Modeling Asymmetric Volatility in the Indian Stock Market

Goudarzi, Hojatallah; Ramanarayanan, C. S.

doi:10.5539/ijbm.v6n3p221

Cited by 44 publications

(37 citation statements)

References 16 publications

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“…It is indeed incredible that this one GARCH (1, 1) model can be sufficiently applied in any financial time series in order to comprehend the volatility dynamics (for example, see Chinzara, 2011;Engle, 2004 andElyasiani et al, 2011). Following the strong financial literature (see, for example, Chinzara, 2011;Engle, 2004;Elyasiani et al, 2011 andGoudarzi &Ramanarayanan, 2010), this research study also applied GARCH (1, 1) to estimate various volatility dynamics. More so, it is also evident from Schwarz Information Criterion (SIC) that lag one is the most appropriate lags to capture the volatility dynamics.…”

Section: Garch (1 1)mentioning

confidence: 99%

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

Khan

Rehman

Khan

et al. 2016

Economic Research-Ekonomska Istraživanja

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This study analyses risk-return trade-off and behaviour of various volatility dynamics including: volatility, its persistence, mean reversion and speed of mean reversion along with asymmetry and leverage effect on the Pakistani stock market by employing aggregate (aggregate market level) and disaggregate (sectoral level) monthly data for the period from 1998 to 2012. Three generalised autoregressive conditional heteroscedasticity models were applied: GARCH (1,1) for various volatility dynamics; EGARCH for asymmetric and leverage effect and GARCH-M for pricing of risk. The outcomes of the study are as follows: first, the volatility shocks are quite persistent but with varying degrees across the sectors. Both the ARCH effect (short-term effect) and GARCH effect (long-term effect) play their role in generating conditional future stock returns volatility. Further, overall the volatility process is mean reverting; however, the speed of mean reversion varies across the sectors. Secondly, the current study finds little evidence of asymmetry and leverage effect at both aggregate and disaggregates data. Thirdly, the pricing of risk (positive risk premium) is also evident, particularly at the disaggregate data in the Pakistani stock market. Finally, this research study sets the implications for both the policy makers and investors.

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Section: Garch (1 1)mentioning

confidence: 99%

Pricing of risk and volatility dynamics on an emerging stock market: evidence from both aggregate and disaggregate data

Khan

Rehman

Khan

et al. 2016

Economic Research-Ekonomska Istraživanja

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“…The results of the study suggested that the persistence of volatility in Chinese stock market is more than Indian stock market. Goudarzi and Ramanarayanan (2011) empirically tested the effects of good and bad news on volatility in the Indian stock markets using asymmetric ARCH models during the global financial crisis of 2008-09. The presence of the leverage effect would imply that the negative innovation (news) has a greater impact on volatility than positive innovation news.…”

Section: Literature Reviewmentioning

confidence: 99%

A study of volatility behaviour of S&P BSE BANKEX return in India: A pragmatic approach using GARCH model

Khan¹,

Javed

2017

Int. j. adv. appl. sci

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The purpose of this study is to know that how the National and International market, namely (S&P BSE SENSEX) (NASDAQ) (SSE) (FTSE) can influence the volatility of (S&P BSE BANKEX) return in India and the factors affecting the volatility for the same. However, the previous studies mostly considered the volatility of stock in the Indian capital market. But the present study mainly focuses on the Bankex return volatility. Here the researcher identified and estimated the mean and variance components of the daily Bankex return using Garch (1, 1) model by explaining the volatility structure of the residuals obtained under the best-suited model for the used data series. The method ML -ARCH (Marquardt) -Normal distribution has satisfied the criterion of model selection based on the three assumptions. These Null Hypotheses deals with the problems firstly, no serial correlation, secondly, the presence of no ARCH effect, thirdly, residual are normally distributed. We have chosen daily data period from 03 rd May, 2012 to 08th January 2016, nearly 914 working days of all markets for estimating Arch-Garch model. The study shows the significant result of ARCH and GARCH effect. The Bankex Return is also significantly affected by endogenous variable (SENSEX return). The NASDAQ composite and SSE composite Index are also statistically significant, In sum-up, the foreign market return volatility or outside shock can influence the volatility of BANKEX return. However, the FTSE 100 was not found statistically significant, revealing that volatility in FTSE return cannot transmit to S&P BSE BANKEX return in India.

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“…H Goudarzi et al (2) in an attempt to examine the volatility of in Indian market arrives at the conclusion that GARCH (1, 1) model fits well into a time series of returns of BSE 500. They have established the adequacy of the model by using ARCH LM test and LB Statistic.A Goyal (3) reported in the study on Exchange Rate Volatility using GARCH models found that quantitative credit restriction, higher interest differentials and policy lending rates depreciate the exchange rates probably due to reduced capital inflow.…”

Section: IImentioning

confidence: 99%