Journal articles: 'Rupee/dollar exchange'

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T., Lakshmanasamy. "Relationship Between Exchange Rate and Stock Market Volatilities in India." International Journal of Finance Research 2, no. 4 (November 8, 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 significant positive effect on the BSE SENSEX return volatility, while the effect of the US dollar/rupee and British pound/rupee exchange rate the volatilities are insignificantly negative. The larger GARCH parameter over the ARCH term indicates that the own lagged values of the stock return cause more volatility in stock returns than the innovations. There exists a highly persistent effect of shocks to the BSE SENSEX return and the volatility effect wanes only slowly

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Kukreja, Mansi. "IMPACT OF SINKING RUPEE ON INDIAS FOREIGN TRADE(THREATS TO EXCHANGE RATE FLUCTUATIONS)." International Journal of Advanced Research 10, no. 08 (August 31, 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 of 80.064 to the dollar in July 2022. The Indian economy, which already had a significant budget and current account imbalance, was negatively impacted by exchange rate pressure. The Indian government made numerous difficult decisions to follow it again on the path. This essay discusses several difficulties brought on by these fluctuations as well as actions taken by the government and central bank to make them effective.

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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) (May 10, 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 accounts for approximately 91% variance of the Dollar-Rupee exchange rates and explain the exchange rate dynamics to a large extent. A few factors that were earlier considered to be important are not as significant as expected.

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Bhat, Aparna Prasad. "The economic determinants of the implied volatility function for currency options." International Journal of Emerging Markets 13, no. 6 (November 29, 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 of the volatility function are identified. Contemporaneous and lead-lag relationships between these determinants and the shape of the volatility function are examined using OLS and multivariate VAR. Impulse response functions are employed to test the strength and persistence of the lead-lag relations. Seasonality of the smile pattern is tested using OLS. Findings The study shows that the implied volatility function for dollar-rupee options is asymmetric and varies with the time to maturity of the option. Historical volatility, momentum and jumps in the exchange rate, time to maturity, traded volume of options and volatility in the stock market appear to Granger-cause the shape of the volatility smile. Feedback causality is observed from the shape of the smile to the volatility, momentum and jumps in the exchange rate and trading volume of currency options. A weak day-of-the-week effect is observed in the pattern of the volatility smile. Practical implications The study sheds light on the potential determinants of the smile and highlights the predictive power of the smile which findings can be useful to market practitioners for pricing and hedging of dollar-rupee options. The study has strong practical implications during a period of increased volatility in the dollar-rupee pair. Originality/value Most of the existing literature regarding implied volatility smiles has focused either on the volatility smile of US equity index options or that of major liquid currencies. There is a need for such studies in the context of options on emerging market currencies such as the Indian rupee which are characterized by thin trading and frequent central bank intervention and signaling. To the best of the author’s knowledge this study is the first to focus on the volatility smile of exchange-traded options on the US dollar–Indian rupee.

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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-Hansen co-integration method allows for one break determined endogenously in three specifications also confirms the long run relationship. Our results, hence, suggest that the monetary model is a valid theory of long run equilibrium condition for the rupee-dollar, rupee-pound, rupee-yen and rupee-euro exchange rates.

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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 (January 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 exchange rate regime as even well behaved fundamentals may not be sufficient to sustain a currency on its peg.

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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 (December 1, 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 all currencies of the industrial countries in question over the period under investigation; for example, it has depreciated by 324.05 percent against the British pound, 406.360 percent against the U.S. dollar, 344.53 percent against the French franc, 498.48 percent against the Swiss franc, 477.78 percent against the German mark and 986.25 percent against the Japanese yen since April 1982. As evidenced by coefficient of variation, Pak rupee has weakened enormously against all currencies of the industrial world, while it has weakened relatively more alarmingly against the Japanese yen, Swiss franc and German mark.

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Adhikari, Deepak. "Impact of Exchange Rate on Trade Deficit and Foreign Exchange Reserve in Nepal: An Empirical Analysis." NRB Economic Review 30, no. 1 (May 11, 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 trade deficit by 0.75 percentage points, the null hypotheses are rejected, thus suggesting that maintaining NPR undervalued with US dollar can improve trade deficit and increase foreign exchange reserves. However, because of pegging with Indian currency, NPR sometimes appreciates in line with Indian currency. This situation could be counterproductive for improving trade deficit and increasing foreign exchange reserve of Nepal. In conclusion, considering the external sector stability as one of the major policy objectives, exchange rate policy can be fine-tuned to correct the trade deficit and maintain adequate foreign exchange reserve to sustain imports and service external debt.

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Sugirtha, R., and Dr M. Babu. "CO- Integration Approach Study of Crude Oil Prices and USD/ INR." Restaurant Business 118, no. 6 (June 15, 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). To check the volatility, the following statistical tools namely descriptive statistic, ADF and GARCH (1,1) model were used. Based on the result, crude price recorded low volatility compared to U.S dollar price during the study period.

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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 (March 28, 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). It is crucial to emphasize that the macroeconomic policies have to be implemented in order to stabilize and reduce the exchange rates volatilities.

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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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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 (March 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 the consistency of the shon-run adjusunent process, given the authorities' longrun target rupee value. The 'revealed' policy is to panly offset the inflation differential between Pakistan and its major trading parUlers. Under plausible conditions, the burden of adjusunent and recessionary conditions are likely to occur in the Pakistani expon sector.

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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 (June 30, 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 the null hypothesis of no cointegration is rejected. Therefore an error correction model is developed and estimated. The estimated results suggest that there is no detectable effect of oil price change on the real exchange rate between the Indian rupee and the U.S. dollar.

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Sharma, Chandan, and Rajat Setia. "Macroeconomic fundamentals and dynamics of the Indian rupee-dollar exchange rate." Journal of Financial Economic Policy 7, no. 4 (November 2, 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 estimate the monetary model in the long- and short-run horizons. Findings – Results of analyses indicate that the macroeconomic fundamentals determine exchange rate in a significant way, but their effect varies sizably across the periods. The IRF illustrate the importance of interest rate in controlling exchange rate volatility. Practical implications – The analysis of the behavior of inter-relationship among macroeconomic variables will help policymakers in a deep-rooted understanding of this complex and time-varying relationship. Originality/value – Most of the existing studies have tested the impact of a single or a few macroeconomic fundamentals on exchange rate. But in the present study, we have tested the impact of a range of important variables, i.e. money supply, real income or output, price level and trade balance. Further, considering the importance of structural breaks in data, they authors have employed standard tests of structural break and incorporated the issue in the cointegration analysis.

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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 (July 31, 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 considered for the study. GARCH (1, 1) model was used to analyze the nexus between exchange rate fluctuations and capitalized indices, and it was further validated by DCC GARCH to evaluate the volatility spillover. The result shows that exchange rate fluctuations have a positive effect on stock market volatility along with a varying degree of incidence on small-cap, mid-cap, and large-cap. DCC α has been found to be significant in USD &amp; GBP for small-cap, and GBP &amp; CNY for mid-cap. On the other hand, USD, Euro, CNY and JPY have a significant impact on the large-cap index in the short-run. Further, it is found that there is long-run spillover effect (DCC β) of exchange rates on all capitalized indices of the Indian stock market, and it is highest in in the large-cap case.

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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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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 (September 26, 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 of three exchange rates, namely Indian rupee/US dollar, Indian rupee/euro and Indian rupee/Japanese yen, during 2011–2016 through Mandelbrot’s single fractal model. Subsequently, maximal overlap discrete wavelet transformation (MODWT) is used to decompose the time series of the individual exchange rates. Random forest and bagging are applied on the decomposed components for predictive modelling.

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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 (January 1, 2023): 18–30. http://dx.doi.org/10.3844/ajebasp.2023.18.30.

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Wang, Kuan-Min. "CAN GOLD EFFECTIVELY HEDGE RISKS OF EXCHANGE RATE?" Journal of Business Economics and Management 14, no. 5 (November 6, 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 that there are two thresholds at –7.5% and –3.7%. These can be divided into regime 1 (exchange rate returns ≤ –7.5%), regime 2 (–7.5% < exchange rate returns ≤ –3.7%), and regime 3 (exchange rate returns > –3.7%). Regarding the effectiveness of gold hedging, regime 2 is higher than is regime 3. The risk hedging effect of regime 1 is not significant because it might be caused by the excessive devaluation of the US dollar in the short-term and the overshooting of the exchange rate adjustment, making gold unable to hedge the devaluation risks of the US dollar.

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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 (January 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 direct international transactions. Using the root mean square errors and mean absolute errors techniques, the study also assess the robustness of measuring one-step-ahead forecasts.

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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 (September 19, 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 efficiency in the Indian currency market. At international level, NSE is dominated by both CME and DGCX in price discovery and in short-term volatility spillovers, while NSE dominates both exchanges in long-term volatility spillovers. Further, NSE dominates SGX in the international information process. The dominance of CME and DGCX over NSE may be on account of their several advantages such as longer trading hours, operations being open even after NSE has shut business, much lower trading costs as well as lower regulatory restrictions. The study provides several significant policy suggestions for improving efficiency of the Indian currency market and is also relevant for foreign portfolio investors (FPIs), domestic investors, researchers and academicians. It contributes to literature on information transmission relating to currency markets in emerging economies.

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Bhat, Aparna Prasad. "An empirical exploration of the performance of alternative option pricing models." Journal of Indian Business Research 11, no. 1 (March 7, 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 extracted by calibrating each model to the prices of traded dollar–rupee call options. These parameters are used to generate out-of-sample model option prices and to construct a delta-neutral hedge for a short option position. Out-of-sample pricing errors and hedging errors are compared to identify the best-performing model. Robustness is tested by comparing the performance of all models separately over turbulent and tranquil periods.FindingsThe study finds that relatively simpler models fare better than more mathematically complex models in pricing and hedging dollar–rupee options during the sample period. This superior performance is observed to persist even when comparisons are made separately over volatile periods and tranquil periods. However the more sophisticated models reveal a lower moneyness-maturity bias as compared to the BSM model.Practical implicationsThe study concludes that incorporation of skewness and kurtosis in the BSM model as well as the practitioners’ approach of using a moneyness-maturity-based volatility within the BSM model (AHBS model) results in better pricing and hedging effectiveness for dollar–rupee options. This conclusion has strong practical implications for market practitioners, hedgers and regulators in the light of increased volatility in the dollar–rupee pair.Originality/valueExisting literature on this topic has largely centered around either US equity index options or options on major liquid currencies. While many studies have solely focused on the pricing performance of option pricing models, this paper examines both the pricing and hedging performance of competing models in the context of Indian currency options. Robustness of findings is tested by comparing model performance across periods of stress and tranquility. To the best of the author’s knowledge, this paper is one of the first comprehensive studies to focus on an emerging market currency pair such as the dollar–rupee.

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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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Ozdemir, Dicle. "Causal Relationship between Agricultural Exports and Exchange Rate: Evidence for India." Applied Economics and Finance 4, no. 6 (October 13, 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 of bidirectional or unidirectional causality between the agricultural exports to real exchange rate in India. It can be concluded that one of the key reasons for high levels of the quantity of agricultural exports in India is long-term economic growth, rather than the real value of Indian Rupee.

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Bhatti, Razzaque H. "Do Expectations Play Any Role in Determining Pak Rupee Exchange Rates?" Pakistan Development Review 36, no. 3 (September 1, 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 ante purchasing power parity, implying that the real exchange rate follows a random walk. These results also suggest that the anticipated inflation rate is higher in Pakistan than in other countries, which tends to encourage the domestic residents to convert their current balances into foreign currency, so that the terms of trade deteriorate and offset much of gains of the continuous devaluation of Pak rupee by undermining external competitiveness.

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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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Bhat, Aparna Prasad. "Who predicts dollar-rupee volatility better? A tale of two options markets." Managerial Finance 45, no. 9 (September 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 GARCH(1, 1)-based volatility. Ordinary least squares regression is used to examine the unbiasedness and informational efficiency of implied volatility. Robustness of the results is tested by using two specifications of implied volatility and realized volatility and comparison across two markets. Findings Implied volatility from both OTC and exchange-traded options is found to contain significant information for predicting ex post volatility, but is neither unbiased nor informationally efficient. The implied volatility of at-the-money options derived using the Black–Scholes–Merton model is found to outperform the model-free implied volatility (MFIV) across both markets. MFIV from OTC options is found to be a better predictor of realized volatility than MFIV from exchange-traded options. Practical implications This study throws light on the predictive power of currency options in India and has strong practical implications for market practitioners. Efficient currency option markets can serve as effective vehicles both for hedging and speculation and can convey useful information to the regulators regarding the market participants’ expectations of future volatility. Originality/value This study is a comprehensive study of the informational efficiency of options on an emerging currency such as the Indian rupee. To the author’s knowledge, this is one of the first studies to compare the predictive ability of the exchange-traded and OTC markets and also to compare traditional model-dependent volatility with MFIV.

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Yasir, Muhammad, Mehr Yahya Durrani, Sitara Afzal, Muazzam Maqsood, Farhan Aadil, Irfan Mehmood, and Seungmin Rho. "An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System." Applied Sciences 9, no. 15 (July 25, 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 rate forecasting. The validity of the model is tested over three currency exchange rates, which are Pak Rupee to US dollar (PKR/USD), British pound sterling to US dollar (GBP/USD), and Hong Kong Dollar to US dollar (HKD/USD). The study also shows the importance of incorporating investor sentiment of local and foreign macro-level events for accurate forecasting of the exchange rate. We processed approximately 5.9 million tweets to extract major events’ sentiment. The results show that this deep-learning-based model is a better predictor of foreign currency exchange rate in comparison with statistical techniques normally employed for prediction. The results present evidence that the exchange rate of all the three countries is more exposed to events happening in the US.

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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 (December 1, 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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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 (September 26, 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 database. Thefindings indicate that foreign reserves and inflation possess an asymmetric relationship withthe exchange rate in long run. The increase in productive inflation only helps to stabilize theexchange rate whereas all other significant variables weakens the Pakistani currency eitherin short -run or long run i.e., decrease in money supply, GDP, inflation and increase in interestrate. The findings will be helpful for the policymakers and economists to implement theirpolicies accordingly to prevent the further depreciation the of Pakistani Rupee against USDollar.

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Khan, Muhammad Arshad, and Saima Nawaz. "Does Pak-Rupee Exchange Rate Respond to Monetary Fundamentals? A Structural Analysis." Pakistan Development Review 57, no. 2 (June 1, 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 responses of exchange rate to shocks, originated from money supply, income, interest rate and inflation differentials, are consistent with the predictions of the flexible-price variant of the monetary model of exchange rate in the short-run. More specifically, the results indicate that inflation and interest rate differential explain maximum variations in exchange rate in the short-run. In essence, results suggest that monetary fundamentals are the key drivers of exchange rate fluctuations in Pakistan, especially in the short-run. JEL Classification: F31, F33, C32, F41 Keywords: Monetary Model, Exchange Rate, SVAR, Pakistan

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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 (October 26, 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 the rubber price, and we examined the possibility of a cross hedge for natural rubber further by using the Pearson correlation coefficient and Granger causality test. We have extended our research to a structural VAR analysis to examine the effect of crude futures and exchange rate shocks on the natural rubber price. Our results showed that there is a short-term relationship between the crude oil futures price, the exchange rates of the US dollar to the Indian rupee, the Malaysian ringgit to the Indian rupee and the Thai baht to the Indian rupee; and the natural rubber price in India. The effort of policymakers to cause the Indian rupee to appreciate against the Thai baht and Malaysian ringgit may increase the natural rubber price in India. Natural rubber traders, growers and consumers can use crude futures to hedge the price risk. The Indian Rubber Board can suggest the VAR(2) model to predict the short-run price for natural rubber.

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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 (June 30, 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 Indian currency by classifying the data into two categories like pre Covid -19 and post Covid -19. Pre Covid including the periods from 2012 -18 and post Covid including 2020 -22. The value of domestic currency declined in comparison to foreign currency, because the demand for foreign currency is more than domestic currency. Thus, the domestic rate of exchange increased and the value of domestic exports become cheaper than foreign imports. The study has used statistical measures such as: Arithmetic Mean (A.M.), Standard deviation {S.D.} and Coefficient of variance (C.V.).

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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 (March 30, 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, LSTM, and Random Forest, in order to determine the best effective tool for predicting exchange rates in a certain context. This research employs a holistic methodology, including a wide range of variables like trade balances, interest rates, and geopolitical events, so providing a multifaceted forecasting approach. The growing economic interdependence between the United States and India highlights the practical importance of precise predictions for many stakeholders, such as traders, investors, and politicians. Furthermore, the study examines the concept of dynamic model updating, a novel attribute that augments flexibility within the ever-changing financial industry. The primary objective of this work is to address a significant need in current academic research by developing a reliable and practical instrument for accurately forecasting the exchange rate between the United States Dollar (USD) and the Indian Rupee (INR). This research is notable for its specific concentration, use of comparative methods, and its potential to make substantial advancements in both academic and practical domains pertaining to currency exchange rate forecasting.

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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 (March 24, 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 currency rate returns are affected by both slump- and bust-market situations, Brazil also tends to be in higher quantiles. The implied volatility indices in the energy market have a substantial and considerable negative impact on the BRICS currencies, with the exception of China, where the effect is only noticeable in the upper extreme quantiles. The policy implications and suggestions are discussed.

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Aravind M. "FX Volatility Impact on Indian Stock Market: An Empirical Investigation." Vision: The Journal of Business Perspective 21, no. 3 (July 10, 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 generalized autoregressive conditional heteroskedasticity (GARCH) is used for modelling foreign exchange (FX) rates volatility and its impact across Indian stock market. The mean equation of the model confirms that any appreciation in Indian rupee will lead to channelization of more funds towards stock market. Further, it is validated that the volatility shocks between the stock market and currency market are quite persistent. Besides the model also points that the volatility attributes are very strong between US dollar and Nifty. The Granger causality test wrap up with a finding that the volatility shocks of British pound have a causal relation with Nifty return. The result of this study will help the domestic as well as foreign investors in favour of portfolio diversification decisions. The study also spots that the policymakers can indirectly intervene into stock market through monitory policy measures.

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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 (December 30, 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 switching to stateone in the next period is less than 0.6 percent.

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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 (April 30, 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 rate fluctuations play a crucial role in shaping the economy of a country, and India is no exception. The exchange rate of the Indian rupee against major world currencies is subject to constant fluctuations, influenced by a multitude of domestic and global factors. These fluctuations have significant implications for various stakeholders, including businesses, consumers, investors, and policymakers. Several factors contribute to the exchange rate fluctuations in India. One of the primary factors is the demand and supply dynamics of foreign exchange. Factors such as trade balance, foreign direct investments, portfolio investments, remittances impact the demand for and supply of foreign currency, Crude oil prices, Gold prices, and thereby affecting the exchange rate. In addition, macroeconomic variables like inflation, interest rates, and economic growth also play a crucial role in determining exchange rate movements. For instance, a high inflation rate in India compared to trading partner countries can lead to a depreciation of the rupee. Global economic conditions and geopolitical events are other significant factors influencing exchange rate fluctuations in India. Economic developments in major trading partners, changes in global commodity prices, and geopolitical tensions can all impact investor sentiment and capital flows, leading to fluctuations in the exchange rate. For example, uncertainties related to Brexit or trade tensions between major economies can trigger volatility in currency markets. Monetary policy decisions by the Reserve Bank of India (RBI) also play a crucial role in influencing exchange rates. Interest rate changes, open market operations, and forex interventions by the central bank can impact the value of the rupee visa-vis other currencies. The RBI often intervenes in the foreign exchange market to stabilize the rupee or prevent extreme volatility. Apart from these factors, market speculation, investor sentiment, and technological advancements in the financial markets can also contribute to exchange rate fluctuations. High-frequency trading, algorithmic trading, and the use of complex financial instruments by market participants can amplify exchange rate movements and increase volatility

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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 rate. However, it was also noticed that the spillover is asymmetric in either of the cases and is higher during phases of high volatility. In an era of flexible exchange rates excessive dependence of the Indian Economy on short term capital flows to finance the current account deficits which raises the dollar demand and exposes the Indian economy to the Monetary Policy of the US, needs to be reduced. Reforms in the nature of capital flows is also the need of the hour.

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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 (July 10, 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 model will be helpful for the government functionaries, monetary policymakers, economists and other stakeholders to identify and forecast the future trend of the exchange rate and make their policies accordingly.

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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 (January 26, 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 (2010).

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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 (April 30, 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 (February 27, 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 (October 10, 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 results show that only the difference between the logarithm of the gross domestic product of Indonesia and the US and the difference between the logarithm of the interest rates of Indonesia and the US have a significant influence on the change in the exchange rate of Indonesia rupiah. The R-squared of 61.6% shows that the observed variation in the dependent variable can be explained by the independent variables, while the remaining 38.4% is influenced by other factors. The conclusion of this study is that the difference in change in the logarithm of gross domestic product and the difference in the logarithm of interest rate have a significant impact on the volatility of the rupee exchange rate.

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Ranjusha, Devasia, and Nandakumar. "COINTEGRATING RELATION BETWEEN EXCHANGE RATE AND GOLD PRICE." International Journal of Research -GRANTHAALAYAH 5, no. 10 (October 31, 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 movement by controlling the exchange rate fluctuations. Likewise it also shows that Exchange rate doesn’t Granger cause to Gold price and vice versa. It means that the time series data of one vasriable cannot be used to predict another.

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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 (July 4, 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 foreign exchange risks. The purpose of this paper is to validate the macro-economic theory that depreciation in domestic currency boosts export as it makes domestic good and services cheaper and appreciation in domestic currency deters export as it makes domestic good and services costlier. The authors are validating this theory for Indian rupee and keeping software services export in the focus. Design/methodology/approach – In this study the authors have done the multiple regression analysis on the obtained time-series data. The research was totally based on the secondary data from Quarter1 (April-June) of FY 2000-2001 to Quarter4 (January-March) of FY 2011-2012. It comprises of data for 48 consecutive quarters. The authors have taken the growth rate, so the final data set consist of data of 47 quarters. The main source of data are published data by RBI. Data have been collected for export of software services, merchandise export, real effective exchange rate, US-dollar-Indian rupee exchange rate, gross domestic product of India and selected countries. Findings – Data analysis leads the authors to the following findings: real effective exchange rate has no significant impact on software services export; US-dollar-Indian rupee exchange rate has no significant impact on software services export; external gross domestic product growth has no significant impact on software services export; and gross domestic product growth of India has no significant impact on software services export. The results obtained from multiple regression analysis are also supported by the results obtained from Granger Causality test. It does not identify any single factor as a major cause of software export. Results shows that the external GDP is having the statistically significant impact on the software export but the low value of R2 denotes that the impact is very low. Originality/value – There are no published studies available which has attempted similar kind of an approach to study using aggregated export data and other macro-economic variables like real effective exchange rate (REER) and GDP growth rate. All previous literatures used REER to measure the impact of the exchange rate on export.

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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 in this article. This research uses three different types of neural network models: ANNs (Artificial Neural Networks), LSTMs (Long Short-Term Memory Networks), and GRUs (Gated Recurring Units). The results depict that GRU’s model is outperforming the other two models.

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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 (November 20, 2015): 185–200. http://dx.doi.org/10.1007/s40953-015-0018-z.

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Journal articles on the topic 'Rupee/dollar exchange'

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