Impulsive Clustering and Leverage Effect of Emerging Stock Market with Special Reference to Brazil, India, Indonesia, and Pakistan

Kumar, Arya; Biswal, Saroj Kanta

doi:10.5373/jardcs/v11sp11/20192925

Cited by 9 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Findings revealed that there was no evidence to support the notion that GARCH is inferior to other models, and the models that performed better were the ones that accommodated a leverage effect. This argument is consistent with Kumar and Biswal (2019). This view is also mirrored in the work of Kilai et al (2018), who acknowledge the shortcomings of GARCH-normal because it underestimates risk.…”

Section: Suggested Alternatives For Non-normal Distributions-unconditional Distributionssupporting

confidence: 68%

“…The presence of the volatility in the stock price makes it possible to earn abnormal profits by risk seeking investors. Kumar and Biswal (2019) employed GARCH econometric models in their study, and the results confirmed the presence of volatility clustering and advantageous effects that affect the future of stock markets. When using GARCH family models to analyse and forecast return volatility, the selection of input variables for forecasting is paramount, as essential conditions will be given for the method to have a stationary solution and perfect matching (Nelson 1990).…”

Section: Literature Reviewmentioning

confidence: 71%

See 1 more Smart Citation

Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Samunderu

Murahwa²

2021

JRFM

View full text Add to dashboard Cite

Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution assumptions alone in measuring risk. Cushioning against risk has always created a plethora of complexities and challenges; hence, this paper attempts to analyse statistical properties of various risk measures in a not normal distribution and provide a financial blueprint on how to manage risk. It is assumed that using old assumptions of normality alone in a distribution is not as accurate, which has led to the use of models that do not give accurate risk measures. Our empirical design of study firstly examined an overview of the use of returns in measuring risk and an assessment of the current financial environment. As an alternative to conventional measures, our paper employs a mosaic of risk techniques in order to ascertain the fact that there is no one universal risk measure. The next step involved looking at the current risk proxy measures adopted, such as the Gaussian-based, value at risk (VaR) measure. Furthermore, the authors analysed multiple alternative approaches that do not take into account the normality assumption, such as other variations of VaR, as well as econometric models that can be used in risk measurement and forecasting. Value at risk (VaR) is a widely used measure of financial risk, which provides a way of quantifying and managing the risk of a portfolio. Arguably, VaR represents the most important tool for evaluating market risk as one of the several threats to the global financial system. Upon carrying out an extensive literature review, a data set was applied which was composed of three main asset classes: bonds, equities and hedge funds. The first part was to determine to what extent returns are not normally distributed. After testing the hypothesis, it was found that the majority of returns are not normally distributed but instead exhibit skewness and kurtosis greater or less than three. The study then applied various VaR methods to measure risk in order to determine the most efficient ones. Different timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset returns was higher. The other steps that followed examined the relationship of the variables, correlation tests and time series analysis conducted and led to the forecasting of the returns. It was noted that these methods could not be used in isolation. We adopted the use of a mosaic of all the methods from the VaR measures, which included studying the behaviour and relation of assets with each other. Furthermore, we also examined the environment as a whole, then applied forecasting models to accurately value returns; this gave a much more accurate and relevant risk measure as compared to the initial assumption of normality.

show abstract

Section: Suggested Alternatives For Non-normal Distributions-unconditional Distributionssupporting

confidence: 68%

Section: Literature Reviewmentioning

confidence: 71%

Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Samunderu

Murahwa²

2021

JRFM

View full text Add to dashboard Cite

show abstract

“…In the study of Amudha and Muthukamu (2018) GARCH family models were used and the researchers confirm the evidence of volatility and leverage effect in the selected stock market. Kumar and Biswal (2019) used the GARCH family model to test the volatility of the select countries' stock prices and confirm the existence of volatility and leverage effect in the select stock market indices.…”

Section: Literature Reviewmentioning

confidence: 99%

An assessment of volatility and leverage effect before and during the period of Covid-19: a study of selected international stock markets

Bhunia

Ganguly²

2020

IJFSM

View full text Add to dashboard Cite

show abstract

“…A series of analyses were conducted by researchers from emerging countries like Kumar and Mishra (2019b), Floros (2008) in Nigeria, Moustafa Abd et al (2011, Angabini andWasiuzzaman (2011) in Malaysia, Ezzat Hassan (2012) of Egypt, Su (2010) in China, Emenike (2010) andFreddie et al (2012) of Saudi Arabia who made a comparison of various models from the GARCH and ARCH family and concluded that GARCH, GJR GARCH, and EGARCH are fitting for the clustering effect, leptokurtosis volatility measurements, and identifying leverage effect. Kumar and Biswal (2019) attempted a study to measure the volatility clustering and leverage effect of top future stock markets from Jan 1st, 2014, to Oct 31st, 2018, by implementing the GARCH family model. The study confirms that EGACRH can be accepted for analyzing the leverage effect while estimating the characteristics of the stock market.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Asymmetric GARCH Models for Predicting Indian Base Metal Price Volatility

Kumar,

Sahoo,

Sahoo

et al. 2024

Folia Oeconomica Stetinensia

Self Cite

View full text Add to dashboard Cite

Research background Many studies have been done in the field of predicting the Volatility of Commodities; however, very little or no analysis has been conducted on any sector, industry, or indices to identify which model is best to understand the asset’s characteristics, as there is a hypothesis that all financial time series can be interpreted by implementing the same model. Purpose The primary objective is to identify different tools developed by the researchers in estimating impulsive clustering and leverage effects. A comparison will be made among the available tools of the GARCH family models to suggest the best tool to forecast and calculate volatility with the least error. Research methodology The data used are historical time series data of Indian base metal indices, i.e., Aluminum (AL), Copper (CO), Lead (LE), Nickel (NI), and Zinc (ZI) from NSE for a period from 1st June 2012 to 31st August 2022 from the official website of NSE of India. The study compared and attempted to identify which GARCH family model is suitable to measure the volatility clustering and leverage effect in Indian base metal indices by reducing the chances of error. Results The study has revealed that the GRACH asymmetric models, while approximating and predicting the financial time series, can enhance the model’s output when it has a high frequency. Here, the asymmetric GARCH models (TARCH, CGARCH, EGARCH, and PARCH) better predict volatility than classic models. Novelty This study is original in its approach, as a previous study stated the presence of volatility or leverage effect by implementing any one tool. However, this study will compare available tools to suggest which is appropriate for which sector. This analysis will support future researchers and practitioners in evaluating volatility clustering and the effect of leverage by implementing the appropriate GARCH family model without believing in a hypothesis that a single model is good enough to predict volatility.

show abstract

Impulsive Clustering and Leverage Effect of Emerging Stock Market with Special Reference to Brazil, India, Indonesia, and Pakistan

Cited by 9 publications

References 0 publications

Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

Return Based Risk Measures for Non-Normally Distributed Returns: An Alternative Modelling Approach

An assessment of volatility and leverage effect before and during the period of Covid-19: a study of selected international stock markets

Exploring Asymmetric GARCH Models for Predicting Indian Base Metal Price Volatility

Contact Info

Product

Resources

About