scholarly journals RISK-RETURN TRADEOFFS IN INDIAN STOCK MARKET: EVIDENCE FROM GARCH MODEL APPROACH

2021 ◽  
pp. 556-566
Author(s):  
Riteshbhai Patel
Keyword(s):  
High Risk ◽  
Stock Market ◽  
Garch Model ◽  
Sample Period ◽  
Asymmetric Volatility ◽  
Empirical Results ◽  
Daily Return ◽  
Risk Return ◽  
Indian Stock Market ◽  
Model Approach

The objective is to examine the risk-return tradeoff in the Indian stock market. The sample period of study is from January 4, 2000 to December 31, 2020. The empirical results shows existence of risk-return tradeoff in the BSE. A positive risk-return tradeoff is found for monthly & annual return series. The market has weak risk-return relationship in daily return series. The CGARCH (1,1) captures the asymmetric volatility effect for all the different frequency based returns. The study has implications for the investors. The riskreturn relationship is stronger and significant in longer duration of investment. The market gives higher return when there is a high risk.

Modeling S&P Bombay Stock Exchange BANKEX Index Volatility Patterns Using GARCH Model

2019 ◽  
pp. 259-268
Keyword(s):  
Time Series ◽  
Banking Sector ◽  
Stock Exchange ◽  
Time Lag ◽  
Garch Model ◽  
Recent Past ◽  
Volatility Clustering ◽  
Asymmetric Volatility ◽  
Empirical Results ◽  
Asymmetric Garch

The main objective of this chapter is to estimate volatility patterns in the case of S&P Bombay Stock Exchange (BSE) BANKEX index in India. In recent past, the Indian banking sector was one of the fastest-growing industries and all major banks have been included in S&P BANKEX index as index benchmark constituent companies. The financial econometric framework is based on asymmetric GARCH (1, 1) model which is performed in order to capture asymmetric volatility clustering and leptokurtosis. Data time lag is considered from the first transaction day of January 2002 to last transaction day of June 2014. The empirical results revealed the existence of volatility shocks in the selected time series and also volatility clustering. The volatility impact has generated highly positive clockwise and impacted actual stocks. Moreover, the empirical findings reveal that the BANKEX index grown over 17 times in 12 years and volatility returns have been found present in listed stocks.

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

2019 ◽  
Vol 18 (2_suppl) ◽  
pp. S183-S212 ◽  
Author(s):  
Suparna Nandy (Pal) ◽  
Arup Kr. Chattopadhyay
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Volatility Spillover ◽  
Exchange Market ◽  
Causality Test ◽  
Asymmetric Volatility ◽  
Indian Stock Market ◽  
Domestic Stock

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

Jump Risk and Heteroscedasticity of KOSPI200 Intra-day Returns

2015 ◽  
Vol 23 (2) ◽  
pp. 243-264
Author(s):  
Min-Goo Hong ◽  
Kook-Hyun Chang
Keyword(s):  
Stock Market ◽  
Estimation Result ◽  
Sample Period ◽  
Jump Risk ◽  
Daily Return ◽  
Korean Stock Market ◽  
To Come ◽  
Daily Returns

This study examines whether KOSPI200 intra-day return has jump risk and heteroscedasticity and we compare the estimation result of intra-day return and that of daily return. The sample covers from January 2, 2004 to July 31, 2014. We use 30-minute intervals for measuring KOSPI200 intra-day return. It seems this study finds the importance of the consideration of the intra-day data in Korean Stock Market. While some of the parameters of the daily returns for the jump are not significant, but those of intra-day returns are significant over the sample period. Also, the intra-day volatility has shown U-shaped or reverse J-shaped curve. In particular the pattern of intra-day volatility seems to come from the jump risk, which is interpreted as the information inflow in the market.

scholarly journals Asymmetric Volatility in the Nepalese Stock Market

2021 ◽  
Vol 22 (1) ◽  
pp. 41-59
Author(s):  
Dinesh Gajurel
Keyword(s):  
Stock Market ◽  
Spillover Effect ◽  
Equity Markets ◽  
Spillover Effects ◽  
Stock Market Index ◽  
International Equity Markets ◽  
Asymmetric Volatility ◽  
Market Index ◽  
International Equity ◽  
Indian Stock Market

This paper investigates the asymmetric volatility behavior of the Nepalese stock market including spillover effects from the US and Indian equity markets. I modeled asymmetric volatility within a generalized autoregressive conditional heteroskdasticy framework using comprehensive data for the Nepal stock market index. The results reveal a very different asymmetry compared to the results in other international equity markets: positive shocks increase volatility by more than negative shocks. The results further suggest that uninformed investors play a significant role in the Nepalese stock market. The spillover effect from the Indian stock market to the Nepalese stock market is negative. Overall, I conclude that a “fear of missing out” (FOMO) of noise traders as well as the deployment of pump and dump schemes are inherent features of the Nepalese stock market. The findings are very useful to policy makers and investors alike.

scholarly journals Impact of index derivatives on Indian stock market volatility-an application of arch and GARCH model

2008 ◽  
Vol 6 (3) ◽  
pp. 39-44
Author(s):  
S. V. Ramana Rao ◽  
Naliniprava Tripathy
Keyword(s):  
Stock Market ◽  
Structural Changes ◽  
Garch Model ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Index Futures ◽  
Market Factors ◽  
Indian Stock Market ◽  
The Impact ◽  
Derivative Products

The present study examined the impact of introduction of index futures derivative and index option derivative on Indian stock market by using ARCH and GARCH model to capture the time varying nature of volatility presence in the data period from October 1995 to July 2006. The results reported that the introduction of index futures and index options on the Nifty has produced no structural changes in the conditional volatility of Nifty but however the market efficiency has been improved after the introduction of the derivative products. The study concludes that financial derivative products are not responsible for increase or decrease in spot market volatility, but there could be other market factors which influenced the market volatility

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