Volatility of Indian Stock Market: An Emperical Evidence

2008 ◽  
Vol 4 (4) ◽  
pp. 53-61 ◽  
Author(s):  
Aman Srivastava
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Stock Exchanges ◽  
Garch Models ◽  
Indian Stock Market ◽  
Class Models

The purpose of this paper is to apply the GARCH-class models to two major stock exchanges of Indian stock markets. The study includes main indices of Bombay Stock Exchange (SENSEX) and that of National stock exchange (NIFTY). GARCH-class models have been applied to analyze the characteristics of the volatility of Indian stock market. The findings suggest that both the Indian stock exchanges have significant ARCH effects and it is appropriate to use ARCH/GARCH models to estimate the process and also demonstrated that there are leverage effects in the markets. That means the investors in those markets are not grown well and they will be heavily influenced by information (good or bad) very easily.

scholarly journals Diffusion Entropy Analysis: Stability of Indian Stock Market

2019 ◽  
Vol 8 (4) ◽  
pp. 9358-9362
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Stock Exchange ◽  
Significant Drop ◽  
Bear Market ◽  
Diffusion Entropy ◽  
The Usa ◽  
The Stability ◽  
Indian Stock Market

The large amount of available data of stock markets becomes very beneficial when it is transformed to valuable information. The analysis of this huge data is essential to extract out the useful information. In the present work, we employ the method of diffusion entropy to study time series of different indexes of Indian stock market. We analyze the stability of Nifty50 index of National Stock Exchange (NSE) India and SENSEX index of Bombay Stock Exchange (BSE), India in the vicinity of global financial crisis of 2008. We also apply the technique of diffusion entropy to analyze the stability of Dow Jones Industrial Average (DJIA) index of USA. We compare the results of Indian Stock market with the USA stock market (DJIA index). We conduct an empirical analysis of the stability of Nifty50, Sensex and DJIA indexes. We find significant drop in the value of diffusion entropy of Nifty50, Sensex and DJIA during the period of crisis. Both Indian and USA stock markets show bull market effects in the pre-crisis and post-crisis periods and bear market effect during the period of crisis. Our findings reveal that diffusion entropy technique can replicate the price fluctuations as well as critical events of the stock market.

An Inquiry into the Persistence of Holiday Effect on Stock Markets in India: Insights and Perspectives on a Seasonal Anomaly

2021 ◽  
pp. 231971452110168
Author(s):  
Meher Shiva Tadepalli ◽  
Ravi Kumar Jain ◽  
Bhimaraya Metri
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Abnormal Returns ◽  
Stock Exchanges ◽  
Indian Market ◽  
Pricing Models ◽  
Market Anomalies ◽  
Holiday Effect ◽  
Stock Market Efficiency

Asset pricing is a key area of literature in analysing and evaluating the stock market efficiency. Though various pricing models made efforts to explain the behaviour of the stocks, the existence of seasonal anomalies in the stock markets creates an opportunity for the investors to generate abnormal returns. The present article emphasizes one of such market anomalies namely, the holiday effect using indices belonging to Indian stock exchanges. Thorough research is performed by including all the prime market-capital and sectoral indices of the National Stock Exchange and the Bombay Stock Exchange. The ARIMAX methodology is adopted to observe the anomaly by considering exogenous variables representing the trading days before the exchange-mandated holidays. Further, the strength of the anomaly is analysed with the incorporation of various stock market reforms and observed to be significantly persistent among most of the Indian market indices (including both the sectoral and the market-capital based indices).

scholarly journals Multidimensional Liquidity: Evidences from Indian Stock Market

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Sharad Nath Bhattacharya ◽  
Pramit Sengupta ◽  
Mousumi Bhattacharya ◽  
Basav Roychoudhury
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Markets ◽  
Trading Volume ◽  
Stock Exchange ◽  
Market Liquidity ◽  
Percentage Error ◽  
Efficiency Coefficient ◽  
Learning Tools ◽  
Indian Stock Market

Various dimensions of liquidity including breadth, depth, resiliency, tightness, immediacy are examined using BSE 500 and NIFTY 500 indices from Indian Equity market. Liquidity dynamics of the stock markets were examined using trading volume, trading probability, spread, Market Efficiency coefficient, and turnover rate as they gauge different dimensions of market liquidity. We provide evidences on the order of importance of these liquidity measures in Indian stock market using machine learning tools like Artificial Neural Network (ANN) and Random Forest (RF). Findings reveal that liquidity variables collectively explains the movements of stock markets. Both these machine learning tools performs satisfactorily in terms of mean absolute percentage error. We also evidenced lower level of liquidity in Bombay Stock Exchange (BSE) than National Stock Exchange (NSE) and findings supports the liquidity enhancement program recently initiated by BSE.

The First Balkan War and the St. Petersburg Stock Exchange

2020 ◽  
Vol 2020 (11-1) ◽  
pp. 40-55
Author(s):  
Pavel Lizunov
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
The State ◽  
Stock Exchanges ◽  
The Balkans ◽  
Military Conflicts ◽  
State Of Affairs ◽  
European Stock Markets

The article shows the reaction of European stock markets and, first of all, the St. Petersburg Stock Exchange to the First Balkan War. Stock market reports demonstrated that stock exchanges were sensitive to any troubling economic, political and military conflicts. Their mood changed depending on hostilities, rumors and false reports about the state of affairs in the Balkans.

scholarly journals Volatility Spillover Effect in Indian Stock Market

2017 ◽  
Vol 5 ◽  
pp. 83-101 ◽  
Author(s):  
Surya Bahadur G. C ◽  
Ranjana Kothari ◽  
Rajesh Kumar Thagurathi
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Spillover Effect ◽  
Stock Index ◽  
Global Markets ◽  
Series Data ◽  
Volatility Spillover ◽  
Volatility Transmission ◽  
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

An Inquiry into the Persistence of Turn-of-the-Month Effect on Stock Markets in India: Insights and Perspectives on a Seasonal Anomaly

2021 ◽  
pp. 227853372199471
Author(s):  
Meher Shiva Tadepalli ◽  
Ravi Kumar Jain ◽  
and Bhimaraya A. Metri
Keyword(s):  
Stock Market ◽  
Capital Markets ◽  
Stock Markets ◽  
Stock Exchange ◽  
Academic Research ◽  
Abnormal Returns ◽  
Stock Exchanges ◽  
Specific Class ◽  
Seasonal Behavior ◽  
Global Stock Markets

Asset pricing in capital markets is a strikingly vibrant area of academic research and is considered as an indicator to evaluate the efficiency of stock markets. Though the explanation for the seasonal behavior of capital markets was attempted by various market models, several anomalies were observed historically. Calendar anomalies that belong to the specific class of seasonal anomalies provided abnormal returns in the global stock markets at regular intervals within and across various calendar years. This article documents the study on one such anomaly—namely, the turn-of-the-month effect in the context of Indian stock indices. In this pursuit, exhaustive research has been carried out considering all the broad-market and sectoral indices of two major stock exchanges, namely, National Stock Exchange and the Bombay Stock Exchange. The study used the ARIMAX methodology with dummy exogenous variables (to represent the turn-of-the-month days) and presented comprehensive findings and learnings. Besides, this article attempts to analyze the changes in the strength and significance of the anomaly in progression with various stock market reforms in both the broad-market and sectoral indices to provide new insights into the efficiency of Indian stock market exchanges.

The Effect of Seasonality over Stock Exchanges in India

2017 ◽  
Vol 4 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Ved Prakash Bansal
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Arima Model ◽  
Regression Equation ◽  
Stock Exchanges ◽  
Monthly Return ◽  
Indian Stock Market ◽  
Return Data

This study investigated to examine stock market seasonality effect in Indian stock market for Bombay Stock Exchange (BSE) 100. The monthly return data of BSE 100 for the period from April, 2001 to March, 2016 was used for analysis. After examining the stationarity of the return series and correlogram, regression equation & ARIMA model is used to find the monthly effect in stock returns in India. The results confirmed the existence of seasonality in stock returns in India.

Efficiency of the Indian Stock Market : An Empirical Study

1995 ◽  
Vol 20 (2) ◽  
pp. 43-52 ◽  
Author(s):  
M S Belgaumi
Keyword(s):  
Correlation Analysis ◽  
Stock Market ◽  
Serial Correlation ◽  
Stock Exchange ◽  
Weak Form ◽  
Stock Exchanges ◽  
Independence Assumption ◽  
Share Prices ◽  
Short Period ◽  
Indian Stock Market

Based on an analysis of 70 companies listed in the ‘A’ list category on the Bombay Stock Exchange, this paper by Belgaumi is an attempt to test the weak form efficiency of the Indian stock market. By subjecting the weekly share prices to Serial Correlation Analysis and Runs Test, the author finds that the Indian stock exchanges are efficient in the weak form and that the independence assumption regarding the movements of share prices over short period holds good.

scholarly journals Co-integration of Karachi Stock Exchange with major South Asian Stock Exchanges

2014 ◽  
Vol 4 (1) ◽  
pp. 106
Author(s):  
Salleh Nawaz Khan ◽  
Mohamad Saad Aslam
Keyword(s):  
Stock Market ◽  
South Asia ◽  
South Asian ◽  
Stock Markets ◽  
Stock Exchange ◽  
Stock Exchanges ◽  
Cross Listing ◽  
The World ◽  
The Subject ◽  
China And Japan

International cross  listing have   amplified  the interest of  academics   and  investors  to the subject  of  co movement among  the  stock  markets of  the world . This  study  investigates the co integration of  Pakistan stock exchange (KSE 100 index) with  major stock exchanges of south Asia . The results reveals that there is no co integration  of  Pakistan’s stock  market  (KSE100  index)  with china and  Japan stock markets.  However   there  is co integration of Pakistan’s stock market (KSE 100 index) with the stock market of India, Indonesia, Malaysia and Singapore. 

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