Foresight for stock market volatility – a study in the Indian perspective

Dixit, Jitendra Kumar; Agrawal, Vivek

doi:10.1108/fs-05-2019-0040

Cited by 8 publications

(7 citation statements)

References 49 publications

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“…Volatilitas harga saham adalah variasi perubahan harga saham yang ditandai dengan jarak antara naik dan turunnya harga saham pada periode waktu tertentu (Nurhaliza, 2021). Menurut Dixit & Agrawal (2020) volatilitas adalah hal yang lumrah di lantai bursa saham karena volatilitas harga saham adalah salah satu hal yang dipertimbangkan oleh investor sebelum mereka mengambil keputusan investasi. Bagi para investor konservatif yang takut untuk mengambil risiko, mereka akan menghindari investasi jika harga saham sedang fluktuatif karena menganggap volatilitas yang tinggi menciptakan ketidakpastian dan tidak mewakili nilai intrinsik saham yang sebenarnya.…”

Section: Kajian Teoriunclassified

Pengaruh Kebijakan Dividen, Leverage, Dan Firm Size Terhadap Volatilitas Harga Saham

Alimuary,

Dermawan

2024

JPA

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The purpose of this study was to determine and analyze the effect of dividend policy, Leverage, and Firm Size on stock price volatility. The research method used was purposive sampling with a total sample of 18 companies listed on the LQ-45 index listed on the Indonesia Stock Exchange (IDX). This research was conducted using secondary data on observations in 2018-2020. Data processing in this research using Microsoft Office 365 software and IBM SPSS Statistics V25.0. The results showed that Dividend Yield, Dividend Payout Ratio and Firm Size did not have a significant effect on Stock Price Volatility, while Leverage had a significant and negative effect on Stock Price Volatility. The implication of this research is the need to know what are the factors that can affect the stock price volatility which will give a good or bad signal to investors in carrying out their investment activities.

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Section: Kajian Teoriunclassified

Pengaruh Kebijakan Dividen, Leverage, Dan Firm Size Terhadap Volatilitas Harga Saham

Alimuary,

Dermawan

2024

JPA

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“…Some authors found PGARCH model to best fit the data from Indian stock market amongst all the models evaluated from the GARCH family (Dixit & Agrawal, 2019). Akigray (Akgiray, 1989) found GARCH(1,1) to be most suitable for modelling daily returns on CRSP indices.…”

Section: Literature Reviewmentioning

confidence: 99%

Modelling Volatility of Size Indices Through Garch and Tgarch Models: Evidence from India

Sharma¹

2023

J. Professional Business Review

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Purpose: The objective of this study was to select the best model for modelling the volatility of log return series of four size indices taken from the Indian stock markets. Theoretical framework: Stock markets throughout the world have been designing various kinds of indices depending upon the requirements of the investors. Accordingly, BSE launched some indices, categorized as size indices, depending upon the market capitalization of the stocks listed on their exchange. Such categorization helps the investors in appropriate decision making. Research on these specific indices is scarce and therefore the aim of the present study. Design/methodology/approach: We analyzed the log return series of four indices, namely S&P BSE Sensex, S&P BSE Large cap index, S&P BSE Mid cap Select index and the S&P BSE Small cap index, by fitting the GARCH (1,1) and the TGARCH (1,1) models for the period from 1 Januray 2018 to 31 December 2022. Findings: The research on modelling volatility is voluminous with varied results as per the various assets and datasets utilized for the purpose. Present study confirms the results from previous study that to model the volatility of size indices a GARCH (1,1) model should suffice and there is no betterment achieved by implementing a TAGARCH (1,1) model. Research, Practical & Social implications: We suggest the implementation of a simple GARCH (1,1) model for all those investors who want to invest in Indian size indices in order to model volatility of these indices. Originality/value: Present study is one of its kinds, investigating size indices specifically, through the two of the most popular GARCH-family models in the Indian context.

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“…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). Dixit and Agrawal (2019) also suggested that the P-GARCH model is the most suitable to predict and forecast stock market volatility for the Bombay Stock Exchange (BSE) and National Stock Exchange (NSE) of India. The authors also highlighted the fact that volatility in financial markets is reflected because of uncertainty in the price, return, unexpected events and non-constant variance, which can be measured through the GARCH models that will give insight for investment decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Samunderu

Murahwa²

2021

JRFM

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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.

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Foresight for stock market volatility – a study in the Indian perspective

Cited by 8 publications

References 49 publications

Pengaruh Kebijakan Dividen, Leverage, Dan Firm Size Terhadap Volatilitas Harga Saham

Pengaruh Kebijakan Dividen, Leverage, Dan Firm Size Terhadap Volatilitas Harga Saham

Modelling Volatility of Size Indices Through Garch and Tgarch Models: Evidence from India

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

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