scholarly journals Foreign Institutional Investment and Stock Market Volatility in India: An Empirical Analysis

1970 ◽  
Vol 3 (4) ◽  
pp. 295-304
Author(s):  
Sharanjit S. Dhillon ◽  
Manjinder Kaur
Keyword(s):  
Stock Market ◽  
Risk Premium ◽  
Stock Exchanges ◽  
Stock Market Volatility ◽  
Institutional Investment ◽  
Daily Data ◽  
Increased Risk ◽  
Depository Receipts ◽  
Current Price ◽  
Indian Stock Market

The two major capital market reforms of (i) entry of Foreign Institutional Investors (FIIs) in Indian stock market (ii) permission to Indian companies for raising capital from foreign stock exchanges by means of American Depository Receipts (ADRs) / Global Depository Receipts (GDRs). Further introduction of two-way fungibility in these instruments of ADRs / GDRs leads to reduction of the sovereignty of Indian stock market. As such, Indian stock market now, is not only sensitive to national events but also more sensitive to international events. Due to the speculative motive of FIIs investment, investment by FIIs is subject to frequent reversals. Volatility is a measure of how far the current price of an asset deviates from its average past prices. Investors demand higher risk premium as a compensation for increased risk due to volatility. A higher risk premium implies higher cost of capital and thus lowers investment. The prevailing inefficiency in emerging securities markets including India further magnifies the problem of volatility.  In this paper, an effort is made to predict stock return volatility and contribution of FIIs investment to that volatility using high frequency data (daily data).

scholarly journals Relationship Between Exchange Rate and Stock Market Volatilities in India

2021 ◽  
Vol 2 (4) ◽  
pp. 244-259
Author(s):  
Lakshmanasamy T.
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Stock Returns ◽  
Exchange Rate Volatility ◽  
Stock Exchanges ◽  
Stock Market Volatility ◽  
Daily Data ◽  
The Us ◽  
Us Dollar ◽  
The Exchange Rate

With increasing globalisation and integration of national stock exchanges, for the global investor, the portfolio risk increases not only from the local stock market volatility but also in the exchange rate risk. This paper examines the exchange rate volatility effect on volatility in stock market return from India’s perspective for the period January 2010 to December 2015, applying ARCH and GARCH estimation. The daily data of the BSE SENSEX returns, exchange rates of US dollar/rupee, British pound/rupee, Euros/rupee are used. It is estimated that the Euro/rupee exchange rate volatility has a significant positive effect on the BSE SENSEX return volatility, while the effect of the US dollar/rupee and British pound/rupee exchange rate the volatilities are insignificantly negative. The larger GARCH parameter over the ARCH term indicates that the own lagged values of the stock return cause more volatility in stock returns than the innovations. There exists a highly persistent effect of shocks to the BSE SENSEX return and the volatility effect wanes only slowly

scholarly journals Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1212
Author(s):  
Pierdomenico Duttilo ◽  
Stefano Antonio Gattone ◽  
Tonio Di Di Battista
Keyword(s):  
Stock Market ◽  
Euro Area ◽  
Risk Premium ◽  
Stock Markets ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Volatility Modeling ◽  
The Impact ◽  
Second Wave ◽  
Daily Returns

Volatility is the most widespread measure of risk. Volatility modeling allows investors to capture potential losses and investment opportunities. This work aims to examine the impact of the two waves of COVID-19 infections on the return and volatility of the stock market indices of the euro area countries. The study also focuses on other important aspects such as time-varying risk premium and leverage effect. This investigation employed the Threshold GARCH(1,1)-in-Mean model with exogenous dummy variables. Daily returns of the euro area stock markets indices from 4th January 2016 to 31st December 2020 has been used for the analysis. The results reveal that euro area stock markets respond differently to the COVID-19 pandemic. Specifically, the first wave of COVID-19 infections had a notable impact on stock market volatility of euro area countries with middle-large financial centres while the second wave had a significant impact only on stock market volatility of Belgium.

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

2021 ◽  
pp. 097226292199098
Author(s):  
Vaibhav Aggarwal ◽  
Adesh Doifode ◽  
Mrityunjay Kumar Tiwary
Keyword(s):  
Stock Market ◽  
Mutual Fund ◽  
Stock Markets ◽  
Large Scale ◽  
Structural Changes ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Equity Investments ◽  
Net Equity ◽  
Indian Stock Market

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.

Stock market predictability

2014 ◽  
Vol 31 (4) ◽  
pp. 354-370 ◽  
Author(s):  
Silvio John Camilleri ◽  
Christopher J. Green
Keyword(s):  
Stock Market ◽  
High Frequency ◽  
Serial Correlation ◽  
Design Methodology ◽  
High Frequency Data ◽  
Frequency Data ◽  
Content Type ◽  
Lag Effects ◽  
Daily Data ◽  
Indian Stock Market

Purpose – The main objective of this study is to obtain new empirical evidence on non-synchronous trading effects through modelling the predictability of market indices. Design/methodology/approach – The authors test for lead-lag effects between the Indian Nifty and Nifty Junior indices using Pesaran–Timmermann tests and Granger-Causality. Then, a simple test on overnight returns is proposed to infer whether the observed predictability is mainly attributable to non-synchronous trading or some form of inefficiency. Findings – The evidence suggests that non-synchronous trading is a better explanation for the observed predictability in the Indian Stock Market. Research limitations/implications – The indication that non-synchronous trading effects become more pronounced in high-frequency data suggests that prior studies using daily data may underestimate the impacts of non-synchronicity. Originality/value – The originality of the paper rests on various important contributions: overnight returns is looked at to infer whether predictability is more attributable to non-synchronous trading or to some form of inefficiency; the impacts of non-synchronicity are investigated in terms of lead-lag effects rather than serial correlation; and high-frequency data is used which gauges the impacts of non-synchronicity during less active parts of the trading day.

Volatility Spillover Between Indian and MINT Stock Exchanges: Portfolio Diversification Implication

2019 ◽  
Vol 67 (3-4) ◽  
pp. 299-311
Author(s):  
Miklesh Prasad Yadav ◽  
Asheesh Pandey
Keyword(s):  
Stock Market ◽  
Spillover Effect ◽  
Stock Exchanges ◽  
Volatility Spillover ◽  
Conditional Correlation ◽  
Causality Test ◽  
Portfolio Managers ◽  
Vector Auto Regression ◽  
Indian Stock Market ◽  
Auto Regression

We examine the spillover effect from the Indian stock market to Mexico, Indonesia, Nigeria and Turkey (MINT) stock markets in order to check if suitable diversification opportunities are available to global portfolio managers investing in India. We apply Granger causality test, vector auto-regression (VAR) and dynamic conditional correlation (DCC)–MGARCH to investigate the level of integration between India and MINT economies. We observe bidirectional causality between India and Nigeria, unidirectional causality in Mexico and Indonesia, while no causality is found between India and Turkey. Our VAR results suggest that none of the MINT economies impact the return of the Indian stock market; rather returns of the Indian stock market are more affected by their own lagged values. Finally, by applying DCC–MGARCH, we observe that there is no volatility spillover from India to any of the MINT economies. We recommend that portfolio managers investing in the Indian economy may explore MINT economies as possible destinations to diversify their risk. Our study has implications for both academia and portfolio managers.

Forecasting the Time Volatility of Emerging Asian Stock Market Index

2007 ◽  
Vol 3 (2) ◽  
pp. 38-51 ◽  
Author(s):  
M. Selvam ◽  
M. Raja ◽  
P. Yazh Mozhi
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Market Volatility ◽  
Stock Market Index ◽  
Market Index ◽  
Asian Market ◽  
Current Price ◽  
One Year ◽  
Almost All ◽  
Derivatives Markets

Volatility is the measure of how far the current price of an asset deviates from its average past prices. Greater the deviation, greater the volatility. It indicates the strength or conviction behind a price movement. Stock market volatility is the function of the arrival of positive and negative market information. Pricing of securities is supposed to be dependent on the volatility of each asset. Matured / developed markets continue to provide over long period of time high returns with low volatility. Emerging markets, except India and China exhibit low returns. The exponential growth in the Asian derivatives markets necessitated the need to test whether the Asian market indices are more volatile or not. The study finds an evidence of time varying volatility, which exhibits clustering, high persistence and predictability for almost all the Asian market indices in the sample. With this background the present paper investigates the dynamic behavior of stock returns of ten market indices from Asian countries, using symmetric GARCH (1,1) model for a period of one year from January 2006 to December 2006.

FX Volatility Impact on Indian Stock Market: An Empirical Investigation

2017 ◽  
Vol 21 (3) ◽  
pp. 284-294 ◽  
Author(s):  
Aravind M.
Keyword(s):  
Stock Market ◽  
Causal Relation ◽  
Research Work ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Indian Rupee ◽  
Currency Market ◽  
Foreign Currencies ◽  
Us Dollar ◽  
Indian Stock Market

Examining the interrelationship between currency market volatility and stock market volatility will create abundant trading opportunities to the investors irrespective of whether the return of one market is moving up or down. This research work intended to examine how the exchange rate volatility between Indian rupee and foreign currencies, such as US dollar, euro, Japanese yen and British pound, can influence the return and volatility of the Indian stock market. The research data extensively cover daily price observations of foreign currencies as well as Nifty index for 1500 days. The generalized autoregressive conditional heteroskedasticity (GARCH) is used for modelling foreign exchange (FX) rates volatility and its impact across Indian stock market. The mean equation of the model confirms that any appreciation in Indian rupee will lead to channelization of more funds towards stock market. Further, it is validated that the volatility shocks between the stock market and currency market are quite persistent. Besides the model also points that the volatility attributes are very strong between US dollar and Nifty. The Granger causality test wrap up with a finding that the volatility shocks of British pound have a causal relation with Nifty return. The result of this study will help the domestic as well as foreign investors in favour of portfolio diversification decisions. The study also spots that the policymakers can indirectly intervene into stock market through monitory policy measures.

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