Modelling time-varying volatility in the Indian stock returns: Some empirical evidence

Tripathy, Trilochan; Gil-Alana, Luís A.

doi:10.1016/j.rdf.2015.04.002

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…Other, marginal differences, emerge with respect to the distributional assumptions: GED and normal distributions now perform better than the t -distribution. Note that consistently with previous findings from Tripathy and Jil-Alana (2015), GED is always selected as the most appropriate distribution for India.…”

Section: Resultssupporting

confidence: 82%

“…They also provide evidence that the use of non-normal error distributions improves the forecasting performance of the models. Along similar lines, Tripathy and Gil-Alana (2015) look at forecasting models for the Indian National Stock Exchange and conclude that models with generalized error distribution (GED) provide superior fit, but their out-of-sample forecast performance is unsatisfactory. Sharma and Vipul (2015) further show that identification of the best performing model within a group of competing models is sensitive to the choice of the loss criterion used to compare performance.…”

Section: The Analysis Of Return Volatility: Methods and Previous Resultsmentioning

confidence: 96%

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Predictability of Return Volatility Across Different Emerging Capital Markets: Evidence from Asia

Vidanage

Carmignani

Singh

2017

South Asian Journal of Macroeconomics and Public Finance

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The importance of return volatility forecasts in policy formation and investment decision-making in emerging countries is growing considerably. However, from an operational perspective, there is no consensus in the literature on which econometric model has the best forecasting performance. To shed new light on this issue, this article compares forecasting models for a selected group of emerging Asian economies: India, Malaysia, Pakistan, Sri Lanka, Singapore and Thailand. Model’s performance is tested using both in-sample and out-of-sample forecasting methods. It is found that a relatively simple asymmetric EGARCH model clearly outperforms other models. JEL Classification: G12, G17

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Section: Resultssupporting

confidence: 82%

Section: The Analysis Of Return Volatility: Methods and Previous Resultsmentioning

confidence: 96%

Predictability of Return Volatility Across Different Emerging Capital Markets: Evidence from Asia

Vidanage

Carmignani

Singh

2017

South Asian Journal of Macroeconomics and Public Finance

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show abstract

“…The results show that shocks to the Ghana equity market are usually transient with minimal instances of persistence and confirm that EGARCH (1,1) is superior in modelling the volatility of returns on the equity market for the study period. In modelling time varying volatility in the context of Indian stock markets using the symmetric and asymmetric GARCH models, Tripathy and Gil-Alana (2015) indicate that the volatility of stock returns is persistent and asymmetric. The study reveals that the model under generalised error distribution appears to be the most suitable one.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamics of Time Varying Volatility in Stock Returns: Evidence from Nepal Stock Exchange

Rana

2020

J. Bus. Soc. Sci. Res.

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This study examines the properties of time varying volatility of daily stock returns in Nepal over the period 2011-2020 using 2059 observations on daily returns of NEPSE index series. The study examines various symmetric and asymmetric GARCH family models using several specifications of error distribution. The results of symmetric GARCH (1,1) and GARCH-M (1, 1) models indicate that there is volatility persistence in daily returns on composite NEPSE index series over the sampled period. However, the estimated results for GARCH-M (1, 1) models show that the stock returns in Nepal offer no significant risk premium to hedge against risk associated with investment in stocks. The study also demonstrates that asymmetric TGARCH (1, 1) and EGARCH (1, 1) models fail to capture the leverage effects on the volatility. Finally, study results show that GARCH (1, 1) with student’s t error distribution model is the best fitted one to capture the volatility persistence of daily returns on NEPSE index series over the sampled period. The findings from this study offers an additional insight in understanding the volatility pattern of daily stock returns in Nepal for the most recent period that helps investors in forming a sound strategy to address the risk pattern of investing in stock market of Nepal.

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“…A comprehensive review of the applications of these models is documented in the literature [e.g., Poon and Granger (2003) and Tripathy and Gil-Alana (2010)]. Overall, despite new developments in volatility modeling, the usefulness of ARCH-type models are notable as they continue to dominate stock market studies until recently [e.g., Guo and Neely (2008), Lee et al (2001), Sanyal et al (2016), Sharma and Vipul (2016), Srinivasan (2011), Tripathy and Gil-Alana (2015), and Zhou and Zhou (2005)]. In this paper, we apply a combination of three models, which are designated for examining information asymmetry: threshold GARCH (TARCH), exponential GARCH (EGARCH), and component GARCH (CGARCH).…”

Section: Methodsmentioning

confidence: 99%

The impact of MILA market-maker facility on volatility of the Colombian stock market

Lukanima

Gómez-Bravo

Sanchez-Barrios

2021

Rev. contab. finanç.

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In 2014, the Colombian Stock Exchange commenced the implementation of its market-maker facility (MMF), aiming at improving market efficiency. This paper examines the impact of the MMF program on three volatility-related aspects: volatility persistence, risk premium, and information asymmetry. This paper provides new insights about the anticipated outcomes of Mercados Integrados Latinoamericanos (MILA) reforms, specifically the MMF on the volatility of the Colombian stock market. This topic has not been fully addressed in the existing literature. This study, therefore, provides useful information for regulators and policy makers, in endeavors to address key issues raised during the World Economic Forum (WEF) of 2016. This paper poses a challenge to policy makers and stock market regulators in Colombia to revisit the reform program and address the factors limiting the effectiveness of market reforms. This paper provides justification for replicating the study to cover other MILA countries due to existing differences in some domestic market policies and structures. The paper employs conditional variance models for measuring volatility persistence, risk premia, and information asymmetry. The models are employed on the COLCAP stock index (Colombia) observed from January 17, 2008 to May 30, 2019. Observations are divided into two samples - pre- and post-MMF. This paper provides evidence of the impacts of the MMF reforms in the Colombian stock market. Specifically, the MMF seems to have an impact on the following aspects: (i) the magnitude in which current returns depend on previous returns has increased; (ii) investment portfolio returns, which are generally low, have declined after the MMF, leading to less risk compensation; (iii) the MMF does not seem to have affected the volatility of market returns and information asymmetry; (iv) volatility persistence increased in magnitude.

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Modelling time-varying volatility in the Indian stock returns: Some empirical evidence

Cited by 12 publications

References 20 publications

Predictability of Return Volatility Across Different Emerging Capital Markets: Evidence from Asia

Predictability of Return Volatility Across Different Emerging Capital Markets: Evidence from Asia

Dynamics of Time Varying Volatility in Stock Returns: Evidence from Nepal Stock Exchange

The impact of MILA market-maker facility on volatility of the Colombian stock market

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