High-Frequency Return-Implied Volatility Relationship: Empirical Evidence from Nifty and India VIX

Chakrabarti, Prasenjit; Kumar, K. Kiran

doi:10.1353/jda.2020.0024

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…The results suggested that the leverage effect is significant for a lagged period. Contradicting the existing literature, (Chakrabarti & Kumar, 2020;Fousekis, 2020) reported an interesting finding that the dynamic association between these series is statistically inadequate to support the traditional hypotheses namely leverage effect and volatility feedback effect. Fousekis (2020) documented a reverse S-shape association between stock returns and VIX in the US, Australia, and the EU markets.…”

Section: Figure1mentioning

confidence: 96%

The Impact of COVID-19 Pandemic on the Relationship between India’s Volatility Index and Nifty 50 Returns

Chittineni

2020

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The paper intends to re-examine the relationship between India’s Implied Volatility Index (IVIX) and Nifty 50 Returns during this COVID-19 pandemic. The study results are important for two reasons, one is to understand whether Indian VIX is fulfilling the purpose of measuring the near future volatility of Nifty 50 during this pandemic, and secondly, it reports the impact of COVID-19 on the investors’ perceptions about the returns and its volatility. The study results documented that the Nifty return and IVIX are moving independently during the COVID-19 pandemic and there is no association between market size and the market move. The one period lagged Nifty returns have a significant influence on the future market volatility. The combined impact of negative and positive Nifty returns on IVIX is not significant during the COVID-19 period. This implies that the Indian investors are not much worried about the fluctuation in the market price or size of the market during the COVID-19 pandemic period. The investors might be taking the market decline as an opportunity to invest and market rise as an opportunity to sell the stocks. Indian investors are much focused on the fundamentals than the market movements during this pandemic. The study results are important for the fund managers, policymakers, and analysts to understand the dynamics of emerging market volatility and the trading behavior of Indian investors.

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Section: Figure1mentioning

confidence: 96%

The Impact of COVID-19 Pandemic on the Relationship between India’s Volatility Index and Nifty 50 Returns

Chittineni

2020

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“…For China and Russia, the fear index impacts the stock market return in a negative way. Before the COVID breakdown, a negative relationship has been found between the Indian VIX and stock market return (Chakrabarti & Kumar, 2020). For Brazil, investors could anticipate instabilities in the Brazilian market by getting indications from the fear index and could identify opportunities to enter and exit the markets (Cainelli et al, 2020).…”

Section: Review Of Literaturementioning

confidence: 99%

Comovement of Fear Index, Stock Returns, Brent Oil Prices in BRIC Countries: The Case of COVID-19

Sharma

Malik²

2022

The Indian Economic Journal

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The study investigates long-run and short-run cointegrating relationship between stock market returns, fear index (VIX), brent crude oil prices and growth in deaths due to the COVID-19 pandemic for BRIC countries using daily data from 23 January 2020 to 24 August 2020 using Autoregressive Distributed Lag (ARDL) model. CUSUM test and serial correlation test estimates point towards the robustness of the model used. The evidence reveals that for India and Brazil, with the outbreak of COVID-19, decrease in crude oil prices and increase in volatility index, the stock returns started declining in the short run, but the impact has declined and the stock returns have regained in the long run. For China, due to the outbreak of COVID-19 and increase in fear index, stock returns declined in the short run, but the Chinese economy has recovered well due to a strong industrial and services sector. For Russia, increase in deaths due to COVID and decline in oil prices has impacted the stock returns in the long- and short run. Due to a decline of 53% for crude oil prices from January 2020 to May 2020, the Russian economy would face the consequences in the long run as well. The results suggest that though BRIC countries were impacted by growth in COVID-19 deaths, but the recovery trajectory and stability has resumed for all countries except Russia. Results of Granger Causality indicate a bidirectional causality between VIX and stock returns for the Indian market.

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“…The author further argued that, by using a risk-mitigating technique when the India VIX increases, the performance of the portfolio consisting of stocks could be improved by shifting allocation from mid-cap to large-cap stocks and vice versa. Chakrabarti and Kumar (2020) examined the short-term association of the India VIX and returns on the NIFTY Index by applying the vector autoregression model to their data at five-minute intervals. In other words, the authors examined the risk-return trade-off concept, assuming a return on the stock market index and risk in the stock market, for which implied volatility is taken as a risk.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, in practice, the CBOE VIX Index is strongly and negatively associated with the S&P 500 Index's returns but positively associated with the volume of the S&P 500 (Fernandes et al 2014). The India VIX Index and the NIFTY 50 Index move in opposite directions (Bantwa 2017;Chakrabarti and Kumar 2020). This relationship is strong when the market is falling.…”

Section: Introductionmentioning

confidence: 98%

Forecasting the Direction of Daily Changes in the India VIX Index Using Machine Learning

Prasad

Bakhshi²

2022

JRFM

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Movements in the India VIX are an important gauge of how the market’s risk perception shifts from day to day. This research attempts to forecast movements one day ahead of the India VIX using logistic regression and 11 ensemble learning classifiers. The period of study is from April 2009 to March 2021. To achieve the stated task, classifiers were trained and validated with 90% of the given sample, considering two-fold time-series cross-validation for hyper-tuning. Optimised models were then predicted on an unseen test dataset, representing 10% of the given sample. The results showed that optimal models performed well, and their accuracy scores were similar, with minor variations ranging from 63.33% to 67.67%. The stacking classifier achieved the highest accuracy. Furthermore, CatBoost, Light Gradient Boosted Machine (LightGBM), Extreme Gradient Boosting (XGBoost), voting, stacking, bagging and Random Forest classifiers are the best models with statistically similar performances. Among them, CatBoost, LightGBM, XGBoost and Random Forest classifiers can be recommended for forecasting day-to-day movements of the India VIX because of their inherently optimised structure. This finding is very useful for anticipating risk in the Indian stock market.

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High-Frequency Return-Implied Volatility Relationship: Empirical Evidence from Nifty and India VIX

Cited by 4 publications

References 18 publications

The Impact of COVID-19 Pandemic on the Relationship between India’s Volatility Index and Nifty 50 Returns

The Impact of COVID-19 Pandemic on the Relationship between India’s Volatility Index and Nifty 50 Returns

Comovement of Fear Index, Stock Returns, Brent Oil Prices in BRIC Countries: The Case of COVID-19

Forecasting the Direction of Daily Changes in the India VIX Index Using Machine Learning

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