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Articles de revues sur le sujet « NIFTY »

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Auteur : Grafiati

Publié le 11 septembre 2023

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1

Vijay, Dr S., et Dr V. Prabakaran. « A study on Bank and IT nifty influence on Nifty 50 ». Journal of University of Shanghai for Science and Technology 23, n^o 12 (18 décembre 2021) : 316–22. http://dx.doi.org/10.51201/jusst/21/121030.

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NIFTY 50 is one of the benchmark indices which is been used in India. The index is built upon by the companies which are represented from various sectors. Each sector is given separate weightage depending upon the representations from each sector. This paper focused on to identify Pairwise Granger Causality between NIFTY 50 Index, NIFTY Bank Index and NIFTY IT Index. The researcher also focused on to identify the influence of Bank NIFTY and IT NIFTY and NIFTY 50 Index. The research was carried out with a total of 532 observations (closing value of each index) of spanned across January 2018 – February 2020. The study revealed that Bank NIFTY Granger Cause IT NIFTY and not vice-versa. NIFTY 50 Index is highly influenced by Bank NIFTY IT NIFTY.

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2

Amuthan, R. « Co relating NIFTY 50 Index Trend’s impact on NSE’s Sector based Indices Growth Momentum in Post COVID-19 led Indian Economy with Special reference to NIFTY Bank, NIFTY Consumer Durables, NIFTY IT and NIFTY Pharma Indices using Arithmetic Modelling ». Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, n^o 6 (5 avril 2021) : 2184–89. http://dx.doi.org/10.17762/turcomat.v12i6.4824.

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The NIFTY 50 is the flagship index on the National Stock Exchange of India Ltd. (NSE). The Index tracks the behavior of a portfolio of blue chip companies, the largest and most liquid Indian securities. It includes 50 of the approximately 1600 companies traded (listed & traded and not listed but permitted to trade) on NSE, captures approximately 65% of its float-adjusted market capitalization and is a true reflection of the Indian stock market. This study probed in to the correlation between NIFTY 50 and NIFTY Bank, NIFTY 50 and NIFTY Consumer Durables, NIFTY 50 and NIFTY IT and NIFTY 50 and NIFTY Pharma Indice.

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3

Narayan, Parab, et Y. V. Reddy. « Exploring the Causal Relationship Between Stock Returns, Volume, and Turnover across Sectoral Indices in Indian Stock Market ». Metamorphosis : A Journal of Management Research 16, n^o 2 (12 novembre 2017) : 122–40. http://dx.doi.org/10.1177/0972622517730140.

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The traditional saying “Market Discounts Everything” is applicable to stock returns, trading volume, and turnover as well. The present study is an analytical attempt to examine the causal relationship between stock returns, trading volume, and turnover across 10 sectoral indices of National Stock Exchange (NSE) for the period 2006–2016. To critically examine this relation, the study uses various statistical techniques such as descriptive statistics, correlation analysis, regression analysis, and econometric tests such as Granger causality test and augmented Dickey–Fuller test. The required analyses have been performed using statistical software E-views, SPSS, and Microsoft Excel. The study noticed a weak positive relationship between stock returns and turnover for Nifty Auto Index, Nifty Bank Index, Nifty Financial Services Index, Nifty Media Index, Nifty Metal Index, and Nifty Private Bank Index. The study also found a significant impact of turnover on stock returns in the case of Nifty Auto Index, Nifty Bank Index, Nifty FMCG Index, Nifty Metal Index, and Nifty Pharma Index and a significant impact of volume on stock returns in the case of Nifty Bank Index, Nifty FMCG Index, and Nifty Pharma Index. Augmented Dickey–Fuller test suggests that there exists no unit root in the data ( p < 1) and the data are stationary. It is evident from the study that the causal relationship between stock returns, turnover, and volume varies across the sectoral indices.

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4

Dharani, M. « Seasonal Anomalies between S&P CNX Nifty Shariah Index and S&P CNX Nifty Index in India ». Journal of Social and Development Sciences 1, n^o 3 (15 avril 2011) : 101–8. http://dx.doi.org/10.22610/jsds.v1i3.633.

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The present study compares the risk and return of the Nifty Shariah index and Nifty index at days, months and quarters wise during the period 2nd January 2007 to 31st December 2010. The raw returns of the both indices are calculated as today price minus yesterday price divided by yesterday price. The t- test has been used to test the mean returns difference between both indices. The average Monday return of the Nifty Shariah index is compared with average return of the Nifty index by using two sample t-test. Like that, the average returns of the remaining of the days of Nifty Shariah index are compared with average returns of remaining days of the Nifty Index. The study finds that there is no difference between average day -wise returns of the Nifty Shariah index and average day return of the Nifty Index during the study period. The study also compares the average January return of the Nifty Shariah index with average January return of the Nifty index, average February return of the Nifty Shariah index with average return of the Nifty index and so on. Finally, the average return of the first, second, third and fourth quarter of Nifty Shariah with average return of the respective first, second, third and fourth quarter of Nifty index are compared. The study finds that there is a significant difference between average return of the Nifty Shariah and Nifty indices in the month of July and September. It is derived from the study that the Muslim Investors are evincing more interest to sell the shares in the market from July to September. The reason being, expenses inconnection with Ramalan Festival during that period. Therefore, the study confirms that Ramalan effect have been prevailing in the Indian Stock Market. Thus, this study reveals that the seasonal variation exits very much in Shariah Index

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5

Stevens, Kerrie. « Nifty Newsletters ». ANZTLA EJournal, n^o 15 (15 septembre 2017) : 94–101. http://dx.doi.org/10.31046/anztla.vi15.460.

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6

Parlante, Nick, David Matuszek, Jeff Lehman, David Reed, John K. Estell et Donald Chinn. « Nifty assignments ». ACM SIGCSE Bulletin 36, n^o 1 (mars 2004) : 46–47. http://dx.doi.org/10.1145/1028174.971318.

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7

Parlante, Nick. « Nifty reflections ». ACM SIGCSE Bulletin 39, n^o 2 (juin 2007) : 25–26. http://dx.doi.org/10.1145/1272848.1272876.

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8

Kim, Tae-Il. « Nifty fifty ». Journal of Periodontal & ; Implant Science 40, n^o 3 (2010) : 103. http://dx.doi.org/10.5051/jpis.2010.40.3.103.

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9

Parlante, Nick. « Nifty assignments ». ACM SIGCSE Bulletin 40, n^o 1 (29 février 2008) : 112–13. http://dx.doi.org/10.1145/1352322.1352173.

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10

Parlante, Nick, Thomas P. Murtagh, Mehran Sahami, Owen Astrachan, David Reed, Christopher A. Stone, Brent Heeringa et Karen Reid. « Nifty assignments ». ACM SIGCSE Bulletin 41, n^o 1 (4 mars 2009) : 483–84. http://dx.doi.org/10.1145/1539024.1509031.

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11

Fuhrman, Jed A., et Douglas G. Capone. « Nifty nanoplankton ». Nature 412, n^o 6847 (août 2001) : 593–94. http://dx.doi.org/10.1038/35088159.

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12

Seppa, Nathan. « Nifty Spittle ». Science News 163, n^o 3 (18 janvier 2003) : 37. http://dx.doi.org/10.2307/4014174.

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13

Parlante, Nick. « Nifty Assignments ». ACM SIGCSE Bulletin 41, n^o 2 (25 juin 2009) : 83–84. http://dx.doi.org/10.1145/1595453.1595479.

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14

Parlante, Nick, Jeffrey Popyack, Stuart Reges, Stephen Weiss, Scott Dexter, Chaya Gurwitz, Joseph Zachary et Grant Braught. « Nifty assignments ». ACM SIGCSE Bulletin 35, n^o 1 (11 janvier 2003) : 353–54. http://dx.doi.org/10.1145/792548.611914.

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15

Parlante, Nick, David Levine, Steven Andrianoff, Aaron J. Gordon, Alyce Brady, Pamela Cutter, Paul Kube, Jefferson Ng et Richard E. Pattis. « Nifty assignment ». ACM SIGCSE Bulletin 37, n^o 1 (23 février 2005) : 371–72. http://dx.doi.org/10.1145/1047124.1047356.

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16

Parlante, Nick, John K. Estell, David Reed, David Levine, Dan Garcia et Julie Zelenski. « Nifty assignments ». ACM SIGCSE Bulletin 34, n^o 1 (mars 2002) : 319–20. http://dx.doi.org/10.1145/563517.563466.

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17

Parlante, Nick. « Nifty assignments ». ACM SIGCSE Bulletin 33, n^o 4 (décembre 2001) : 25–27. http://dx.doi.org/10.1145/572139.572162.

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18

Parlante, Nick, John Cigas, Angela B. Shiflet, Raja Sooriamurthi, Mike Clancy, Bob Noonan et David Reed. « Nifty assignments ». ACM SIGCSE Bulletin 39, n^o 1 (7 mars 2007) : 497–98. http://dx.doi.org/10.1145/1227504.1227479.

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19

Parlante, Nick, Steven A. Wolfman, Lester I. McCann, Eric Roberts, Chris Nevison, John Motil, Jerry Cain et Stuart Reges. « Nifty assignments ». ACM SIGCSE Bulletin 38, n^o 1 (31 mars 2006) : 562–63. http://dx.doi.org/10.1145/1124706.1121516.

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20

Ashri, Dhananjay, Bibhu Prasad Sahoo, Ankita Gulati et Irfan UL Haq. « Repercussions of COVID-19 on the Indian stock market ». Linguistics and Culture Review 5, S1 (17 novembre 2021) : 1495–509. http://dx.doi.org/10.21744/lingcure.v5ns1.1792.

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The present paper determines the repercussions of the coronavirus on the Indian financial markets by taking the eight sectoral indices into account. By taking the sectoral indices into account, the study deduces the impact of virus outbreak on the various sectoral indices of the Indian stock market. Employing Welch's t-test and Non-parametric Mann-Whitney U test, we empirically analysed the daily returns of eight sectoral indices: Nifty Auto, Nifty FMCG, Nifty IT, Nifty Media, Nifty Metal, Nifty Oil and Gas, Nifty Pharma, and Nifty Bank. The results unveiled that pandemic had a negative impact on the automobile, FMCG, pharmaceuticals, and oil and gas sectors in the short run. In the long run, automobile, oil and gas, metals, and the banking sector have suffered enormously. The results further unveiled that no selected indices underperformed the domestic average, except NIFTY Auto.

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21

P.R, Roshni, et E. Sulaiman. « PERFORMANCE OF NIFTY 50 EXCHANGE TRADED FUNDS ». International Journal of Advanced Research 9, n^o 02 (28 février 2021) : 77–83. http://dx.doi.org/10.21474/ijar01/12420.

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The study evaluated the performance of selected Nifty 50 ETFs tracking Nifty 50 Index listed in National Stock Exchange in India during a period of six years starting from 1st April, 2014 to 31st March, 2020. The performance of ETFs is measured using Average Daily Returns, CAGR, HPR, Standard Deviation, Tracking Error, R squared and Beta. It is found that there is difference in the risk-return pattern of Nifty 50 ETFs and its index Nifty 50. Aditya Birla Nifty ETF is the performing fund among the selected ETFs.

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22

Siddiqui, Saif, et Safika Praveen Sheikh. « Modelling the Return of Shariah with Underlying Indices of National Stock Exchange of India : A Case of 3SLS and GMM Estimation ». Journal of Emerging Economies and Islamic Research 4, n^o 2 (31 mai 2016) : 6. http://dx.doi.org/10.24191/jeeir.v4i2.9082.

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Shariah indices can be used to construct socially reliable investment products that are attractive for those, who do not wish to invest in undesired business. National Stock Exchange of India introduced Nifty 50 Shariah and Nifty 500 Shariah indices to provide alternative i The study is an attempt to reveal the relationship between Nifty 50 Shariah and Nifty 500 Shariah with their underlying indices, Nifty 50 and Nifty 500.For this purpose a period of 01/01/2007 to 31/12/2015 is taken. Based on various objectives, techniques like Descriptive statistics, Correlation, Co 3SLS and GMM estimation are used. It is concluded that return of Shariah Indices are better and risk is lesser, than underlying indices. These indices are the better option for portfolios.

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23

Singh, Gurmeet. « Estimating Optimal Hedge Ratio and Hedging Effectiveness in the NSE Index Futures ». Jindal Journal of Business Research 6, n^o 2 (4 septembre 2017) : 108–31. http://dx.doi.org/10.1177/2278682117715358.

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This study attempts to study and suggest an optimal hedge ratio to Indian investors and traders by examining the three main indices of National Stock Exchange of India (NSE), namely, NIFTY, Bank NIFTY, and IT NIFTY, over the sample period from January 2011 to December 2015. The present study estimated the hedge ratio through six econometric models, namely, OLS, GARCH, EGARCH, TARCH, VAR, and VECM, in the minimum variance hedge ratio framework as suggested by Ederington (1979). The findings of the present study confirm the theoretical properties of Indian cash and futures market and suggest that the optimal hedge ratio estimated through EGARCH model was lowest for the NIFTY and Bank NIFTY, and that for IT NIFTY, the OLS model shows the lowest optimal hedge ratio as compared to that estimated through other models.

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24

Babu, Manivannan, A. Antony Lourdesraj, C. Hariharan, Gayathri Jayapal, G. Indhumathi, J. Sathya et Chinnadurai Kathiravan. « Dynamics of Volatility Spillover between Energy and Environmental, Social and Sustainable Indices ». International Journal of Energy Economics and Policy 12, n^o 6 (28 novembre 2022) : 50–55. http://dx.doi.org/10.32479/ijeep.13482.

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The purpose of this research was to examine the dynamics of volatility spillover between energy and environmental, social, and sustainable indices. COVID19 prompted the research to select April 2019 to March 2022 as a sample period, and the respective data (Daily Prices) of the Nifty Energy and Nifty ESG indices were obtained from the National Stock Exchange of India Limited. The outcomes of the study confirmed that the daily returns of Nifty Energy and Nifty 100 ESG indices were not normally distributed and reached stationarity at level difference. Further, the study employed GARCH Models such as ARCH, GARCH (1,1), and GARCH-M to determine conditional volatility, and it validated the ARCH influence on the daily returns of the Nifty Energy and Nifty 100 ESG, during the study period

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25

Powell, John Y. « The Nifty Nine ». Residential Treatment For Children & ; Youth 9, n^o 2 (4 mai 1992) : 85–95. http://dx.doi.org/10.1300/j007v09n02_08.

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26

Mervis, J. « A NIFty Idea ». Science 320, n^o 5875 (25 avril 2008) : 437d. http://dx.doi.org/10.1126/science.320.5875.437d.

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27

JACOBY, MITCH, et LAUREN K. WOLF. « NIFTY AT FIFTY ». Chemical & ; Engineering News 88, n^o 10 (8 mars 2010) : 13–17. http://dx.doi.org/10.1021/cen-v088n010.p013.

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28

Parlante, Nick, Mike Clancy, Stuart Reges, Julie Zelenski et Owen Astrachan. « Nifty assignments panel ». ACM SIGCSE Bulletin 33, n^o 1 (mars 2001) : 412–13. http://dx.doi.org/10.1145/366413.364797.

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29

Parlante, Nick, Owen Astrachan, Mike Clancy, Richard E. Pattis, Julie Zelenski et Stuart Reges. « Nifty assignments panel ». ACM SIGCSE Bulletin 31, n^o 1 (mars 1999) : 354–55. http://dx.doi.org/10.1145/384266.299809.

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30

D., Bhuvaneshwari. « Impact of Covid-19 on the Financial Sector Indices ». International Research Journal of Business Studies 14, n^o 2 (15 novembre 2021) : 137–45. http://dx.doi.org/10.21632/irjbs.14.2.137-145.

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This study is an attempt to assess the impact of Covid-19 and the lockdown pronounced thereof on the Nifty sectoral indices with specific reference to the financial sector indices owing to their significance in the economy. The OLS regression, Granger Causality and Impulse Response Function were estimated to measure the changes in the future responses of Nifty 50 to the changes in the select sectoral indices, namely, Nifty Bank, Nifty Financial Services and Nifty Private Banks and Nifty PSU Banks for the period consisting two sub-periods, i.e., the first sub-period from April 2019 to March 2020 are assumed as the preCovid-19 period and the second sub-period from April 2020 to March 2021 is assumed as the period during Covid-19. The results indicated that the shock of the Covid-19 had an impact on the financial sector indices in India during the Covid-19 period.

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31

Dharani, M. « Equanimity of Risk and Return Relationship between Shariah Index and General Index in India ». Journal of Economics and Behavioral Studies 2, n^o 5 (15 mai 2011) : 213–22. http://dx.doi.org/10.22610/jebs.v2i5.239.

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The present study empirically examines the risk and return of the Nifty Shariah index and Nifty index during the period 2nd January 2007 to 31st December 2010. The sample period is further divided into bull market period and bear market period based on the movement of the both indices during the study period. The objective of the study is to analyse the performance of the Islamic index and common index and to test whether any significant difference between both indices in India. Based on the previous studies, the present paper employs Risk adjusted measurement such as Sharpe index, Treynor Index and Jensen alpha. The t- test is used to test the mean returns difference between both indices. The study found that Nifty Shariah has been underperformed during the sample and sub sample period. According to ttest, the mean difference between both indices has not been significant which reveals both are consistent. The risk adjusted returns for the both indices reveals that both were underperforming with respect to risk free rate of return. The study has also disclosed the low volatile nature of Nifty Shariah than Nifty index. Finally, the study concludes that Nifty Shariah and Nifty indices in India are performing in a similar manner.

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32

Kotha, Kiran Kumar, et Shreya Bose. « Dynamic Linkages between Singapore and NSE listed NIFTY Futures and NIFTY Spot Markets ». Journal of Prediction Markets 10, n^o 2 (27 janvier 2017) : 1–13. http://dx.doi.org/10.5750/jpm.v10i2.1253.

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This study examines the dynamic linkages of Nifty stock index and Nifty index futures contract traded on the home market, National Stock Exchange (NSE) and on the off-shore market, Singapore Stock Exchange (SGX). The study uses daily closing prices of the Nifty index and the Nifty futures contract traded on both the exchanges for the period July 15, 2010 to July 15, 2016. The study finds a causality running from the returns of the spot market to the returns from the Nifty futures market in both the exchanges, NSE and SGX, with the help of Vector Error Correction model and Granger causality test. Variance Decomposition and Impulse Response Analysis also confirm that the spot market is the leading market in price discovery, making it the most efficient amongst others.

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33

N., Dileep, et G. Kotreshwar. « INTER RELATIONSHIP BETWEEN RAINFALL INDEX AND NIFTY INDEX : AN EMPIRICAL STUDY ». JOURNAL OF APPLIED FINANCIAL ECONOMETRICS 3, n^o 1 (2022) : 53–63. http://dx.doi.org/10.47509/jafe.2022.v03i01.03.

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The proposed study is an attempt to determine whether a relationship exists between rainfall index and NSE Nifty index. The study used the monthly mean rainfall data and monthly closing price of Nifty index. The study applied Augmented Dickey-Fuller (ADF) test, correlation analysis, the GARCH (1,1) model, and the Granger Causality test to analyse the interrelationship. The results of correlation matrix show that there is no interrelationship between the two variables. The GARCH (1,1) model found that the NSE Nifty index is not affected by the rainfall index and Granger Causality test displays that rainfall index does not Granger Cause the Nifty index. According to the authors’ knowledge, this is the first empirical study to determine the interrelationship between the rainfall index and the Nifty Index over a longer period of time.

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N, Dileep, et G. Kotreshwar. « Interrelationship between Rainfall Index and Nifty Index : An Empirical Study ». Gyan Management Journal 16, n^o 2 (7 novembre 2022) : 47–54. http://dx.doi.org/10.48165/gmj.2022.16.2.7.

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The proposed study is an attempt to determine whether a relationship exists between rainfall index and NSE Nifty index. The study used the monthly mean rainfall data and monthly closing price of Nifty index. The study applied Augmented Dickey-Fuller (ADF) test, correlation analysis, the GARCH (1,1) model, and the Granger Causality test to analyse the interrelationship. The results of correlation matrix show that there is no interrelationship between the two variables. The GARCH (1,1) model found that the NSE Nifty index is not affected by the rainfall index and Granger Causality test displays that rainfall index does not Granger Cause the Nifty index. According to the authors’ knowledge, this is the first empirical study to determine the interrelationship between the rainfall index and the Nifty Index over a longer period of time.

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35

P. Sakthivel, S. Rajaswaminathan, R. Renuka et N. R.Vembu. « Dynamic Relationship between Crude Oil and Stock Prices in India : Before And After the Subprime Financial Crisis 2008 ». GIS Business 14, n^o 6 (26 novembre 2019) : 96–104. http://dx.doi.org/10.26643/gis.v14i6.11683.

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This paper empirically discovered the inter-linkages between stock and crude oil prices before and after the subprime financial crisis 2008 by using Johansan co-integration and Granger causality techniques to explore both long and short- run relationships. The whole data set of Nifty index, Nifty energy index, BSE Sensex, BSE energy index and oil prices are divided into two periods; before crisis (from February 15, 2005 to December31, 2007) and after crisis (from January 1, 2008 to December 31, 2018) are collected and analyzed. The results discovered that there is one-way causal relationship from crude oil prices to Nifty index, Nifty energy index, BSE Sensex and BSE energy index but not other way around in both periods. However, a bidirectional causality relationship between BSE Energy index and crude oil prices during post subprime financial crisis 2008. The co-integration results suggested that the absence of long run relationship between crude oil prices and market indices of BSE Sensex, BSE energy index, Nifty index and Nifty energy index before and after subprime financial crisis 2008.

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36

K. Riyazahmed, K. « VOLATILITY SPILLOVER AND PANDEMIC – ANALYSIS OF SELECTED SECTORAL INDICES IN INDIA ». Economic Thought journal 67, n^o 6 (22 décembre 2022) : 655–70. http://dx.doi.org/10.56497/etj2267602.

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The COVID-19 pandemic has impacted economies worldwide, and it has been reflected in their stock markets, as well. The effect was evident in the Indian stock markets, yet the nature and level of this impact are not very clear. This paper examines the short- and long-term spillover in the volatility between coronavirus cases on the broader market index, Nifty 50, and the indices of selected sectors: information technology, healthcare, and pharmaceuticals. Data for the period from January 2020 to July 2022 has been analyzed. The Dynamic Conditional Correlation GARCH model was used for analyzing the volatility spillover of coronavirus cases on Nifty 50, Nifty IT, Nifty Healthcare, and Nifty Pharma. The results show that there has been a significant long-term volatility spillover of infections on the broader market index, Nifty 50. However, there is no long-term persistence of COVID-19 on the sectoral indices. Only Pharma and Healthcare have exhibited significant short-term persistence. All the indices were negatively correlated with case numbers. Even though the sectoral indices did not exhibit significant long-term volatility spillover, they were positively correlated with the broader market index, Nifty 50, which in turn showed the significant long-term persistence of COVID-19. The results of the study are useful to policymakers and investors to understand the level of market impact due to the pandemic.

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37

Gangwani, Mayank, et Dhun Sehrawat. « Covid-19-A Baleful Aftermath for the Stocks of Indian Pharmaceutical Companies ». International Journal of Science, Engineering and Management 9, n^o 9 (13 septembre 2022) : 21–31. http://dx.doi.org/10.36647/ijsem/09.09.a004.

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Covid-19 catastrophe has not spared any market across the world due to widespread disruptions in its supply chain operations. In today's world, however, stock markets serve as a catalyst for a country's economic and financial development. But, with the development of Covid-19 infection and widespread lockdown in the majority of countries, its stock market has plummeted even further into the depths. Therefore, to determine whether the Covid-19 outbreak has impacted the expected return of Nifty Pharma and stock return of 3 leading pharmaceutical companies Cipla, Dr. Reddy's, and Sun Pharma in the before-Covid-19 period (1 Jan 2019 to 20 Mar 2020) and the after-Covid-19 period (23 Mar 2020 to 20 Mar 2021) this study is being carried out. The study aims to uncertain whether there exists a meaningful link between the S&P CNX Nifty index's forecasted market return and the expected security return of Nifty Pharma before and after Covid-19. To evaluate the relevant hypotheses, descriptive statistics event study technique, non-parametric testing, particularly the Wilcoxon Signed test, and regression analysis were used. It was discovered that the expected returns of Nifty Pharma and the expected security returns of Cipla, Dr. Reddy's, and Sun Pharma did not vary significantly before and throughout the Covid-19 epidemic period. However, for both before and throughout the Covid-19 era, there was a link between the predicted market return of the S&P CNX Nifty index and the security return of Nifty Pharma, implying that the benchmark index S&P CNX Nifty was influenced by Nifty Pharma's performance.

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Garg, Deeksha. « Does the Nature of Index and Liquidity Influence the Mispricing in Future Contracts in India ? » Applied Finance Letters 9, SI (18 novembre 2020) : 15–22. http://dx.doi.org/10.24135/afl.v9i2.244.

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In this study, we investigate the variations in the mispricing of futures in Nifty (benchmark index), Bank Nifty and Nifty IT. Using a regression model on 1230 observations for the period of 1 January 2014 to 31 December 2018, we find no significant mispricing exists in the last week to the expiry of the contract in all three indices. This finding supports the existing literature that as the contract moves towards the maturity date, its value converges the market value. However, the main highlight of the paper is to reveal the difference in the life of mispricing in different indices. This difference in mispricing can be allocated to the liquidity in that indices. We report that being the most liquid, Bank Nifty is having mispricing only in 1 week (first week) of the contract, after that no significant mispricing exists in mispricing, Nifty shows significant mispricing for the first two weeks and Nifty IT shows mispricing for all weeks except last week. This is the pioneering work which considers the sectoral differences while evaluating futures mispricing. The findings of this study will provide a useful insight to the regulator and investors.

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Victor, Vijay, Dibin K K, Meenu Bhaskar et Farheen Naz. « Investigating the Dynamic Interlinkages between Exchange Rates and the NSE NIFTY Index ». Journal of Risk and Financial Management 14, n^o 1 (5 janvier 2021) : 20. http://dx.doi.org/10.3390/jrfm14010020.

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This study aims at examining the short-run and long-run dynamic linkages among exchange rates and stock market index in India through a structured cointegration and Granger causality tests. Daily exchange rates of USD, EUR, CNY, JPY, and GBP to INR along with the daily movement of NSE NIFTY for a period spanning 13 years from 6 September 2005 to 31 December 2018 were used for the analysis. The results reveal that there is no evidence for a stable long-run relationship between NSE NIFTY and the exchange rates under study. However, the VAR-based Granger causality test shows that USD, JPY, and CNY have short-run causal relationship with NSE NIFTY. The NSE NIFTY also seemed to have an influence on USD expressed in terms of Indian rupee. The impulse response analysis further supports the results of the Granger causality test and provides information on the time required for the NSE NIFTY index to recover from a shock caused by the fluctuation in exchange rates.

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Gadasandula, Krishna. « Effect of Macroeconomic Determinants on Indian Stock Market ». Asian Journal of Managerial Science 8, n^o 2 (5 mai 2019) : 22–27. http://dx.doi.org/10.51983/ajms-2019.8.2.1556.

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Stock market is one of the important forms of investment. The prices of stock markets are affected by much macro-economic factors. The study investigates the relationships between the Indian stock market index (NSE Nifty) and four macroeconomic variables, namely, GDP, Inflation, Exchange Rate and Bank Rate. The data is collected on a quarterly basis for the time period March 2000 to December 2017. The study employs the Johansen’s co-integration approach to the long-run equilibrium relationship between stock market index and macroeconomic variables. For causality analysis, the study carried out Granger and Geweke causality tests. From this paper it is observed that the Granger causality test results do not demonstrate the presence of any bidirectional causality. The results show the unidirectional causal associations running from GDP to Inflation, Bank Rate to GDP, Exchange Rate to GDP, NIFTY Index to GDP, Exchange Rate to Inflation, NIFTY Index to Inflation, and Bank Rate to NIFTY Index. Apart from that, the results also show no causal association between Inflation and Bank Rate, Bank Rate and Exchange Rate, and Exchange Rate and NIFTY Index. However, the bidirectional causal associations appear. When we look into the results of Geweke causality analysis shows that bidirectional causal associations exist between Inflation and Bank Rate, and Exchange Rate and Nifty Index.

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K, Ramya, et Bhuvaneshwari D. « Dynamic Interaction Between Nifty 50 and Nifty Sectoral Indices : An Empirical Study on Indian Stock Indices ». NMIMS Management Review 29, n^o 02 (12 avril 2021) : 17–24. http://dx.doi.org/10.53908/nmmr.290202.

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This study aims to determine the cointegrating and causal relationship between Nifty 50 and Nifty sectoral indices. Historical index data of the select indices were collected from the National Stock Exchange (NSE) database for the period Jan 2014 - Dec 2018. Appropriate Econometric tools - Augmented Dickey-Fuller (ADF) test, Phillips and Perron (PP) test, regression model, Granger causality test, and Johansen cointegration test were used to analyze the data. The findings of the study imply that the movements of Nifty sectoral index prices could determine the flow of stock index prices, i.e., Nifty 50 and vice versa during the period of the study which could also help the policymakers and financial planners in providing financial awareness to investors and clients in decision making.

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Slivka, Ronald T., Jie Yang et Weiyu Wan. « Nifty Futures Rollover Strategies ». Indian Journal of Finance 11, n^o 7 (1 juillet 2017) : 7. http://dx.doi.org/10.17010/ijf/2017/v11i7/116563.

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Siegel, Jeremy J. « The Nifty-Fifty Revisited ». Journal of Portfolio Management 21, n^o 4 (31 juillet 1995) : 8–20. http://dx.doi.org/10.3905/jpm.1995.8.

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Lithic, I. M. « Invocation : FIFTY is Nifty ». SubStance 52, n^o 1 (2023) : 6–7. http://dx.doi.org/10.1353/sub.2023.a900519.

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Singh, Kamaljit, et Vinod Kumar. « Dynamic linkage between nifty-fifty and sectorial indices of national stock exchange ». American Journal of Economics and Business Management 3, n^o 2 (25 mars 2020) : 17–27. http://dx.doi.org/10.31150/ajebm.v3i2.148.

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The main objective of this paper is to analyze the trend and pattern of the Nifty-Fifty and sectorial indices. An attempt has been also made to find out the causal relationship among the Nifty-Fifty and NSE sectorial Indices. The unit root test and Granger-causality test has been applied to check the causal relationship between Nifty-Fifty and sectorial indices. The finding of the study shows that the financial service sector had performed better and followed by the banking sector among all the indices while the Pharma sector and the Realty sector were Under-performed in comparison to other indices. The Nifty-Fifty has been found less volatile in comparison to other sectorial indices however Realty sector indices show the highest volatility during the study period.

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Matharu, Taran. « Interlinking the Shariah Compliant Stocks in India : An Analysis Using Granger Causality ». Ushus - Journal of Business Management 18, n^o 3 (1 juillet 2019) : 39–50. http://dx.doi.org/10.12725/ujbm.48.4.

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With the growth of the Shariah-compliant stocks in India, the opportunities are also increasing. The investors need to know how they can predict the future of the stocks by studying a smaller number of stocks. This paper deals with the link between different Shariah-compliant stocks available in India. A research was done on S&P BSE 500 Shariah, Nifty Shariah 25, Nifty 50 Shariah and NIFTY 500 Shariah. The study revealed the interdependence of the shares. It helped locate the function as well as the driver of the stock.

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V.N, Vishweswarsastry, Binoy Mathew et Aisha Banu. « Efficient Model Selection for Nifty Index and Impact of Money Supply, Gold and Exchange Rate on S&P Nifty 50 ». IRA-International Journal of Management & ; Social Sciences (ISSN 2455-2267) 7, n^o 2 (10 juin 2017) : 309. http://dx.doi.org/10.21013/jmss.v7.n2.p22.

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<div><p><em>The economic importance of stock market results from marketability which is increased or increasing resulting from quotation of shares from a stock exchange. Stock Indices attracts most of the investors and volatile to market conditions which affects the return of the investors. The primary objective of the paper is to study the effect of macroeconomic factors on NIFTY secondly to analyse and check the stationarity among various economic indicators and Nifty and thirdly to judge the best model for estimating the nifty. The methodology applied for the study is analytical and an econometric tool like ADF test, ARDL, Pairwise granger causality test and VAR is applied for regressing the index. The Variables was stationary at first order of difference for the nifty to regress for future period which aids investors in decision making.</em></p></div>

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SACHDEVA, J. K., et Jyoti Nair. « Cointegration of East Asian, Indian and European Marketsâ€“ A Study of Impact on Indian Bourses ». Journal of Global Economy 14, n^o 1 (8 novembre 2018) : 3–27. http://dx.doi.org/10.1956/jge.v14i1.490.

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With huge investments flowing from all over the world to India, FIIs (Foreign Institutional Investors) and DIIs (Domestic Institutional Investors), retail investors, investment advisors, brokers and portfolio consultants keep abreast with latest research on fundamentals and technicals. Interdependence between stock markets is an important aspect of international portfolio management. In this paper, impact of Asian Indices like Hang Sang, KOSPI, SET SIT and TSEC on opening prices of Indian index Nifty was studied with various tools like Johansen Cointegration Test, VAR Granger Causality and Pairwise Granger Causality test. Similarly impact of European indices like CAC, FTSE, Euronext, DAX and SMI on Nifty closing prices were studied with same tools. The 3 months, 6 months, one year and 5 year data were subjected to experiment whether series are cointegrated. It was observed that series are cointegrated at very short-term level but for longer period they are not cointegrated, however, they influence others. VAR Granger Causality Test and Pairwise Granger Causality reveal that Hang Sang, KOSPI, SIT and TW (TSEC of Taiwan) impact Nifty Open prices. Nifty influences only TW. KOSPI influences Hang Sang and SET. SET influences KOSPI and TW. Similarly, VAR Granger Causality Test and Pairwise Granger Causality also reveal Nifty closing prices influence CAC, DAX, FTSE

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Karthikeyan, P. « Correlation Analysis of Public Sector Banks and Nifty Banks with Nifty in NSE ». Asian Journal of Research in Business Economics and Management 7, n^o 5 (2017) : 271. http://dx.doi.org/10.5958/2249-7307.2017.00055.x.

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Karmakar, Madhusudan. « Price Discoveries and Volatility Spillovers in S&P CNX Nifty Future and its Underlying Index CNX Nifty ». Vikalpa : The Journal for Decision Makers 34, n^o 2 (avril 2009) : 41–56. http://dx.doi.org/10.1177/0256090920090204.

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In a perfectly functioning world, every piece of information should be reflected simultaneously in the underlying spot market and its futures markets. However, in reality, information can be disseminated in one market first and then transmitted to other markets due to market imperfections. And, if one market reacts faster to information than the other, a lead-lag relation is observed The lead-lag relationship in returns and volatilities between spot and futures markets is of interest to academics, practitioners, and regulators. In India, there are very few studies which have investigated the lead-lag relationship in the first moment of the spot and futures markets This study investigates the lead-lag relationship in the first moment as well as the second moment between the S&P CNX Nifty and the Nifty future. It also investigates how much of the volatility in one market can be explained by volatility innovations in the other market and how fast these movements transfer between these markets. It conducts Multivariate Cointegration tests on the long-run relation between these two markets. It investigates the daily price discovery process by exploring the common stochastic trend between the S&P CNX Nifty and the Nifty future based on vector error correction model (VECM). It examines the volatility spillover mechanism with a bivariate BEKK model. Finally, this study captures the effects of recent policy changes in the Indian stock market. The results reveal the following: The VECM results show that the Nifty futures dominate the cash market in price discovery. The bivariate BEKK model shows that although the persistent volatility spills over from one market to another market bi-directionally, past innovations originating in future market have the unidirectional significant effect on the present volatility of the spot market. The findings of the study thus suggest that the Nifty future is more informationally efficient than the underlying spot market. These findings may provide insights on the information transaction and index arbitrage between the CNX Nifty and futures markets.

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