Volatility Of Indian Stock Market Research Articles

Factors Affecting Volatility in Indian Stock Market

Factors Affecting Volatility in Indian Stock Market

Structural change, information asymmetry and volatility in Indian stock market: evidence from pre-and-post-COVID-19 outbreak

Structural change, information asymmetry and volatility in Indian stock market: evidence from pre-and-post-COVID-19 outbreak

The Impact of Directional Global Economic Policy Uncertainty on Indian Stock Market Volatility: New Evidence

The Impact of Directional Global Economic Policy Uncertainty on Indian Stock Market Volatility: New Evidence

The effect of COVID-19 on Indian stock market volatility: can economic package control the uncertainty?

PurposeThe stock market has shown fluctuating degrees of volatility because of the recent COVID-19 pandemic in India. The present research aims to investigate the effect of the COVID-19 on the stock market volatility, and whether the economic package can control the market volatility or not, measured by a set of certain sector-level economic features and factors such as resilience variables.Design/methodology/approachWe examine the correlation matrix, basic volatility model and robustness tests to determine the sector-level economic features and macroeconomic factors helpful in diminishing the volatility rising because of the COVID-19.FindingsThe outcomes of this study are significant as policymakers and financial analysts can apply these economic factors to set policy replies to handle the unexpected fluctuation in the stock market in sequence to circumvent any thinkable future financial crisis.Originality/valueThe originality of the paper is to measure the variables affecting the stock market volatility due to COVID-19, and understand the impact of capital market macroeconomic variables and dummy variables to theoretically explain the COVID-19 impact on stock market volatility.

Open market share repurchase and price volatility in Indian stock market

Open market share repurchase and price volatility in Indian stock market

The outbreak of COVID-19 pandemic and its impact on volatility of Indian stock market: evidence from top ten stocks of Bombay Stock Exchange

The outbreak of COVID-19 pandemic and its impact on volatility of Indian stock market: evidence from top ten stocks of Bombay Stock Exchange

The outbreak of COVID-19 pandemic and its impact on volatility of Indian stock market: evidence from top 10 stocks of Bombay Stock Exchange

The outbreak of COVID-19 pandemic and its impact on volatility of Indian stock market: evidence from top 10 stocks of Bombay Stock Exchange

Dynamics of Indian stock market volatility

The market volatility is one of the most pioneering topics in financial literature. If any potential investor understands the pattern of volatility, the prediction becomes easier and bagging profits becomes convenient. It has been observed that whenever a new piece of information hit the market, investors react in their own respective ways. Given piece of information may or may not be that useful for the prediction purposes. So, this research study is basically focusing on budgetary announcements and their impact on market sentiments and investment behaviour. The present study however, concentrated on volatility clustering before and after the event.

Dynamics of Indian stock market volatility

Dynamics of Indian stock market volatility

Impact of Foreign Fund Flows on Volatile Indian Stock Market

This study examines the impact of foreign funds flows in terms of FDI and FPI on Indian stock market. Foreign fund flows in and out of Indian stock markets are now an ample portion of the market activity because FDI influences the growth directly and FPIs investment affects the growth indirectly by improving equity market performance of the host country. This study is based on yearly time series data for the period from 1992-93 to 2019-20. While analysing the data, descriptive statistics, correlation analysis, ADF unit root test, co integration and Wald test have been used. There is a positive relationship of FDI and FPI with Sensex. The volatility of Sensex is influenced by prior volatility and foreign fund flows. The Johansen co integration test results indicate that all the variables have a long-run relationship of the same order. The Wald test results confirm that there is a significant short-run causal relationship of FDI and FPI with Indian stock market. These findings suggest that volatility of Indian stock market and foreign flows have increased over the period of study.

Do Lower Foreign Flows and Higher Domestic Flows Reduce Indian Equity Market Volatility?

This study examines the relationship that both domestic and foreign institutional net equity flows have with the India stock markets. The motivation behind is the study to examine whether increased net equity investments from domestic institutional investors has reduced the influence of foreign equity flows on the Indian stock market volatility. Our results indicate that only during periods in which domestic equity inflows surpass foreign flows by a significant margin, as seen during 2015–2018, is the Indian stock market volatility not significantly influenced by foreign equity investments. However, during periods of re-emergence of strong foreign net inflows, the Indian market volatility is still being impacted significantly, as has been observed since 2019. Furthermore, we find that both large-scale net buying and net selling by domestic funds increased the stock market volatility as observed during 2015–2018 and COVID-impacted year 2020 respectively. The implications of this study are multi-fold. First, the regulators should discuss with industry bodies before enforcing major structural changes like reconstituting of mutual fund investment mandate in 2017 which forced domestic funds to quickly change portfolio allocation amongst large-cap, mid-cap and small-cap stocks resulting in higher stock market volatility. Second, adequate investor educational and awareness programmes need to be conducted regularly for retail investors to minimize herd behaviour of investing during market rise and heavy redemptions at times of fall. Third, the economic policies should be stable and forward-looking to ensure foreign investors remain attracted to the Indian stock markets at all times.

Critical study of Indian stock market relationship with domestic (DIIs) and foreign institutional investors (FIIs)

This is an investigation to analyse the investment behaviour of various institutional investors and their relationship with volatility of stock market in India. Major participants of stock market, FIIs and DIIs are risk taker for return on investment. Around the world, Indian stock market is one of the favourite avenues for investment for institutional investors. Time series analysis of data for the period of 13 years has been done to check the volatility of Indian stock market, investment behaviour of FIIs & DIIs. Unit Root Test, Correlation, VAR and Granger Causality test has been applied with the help of Eviews. Finding of the study suggested that there is strong relationship and impact of investment behaviour of FIIs and DIIs on volatility of Indian stock market.

Asset Pricing and Volatility of Indian Stock Market

This paper shows a basic market portion model of two sorts of dealers, for example, fundamentalists and chartists under a market situation. It is discovered that the Asset costs, riches elements and market conduct are portrayed by the elements of the basic deterministic framework. We give the hypothetical and exact contentions for a recessed shape for the security advertise line, or a reducing minimal premium for showcase hazard. In capital market balance with basic portfolio limitations, various financial specialists by and large hold various arrangements of theoretical or tricky protections. We show that the volatility depends on covariance of aggregate risk aversion and the stock returns. We found that the heterogeneity increases volatility and thereby it leads in rise of beta. Here the statistical approach is provided to estimate the volatility of the assets included in the portfolio. This paper draws out the connection between the alpha (returns) and beta (hazard). A sober minded examination of securities exchange information affirms the presence of a critical and solid, inward cross-sectional connection between normal return and gauge past market beta. This paper additionally reports a descending slanting of security advertises line, which is more bewildering than the normal and customary "smoothed" SML. We additionally found that the incline of SML turns out to be increasingly "modified" when speculators become overweening with expanded exchanging volume. Here high-beta stocks are the most exchanged stocks and are connected with the least hazard balanced returns and bringing about a way more noteworthy gainfulness of the wagering against-beta. Hence the asset pricing can be made with the help of security market line or characteristic line which best describes the prominent attributes of the securities that are leaping on the market line. The Capital asset pricing model can be perfectly justified with the help of SML.

An Empirical Model for the Indian Foreign Investment and Stock Market Volatility: Evidence From ARDL Bounds Testing Analysis

Foreign investment is a major factor to determine volatility in the stock market. To discover the influence on Stock Market volatility of foreign investment we have considered FE, FD, and FDI as proxy variables of foreign investment and Indian stock market volatility is represented by Indian vix. The period for this study is 2009 to 2017 (monthly data). To address this issue of volatility in the long/short-run we have applied the ARDL. The preference given to the ARDL model over Johansen co-integration is to the difference in the order of integration among the variables. ARDL model allows us to combine the I(0) and I(1) series whereas I(1) required in the case of Johansen approach. Results of unit root confirm the I(0)/I(1) order of integration, which allows us to apply the ADRL bound test. F-statistics is higher than the upper bound critical value at 10%, 5% and providing the evidence of co-integration among variables at a 5% level of significance. Hence, there is a long-run relationship amid the variables. Long-run form results show the negative sign of the coefficient and it is significant. The ECM value is (-0.9671) and it confirms that nearly 96.71 % of the inaccuracy rose in each period and automatically corrected in specified time period.

Volatility Spillover from Institutional Equity Investments to Indian Volatility Index

The interdependence between stock market volatility and institutional flows in both developed and emerging economies have evolved over the past few decades. While earlier studies have mostly considered historical volatility, this study examines the relationship of both foreign and domestic equity flows with the implied volatility in the Indian stock markets measured by the VIX index. The period of the study is January 2010 to July 2019. We find that the value of net FII flows impacts the fluctuations in the volatility of the Indian stock market. However, the past volatility in FII flows does not impact the fluctuation in the volatility of the Indian stock market. Further, in the case of mutual funds, the value of flows does not significantly impact the fluctuation in Indian stock market volatility, but past volatility in net inflows does. This study can help investors to enhance portfolio return.

Volatility spillover from institutional equity investments to Indian volatility index

The interdependence between stock market volatility and institutional flows in both developed and emerging economies have evolved over the past few decades. While earlier studies have mostly considered historical volatility, this study examines the relationship of both foreign and domestic equity flows with the implied volatility in the Indian stock markets measured by the VIX index. The period of the study is January 2010 to July 2019. We find that the value of net FII flows impacts the fluctuations in the volatility of the Indian stock market. However, the past volatility in FII flows does not impact the fluctuation in the volatility of the Indian stock market. Further, in the case of mutual funds, the value of flows does not significantly impact the fluctuation in Indian stock market volatility, but past volatility in net inflows does. This study can help investors to enhance portfolio return.

Sentiment Analysis of Indian Stock Market Volatility

Abstract Traditional empirical models analyze impact of sentiments on financial market volatility using macroeconomic fundamentals or financial indicators. In this paper, recent methods of text based sentiment analysis of market from relevant news articles regarding economy and financial market are used. Two distinct market sentiments namely, positive and negative sentiments are constructed using different emotions which are identified through standard natural language processing methods. Further, the paper aims at proposing an augmented version of asymmetric GARCH model of conditional volatility for Indian stock exchange, Sensex, during the time period of April 19, 2007 to January 10, 2020 by incorporating aforementioned market sentiments. Empirical findings suggest dominant impact of negative market sentiment over positive one and it also provides evidence of noise trading in financially immature Indian stock market.

‘Indian Stock Market Volatility’: A Study of Inter-linkages and Spillover Effects

The article attempts to examine interdependence between Indian stock market and other domestic financial markets, namely, foreign exchange market, bullion market, money market, and also Foreign Institutional Investor (FII) trade and foreign stock markets comprising one regional stock market represented by Nikkei of Japan and other stock market for the rest of the world represented by Standard & Poor’s (S&P) 500 of the USA. Attempts are also made to examine asymmetric volatility spillover, first, between the Indian stock market and other domestic financial markets and second, between the Indian stock market and global stock markets (represented by Nikkei and S&P 500) along with the foreign exchange market. To measure linear interdependence among multiple time series of financial markets multivariate Vector Autoregression (VAR) analysis, Granger causality test, impulse response function and variance decomposition techniques are used. For estima-ting the volatility spillover among the aforesaid markets Dynamic Conditional Correlation-Multivriate-Threshold Autoregressive Condi-tional Heteroscedastic (DCC-MV-TARCH) (1, 1) model is applied on daily data for a quite long period of time from 01 April 1996 to 31 March 2012. The results of multi­variate VAR analysis, Granger causality test, variance decomposition analysis and impulse response function estimation establish significant interdependence between domestic stock market and different other financial markets in India and abroad. The results of DCC-MV-TARCH (1, 1) model estimation further show signi- ficant asymmetric volatility spillover between the domestic stock market and the foreign exchange market and also from the domestic stock market to bullion market and changes in gross volume of FII trade. We also find (a) both way asymmetric volatility spillover between the domestic stock market and the Asian stock market and (b) its unidirectional movement from the world stock market to the domestic stock market. The results of the study may help market regulators in setting regulatory policies considering the inter-linkages and pattern of volatility spillovers across different financial markets. JEL Classification: G15, G17

An analytical study on effect of FIIs & DIIs on Indian stock market

Purpose: The purpose of this research is to examine the effect of FIIs and DIIs activities on volatility of Indian stock market. This study also examined the causal relationship between FIIs and DIIs.Design/Methodology/Approach: This study uses Nifty, Nifty Next 50, BSE Sensex, and BSE 100 to represent Indian stock market. Ganger Causality test is used to see causal relationship between FIIs and DIIs. TGARCH model is used to check volatility of Indian stock market.Practical implication: This will also help investors to make investment decisions, especially investing in these indices as they will be able to forecast effect of recent news and historical volatility of returns.Originality/value: This paper will be useful to investors investing in these four major indices.

Measurement of time varying Volatility and its relation with noise Trading: A Study on Indian Stock Market using Garch Model

Indian stock market has seen so many changes during last two decades. The role of behavioural finance has also emerged in the last few years. Thus, keeping in view the importance of behavioral finance the impact of noise trading has been checked. The present study is conducted to examine the impact of noise trading on volatility of Indian stock market specifically in equity market and has considered Nifty 50, Nifty 100 and Nifty 100 Liquid 15 indices of NSE (National Stock Exchange) for a period of approximately a decade, i.e., January 2007 to December 2017. For the alleged purpose, a total of 2501 daily observations of closing value of banking companies of Nifty 50 have been considered for all empirical tests for the study period. GARCH model has been applied to study the impact of noise trading on banking stocks of Nifty 50. The results indicate that the behaviour is not found to be same for all the companies. These results will be helpful for traders in identifying the stocks which are popular among the noise traders. The price movement in these stocks is not efficient due to the involvement of noise traders.