A Study on Unfolding Volatility and Leverage Effect in Indian Stock Market

GIS Business ◽  
1970 ◽  
Vol 13 (2) ◽  
pp. 7-14
Author(s):  
Shabarisha Narayan, ◽  
Madegowda J.
Keyword(s):  
Stock Market ◽  
Random Variable ◽  
Leverage Effect ◽  
Symmetric Model ◽  
Volatility Forecasting ◽  
Volatility Clustering ◽  
Indian Stock Market ◽  
Forecasting Volatility

Return is the major attribute of an investment asset that can be considered as a random variable. The variability in return can be expressed as volatility. Forecasting volatility and modelling are the most prolific areas for the research. Volatility and Leverage effect are the two crucial stipulations to study market contradictions and trends that prevail for a drawn-out period. It is observed that when volatility beams the markets soar and when markets roar the volatility fades away. Leverage has a larger scope in managing volatility when investors tend to shuffle their positions. This literature aims to identify the volatility clustering and leverage effect caused to NSE NIFTY 50 index. The study contrasts volatility clustering using symmetric model of i.e., GARCH (1,1). Leverage effects is studied and compared using TGARCH and EGARCH models.

Volatility in the Indian Stock Market: Evidence from S&P CNX 500

2020 ◽  
Vol 17 (4) ◽  
pp. 1826-1830
Author(s):  
V. Shanthaamani ◽  
V. B. Usha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Conditional Variance ◽  
Leverage Effect ◽  
Volatility Clustering ◽  
Expected Stock Returns ◽  
High Volatility ◽  
Indian Stock Market ◽  
Daily Returns

This paper uses the Generalized Autoregressive Conditional Heteroskedastic models to estimate volatility (conditional variance) in the daily returns of the S&P CNX 500 index over the period from April 2007 to March 2018. The models include both symmetric and asymmetric models that capture the most common stylized facts about index returns such as volatility clustering and leverage effect. The empirical results show that the conditional variance process is highly persistent and provide evidence on the existence of risk premium for the S&P CNX 500 index return series which support the positive correlation hypothesis between volatility and the expected stock returns. Our findings also show that the asymmetric models provide better fit than the symmetric models, which confirms the presence of leverage effect. These results, in general, explain that high volatility of index return series is present in Indian stock market over the sample period.

scholarly journals Volatility Analysis of Nepalese Stock Market

2009 ◽  
Vol 5 (1) ◽  
pp. 76-84 ◽  
Author(s):  
Surya Bahadur G.C.
Keyword(s):  
Stock Market ◽  
Conditional Heteroskedasticity ◽  
Time Varying ◽  
Stock Market Volatility ◽  
Volatility Clustering ◽  
Business Studies ◽  
Volatility Models ◽  
Modeling And Forecasting ◽  
Volatility Analysis ◽  
Forecasting Volatility

Modeling and forecasting volatility of capital markets has been important area of inquiry and research in financial economics with the recognition of time-varying volatility, volatility clusturing, and asymmetric response of volatility to market movements. Given the anticipated growth of the Nepalese stock market and increasing interest of investors towards investment in Nepalese stock market, it is important to understand the pattern of stock market volatility. In the paper, the volatility of the Nepalese stock market is modeled using daily return series consisting of 1297 observations from July 2003 to Feb 2009 and different classes of estimators and volatility models. The results indicate that the most appropriate model for volatility modeling in Nepalese market, where no significant asymmetry in the conditional volatility of returns was captured, is GARCH(1,1). The study revealed strong evidence of time-varying volatility, a tendency of the periods of high and low volatility to cluster and a high persistence and predictability of volatility in the Nepalese stock market.Key words: Conditional heteroskedasticity, ARCH, GARCH, volatility clustering, leverage effect, Nepalese Stock MarketThe Journal of Nepalese Business Studies Vol. V, No. 1, 2008, December Page: 76-84

Calendar anomaly: unique evidence from the Indian stock market

2018 ◽  
Vol 15 (1) ◽  
pp. 87-108
Author(s):  
Harshita Harshita ◽  
Shveta Singh ◽  
Surendra S. Yadav
Keyword(s):  
Stock Market ◽  
Autocorrelation Function ◽  
Abnormal Returns ◽  
Content Type ◽  
Volatility Clustering ◽  
Year Effect ◽  
Augmented Dickey Fuller ◽  
Short Term Investment ◽  
Investment Portfolios ◽  
Indian Stock Market

Purpose The purpose of this paper is to ascertain the monthly seasonality in the Indian stock market after taking into consideration the market features of leptokurtosis, volatility clustering and the leverage effect. Design/methodology/approach Augmented Dickey-Fuller, Phillips-Perron and Kwaitkowski-Phillips-Schmidt-Shin tests are deployed to check stationarity of the series. Autocorrelation function, partial autocorrelation function and Ljung-Box statistics are employed to check the applicability of volatility models. An exponential generalized auto regressive conditionally heteroskedastic model is deployed to test the seasonality, where the conditional mean equation is a switching model with dummy variables for each month of the year. Findings Though the financial year in India stretches from April to March, the stock market exhibits a November effect (returns in November are the highest). Cultural factors, misattribution bias and liquidity hypothesis seem to explain the phenomenon. Research limitations/implications The paper endeavors to provide a review of possible explanations behind month-of-the-year effect documented in literature in the past four decades. Further, the unique evidence from the Indian stock market supports the argument in the literature that monthly seasonality, by nature, may not be a consistent/robust phenomenon. Therefore, it needs to be examined from time to time. Originality/value As the seasonality in the stock market and resultant anomalies are dynamic phenomena, the paper reports the current seasonality/anomalies prevalent in the Indian market. This would aid investors in designing short-term investment portfolios (based on anomalies present) in order to earn abnormal returns.

Stock Market Asymmetry and Investors’ Sensation on Prime Minister: Indian Evidence

2020 ◽  
Vol 9 (2) ◽  
pp. 148-161
Author(s):  
Subrata Roy
Keyword(s):  
Random Walk ◽  
Market Efficiency ◽  
Prime Minister ◽  
Leverage Effect ◽  
Return Volatility ◽  
Volatility Forecasting ◽  
Volatility Clustering ◽  
Prime Ministers ◽  
National Congress ◽  
Market Asymmetry

This study empirically examines the growth of return, volatility shocks, market efficiency and investors’ sentiment on prime ministers during their administration as a prime minister. Thus, various volatility forecasting measures are applied. It is observed that BSE return does not follow a random walk and inefficient during their tenures as a prime minister. ARCH measure confirms about volatility clustering. According to the EGARCH measure leverage effect does not exist, but the presence of this effect based on TARCH during the tenure of few prime ministers. Finally, the investors are trustful to those prime ministers who are elected from the Indian National Congress according to the growth of return.

scholarly journals Checking presence of excess volatility in forecasting volatility of a set of market indexes through an empirical comparison of three GARCH models

2014 ◽  
Vol 3 (3) ◽  
pp. 385-394
Author(s):  
Islem Ahmed Boutabba
Keyword(s):  
Empirical Study ◽  
Efficient Market Hypothesis ◽  
Garch Models ◽  
Leverage Effect ◽  
Empirical Comparison ◽  
Behavioural Finance ◽  
Volatility Clustering ◽  
Excess Volatility ◽  
Market Indexes ◽  
Forecasting Volatility

Classical financial theory is based on Efficient Market Hypothesis (EMH). Several researchers likeSchiller (1981) (1990), Le Roy and Porter (1980) have extensively argued for the invalidity of EMH. Volatility excess has been detected and highlighted by many researchers; however it has not been explained very well by EMH. For this reason, we conducted an empirical study to identify the variable characteristics of volatility by comparing three GARCH models (GARCH, E-GARCH and GRJ-GARCH) over five different market indexes to examine prediction of returns volatility.This comparison led us to detect several volatility characteristics like volatility clustering and leverage effect. This change in volatility regime is an irrefutable proof of the presence of volatility excess.Given the inability of classical financial theory in explaining volatility excess, researchers started to focus on behavioural finance (Barret and Saphister (1996)).

scholarly journals Checking presence of excess volatility in forecasting volatility of a set of market indexes through an empirical comparison of three GARCH models

2014 ◽  
Vol 4 (2) ◽  
pp. 573-579
Author(s):  
Islem Boutabba
Keyword(s):  
Empirical Study ◽  
Efficient Market Hypothesis ◽  
Garch Models ◽  
Leverage Effect ◽  
Empirical Comparison ◽  
Behavioural Finance ◽  
Volatility Clustering ◽  
Excess Volatility ◽  
Market Indexes ◽  
Forecasting Volatility

Classical financial theory is based on Efficient Market Hypothesis (EMH). Several researchers like Schiller (1981) (1990), Le Roy and Porter (1980) have extensively argued for the invalidity of EMH.Volatility excess has been detected and highlighted by many researchers; however it has not been explained very well by EMH. For this reason, we conducted an empirical study to identify the variable characteristics of volatility by comparing three GARCH models (GARCH, E-GARCH and GRJ-GARCH) over five different market indexes to examine prediction of returns volatility. This comparison led us to detect several volatility characteristics like volatility clustering and leverage effect. This change in volatility regime is an irrefutable proof of the presence of volatility excess.Given the inability of classical financial theory in explaining volatility excess, researchers started to focus on behavioural finance (Barret and Saphister (1996)).

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