Indian Stock Indices Research Articles

Investigating liquidity constraints as a channel of contagion: a regime switching approach

The present study investigates the timing and repercussion of the subprime crisis of 2008–09 in a regime-switching model. The interdependence and co-movement of financial markets in different countries has been enhanced due to the globalization of international trade, and investment trends can spread globally as a result of investors owning international portfolios. This study uses a regime-switching model to illustrate the timing of the crisis regime and calm regime for United States (US) stock index returns and the corresponding impact on Indian stock index returns. The Indian stocks investigated are classified into “remote” and “reachable” stocks, and different effects are found for these two types. It is found that shocks originating in the US can be transferred to the Indian reachable market as a result of foreign investors. There is, however, a less persistent impact on remote stocks. Accordingly, the study contributes to the literature on the material impacts of the crisis resulting from liquidity constraints and fear of contagion among investors.

Scrutinizing Causal Relationship Between Stock Market Development and Economic Growth: Case of India

With the rapid changing trends in financial system it becomes imperative to identify the vitality of financial system and its importance in context of growth of an economy. The present study analysed relationship between the selected indicators of Indian Stock Exchange and index of industrial production by considering the duration 1996-1997 to 2015-2016. The autoregressive distributed lag technique is applied to identify the long-run cointegration and vector error correction technique is used to identify the long-run and short-run relationship between the variables. A detailed analysis indicates that a long-run cointegration relationship do exist between the variables of stock market and economic growth. The study recommends relaxation in some of the stringent requirements for entry into the stock market to ensure that more companies are listed, as industries are the large user of external funds and they grow faster in countries with higher opportunity of growth for stock market sector.

Air Pollution and Stock Returns: Evidence from NSE and BSE of India

Air pollution issue has become an important environmental problem in India. This paper proposes to examine the influence of Delhi Air Pollution on the two Indian stock indices, using Descriptive Statistics, Unit Root, and OLS regression. The analysis of the study found that Delhi Air Pollution did create a statistically significant effect on Nifty. This is the first study of this type to look into the effects of air quality issue on stock market indices in India

Demonetization and Its Effects on BSESENSEX and Some Sectoral Indices: An Exploratory Econometric Analysis

This study has carried out somepreliminary time series analyses to examine the impacts of demonetization which was carried outin India on 8 November 2016, on the well-known Indian stock index, BSESENSEX,and four major sectoral indices viz., BSE BANKEX,BSE Auto, BSE Reality and BSE Smallcap,using daily level time series data covering the period 1January 2016 to 31May2017. Apart from examining the stationarity/nonstationarity property and existence of structural breaks after demonetizationin these series, the paper has also studied the trend behavior and returns models for both the pre- and post-demonetization periods. This study has found that while there is more than one break in all the five series at their level values, there is only one structural break after demonetization. It has also been found that the trend function for all the index series broadly gives support to this finding of one break after demonetization. Further, some changes have also been observed in the stationary models for the two sub-periods of pre- and post-demonetization for all but BSE Auto index. Finally, except for BSESENSEX to some extent, no change in the status of stationarity/nonstationarity in the pre- and post- demonetization periods has been found in the other series.

Macroeconomic surprises and stock market responses—A study on Indian stock market

AbstractThis study analyzes the sensitivity of a series of Indian stock indices for the astonishing component of monetary and macroeconomic policy with the data set from 1 April 2004 to 31 July 201...

Relationship between Changing Oil Prices and Stock Index Movements Evidence from Nifty

Researches at different phases has been conducted to understand the movement of Indian stock indices and the factors which affect them the most. The past literatures have concluded that several micro and macro-economic variables have demonstrated both positive and negative relationship on stock returns. The current study aims to understand the association between the Indian stock market and fluctuating crude oil prices. Several literature have concluded that a country's economic activity influence their demand for crude oil and which in turn is responsible for the volatility in the prices. The objective of the study is to understand the long run and short run association between changing crude prices and the stock market returns. Test for Stationarity of data has been performed. Johansen Cointegration test for long run and Granger Causality test to understand the short run has been used in the study.

Role of Algorithmic and Co-Location Trading on the Speed of Information Adjustments: Evidence from India

ABSTRACTWe investigate whether increased Algorithmic Trading (AT) intensity caused by the introduction of Co-location trading (CLT) facilities improve the productive efficiency of the Indian stock indices. We measure the change in the speed of information adjustment and change of persistence before and after the introduction of CLT for Indian Indices. We report an improvement in the overall productive efficiency of the leading Indian Indices, Midcap and Smallcap indices being the prominent beneficiaries. Our work contributes to the empirical literature on the ongoing debate on the benefits of AT and its role in improving market efficiency, especially in the emerging markets context.

Effects of Market Reforms and External Shocks on Indian Stock Indices: Evidence on Structural Breaks and Weak-Form Efficiency

This study explores stock market efficiency in India after allowing for potential structural changes induced by reforms processes and/or external shocks. The endogenous determination of structural break dates, using mostly Clemente, Montanes, & Reyes (1998) (CMR) methodology, allows us to identify important events in this respect. External shocks such as occasional stock market scams, policy and political regime changes, oil price shocks and the effect of global market meltdowns have caused abrupt or one time changes in the series mean (additive outlier model), while the reforms processes stand out to be the single most important cause for the gradual shifts in the level of stock indices (innovation outlier model). This underlines the importance of institution building and the domestic policy stance in countering external shocks.

Volatility Spillover Effect in Indian Stock Market

The study aims to empirically examine the transmission of volatility from global stock markets to Indian stock market. The study is based on time series data comprising of daily closing stock market indices from National Stock Exchange (NSE), India and major foreign stock exchange of the three countries one each from America, Europe and Asia making the highest portfolio investment in Indian stock market. The study period covers 11 years from 1st January, 2005 to 31st December, 2015 comprising a total of 2731 observations. The Indian stock index used is CNX Nifty 50 and the foreign indices are S & P 500 from USA, FTSE 100 from UK, and Nikkei 225 from Japan. The results reveal that the Indian stock market return is co-integrated with market returns of US, UK and Japanese stock markets. Therefore, the return and hence volatility of Indian stock market is associated with global markets which depicts that it is getting integrated with global financial markets. The results provide empirical evidence for volatility transmission or volatility spillover in the Indian stock market from global markets. There exists inbound volatility transmission from US market to Indian stock market. The Indian and UK stock market have bi-directional volatility transmission. However, there exists presence of only outbound volatility transmission from Indian stock market to Japanese stock market. The volatility transmission from global markets to India is rapid with the spillover effect existing for up to three days only.Janapriya Journal of Interdisciplinary Studies, Vol. 5 (December 2016), page: 83-101

Testing the Weak-Form Market Efficiency in the Indian Stock Market : Evidence from the Bombay Stock Exchange Index (BSE) Sensex

Performance of the stock markets is considered as a very important tool to measure the performance of the economy. In recent years, the Indian stock market has witnessed a tremendous growth in all the facets of trading , that is, number of companies listed, market capitalization, membership, value of trading ,volume of trading per day, and so forth. The Indian benchmark stock index SENSEX by June 2015 had grown massively to over 27,780.83 from 3,658 in January 1998. This unprecedented growth in the Indian stock market raises the interest over the efficiency of the stock market. The present paper tested the weak-form of market efficiency in the Indian stock market by testing the random walk hypothesis in the return series. According to the random walk hypothesis, the stock movements are random and unpredictable. Weighted index of the Bombay Stock Exchange (SENSEX) was examined for the study from 1998 to July 2015 by using daily data and weekly data. A battery of tests were applied on the data , that is, autocorrelation test, unit root test, and variance ratio test. The empirical evidence found from the autocorrelation test conclusively rejected the serial dependency in the series observed, and hence proclaimed the existence of the random walk hypothesis in the Indian stock market. ADF, DF-GLS, PP, and KPSS tests were performed to find the significance of unit root, and the results from the unit root test were consistent with the autocorrelation test. Similar and very strong evidence was found from the results of the variance ratio test also. Reasonable empirical evidence was found to prove the weak-form of market efficiency in the Indian stock market through this paper.

Price discovery in Indian stock index futures market: new evidence based on intraday data

The purpose of this study is to revisit price discovery process in Indian stock market for spot and futures of S&P CNX Nifty, by using high-frequency data to gain fresh insights. The sample consists of high-frequency data for the period from January 2014 to August 2015. Stationarity and cointegration test results reveal that spot and futures prices are I(1) and cointegrated. Three different econometric methodologies – component share method of (Gonzalo and Granger, 1995), information share method of (Hasbrouck, 1995), and modified information share of (Lien and Shrestha, 2009) – have been employed to determine the extent of price discovery contribution by spot and futures markets. The results reveal that futures market is performing its price discovery function. These results support the notion that futures market in more efficient vis-a-vis spot market in India. Price discovery is a main function of futures market and has implications for asset pricing, portfolio allocation, investment strategy formation, and market efficiency. This study might be helpful for regulators and policymakers to form market structure policies and guidelines for equity markets.

Intraday price discovery in Indian stock index futures market: new evidence from neural network approach

Intraday price discovery in Indian stock index futures market: new evidence from neural network approach

Enhanced indexing for risk averse investors using relaxed second order stochastic dominance

The less efficient markets offer scope for enhanced indexing (EI), an investment strategy of portfolio selection which seeks to earn more return than the benchmark index. In this context, we examine the use of relaxed second order stochastic dominance (RSSD) by introducing underachievement and overachievement variables in the second order stochastic dominance (SSD), for EI. We propose a linear optimization model that maximizes the mean return subject to the constraints formed using RSSD. We impose bounds on the ratio of the total underachievement to the sum of total underachievement and total overachievement variables depicting the risk-return tradeoff in the model. The proposed model for EI is inspired from many applications of SSD and almost SSD (ASSD). We examine the performance of the proposed model with the SSD model, EI model of maximizing mean return and minimizing the underperformance (MM) from the benchmark index, \(\epsilon \)-almost second order stochastic dominance (\(\epsilon \)-ASSD) model, and the naive 1/N portfolio, on two Indian stock indices, CNX 100 and CNX 200, through a rolling window strategy. To widen the empirical analysis, we also compare all models on the eight publicly available real financial data sets from Beasley OR library through a single window strategy. The portfolios from the proposed model are shown to produce statistically significant mean excess return and excess Sharpe ratio (both from the benchmark indices) more often than the MM and \(\epsilon \)-ASSD models. Also, the portfolios from the proposed model always have smaller violation area in SSD constraints from benchmark indices than the MM and \(\epsilon \)-ASSD models.

Intraday price discovery in Indian stock index futures market: new evidence from neural network approach

Using neural network approach, this study revisits the price discovery relationship between spot and futures prices of S&P CNX Nifty Index of India. This study uses minute-by-minute data of 167 trading days (from January 2015 to August 2015). Empirical results reveal that there exists a bi-directional lead-lag relationship between spot and futures markets, but the causality from the spot market to the futures market is more dominant as compared to that of running from the futures market to the spot market. Root mean squared error of the results indicates that the incorporation of spot returns in modelling futures returns improves the results by 62.37%. Whereas, the inclusion of futures returns in modelling spot returns improves the model by only 44.60%. Price discovery dynamics has significant implications for market participants, regulators, and exchanges. These findings could be helpful for portfolio management, market structure design, and the implementation of arbitrage and hedging strategies.

Do Global Oil Price Changes Affect Indian Stock Market Returns?

The present research work is conducted to analyze whether changes in oil prices at global level affect the stock market returns in Indian market. Daily closing stock market price data from National Stock Exchange (NSE) and daily oil prices, for the period beginning from January 2010 to December 2014, are taken into consideration. VAR model of co-integration is used to test the relation between the two variables. It is found that there is no long term integration between oil price and Indian stock index series. Apart from long term, these series do not even cause each other in short run. Very weak correlation, about 3.21 percent, was observed in the series that may be due to recent fall in crude oil prices at global level. Individual/ institutional investors, portfolio managers, corporate executives, policy makers and practitioners may draw meaningful conclusions from the findings of the present study while operating in stock markets. Research like this may help diverse stakeholders in management of their existing portfolios as their portfolio management strategies may be, up to some extent, dependent upon such research work.This paper is an attempt to fill the research gap that exists for Indian stock market in terms of its relationship with global oil prices.

Price discovery in Indian stock index futures market: new evidence based on intraday data

This study revisits price discovery process in Indian stock index futures market for spot and futures of S&P CNX Nifty, by using high-frequency one-minute time-interval data for the period from January 2011 to August 2015 to gain fresh insights. Stationarity and cointegration tests reveal that spot and futures prices are I (1) and cointegrated. Three different econometric methodologies - component share method of Gonzalo and Granger (1995), information share method of Hasbrouck (1995), and modified information share of Lien and Shrestha (2009) have been employed to determine the extent of price discovery contribution by spot and futures markets. The results reveal that futures market is performing its price discovery function which satisfies the transaction cost hypothesis. Also, these results support the notion that futures market is more efficient vis-a-vis spot market in India. Price discovery is a main function of futures market and has implications for asset pricing, portfolio allocation, investment strategy formation, and market efficiency. This study might be helpful for regulators and policymakers to form market structure policies and guidelines for equity markets.

Impact of Key Macroeconomic Variables on Indian Stock Index

Impact of Key Macroeconomic Variables on Indian Stock Index

Speed of Information Assimilation in Indian Stock Indices

The waves of economic reforms have resulted in the exponential growth in the international trade and business over the past two decades. As a result, the stock markets are integrated thus capturing the news from the domestic as well as global markets. The speed and efficiency with which the markets incorporate this information into the prices is one of the interesting domains of research in the finance literature. Understanding the behaviour of stock price patterns are of immense use to investors and portfolio managers in asset allocation strategies and diversification. Theobjective ofthisstudyis tomeasurethespeed ofinformation assimilation in twobroad market indices in the Indian equity market viz., Sensex and Nifty over a period of nine years that span from January 2005 to December 2013. The sample is sub-divided into three periods to unfold the speed of information adjustment in pre-crisis period, crisis period and post financial crisis period using the Generalized Auto Regressive Conditional Heteroskedasticity (GARCH) Approach. The study found that the market placed more relevance to existing information than the new information across the economic cycles. Better assimilation of existing information was found in the stock prices. The volatility persistence was found to be very high during the crisis period.

Estimating Long-term Volatility on National Stock Exchange of India

Abstract This paper aims to investigate long-term volatility of National Stock Exchange of India based on Generalize Autoregressive Conditional Heteroskedasticity (GARCH) models. The CNX-100 index is one of diversified Indian stock index which includes 38 sectors of economy. It represents about 81.57% of free float market capitalization of stocks which listed on NSE (National Stock Exchange, India) from ending March, 2014. This paper empirically tests volatility clusters of CNX 100 index using data set from 10.2007 to 07.2014. The empirical analysis is based on stock market returns - daily observations (1698) and provides additional insight regarding volatility patterns. The final results (outcomes) suggested the existence of abnormal minor and major shocks, strong volatility and bullish growth ranges after 2013. CNX - 100 stock market index behavior is found to be more volatile clockwise after 2013, so that market clusters and the influence from different sectors (i.e 38 sectors of economy) creates great opportunity for investors, financial practitioners and academics.

Negative Return-Volume Relationship in Asian Stock Markets: Figarch-Copula Approach

We explore the potential dependence among different Asian stock markets, using several different statistical models. Extreme return-volume dependence in Hong Kong Seng Index, Bombay Stock Exchange, Indonesia Composite Index and Bursa Malaysia has been examined by using FIGARCH-Copula and GARCH-Copula approach. We have used Gaussian, Student-t, Frank, Clayton, Survival Clayton and Gumbel copulas. Based on Akaike information criterion (AIC), we found that using FIGARCH model for return series improves the results of copula parameter estimation. According to our finding, Hong Kong and Indian stock indices showed weak upper tail dependence between return and volume. Further, we have found that the extremely low returns for Malaysia and Indonesia stock indices are followed by high volumes, providing evidence of leverage effect. Our investigation shows that Malaysia and Indonesia stock indices are sensitive to bad news rather than good news.