National Stock Exchange Nifty Research Articles

Measuring Asymmetric Volatility of Bank Nifty Index using Egarch Model

The paper examines the significance of volatility models in forecasting future volatility for effective portfolio allocation and risk reduction. It compares the performance of symmetric and asymmetric models in estimating conditional variance, and linear versus non-linear GARCH models. Using secondary data from the National Stock Exchange's Nifty Bank index, the study applies Exponential GARCH (1,1) to measure asymmetric volatility and conducts various tests to confirm the suitability of the data for analysis. The results indicate clustering of volatility in Nifty Bank returns over a four-year period, with the presence of asymmetrical effects and leverage constants. The study concludes that negative information has a greater impact on volatility than positive surprises, and that market fluctuations are inversely related to stock market performance. This research provides valuable insights for portfolio selection, risk management, and asset pricing in the context of increasing volatility across various markets and industries.

Does the random walk hypothesis hold for Indian stock markets during parliamentary elections?

In this study, we examine the random walk hypothesis for two well-known Indian indices, BSE (Sensex) and NSE (Nifty), by considering country-specific political events (parliamentary elections). Two pertaining questions were studied. First, efficiency with respect to weak form, semi-strong form, and strong form; and second, the random walk pattern of the return by applying the new variance ratio tests. We use 21 years of daily closing stock price data of both the National Stock Exchange (NSE) Nifty Index and the Bombay Stock Exchange (BSE) Sensex Index. The hypothesis is tested by using both conventional and new variance ratio tests: The Lo–MacKinlay variance ratio test, the Chow–Denning test, and Wright’s Rank and Sign test. All three tests report that the return does not follow the random walk for the full sample, suggesting the possibility of making gains by exploiting various investing strategies. It was found that both Indian indices follow the random walk hypothesis during the phase of parliamentary elections. This study contributes to the existing literature on the Efficient Market Hypothesis (EMH).

Intraday price discovery and volatility transmission between the dual-listed stock index futures and spot markets – new evidence from India

PurposeThis study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.Design/methodology/approachThis study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.FindingsThis study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.Practical implicationsThese findings can potentially help market participants, policymakers, stock exchanges and regulators.Originality/valueUnlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

Relationship of Stock Market Indices and Individual stocks: A Descriptive Study

Indices of the stock market and particular stocks are intertwined in India. Stock market indices like the Bombay Stock Exchange Sensex and the NSE (National Stock Exchange) Nifty act as gauges of the state of the market. They provide a glimpse of the larger market by representing a selection of stocks, typically the most well-known and actively traded ones. Changes in stock market indexes frequently reflect the general mood and market trends. In general, rising indices signify that most of the stocks that make up the index are experiencing upward price fluctuations. The same is true when indexes drop; this indicates a market trend in the opposite direction. Contrarily, specific corporations that are listed on the stock exchange are represented by individual stocks. The performance of each of an investor's or trader's individual stock holdings is measured against stock market indices. They determine relative performance by comparing the returns on their equities to the performance of the index. Investors who wish to successfully navigate the Indian stock market must have a solid understanding of the relationship between stock market indices and specific stocks.

The Effect of Energy Prices on Stock Indices in the Period of COVID-19: Evidence from Russia, Turkey, Brazil, and India

Petroleum and natural gas, which are among the most used energy sources in the world, have a significant impact on financial markets and macroeconomic indicators as they are used as raw materials in many fields. For this reason, Russia, Turkey, Brazil, and India, as energy importers and developing countries, may be affected positively or negatively by changes in energy prices. The main purpose of this study is to examine the correlation between Brent oil, crude oil (WTI), and natural gas (NG) prices and Moscow Stock Exchange Index (RTSI), Borsa Istanbul Index (XU100), Bovespa Brazilian Stock Exchange Index (BVSP), and Indian National Stock Exchange Nifty 50 Index (NSEI). In the study, weekly data between 16.02.2020 and 26.12.2021 were examined. Vector autoregressive (VAR) model was used to examine the correlation between the variables included in the analysis, and the direction of the correlation between the variables was determined by the Granger causality test. According to the results of the VAR model, Brent oil and crude oil prices have significant effects on the indices included in the analysis; however, natural gas price does not have a significant effect on indices, Brent oil, and crude oil prices. On the other hand, the results of the Granger causality test confirm the findings of the VAR analysis. Granger causality test results reveal that in Granger's sense, only BVSP and NSEI are the cause of Brent oil price, RTSI, BVSP, NSEI, and XU100 are the cause of WTI, and WTI is the cause of NSEI.

An Analysis of the Macroeconomic Variables Impacting the Indian Stock Market at NSE Nifty 50

The main purpose of the study was to understand a long-term relationship of the external macroeconomic factors and the Nifty 50 index of the National Stock Exchange (NSE). Five major macroeconomic factors namely, foreign exchange reserves, prices of crude oil, foreign institutional investment, balance of payments, and gold prices were considered to know the cause of these external macroeconomic factors on NSE Nifty 50. Monthly data of the above variables were being considered for the study from January 2016 to December 2020, that is, for a period of 5 years. To understand the relationship among the variables, an attempt was made to use the Augmented Dickey – Fuller (ADF) test to ascertain whether the data used were stationary or not, and to know the extent of influence of independent variables on dependent variables by running the multiple regression. It was observed that out of all the factors considered for the study, bearing crude oil, no factor had a relationship with the market index (NSE Nifty 50). It was also found that the stock market became a weak form as no significant relationship existed during the study period.

Friday the 13th effect on Indian stock market

Purpose: This paper attempts to investigate the presence of Friday the 13th Effect in the Indian stock market. Design/methodology/approach: This paper tests the presence of the Friday the 13th Effect using different sets of hypotheses for 7 days, 15 days and normal versus Friday the 13th by using statistical methods. Findings: The findings of the study do not support the presence of Friday the 13th Effect for all cases. There are few months for certain specific years where the effect was seen. Research limitations/implications: The Friday the 13th effect has been examined for two major indices of the Indian market, i.e., the Bombay Stock Exchange Index SENSEX and the National Stock Exchange Nifty Fifty Index. However, there are other major and sectoral indices as well where in the effect may be checked. Practical implications: The study results indicate that Indian stock market shows phased anomaly. The effect of Friday the 13th is seen only in some cases during certain years only. Originality/value: Friday the 13th effect has been mostly checked for developed nations and again there has been less work done with respect to this particular market anomaly. The present research is an original work done for emerging market naming India.

Identifying Significant Macroeconomic Indicators for Indian Stock Markets

The macroeconomic indicators play a major role in all the stock markets, and they vary from nation to nation. This paper identifies the influence of macroeconomic indicators on the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE) of India. Total of forty-four macroeconomic indicators for eight years from the year 2011 to 2018 are considered in this study. The macroeconomic factors are aggregated and considered in average monthly form. The proposed method finds the correlation matrix of all considered macroeconomic indicators. The need for dimensionality reduction and the existence of multicollinearity are proven using validation techniques such as the Kaiser-Meyer-Olkin and Bartlett tests. The Principal Component Analysis (PCA) method is used to reduce the dimensionality to seven factors and then PCA with the varimax rotation method is applied to find factors with maximum variation. In addition, the influence of these seven factors on the NSE Nifty and BSE SENSEX indices are analyzed using regression. Finally, an Artificial Neural Network is used to predict stock market movement with the help of macroeconomic indicators. Accuracy of 92% and 87% are obtained on NSE NIFTY and BSE SENSEX respectively.

Does the Pre-Open Auction Market Improve Efficiency of Price Discovery in Stock Markets? Evidence from India

The paper analyzed the impact of opening call auction on the efficiency of price discovery at the National Stock Exchange (NSE), India by studying the returns and volatility behavior of one benchmark index (NSE's Nifty) and 10 Nifty component companies selected on random. The paper used the closing prices for the period 3 years before and 3 years after the introduction of the call auction market in 2010. Descriptive statistics, one-way ANOVA, augmented Dickey-Fuller unit-root Test, and ARCH-GARCH type methodology was employed for the analysis. The paper found no significant difference in the returns during the two periods, though a reduction in volatility was observed. The introduction of the pre-open auction market resulted in an improvement in the efficiency of price discovery of various stocks. The findings of the paper offer valuable inputs for stock market regulators as well as investors.

An Examination of Carbon Disclosure Practices in India

This paper analyses the current responsible practices in carbon accounting reporting by Indian NSE (National Stock Exchange) Nifty firms within their disclosed financial statements, to establish a baseline understanding of current their accounting practices to assess whether they are meaningful and transparent. Realizing the significant growth in the number of companies reporting their financial statements on the web, we survey their financial information pages for a clue to the carbon disclosure and reporting practices as followed by them. Significantly large numbers of firms in NSE Nifty demonstrate their concern for the environment and indicate their voluntary willingness to address the ill-effects of carbon emission. The findings suggest that comparable information about the relative performance of firms in India cannot be discerned from carbon related disclosures. Further, there is a significant diversity in how firms are accounting for emission allowances across India and different approaches have resulted in materially different disclosures in the financial statements.

A Fusion Model Integrating ANFIS and Artificial Immune Algorithm for Forecasting Indian Stock Market

Stock market forecasting provides challenging and interesting task to both investors and academic researchers because trading decision at an appropriate time makes more profit for investors. In present study, a new approach has been proposed to integrate Adaptive Neuro-Fuzzy Inference System (ANFIS) with Artificial Immune Algorithm (AIA) for predicting the future index value of National Stock Exchange (NSE) of India. In order to make an efficient forecasting model, ANFIS is employed to optimize decision-making process and an efficient artificial immune algorithm is adopted to adjust membership function parameters of Fuzzy Inference System (FIS). The proposed system was simulated using daily closing value of NSE Nifty data and well-known technical indicators as input data values and output is the predicted future index value of NSE Nifty. Simulation results of our fusion model have been compared with other soft computing models and actual NSE Nifty data as benchmark. The experimental results showed that the proposed forecasting model yielded significantly higher forecasting accuracy values than other forecasting models.

An econometric analysis of the lead‐lag relationship between India's NSE Nifty and its derivative contracts

PurposeThe purpose of this paper is to examine the lead‐lag relationships between the National Stock Exchange (NSE) Nifty stock market index (in India) and its related futures and options contracts, and also the interrelation between the derivatives markets.Design/methodology/approachThe paper uses serial correlation of return series and autoregressive moving average (ARMA) model for studying the lead‐lag relationship between hourly returns on the NSE Nifty index and its derivatives contracts like futures, call and put options. Further, the lead‐lag relation between hourly returns of the derivatives contracts among themselves is also studied using ARMA models.FindingsThe ARMA analysis shows that the NSE Nifty derivatives markets tend to lead the underlying stock index. The futures market clearly leads the cash market although this lead appears to be eroding slightly over time. Although the options market leads the cash overall, there is some feedback between the two with the underlying index leading at times. Further, it is found that the index call options lead the index futures more strongly than futures lead calls, while the futures lead puts more strongly than the reverse.Practical implicationsThe results imply that the derivative contracts on NSE Nifty lead the underlying cash market. Thus, the derivative markets are indicative of futures price movements and this will certainly be helpful to potential investors to design their risk‐return portfolio while investing in stocks and derivatives contracts.Originality/valueThis paper is an original piece of work towards exploring the lead‐lag relation between NSE Nifty and the derivative contracts. The issue of price discovery on futures and spot markets and the lead‐lag relationship are topics of interest to traders, financial economists, and analysts.

Effect of futures trading on spot‐price volatility: evidence for NSE Nifty using GARCH

PurposeThe paper aims to study the impact of the introduction of Nifty index futures on the volatility of the Indian spot markets by use of econometric models.Design/methodology/approachThe study considered six measures of volatility, the dynamic linear regression model, and the GARCH models to investigate volatility in National Stock Exchange (NSE) Nifty prices both before and after the onset of futures trading.FindingsThe GARCH analysis confirmed no structural change after the introduction of futures trading on Nifty, and found that whilst the pre‐futures sample was integrated, the post‐futures sample was stationary. Spot returns volatility is found to be less important in explaining spot returns after the advent of futures trading in NSE Nifty.Practical implicationsThe results imply that futures markets serve their prescribed role of improving pricing efficiency and improve the quality of information flowing to spot markets. This will enable investors to prudently structure their strategies investing in both spot and futures markets.Originality/valueThis study is an original piece of work towards exploring the impact of the introduction of futures trading on cash market volatility in an emerging economy like India.

An Econometric Study of Lead-Lag Relationship between Futures and Spot Markets in India

The temporal relation between stock index and index futures has been an area of interest to academicians, regulators, and practitioners alike as it gives an idea about the efficiency of the market, its volatility, and arbitrage opportunities, if any. The issue of price discovery on futures and spot markets and the lead-lag relationship are topics of interest to traders, financial economists, and analysts. Although futures and spot markets react to the same information, the major question is which market reacts first. This paper examines the lead-log relationship between futures and spot markets in India. For both available stock index futures contracts, i.e., NSE Nifty and BSE Sensex of National Stock Exchange (NSE) and Bombay Stock Exchange (BSE) respectively, we employ a Bivariate GARCH model to explain price discovery of futures market over the period from July 2000 to March 2007. Empirical results confirm that futures market plays a price discovery role, implying that futures prices contain useful information about spot prices (in line with similar findings in the literature). We evidence a bidirectional lead-lag relationship between spot and futures prices in both the stock indices and this is relatively stronger in NSE Nifty than in BSE Sensex. Futures markets are more informationally efficient than underlying stock markets in India during the period 2001-7. These findings are helpful to financial managers and traders dealing with Indian stock index futures.

An Empirical Verification of Cointegration and Causality in Indian Stock Markets

The study examines the empirical validity of cointegration and causality between the two dominating Indian stock markets: the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). The daily closing values of the BSE Sensex and the NSE Nifty indices are retrieved from the PROWESS online database maintained by the Centre for Monitoring Indian Economy (CMIE). The study covers a period of nine years spanning from 1 January 1997 to 31 December 2005. The analysis reveals the existence of market integration between the BSE and the NSE. This demonstrates information dissemination between these two markets. The study further confirms the role of NSE as a dominating factor over BSE.