CNX Nifty Index Research Articles

Ad-Hoc Black–Scholes vis-à-vis TSRV-based Black–Scholes: Evidence from Indian Options Market

This research paper examines one-day-ahead out-of-sample performance of the volatility smirk-based options pricing models, namely, Ad-Hoc-Black–Scholes (AHBS) models on the CNX Nifty index options of India. Further, we compare the performance of these models with that of a TSRV-based Black–Scholes (BS) model. For the purpose, the study uses tick-by-tick data. The results on the AHBS models are highly satisfactory and robust across all the subgroups considered in the study. Notably, a daily constant implied volatility based ad-hoc approach outperforms the TSRV-based BS model substantially. The performance of the ad-hoc approaches improves further when the smile/smirk effect is considered. For the estimation of the implied volatility smile, we apply three weighting schemes based on the Vega and liquidity of the options. All the schemes offer equally competing results. The major contribution of the study to the existing literature on options pricing is in terms of the ex-ante examination of the ad-hoc approaches to price the options by calibrating volatility smile/smirk on a daily basis.

Influence of Shareholders’ Activism and Firm-level Variables on the Corporate Governance Quality in India

Corporate governance (CG) gained widespread prominence as a medium for boosting corporate performance especially after the financial crisis of 2008. With limited empirical work on factors influencing the CG quality (CGQ), this article focuses on the construction of the CG Index (CGI) by considering a total of 64 attributes encapsulated in five sub-indices followed by the investigation of effect of shareholder activism and firm-level variables on it. The estimations are based on companies listed in S&P CNX NIFTY Index from the financial year 2008–2013. The examination of results relays that both shareholder activism and firm-level variables have a significant impact on the CGQ of the firms. The significant impact of Disclosure and Board Index on the performance of firms emphasised the importance of disclosure norms in driving the performance by improving investor perceptions through higher transparency. And active board contributes in dispelling agency and managerial issues assisting in improving firm’s value. Our findings imply that shareholder activism and firm-level variables help in bolstering the quality of CG. Large concentrated holdings limit the power in few hands that deter the use of effective shareholder activism and thus should be reduced to enhance the quality of governance. The policymakers and regulators are needed to pressurise institutional investors for active participation in the companies’ routine decisions to increase vigilance regarding CG issues.

Behaviour and determinants of implied volatility in Indian market

Purpose The purpose of this paper is to understand the patterns of the implied volatility (IV) of the Indian index option market and its relationship with moneyness (called the volatility smile). Its goal is also to ascertain the determinants of IV. Design/methodology/approach For this purpose, IVs were computed from the daily call and put data of CNX Nifty index options from April 2004 to March 2014. The patterns of IVs were analysed using univariate parametric tests. Multivariate regression analyses were conducted to understand the relationships observed. Resultantly, vector autoregressions were performed to assess the determinants of IV. Findings The results suggested that there was asymmetric volatility across time and strike prices using alternative measures of moneyness. Furthermore, it was found that the IV of lower strike prices was significantly higher (lower) than that of higher strike prices for call (put) options. Put IV was observed to be higher than call IV irrespective of any attributes. The results further showed that current-month contracts have significantly higher IV than those for next month and those were followed by far-month contracts. Nifty futures’ volumes and momentum were found to be significant determinants of IV. Practical implications The behaviour of the volatility smile is important when accounting for the Vega risks in the portfolios of hedge fund managers. While taking a position, besides the Black-Scholes-Merton (BSM) model’s input factors, investors must consider the previous behaviour of volatility, a market’s microstructures and its liquidity for a put option contract. They must also consider the attributes of the underlying for a call option contract. Originality/value This is the first decadal study (the longest span of data for any international study on this subject) to confirm the existence of the volatility smile for the index options market in India. It examines and confirms the smile’s asymmetry patterns for different definitions of moneyness, as well as option types, the tenure of options contracts and the different phases of market conditions. It further helps to identify the determinants of IV and so has renewed importance for traders.

The Empirical Investigation of Causal Relationship between Intraday Return and Volume in Indian Stock Market

This article examines the causal relationship between intraday return and volume by using 1-minute intraday data of 35 stocks of S&P CNX Nifty index during the period from April 2007 to March 2011. The empirical analysis provides evidence to the mixture of distribution hypothesis (MDH), as a majority of stocks of S&P CNX Nifty index show no causal relationship between the intraday return–volume relationships. However, this study finds evidence of significant causal and lead–lag relations between the intraday return–volume associations for some stocks. These findings reveal strong indication of uni- and bi-directional causality, thus supporting the sequential information arrival hypothesis (SIAH) which suggests that lagged values of volume provide the predictability component of current return (vice versa).

Semi-Month Effect in S&P CNX NIFTY Turnover and Retail Participation in Indian Capital Market

<div><p><em>NIFTY is the bellwether index followed by most of the traders in Indian stock markets. The turnover in the S&P CNX NIFTY constituent shows that the scripts are mostly traded on every session of trading. The study is carried out to check the semi moth effect in S&P CNX NIFTY which may helpful for retail investors to make investment decision. This study is carried out using the monthly movement of S&P CNX NIFTY index of NSE India Ltd for the period April 01, 2004 to March 31, 2015. The statistical significance of semi month effect is tested using descriptive statistics and paired “t” test. The outcome of the analysis shows that there is the presence of semi-month effect in index returns and suggestions to retail investors to time the investments based on the market movement.</em></p><p> </p></div>

Impact of Ownership Structure and Board Composition on Corporate Performance in Indian Companies

This study takes into account the National Stock Exchange (NSE)-listed Indian companies, which constitute the CNX Nifty Index, for the period from 1 April 2009 to 31 March 2013. Tobin’s Q, market value added, cash earnings per share and return on capital employed have been used as the corporate performance variables in the study; the first two being market-based and the last two being accounting-based measures. Board size (BS), board composition, ownership structure, multiplicity of directorship, chief executive officer (CEO) duality, CEO tenure and executive remuneration have been used as corporate governance surrogates from different dimensions along with other widely used independent variables to see their effect on corporate performance in a panel-data-based regression. BS and foreign promoters’ (FPs’) shareholdings have been identified to have a positive impact on more than one corporate performance variable. Among the remaining independent variables, assets turnover is positively related with the performance variables.

Sequential Information Arrival Hypothesis: More Evidence from the Indian Derivatives Market

The study attempts to gather additional evidence on the sequential information arrival hypothesis by examining the dynamic relationship between the trading volume and the volatility of CNX Nifty index futures traded on the National Stock Exchange of India. The documented result using a linear Granger causality model shows a unidirectional causality from volatility to trading volume, thus rejecting the theory of sequential arrival of information in Nifty index futures. Further, under a non-linear GARCH framework of analysis, it is reinforced that a lagged trading volume has no explanatory power of current conditional volatility, even in the presence of market depth surrogated by open interest in the Nifty futures market. Consequently, given the trading volume, the possibility of forecasting the price variability of Nifty futures is rejected.

Pricing Efficiency in CNX Nifty Index Options Using the Black–Scholes Model: A Comparative Study of Alternate Volatility Measures

This article attempts to determine the method of volatility estimation that prices the CNX Nifty Index options closest to the theoretical price as computed by the Black–Scholes (1973) model. Volatility has been estimated using simple variance, implied volatility, volatility index and the asymmetrical exponential generalised auto-regressive conditional heteroskedasticity (EGARCH) (1,1) model with generalised error distribution innovations. The trend in mispricing has been studied using error estimates and non-parametric tests. Our findings indicate significant mispricing in CNX Nifty Index options. The results of our study will have major implications for investors who use options as part of their portfolios and corporates who use them for risk hedging. Our study is important, as there are only a few studies that examine the pricing efficiency of options with a focus on volatility modelling. Also, our study spans a longer time period than the previous studies. JEL Classification: G14, G32, C14

Volatility–volume causality across single stock spot–futures markets in India

ABSTRACTThis study examines the causal relationships between volatility and volume across spot and futures market for the 50 constituent stocks of the CNX NIFTY Index. Granger non-causality tests implemented using vector autoregression (VAR) and asymmetric VAR models indicate the presence of significant causal relations from both the spot and futures volume to both the spot and futures volatility. Bidirectional causal relationships between spot and futures volume were observed for almost all stocks but few stocks displayed a similar relationship between volatilities. The results highlight the importance of volume in absorbing information and its behaviour as the conduit of information.

Intraday price discovery in Indian stock index futures market: new evidence from neural network approach

Using neural network approach, this study revisits the price discovery relationship between spot and futures prices of S&P CNX Nifty Index of India. This study uses minute-by-minute data of 167 trading days (from January 2015 to August 2015). Empirical results reveal that there exists a bi-directional lead-lag relationship between spot and futures markets, but the causality from the spot market to the futures market is more dominant as compared to that of running from the futures market to the spot market. Root mean squared error of the results indicates that the incorporation of spot returns in modelling futures returns improves the results by 62.37%. Whereas, the inclusion of futures returns in modelling spot returns improves the model by only 44.60%. Price discovery dynamics has significant implications for market participants, regulators, and exchanges. These findings could be helpful for portfolio management, market structure design, and the implementation of arbitrage and hedging strategies.

On the relationship between implied volatility index and equity index returns

Purpose – 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. Design/methodology/approach – 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). Findings – 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. Practical implications – 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. Originality/value – 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.

Performance of the Heston’s Stochastic Volatility Model: A Study in Indian Index Options Market

This study attempts to analyse one-day-ahead out-of-sample performance of the stochastic volatility model of Heston (SVH) in the Indian context. Also, the study compares the ex-ante performance of the SVH with that of a Two-Scale-Realised-Volatility (TSRV)-based Black-Scholes model (BS) using the liquidity-weighted performance metrics. For the purpose, we utilise the tick-by-tick data of the CNX Nifty index and options thereon, the most liquid equity options in the world in terms of the number of contracts traded1. Additionally, the study compares the two models across subgroups based on the moneyness, volatility of the underlying and time-to-expiration of the options. The results establish that the SVH model is better than the BS model in pricing equity index options. Further, the SVH model appears to be superior across all the subgroups, for both call options and put options.

Exploring the usage of econometric techniques in nonlinear machine learning and data mining

The present study, investigates the inter-linkage of the Indian spot market with other global markets and the predictability of S&P CNX NIFTY Index returns with a set of five new international market returns as input variables in artificial neural networks (ANNs). Identifying the right set of exogenous input variables using conventional techniques like OLS, Granger causality and cross correlation substantially increased the predictability of financial time series like stock return in the Indian context. The performance of the ANN model in forecasting NIFTY index returns is evaluated by comparing it for different sample periods in terms of forecasting error functions with statistical measures like mean absolute error, root mean square error, mean absolute percentage error and mean square error. The findings suggest that higher accuracy of the predictive power of neural network is largely influenced by the input variables.

Volatility Informed Trading in the Options Market: Evidence from India

The purpose of this paper is to investigate the trading activity in options market based on information about expected future volatility in spot market. We employ Common Implied Volatility as a measure of expected volatility and options volume and changes in Open Interests as measures of options trading activity. We first test for simultaneous information flow in the two markets using multiple regression technique. Next, we test for information based or hedge based use of options using Trivariate Vector-auto Regression framework. We further consider the classes of options moneyness and the market trends in our analysis to examine if the trader’s preference of options changes with change in description of options intrinsic value and market environment. We use daily closing data of S&P CNX Nifty Index options traded on National Stock Exchange, India. We, for the most part, find negative and significant relationship in contemporaneous regression suggesting active trading by arbitrageurs. A feedback relationship is observed in vector auto regression analysis suggesting that options are traded in India for both information based trading and hedging purposes. We also observe the relationship to be varying when market trends and classes of options moneyness are considered. This indicates that traders are not indifferent in their choice of trading venue when market conditions and factors change. The results of this study are helpful for traders in managing the risk and return of their portfolio based on volatility forecast. This study is distinctive as it examines the scarcely researched area of volatility informed trading in an emerging market set up.

Impact Of Union Budget On Indian Stock Market

This paper analyzes the impact of Union budget on NSE’s CNX NIFTY Index. The impact is measured in terms of daily average returns and volatility over the short term, medium term and long term period in pre and post budget period. The data has been collected for five budget periods from 2011 to 2015. The statistical tools used are paired T-test and F-test. Paired T-test is conducted on average returns and F-test is conducted on variances over the period i.e., 3, 10 and 30 days in pre and post budget period. The maximum impact of budget is seen in short term then it gradually decreases in medium term and finally diminishes in the long term. The implication of this paper is that the investor should fear from investing in the stock market around the budget period.

Cointegration and Causality between Macroeconomic variables and Stock Prices: Empirical Analysis from Indian Economy

<p class="ber"><span lang="EN-IN">The study aims at examining how fiscal fundamental macroeconomic variables affect the performance of the stock market in India by using monthly data from April 2004- July 2015. The study makes use of Ng-Perron unit root tests to check the non-stationarity property of the series; the Auto Regressive Distributed Lag (ARDL) bounds test and a Vector Error Correction Model (VECM) for testing both short and long run dynamic relationships. The variance decomposition (VDC) is used to predict the exogenous shocks of the variables. The findings of the bounds test </span><span lang="EN-GB">confirm that there exists a long-run co-integrating relationship between different macroeconomic variables and the stock prices in India. The ARDL result suggests a long-run negative relationship exists between crude oil prices, inflation and stock prices. The results of the influence of both the variables on stock prices are consistent in the short run as well. The results of the short-run estimation confirm positive and significant relationship for Gold, T-bill rates and Real Effective Exchange Rate. The VECM result shows a bidirectional causality is running between Inflation and CNX nifty index. Further, the result indicates the presence of long run causality for the equation with a CNX nifty index as the dependent variable. The results of VDC analysis and IRF show that a major percentage of stock prices change is its own innovative shocks. The study implies that appropriate policy measures should be taken by the proficient authorities for the purpose of controlling inflation, which ultimately leads to the control of volatility of the stock market.</span></p>

A Comparative Study on the Performance of Gold ETF and Infrastructure ETF as Against CNX Nifty Index

A Comparative Study on the Performance of Gold ETF and Infrastructure ETF as Against CNX Nifty Index

Looking into the relationship between implied and realized volatility: a study on S&P CNX Nifty index option

This paper looks into the robustness of implied volatility against the backward looking volatility of S&P CNX Nifty index option in India by considering both ‘in the sample’ and ‘out of the sample’ framework. The backward looking volatility is measured by employing Moving Average (MA), Exponential Weighted Moving Average (EWMA), Generalized Auto Regressive Conditional Heteroskedisticity (GARCH) and Exponential Generalized Auto Regressive Conditional Heteroskedisticity (EGARCH) model with Generalized Error Distribution (GED). The study computed the realized volatility of S&P CNX Nifty by using the overlapping database to match with the expiration of the corresponding option contract. This leads to the problem of minimizing the standard error of the estimator as per the OLS method. This problem has been resolved by employing Generalized Methods of Moments (GMM). The study period of the analysis is spanning over the period from 4th June 2001 to 23rd June 2011. The determinations of the study suggest that Conditional Volatility gives a superior forecast of realized volatility than forward looking volatility and other backward looking volatility. At the same time the analysis shows that implied volatilities are biased and inefficient estimates over the remaining life of the option contract. In ‘out of the sample analysis’, the family of ARCH models outperformed all other forecasting models with respect to predicting 30 days ahead volatility.

On the Relationship of Ex-ante and Ex-post Volatility: A Sub-period Analysis of S&P CNX Nifty Index Options

In this article, the information content of implied volatility is studied at sub-periods (i.e., pre- and post-crises of 2007–09). The main objective is to judge the predictive power of implied volatility in the pre- and post-crises period, using at-the-money (ATM) non-overlapping monthly implied volatilities of Nifty Index options. A simple ordinary least squares (OLS) estimation is used to analyse the information content of implied volatility in sub-periods. An autoregressive-moving average (ARMA) structure is analysed for the assessment of times series property of ex-ante and ex-post volatility. An autoregressive distributed lag (ARDL) model is adopted to choose the most advantageous forecasting model for predicting the future volatility. The OLS estimation shows that implied volatility is more biased in the pre-crises period. The two-stage least squares (2SLS) estimation clearly explains that implied volatility is an unbiased estimate of the future realised volatility. An ARMA (1,1) and ARDL (1,0) is the best model of future volatility estimation. This study explains that for Indian derivative market, volatility estimates based on options are useful for the pricing of derivative instruments and portfolio risk management. JEL Classification: G13, G14, C53

Impact of Weather on Return and Volatility: Evidence from Indian Stock Market

This study examines the impact of Weather factors on return and volatility of the Indian stock market. The study uses the daily data of top four metros and tests its impact on the return and volatility of S&P CNX Nifty index from January 2008 to December 2013. This study applies GARCH (1, 1) model and find that the stock returns are influenced by temperature in Chennai and the stock return volatility influenced by the temperature in Mumbai, Delhi and Kolkata.