the run up to devaluation in the globalization period the monetary authorities in Pakistan were acting to reduce domestic credit; but that c) additional pressure was brought against the Pak-Rupee from speculative sources. These findings relate to current thinking on the choice of the exchan
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7

Bhatti, Razzaque H. "Determining Pak Rupee Exchange Rates vis-à-vis Six Currencies of the Industrial World: Some Evidence Based on the Traditional Flow Model." Pakistan Development Review 40, no. 4II (2001): 885–97. http://dx.doi.org/10.30541/v40i4iipp.885-897.

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Pak-rupee exchange rates vis-à-vis many currencies of the industrial world have weakened continuously and persistently since Pakistan abandoned fixed exchange rates in April 1982. This proposition is strongly supported by descriptive test statistics, as shown in Table 1, such as mean, standard deviation and coefficient of variation of six Pak rupee exchange rates—against the U.S. dollar, British pound, German mark, Japanese yen, Swiss franc and French franc—over the period 1982q1-2000q4. Based on these descriptive statistics, it is evident that Pak rupee has depreciated persistently against al
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8

Adhikari, Deepak. "Impact of Exchange Rate on Trade Deficit and Foreign Exchange Reserve in Nepal: An Empirical Analysis." NRB Economic Review 30, no. 1 (2018): 35–48. http://dx.doi.org/10.3126/nrber.v30i1.52299.

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The objective of the study is to examine the impact of exchange rate on trade deficit and foreign exchange reserve in Nepal. The hypotheses of the study are: (a) there is no significant positive association between nominal exchange rate and foreign exchange reserve and (b) there is no significant relationship between nominal exchange rate of Nepalese rupee with US dollar and trade deficit. As empirical analysis shows that one percentage point depreciation of the Nepalese rupee (NPR) with respect to US dollar results in an (a) increase in reserve by 0.82 percentage points and (b) decline in tra
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9

Sugirtha, R., and Dr M. Babu. "CO- Integration Approach Study of Crude Oil Prices and USD/ INR." Restaurant Business 118, no. 6 (2019): 140–44. http://dx.doi.org/10.26643/rb.v118i6.8004.

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The crude oil price and US dollar/INR influence the value of Indian rupee as well as values of currencies of other countries . Over the past decades, oil price and US dollar dominate the overall global markets. The crude oil price and US dollars instability bond with the economic growth and welfare of a country. Hence the study examined the volatility of crude oil price and US dollar in the Indian commodity market, during the study period from 2009 to 2018. US dollar price were collected from the Reserve Bank of India (RBI) and crude oil price were collected from Multi Commodity Exchange (MCX)
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10

Quang My, Nguyen, and Mustafa Sayim. "The Impact of Economic Factors on the Foreign Exchange Rates between USA and Four Big Emerging Countries: China, India, Brazil and Mexico." International Finance and Banking 3, no. 1 (2016): 11. http://dx.doi.org/10.5296/ifb.v3i1.9108.

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This study examines the impact of macro-economic factors on the foreign exchange rates between USA and four big emerging countries: India, Mexico, Brazil and China for the period of 2005 to 2014. This study uses Enter and Stepwise multiple regression methods to investigate the impact of market fundamental on the exchange rates. The empirical findings reveal that the macro-economic factors significantly predict and influence the exchange rates between USD/CNY (US dollar/Chinese yuan), USD/INR (US dollar/Indian rupee), USD/BRL (US dollar/ Brazilian real), and USD/MNX (US dollar/Mexican pesos). I
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11

Naresh, G., and S. Ananda. "Bitcoin prices and rupee-dollar exchange rates during COVID-19." International Journal of Electronic Finance 10, no. 3 (2021): 180. http://dx.doi.org/10.1504/ijef.2021.115661.

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12

Ananda, S., and G. Naresh. "Bitcoin prices and rupee-dollar exchange rates during COVID-19." International Journal of Electronic Finance 10, no. 3 (2021): 180. http://dx.doi.org/10.1504/ijef.2021.10038663.

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13

Ahmed, Mohammad. "Pakistan's Exchange Rate Policy: An Econometric Investigation." Pakistan Development Review 31, no. 1 (1992): 49–74. http://dx.doi.org/10.30541/v31i1pp.49-74.

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This paper examines empirical determinants of the Pakistani rupee exchange rate since the advent of the managed float in 1982. The behaviour of the nominal exchange rate results from policy intervention carried out by the monetary authorities. Various testable hypotheses are developed in order to discern the factor(s) which can be the determinants of the nominal rupee exchange rate. In the shon run, authorities follow a contingent policy rule with respect to movements of the U. S. dollar against the SDR. Based on vector autoregression techniques, the error correction model is employed to check
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14

Kaushik, Neetu, Raja Nag, and Kamal P. Upadhyaya. "Oil Price And Real Exchange Rate: The Case Of India." International Business & Economics Research Journal (IBER) 13, no. 4 (2014): 809. http://dx.doi.org/10.19030/iber.v13i4.8688.

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This paper studies the effect of oil price change on the real exchange rate between the Indian rupee and the U.S. dollar. For that, a model is developed which is based on a monetary model of exchange rate which incorporates the real GDP, real money balances, and the interest rates of both the home and foreign country and the real price of the crude oil. Quarterly time series data from 1996 to 2012 is used. Before estimating the model, the time series properties of the data are diagnosed in order to ensure the stationarity of the data. The data series are found to be integrated of order one and
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15

Sharma, Chandan, and Rajat Setia. "Macroeconomic fundamentals and dynamics of the Indian rupee-dollar exchange rate." Journal of Financial Economic Policy 7, no. 4 (2015): 301–26. http://dx.doi.org/10.1108/jfep-11-2014-0069.

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Purpose – This paper aims to examine the relationship between Indian rupee-US dollar exchange rate and the macroeconomic fundamentals for the post-economic reform period. Design/methodology/approach – The authors have used an empirical model which includes a range of important macroeconomic variables based on the basic monetary theories of exchange rate determination. At the first stage of the analysis, they have tested structural break in the data. Subsequently, they have employed the fully modified ordinary least square, Wald’s coefficient restriction and impulse response functions (IRF) to
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16

Mohanty, Debasis, Amiya Kumar Mohapatra, Sasikanta Tripathy, and Rahul Matta. "Nexus between foreign exchange rate and stock market: evidence from India." Investment Management and Financial Innovations 20, no. 3 (2023): 79–90. http://dx.doi.org/10.21511/imfi.20(3).2023.07.

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This study examines the impact of foreign exchange rate fluctuations on various NSE capitalized indices of India. Five exchange rates were chosen based on trading contracts in the currency derivative segment of NSE. These exchange rates are US Dollar-Indian Rupee (USD/INR), Euro-Indian Rupee (EUR/INR), Great Britain Pound-Indian Rupee (GBP/INR), Chinese Yuan-Indian Rupee (CNY/INR) and Japanese Yen-Indian Rupee (JPY/INR), which are used as a regressor in this study. The data of NSE Nifty large-cap 100, Nifty mid-cap 100 and Nifty small-cap from December 1, 2012 to December 1, 2022 was considere
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17

Gupta, Hemendra, Deepak Tandon, Neelam Tandon, and Upendra Nath Shukla. "Cointegration of MIBOR with rupee-dollar and rupee-yen exchange rates: estimating volatility spillovers and asymmetry." Afro-Asian J. of Finance and Accounting 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/aajfa.2022.10045006.

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18

Ghosh, Indranil, and Tamal Datta Chaudhuri. "Fractal Investigation and Maximal Overlap Discrete Wavelet Transformation (MODWT)-based Machine Learning Framework for Forecasting Exchange Rates." Studies in Microeconomics 5, no. 2 (2017): 105–31. http://dx.doi.org/10.1177/2321022217724978.

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Foreign currency is bought and sold in the financial markets, every minute, every day, on trading days, like any commodity or stocks of companies. The players in this market are (a) people with underlying interest in foreign currency such as exporters and importers who are continuously hedging in futures or options markets, (b) speculators and (c) arbitrageurs. This paper focuses on this microeconomic flavour of foreign currency as a continuously tradable product and presents a granular framework for forecasting the exchange rate. We initially investigate year-wise inherent nature of movements
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19

Kulal, Abhinandan, Deepak Kallige Vishwanath, and Sanath Kumar Kanthila. "Dynamic Relationship Between Rupee-Dollar Exchange Rate and Major Economic Indicators." American Journal of Economics and Business Administration 15, no. 1 (2023): 18–30. http://dx.doi.org/10.3844/ajebasp.2023.18.30.

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20

Wang, Kuan-Min. "CAN GOLD EFFECTIVELY HEDGE RISKS OF EXCHANGE RATE?" Journal of Business Economics and Management 14, no. 5 (2013): 833–51. http://dx.doi.org/10.3846/16111699.2012.670133.

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This study tests whether gold can effectively hedge exchange rate risks. We take into account the asymmetric characteristic of exchange rate fluctuations and use the dynamic panel threshold model in order to select gold prices in major gold-related currencies in the world: the Australian dollar, the Canadian dollar, the euro, the Indian rupee, the Japanese yen, the South African rand, and the British pound. Using monthly data from January 1999 to January 2010, with lagged one-period exchange rate returns (US dollar depreciation rate) as the threshold variable, the estimation results suggest th
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21

Khan, Abdul Jalil, and Parvez Azim. "One-Step-Ahead Forecastability of GARCH (1,1): A Comparative Analysis of USD- and PKR-Based Exchange Rate Volatilities." LAHORE JOURNAL OF ECONOMICS 18, no. 1 (2013): 1–38. http://dx.doi.org/10.35536/lje.2013.v18.i1.a1.

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This study aims to capture volatility patterns using GARCH (1,1) models. It evaluates these models to obtain one-step-ahead forecastabilities by employing four major forecasting evaluation criteria, and compares two different currencies— the Pakistan rupee and the US dollar—as domestic and foreign currency-valued exchange rates, respectively. The results show that using an international vehicle currency is favorable in Pakistan’s context. However, the Kuwaiti dinar, Canadian dollar, US dollar, Singapore dollar, Hong Kong dollar, and Malaysian ringgit are found to be preferable when performing
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22

Sehgal, Sanjay, and Mala Dutt. "Domestic and International Information Linkages for the US Dollar/Indian Rupee Contracts: An Empirical Study." Management and Labour Studies 43, no. 4 (2018): 205–33. http://dx.doi.org/10.1177/0258042x18791625.

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This study examines price discovery and volatility linkages between USD/INR spot and futures contracts in India and between USD/INR futures contracts on National Stock Exchange of India Limited (NSE), India and on three international exchanges, namely Singapore Exchange (SGX), Dubai Gold and Commodity Exchange (DGCX) and Chicago Mercantile Exchange (CME), from 29 August 2008 to 30 March 2015. Findings show that, at domestic level, the futures dominate spot in the Indian currency market; these findings are stronger than those in an earlier study, indicating improved pricing as well as hedging e
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23

Bhat, Aparna Prasad. "An empirical exploration of the performance of alternative option pricing models." Journal of Indian Business Research 11, no. 1 (2019): 23–49. http://dx.doi.org/10.1108/jibr-04-2018-0114.

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PurposeThe purpose of this paper is to ascertain the effectiveness of major deterministic and stochastic volatility-based option pricing models in pricing and hedging exchange-traded dollar–rupee options over a five-year period since the launch of these options in India.Design/methodology/approachThe paper examines the pricing and hedging performance of five different models, namely, the Black–Scholes–Merton model (BSM), skewness- and kurtosis-adjusted BSM, NGARCH model of Duan, Heston’s stochastic volatility model and anad hocBlack–Scholes (AHBS) model. Risk-neutral structural parameters are
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24

Shukla, Upendra Nath, Neelam Tandon, Deepak Tandon, and Hemendra Gupta. "Cointegration of 'MIBOR' with rupee-dollar and rupee-yen exchange rates: estimating volatility spill-overs and asymmetry." Afro-Asian J. of Finance and Accounting 12, no. 6 (2022): 691. http://dx.doi.org/10.1504/aajfa.2022.127942.

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25

Ozdemir, Dicle. "Causal Relationship between Agricultural Exports and Exchange Rate: Evidence for India." Applied Economics and Finance 4, no. 6 (2017): 36. http://dx.doi.org/10.11114/aef.v4i6.2696.

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In this paper we empirically investigate the causal link between agricultural exports and real exchange rate in India employing linear and nonlinear causality analysis. We carry out our investigation using annual index of the quantity of agricultural exports in India and real US Dollar to Rupee exchange rate data which cover the period between 1961 and 2013. We find that there are no significant changes in the linear and nonlinear causal relations between agricultural exports and exchange rates over the sample period under investigation. However, our investigation does not provide any evidence
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26

Bhatti, Razzaque H. "Do Expectations Play Any Role in Determining Pak Rupee Exchange Rates?" Pakistan Development Review 36, no. 3 (1997): 263–73. http://dx.doi.org/10.30541/v36i3pp.263-273.

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This paper presents some evidence on the role of expectations in the determination of Pak rupee exchange rates vis-à-vis the dollar, pound, and yen over the period 1982:1– 1993:7. Results of cointegration and coefficient restriction tests in two out of three cases are supportive of the view of exchange rate determination in postulating that in efficient markets in which uncertainty and expectations about the future are dominant, the equilibrium nominal exchange rate is determined not only by current relative prices but also by the expected real exchange rate. These results are supportive of ex
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27

Hsing, Yu. "Determinants of the Indian rupee/US dollar exchange rate and policy implications." International Journal of Economics and Business Research 10, no. 2 (2015): 105. http://dx.doi.org/10.1504/ijebr.2015.070977.

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28

Bhat, Aparna Prasad. "Who predicts dollar-rupee volatility better? A tale of two options markets." Managerial Finance 45, no. 9 (2019): 1292–308. http://dx.doi.org/10.1108/mf-09-2018-0416.

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Purpose The purpose of this paper is to examine whether volatility implied from dollar-rupee options is an unbiased and efficient predictor of ex post volatility, and to determine which options market is a better predictor of future realized volatility and to ascertain whether the model-free measure of implied volatility outperforms the traditional measure derived from the Black–Scholes–Merton model. Design/methodology/approach The information content of exchange-traded implied volatility and that of quoted implied volatility for OTC options is compared with that of historical volatility and a
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29

Yasir, Muhammad, Mehr Yahya Durrani, Sitara Afzal, et al. "An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System." Applied Sciences 9, no. 15 (2019): 2980. http://dx.doi.org/10.3390/app9152980.

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Financial time series analysis is an important research area that can predict various economic indicators such as the foreign currency exchange rate. In this paper, a deep-learning-based model is proposed to forecast the foreign exchange rate. Since the currency market is volatile and susceptible to ongoing social and political events, the proposed model incorporates event sentiments to accurately predict the exchange rate. Moreover, as the currency market is heavily dependent upon highly volatile factors such as gold and crude oil prices, we considered these sensitive factors for exchange rat
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30

Shylajan, C. S., Sreejesh S, and Suresh K G. "Rupee-Dollar Exchange Rate and Macroeconomic Fundamentals: An Empirical Analysis Using Flexible-Price Monetary Model." Journal of International Business and Economy 12, no. 2 (2011): 88–105. http://dx.doi.org/10.51240/jibe.2011.2.5.

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This paper empirically investigates the link between Indian rupee-US dollar exchange rates and a set of macroeconomic fundamentals using flexible-price monetary model (FPMM) for the period 1996 M1 to 2010 M12. The Johanson-Juselius cointegration test result indicates the existence of long run relationship between exchange rate and the macroeconomic variables, implying the validity of FPMM model in Indian context even though there is no short run casual relationship exist in the VECM analysis.
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31

Daniel, Linda Nalini, Muhammad Asad Ullah, and Mosab I. Tabash. "Mapping the Causal Connections among Exchange Rate Indicators and Exchange Rate: New Evidence from NARDL Econometric Approach." Pakistan Business Review 25, no. 2 (2023): 171–89. http://dx.doi.org/10.22555/pbr.v25i2.923.

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The aim of the study is to find out the symmetric or asymmetric relationship betweenthe macroeconomic fundamentals and exchange rate of Pakistani Rupee against the US Dollar which has never been analyzed briefly in previous literature. The NARDL approach hasbeen applied with the selected macroeconomic fundamentals i.e., GDP, foreign reserves, inflation rate, interest rate, oil price, gold price, trade balance, and money supply for thedata analysis. The data of exchange rate and selected macroeconomic fundamentals havebeen taken during the time period of 2011 to 2022 from the official IMF IFS d
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32

Khan, Muhammad Arshad, and Saima Nawaz. "Does Pak-Rupee Exchange Rate Respond to Monetary Fundamentals? A Structural Analysis." Pakistan Development Review 57, no. 2 (2018): 175–202. http://dx.doi.org/10.30541/v57i2pp.175-202.

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This study empirically examines the contribution of monetary fundamentals in explaining nominal exchange rate movements in the case of Pak-rupee vis-à-vis US-dollar over the period 1982Q2 to 2014Q2. The empirical results support the existence of cointegration relationship between nominal exchange rate and monetary fundamentals. The results reveal that relative money stocks and real income are the key drivers of exchange rate determination in Pakistan in the long-run. For dynamic interaction, the Structural Vector Autoregressive (SVAR) method is applied. Results from the SVAR show that the resp
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Kumar, Kepulaje Abhaya, Prakash Pinto, Iqbal Thonse Hawaldar, Cristi Spulbar, and Ramona Birau. "Crude oil futures to manage the price risk of natural rubber: Empirical evidence from India." Agricultural Economics (Zemědělská ekonomika) 67, No. 10 (2021): 423–34. http://dx.doi.org/10.17221/28/2021-agricecon.

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The trading of natural rubber derivatives in the Indian commodity exchanges was banned several times in the past. Hence, in India, the derivatives on natural rubber are not traded actively and regularly. We have examined the possibility of a forecast model and a cross hedge tool for the natural rubber price by using crude oil futures in India. Results of the Johansen cointegration test proved that there is no cointegration equation in the model; hence, there is no scope to develop long-run models or error correction models. We have developed a vector autoregressive [VAR(2)] model to forecast t
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Shravan, Mr. "Impact of COVID-19 on Indian Currency." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 3656–66. http://dx.doi.org/10.22214/ijraset.2022.44718.

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Abstract: In this article the author examines the impact of COVID -19 on Indian rupee. The data has been collected through secondary method such as RBI bulletin, newspapers and other research papers. The objectives of the study to investigate the impact of COVID -19 on Indian rupee, currency growth, FDI, export - import, economic stability in Indian economy and protection of Indian rupee against the U.S. dollar by making efficient economic policies. This paper cover the data of exchange rate from 2012 to 2022 to know the impact of corona. Further, this study observe the effect of Covid -19 on
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Rami, Khyati, Ansh Rajput, Navin Shripathi, Jay Patel, and Roshni Patel. "Comparative Analysis of ML Models for Currency Exchange Rate Prediction." International Journal of Computer Science and Mobile Computing 13, no. 3 (2024): 27–43. http://dx.doi.org/10.47760/ijcsmc.2024.v13i03.004.

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The primary aim of this research is to improve the prediction of the exchange rate between the United States Dollar (USD) and the Indian Rupee (INR), which is an area that has received little attention in the field of financial forecasting. In contrast to widespread methodologies that consolidate results over several currency pair, this study specifically concentrates on the USD to INR pair, recognising the distinctive economic and political dynamics between the United States and India. This study aims to do a comparative analysis of four various machine learning models, namely RNN, ARIMA, LST
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Qabhobho, Thobekile. "Assessing the Asymmetric Effect of Local Realized Exchange Rate Volatility and Implied Volatilities in Energy Market on Exchange Rate Returns in BRICS." International Journal of Energy Economics and Policy 13, no. 2 (2023): 231–39. http://dx.doi.org/10.32479/ijeep.13685.

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This paper investigates the leverage effect of local realised exchange rate volatility and implied volatilities in energy market on exchange rate returns in BRICS for the period May 7, 2012 to March 31, 2022, using the quantile regression technique. This paper reveals that oil implied volatility shocks (OVX changes) have a significant negative impact on Russian-U.S. Dollar exchange rate returns in all quantiles. When it comes to the Indian rupee and Chinese RMB returns/Dollar, the adverse effects of OVX are most apparent in both normal and booming market conditions. Although South Africa's cur
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37

Aravind M. "FX Volatility Impact on Indian Stock Market: An Empirical Investigation." Vision: The Journal of Business Perspective 21, no. 3 (2017): 284–94. http://dx.doi.org/10.1177/0972262917716760.

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Examining the interrelationship between currency market volatility and stock market volatility will create abundant trading opportunities to the investors irrespective of whether the return of one market is moving up or down. This research work intended to examine how the exchange rate volatility between Indian rupee and foreign currencies, such as US dollar, euro, Japanese yen and British pound, can influence the return and volatility of the Indian stock market. The research data extensively cover daily price observations of foreign currencies as well as Nifty index for 1500 days. The general
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Shaikh, Ehsan Ahmed, and Shahida Wizarat. "Empirical Investigation of Real Exchange Rate between Pak Rupee and US Dollar Employing Markov Switching-AR Model." GMJACS 12, no. 2 (2022): 38–51. http://dx.doi.org/10.59263/gmjacs.12.02.2022.252.

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This study empirically investigates real exchange rate between Pak Rupee and US Dollaremploying a two state Markov Switching-AR Model. Bai-Perron test for multiple structural breaks foundthree structural breaks in the series. Estimation results of Markov Switching-AR model reveal that if thereal exchange rate is in state one, its probability of staying in same state in the next period is greaterthan 99 percent whereas switching to second state is 0.7 percent. Whereas, if real exchange rate is instate two, its probability of staying to the same state is 99 percent and its probability of switchi
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Shah, Mohammad Samal. "Analysing the Factors Behind Exchange Rate Fluctuations in India." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 969–92. http://dx.doi.org/10.22214/ijraset.2024.59951.

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Abstract: The study gives an overview of the various determinants of the exchange rate movements in India. Out of the multiple factors affecting the Rupee-Dollar value the impact of Interest rate differential, Trade deficit of India, Foreign Net investment inflows to India, Oil prices, and Gold prices (in the short term) on the exchange rate has been studied using Regression analysis and correlation and the role they played by the above mentioned variables in determining the exchange rate during the Global Financial Crisis of 2008-2010 and during the Covid-19 Period from 2020-2023. Exchange ra
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Patnaik, Anuradha. "International Transmission of Monetary Policy: The Usa to India." International Letters of Social and Humanistic Sciences 54 (June 2015): 53–62. http://dx.doi.org/10.18052/www.scipress.com/ilshs.54.53.

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The present study attempts measure the transmission of monetary impulse from the USA to India by trying to quantify the extent of volatility spillover from the US monetary policy to the exchange rate and interest rate of India. By applying a t-DCC MGARCH model to daily data on Fed Funds Rate, Rupee Dollar Exchange Rate and the Call Money rate of India it was found that there is considerable volatility spillover from the Fed Rate to the exchange rate. Spillover is also clearly evident in case of the call rate. The extent of spillover is higher for the foreign exchange rate than the call money r
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Asadullah, Muhammad, Nawaz Ahmad, and Maria José Palma Lampreia Dos-Santos. "Forecast Foreign Exchange Rate: The Case Study of PKR/USD." Mediterranean Journal of Social Sciences 11, no. 4 (2020): 129. http://dx.doi.org/10.36941/mjss-2020-0048.

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The main aim of this paper is to forecast the future values of the exchange rate of the USD. Dollar (USD) and Pakistani Rupee (PR). For this purpose was used the ARIMA model to forecast the future exchange rates, because the time series was stationary at first difference. Data reported to five years ranging from the first day of April 2014 to 31st March 2019. The results proved that ARIMA (1,1,9) is the most suitable model to forecast the exchange rate. The difference between the forecasted values and actual values are less than 1%; therefore, it was found that the ARIMA is robust and this mod
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Hidhayathulla, Dr A., and Mahammad Rafee.B. "Relationship between Crude oil price and Rupee, Dollar Exchange Rate: An Analysis of Preliminary Evidence." IOSR Journal of Economics and Finance 3, no. 2 (2014): 01–04. http://dx.doi.org/10.9790/5933-03220104.

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Ali Shah G.Syed, Dr Anwar, Faiz M.Shaikh, Abdul Sattar Shah, and Muhammad Akram. "COINTEGRATION BETWEEN EXCHANGE RATE AND INTEREST RATE DIFFERENTIAL: THE CASE OF PAK RUPEE/US DOLLAR." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 10, no. 5 (2015): 2146–50. http://dx.doi.org/10.24297/ijmit.v10i5.617.

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Empirical literature is inconclusive whether a relationship exists between Interest Rate Differential and exchange rate. However, theoretical literature suggests that such positive relationship exists between these variables. Monthly interest rate data between Pakistan and USA from Jan 2001 to December 2010 was taken and co-integration technique was applied to empirically test the relationship between both variables. The results show no relationship exists between these variables. This results supports the findings of previous studies of Edison and Pauls (1993) and Shah,A and Saeed Ur Reham (2
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Perwej, Yusuf. "Forecasting Of Indian Rupee (INR) / US Dollar (USD) Currency Exchange Rate Using Artificial Neural Network." International Journal of Computer Science, Engineering and Applications 2, no. 2 (2012): 41–52. http://dx.doi.org/10.5121/ijcsea.2012.2204.

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Kar, Rituparna, and Nityananda Sarkar. "Mean and volatility dynamics of Indian rupee/US dollar exchange rate series: an empirical investigation." Asia-Pacific Financial Markets 13, no. 1 (2007): 41–69. http://dx.doi.org/10.1007/s10690-007-9034-0.

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Rofi'i, Yulianto Umar. "Pengaruh Indeks Harga Konsumen, Jumlah Uang Beredar, Produk Domestik Bruto, Suku Bunga, dan Neraca Pembayaran Terhadap Nilai Tukar Rupiah." Jurnal EMT KITA 7, no. 4 (2023): 1139–48. http://dx.doi.org/10.35870/emt.v7i4.1568.

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This study aims to analyze the relationship between various assumed economic variables and exchange rate fluctuations of the Rupee against the US dollar. The variables considered include the logarithmic difference of the consumer price index of Indonesia and the United States, the logarithmic difference of the money supply of Indonesia and the United States, the logarithmic difference of the gross domestic product of Indonesia and the United States, the logarithmic difference of the Indonesian and US interest rates and the logarithm of the Indonesian balance of payments. The data analysis resu
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Ranjusha, Devasia, and Nandakumar. "COINTEGRATING RELATION BETWEEN EXCHANGE RATE AND GOLD PRICE." International Journal of Research -GRANTHAALAYAH 5, no. 10 (2017): 263–69. http://dx.doi.org/10.29121/granthaalayah.v5.i10.2017.2303.

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The very purpose of this paper is to analyse the relationship between gold price and Rupee – Dollar exchange rate in India. The study utilises the annual data of exchange Rate (ER) and Gold Price (GP) from 1970 to 2015 to determine the relationship. Different econometric tools like Unit root test, Johansen co integration test, Vector error correction model, Granger causality test are used for detecting the long run relation, if any between the mentioned variables. The result shows that there exists a long run cointegrating relation between the variables. That is we can stabilise the Gold Price
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Upadhyay, Parijat, and Saikat Ghosh Roy. "Impact of exchange rate movement and macro-economic factors on exports of software and services from India." Benchmarking: An International Journal 23, no. 5 (2016): 1193–206. http://dx.doi.org/10.1108/bij-04-2014-0034.

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Purpose – The information technology (IT) sector in India is the leading exporter from the service sector domain and also is a significant contributor to the overall export kitty of India. The IT sector’s contribution in total Indian exports (merchandise plus services) increased from less than 4 percent in FY1998-1999 to about 25 percent in FY2011-2012 as per IT industry nodal body National Association of Software and Services Companies and the central bank of the country, the Reserve Bank of India (RBI). As this industry earns most of its revenue in foreign currencies it is exposed to the for
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Pandey, Trilok Nath, Nrusingha Tripathy, Sarbeswar Hota, and Bichitrananda Patra. "Empirical analysis of machine learning techniques for prediction of indian exchange rate." Journal of Statistics & Management Systems 26, no. 1 (2023): 13–22. http://dx.doi.org/10.47974/jsms-943.

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Throughout the past few decades, there has been a dramatic surge in the currency market. The changes play an important role in balancing the market’s characteristics. As a result, accurate change price forecasting is essential to improve the success rate of many businesses and fund managers. Despite the fact that the market is renowned for its erratic behavior and volatility, there are organizations like agencies, banks, and others. In order to estimate the extraneous interchange rate of the dollar against the rupee with a high degree of accuracy, we used three distinct types of methodologies
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Chaturvedi, Anoop, and Arvind Shrivastava. "Bayesian Analysis of Structural Changes in a Linear Regression Model: An Application to Rupee-Dollar Exchange Rate." Journal of Quantitative Economics 13, no. 2 (2015): 185–200. http://dx.doi.org/10.1007/s40953-015-0018-z.

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