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Earlier studies establish a positive relationship between volatility index (VIX) and the stock index returns. These studies are mainly restricted to developed markets and research in this regard in emerging markets is scarce. The purpose of this paper is to fill this gap.

The paper studies the direct and cross‐sectional relationship of India VIX in relation to three important parameters: viz., stock beta, market to book value of equity and market capitalization. The paper constructs value weighted portfolio sorted on the basis viz., stock beta, market to book value of equity and market capitalization. The paper employs three‐factor multiple regression to find out the results.

The paper finds that India VIX has a positive and significant relationship with the returns of the value‐weighted high‐low portfolios sorted on the basis of the above parameters. The paper examines the behavior of India VIX in the presence of the above two parameters. The India VIX yields a positive and significant relationship with the above sorted portfolio returns.

India VIX was recently introduced in November, 2007 and therefore the research is expected to suffer from small sample bias.

The findings suggest India VIX is a distinct risk factor capable of predicting the price discovery mechanism of the market.

In the rapidly expanding emerging markets the introduction of Volatility Index is a recent phenomenon. Research in this regard is scarce, particularly in the area of finding predictive ability of the Volatility Index. This research is in this direction and would definitely help the market regulators and policy‐makers with their understanding of the market and market direction. It would help them to correct the market imbalances and avert crisis, which has been recently witnessed.

This paper aims to understand the effect of a change in Economic Policy Uncertainty (EPU) of India and the USA on investors' sentiment in the Indian context, consisting of Sensex returns and volatility index (Vix).

The authors employ bounds testing approach to cointegration to capture the short-and long-run effects of EPU on investors' sentiment, along with impulse response functions and variance decompositions to check the effect of a shock on Sensex and Vix.

The study concludes the existence of a cointegrating relationship for both models, that is, Vix and Sensex. In the long-run, changes in EPU_India affect Vix and Sensex positively and negatively, respectively. On the other hand, EPU_USA affects Vix and Sensex positively. Furthermore, Gregory and Hansen (1996) cointegration with endogenous structural break reveals a long-run cointegrating relationship for both models.

The effect of EPUs on investors' sentiment reveals that when there is an uncertain event that adversely affects the stock prices, investors should not make haste to take a decision as the impact on stock prices perturbation might be temporary. Therefore, one should persevere for the dip in prices to hit the desired target.

Various studies look at the effect of cross-country EPU on the home country, However, there is no such study in the Indian context. The present study examines the impact of India's EPU on investors' sentiments after controlling the USA's EPU, one of India's largest trading partners and a key determinant of global economic policy.

The study examines the effect of increased Economic Policy uncertainty on analyst optimism in the Indian market. The study also explores whether the SEBI Research Analyst Regulation, 2014, has effectively contained the optimistic nature of analysts.

The study is based on firms in the Indian market. The sample period is 2003–2020. It runs a linear panel regression to measure the impact of Economic Policy uncertainty on the optimism level of analysts' forecasts and recommendations, controlling for firm fixed effects. Further, the impact of the SEBI Research Analyst Regulation, 2014, has been assessed with the help of the difference-in-difference approach.

The Economic Policy uncertainty is significantly and positively related to the analyst optimism, reflected in the forecast bias and recommendation in the Indian context. The experience of analysts and the age of the firm positively drive optimism. However, introducing the Research Analyst Regulation by SEBI led to a decline in analyst optimism. The regulation decoupled the analysts' compensation from brokerage service transactions. Thus, the results suggest that the regulation has effectively curbed the incentive to produce optimistic output.

This is the first study in the Indian market to assess the impact of uncertainty on analyst output. It also investigates the effectiveness of the first analyst-specific regulation in India, i.e. The Research Analyst Regulation, 2014.

This study aims to examine the role of economic political uncertainty (EPU) on various corporate policies, namely, cash reserves, investment, capital structure and operating activities of Indian listed firms.

To assess the influence of policy-related uncertainties, the author uses the India-specific EPU news-based index constructed by Baker et al. (2016) as a proxy for policy uncertainties. This study uses data from listed Indian firms spanning the period 2003 to 2019. The author uses panel regression models with firm-fixed effects to analyze the impact of EPU on corporate policies, including cash reserves, leverage and CAPEX, while considering key control variables.

In response to heightened EPU, firms tend to increase their cash reserves, curtail their investment activities and favour secured financing options. Notably, this study aligns with the “real options” framework, demonstrating that firms with substantial investment irreversibility significantly reduce their capital expenditures during periods of elevated EPU. Additionally, the results reveal that rising EPU corresponds to heightened borrowing costs and increased operating expenses for firms.

In contrast to prior research that predominantly investigated the impact of EPU on the decisions of listed firms in developed markets, this study delves into the role of EPU on corporate policies among listed firms in India. This focus is particularly relevant, given the significant policy changes that have transpired in the Indian business landscape in recent years.

The purpose of this paper is to analyze the asymmetric contemporaneous relationship between implied volatility index (India VIX) and Equity Index (S & P CNX Nifty Index). In addition, the study also analyzes the seasonality of implied volatility index in the form of day-of-the-week effects and option expiration cycle.

This study employs simple OLS estimation to analyze the contemporaneous relationship among the volatility index and stock index. In order to obtain robust results, the analysis has been presented for the calendar years and sub-periods. Moreover, the international evidenced presented for other Asian markets (Japan and China).

The empirical evidences reveal a strong persistence of asymmetry among the India VIX and Nifty stock index, at the same time the magnitude of asymmetry is not identical. The results show that the changes in India VIX occur bigger for the negative return shocks than the positive returns shocks. The similar kinds of results are recorded for the Japan and China volatility index. Particularly, the analysis also supports that India VIX holds seasonality, on the market opening VIX observed to be at its high level, and on the subsequent days it remains low. The results on the options expiration unfold the facts that India VIX remains more normal on the day of expiration.

The asymmetric relation and seasonal patterns are quite useful to the volatility traders to price the financial assets when market trades in the high- and low-volatility periods.

There is a lack of studies of this kind in the context of emerging markets like India; hence, this is an attempt in this direction. The study provides an insight to the NSE to launch some derivative products (i.e. F & Os) on India VIX that can generate more liquidity in the market for the volatility traders.

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.

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.

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.

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.

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.

This paper aims to propose the implied volatility index for the US dollar–Indian rupee pair (INRVIX). The study seeks to examine whether INRVIX truly reflects future USDINR (US Dollar-Indian rupee) volatility and signals profitable currency trading strategies.

Two measures of INRVIX are constructed and compared: a model-free version based on the methodology adopted by the Chicago Board of Options Exchange (CBOE) and a model-dependent version constructed from Black–Scholes–Merton-implied volatility. The proposed INRVIX is computed by tweaking some parameters of the CBOE methodology to ensure compatibility with the microstructure of the Indian currency derivatives market. The volatility forecasting ability of INRVIX is compared to that of a generalized autoregressive conditional heteroscedasticity (1,1) model. Ordinary least squares regression is used to examine the relationship between n-day-ahead USDINR returns and different quantiles of INRVIX.

Results indicate that INRVIX based on the model-free approach reflects ex post volatility in a better manner than its model-dependent counterpart, although neither measure is found to be an unbiased and efficient forecast. Subsample analysis across tranquil and turbulent periods corroborates the results. The volatility forecasting performance of INRVIX is found to be better than that of forecasts based on historical time-series. These results are consistent with similar studies of developed market currencies. The study does not find any significant relationship between extreme levels of INRVIX and the profitability of trading strategies based on such levels, which is contrary to results from the equity options market.

Foreign exchange volatility affects the costs of international trade and the external sector competitiveness of Indian multinationals. It is a significant risk factor for financial institutions and traders in the financial markets. An implied VIX for the USDINR could serve as an indicator of expected foreign exchange risk. It could thus provide a signal for a possible intervention in the forex market by the regulator. Regulators could introduce volatility derivative contracts based on the INRVIX. Such contracts would enable hedging of the pure volatility risk of dollar–rupee exposure. Thus, the study has practical implications for investors, hedgers, regulators and academicians alike.

To the author’s knowledge, this is one of a few studies to construct an implied VIX for an emerging currency like the rupee. The study is based on up-to-date sample data that includes the recent COVID-19 market crash. A novel contribution of this paper is that in addition to examining whether INRVIX contains information about future USDINR volatility, and it also examines the signalling power of INRVIX for currency trading strategies.

The purpose of this paper is to examine the statistical properties of the volatility index of India, India Vix (Ivix), its relationship with the Indian stock market and its predictive power for forecasting future variance. Further, the paper examines the volatility transmission between India and developed markets.

The study uses quantile regression and VAR techniques to examine the empirical issues.

The results of the study show that Ivix returns are negatively related to stock market returns and the leverage effect is only significant around the middle of the joint distribution. The asymmetric response of Ivix is also not observed in the left tail and is significant again around the centre of the distribution. Monthly volatility forecasts obtained from Ivix contain important information about future market volatility. Finally, overnight volatility movements from the US market have significant effect on the Indian market's volatility and transmission in opposite direction was not observed.

If Ivix is included in a stock portfolio when the market moves up, Ivix may not fall significantly, consequently, the portfolio returns are not negatively effected. But, when market declines sharply, i.e. for large losses, Ivix may not move up significantly in the opposite direction, thereby not providing the much‐needed insurance to the portfolio returns. But for normal/average market declines, volatility derivatives on Ivix may be useful as portfolio insurance tools.

The paper is novel in employing quantile regression methodology to examine the empirical relationships of a volatility index. Volatility spillovers between emerging and developed markets are studied using volatility indices that are ex ante.

This study constructs time series model, artificial neural networks (ANNs) and statistical topologies to examine the volatility and forecast foreign exchange rates. The Mauritian forex market has been utilized as a case study, and daily data for nominal spot rate (during a time period of five years spanning from 2014 to 2018) for EUR/MUR, GBP/MUR, CAD/MUR and AUD/MUR have been applied for the predictions.

Autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models are used as a basis for time series modelling for the analysis, along with the non-linear autoregressive network with exogenous inputs (NARX) neural network backpropagation algorithm utilizing different training functions, namely, Levenberg–Marquardt (LM), Bayesian regularization and scaled conjugate gradient (SCG) algorithms. The study also features a hybrid kernel principal component analysis (KPCA) using the support vector regression (SVR) algorithm as an additional statistical tool to conduct financial market forecasting modelling. Mean squared error (MSE) and root mean square error (RMSE) are employed as indicators for the performance of the models.

The results demonstrated that the GARCH model performed better in terms of volatility clustering and prediction compared to the ARIMA model. On the other hand, the NARX model indicated that LM and Bayesian regularization training algorithms are the most appropriate method of forecasting the different currency exchange rates as the MSE and RMSE seemed to be the lowest error compared to the other training functions. Meanwhile, the results reported that NARX and KPCA–SVR topologies outperformed the linear time series models due to the theory based on the structural risk minimization principle. Finally, the comparison between the NARX model and KPCA–SVR illustrated that the NARX model outperformed the statistical prediction model. Overall, the study deduced that the NARX topology achieves better prediction performance results compared to time series and statistical parameters.

The foreign exchange market is considered to be instable owing to uncertainties in the economic environment of any country and thus, accurate forecasting of foreign exchange rates is crucial for any foreign exchange activity. The study has an important economic implication as it will help researchers, investors, traders, speculators and financial analysts, users of financial news in banking and financial institutions, money changers, non-banking financial companies and stock exchange institutions in Mauritius to take investment decisions in terms of international portfolios. Moreover, currency rates instability might raise transaction costs and diminish the returns in terms of international trade. Exchange rate volatility raises the need to implement a highly organized risk management measures so as to disclose future trend and movement of the foreign currencies which could act as an essential guidance for foreign exchange participants. By this way, they will be more alert before conducting any forex transactions including hedging, asset pricing or any speculation activity, take corrective actions, thus preventing them from making any potential losses in the future and gain more profit.

This is one of the first studies applying artificial intelligence (AI) while making use of time series modelling, the NARX neural network backpropagation algorithm and hybrid KPCA–SVR to predict forex using multiple currencies in the foreign exchange market in Mauritius.

In this paper, our aim is to estimate the time varying correlations between Bitcoin, VIX futures and CDS indexes and to examine in what ways these assets can act as beneficial hedge and safe haven mechanisms, useful for facing, or attenuating, the major world equity markets related risks and volatilities.

Our methodology consists to model each pair equity/asset indices by bivariate symmetric and asymmetric dynamic conditional models (A) DCC to evaluate the portfolio design associated implications on both daily and weekly collected data base, with regard to the period ranging from July, 2010 to January 2018. To assess the extent to which the Bitcoin, VIX futures and sovereign CDS may stand as diversifiers, i.e. as hedging or safe haven instruments against the various stock indexes, we adopt the same method applied by Baur and Lucey (2010).

Empirical results show that the hedging and safe haven roles associated with the three hedging instruments tend to differ noticeably across time horizons and model used. The interest brought about by treating this issue is twofold. On the one hand, it should provide useful guidelines to investors through helping them opt for the most effective and beneficial strategies, whereby they could efficiently hedge the equity markets related extreme risks and volatilities. On the other hand, it is intended to highlight the applied models' specifications associated impacts.

The interest brought about by treating this issue is twofold. On the one hand, it should provide useful guidelines to investors and financial advisors through helping them opt for the most effective and beneficial of the strategies, whereby they could efficiently hedge the equity markets related extreme risks and volatilities. On the other hand, it is intended to highlight the applied models' specifications associated impacts.

Study of Bitcoin can be considered as safe haven or hedge or diversifier instrument. Compare between Bitcoin, VIX and CDs.