Stock Market Volatility Modeling and Forecasting with a Special Reference to BSE Sensex

Malepati, Venkataramanaiah; Latha, Challa Madhavi; Kolusu, Siva Nageswara Rao

doi:10.1108/s0196-382120190000035005

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Lingaraja et al (2020) examined the impact of demonetization on the volatility of the stock prices of public sector banks in India with a focus on the BSE and found that volatility significantly increased after the program's introduction. Malepati et al (2019) focused on modeling and forecasting the stock market volatility of BSE Sensex, using various techniques such as GARCH, EGARCH, and ARIMA, and found that GARCH (1, 1) was the most accurate model. Malhotra (2018) examined the cointegration and Granger causality between the Indian and US stock indexes, and found evidence of long-run equilibrium relationship and bi-directional causality between the two markets.…”

Section: Stock Performance and Macroeconomic Factormentioning

confidence: 99%

“…Malepati et al. (2019) focused on modeling and forecasting the stock market volatility of BSE Sensex, using various techniques such as GARCH, EGARCH, and ARIMA, and found that GARCH (1, 1) was the most accurate model. Malhotra (2018) examined the cointegration and Granger causality between the Indian and US stock indexes, and found evidence of long‐run equilibrium relationship and bi‐directional causality between the two markets.…”

Section: Literature Reviewmentioning

confidence: 99%

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Causality and volatility spillovers of banks' stock price returns on BSE Bankex returns

Subburayan

2023

J Corp Accounting Finance

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This paper investigates the causal relationships and volatility spillovers between the BSE Bankex index and the stock prices of five major Indian banks (Axis Bank, HDFC Bank, ICICI Bank, Kotak Bank, and SBI). Daily data from January 2, 2018 to March 8, 2023 are used, and statistical techniques such as descriptive statistics, Unit Root test, Cointegration test, Ganger Causality test, OLS regression, and GARCH model are employed. The study finds bidirectional causal relationships between the bank stocks and BSE Bankex returns, suggesting that the movement of the bank stocks significantly affects the overall market returns and vice versa. The study also finds significant volatility spillovers between the bank stocks and BSE Bankex returns, implying that the shocks in the bank stocks affect the market returns and vice versa. The study's outcomes have practical implications for investors and policymakers. Investors can use the results to make informed investment decisions in the Indian stock market, while policymakers can use the findings to monitor the financial stability of the banking sector and design appropriate policy interventions to address any potential financial crises. Overall, the study's findings suggest that policymakers should proactively monitor and manage market risks to safeguard overall financial stability.

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Section: Stock Performance and Macroeconomic Factormentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Causality and volatility spillovers of banks' stock price returns on BSE Bankex returns

Subburayan

2023

J Corp Accounting Finance

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“…Oueslati and Hammami (2018) have forecasted stock returns in Saudi Arabia and Malaysia applying regression model and it is proved that stock return predictability is stronger in the expansions stage than during the recession. Malepati (2019) has investigated the volatility patterns in the Bombay Stock Exchange returns applying the GARCH (1,1) model, and the result of the model shows that the volatility index depicted volatility clustering with varied results at the different stages of the study period.…”

Section: Critical Review Of Volatility Studiesmentioning

confidence: 99%

Review on volatility and return analysis including emerging developments: evidence from stock market empirics

Kashyap

2022

JM2

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Purpose This paper aims to analyze and give directions for advancing research in stock market volatility highlighting its features, structural breaks and emerging developments. This study offers a platform to research the benchmark studies to know the research gap and give directions for extending future research. Design/methodology/approach The author has performed the literature review, and, reference checking as per the snowballing approach. Firstly, the author has started with outlining and simplifying the significance of the subject area, the review illustrating the various elements along with the research gaps and emphasizing the finding. Findings This work summarizes the studies covering the volatility, its properties and structural breaks on various aspects such as techniques applied, subareas and the markets. From the review’s analysis, no study has clarified the supremacy of any model because of the different market conditions, nature of data and methodological aspects. The outcome of this research work has delivered further magnitude to research the benchmark studies for the upcoming work on stock market volatility. This paper has also proposed the hybrid volatility models combining artificial intelligence with econometric techniques to detect noise, sudden changes and chaotic information easily. Research limitations/implications The author has taken the research papers from the scholarly journal published in the English language only and the author may also consider other nonscholarly or other language journals. Originality/value To the best of the author’s knowledge, this research work highlights an updated and more comprehensive framework examining the properties and demonstrating the contemporary developments in the field of stock market volatility.

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Optimized FOREX Rate Prediction Using Hybrid Machine Learning Algorithm

Latha,

Bhuvaneswari,

Soujanya

2024

Lecture Notes in Networks and Systems

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Stock Market Volatility Modeling and Forecasting with a Special Reference to BSE Sensex

Cited by 5 publications

References 8 publications

Causality and volatility spillovers of banks' stock price returns on BSE Bankex returns

Causality and volatility spillovers of banks' stock price returns on BSE Bankex returns

Review on volatility and return analysis including emerging developments: evidence from stock market empirics

Optimized FOREX Rate Prediction Using Hybrid Machine Learning Algorithm

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