Price discovery in Indian stock index futures market: new evidence based on intraday data

Inani, Sarveshwar Kumar

doi:10.1504/ijicbm.2017.10000830

Cited by 2 publications

(2 citation statements)

References 22 publications

(27 reference statements)

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“…These results are in line with the findings of many previous studies regarding price discovery between spot and futures prices of Nifty index (Kumar and Chaturvedula, 2013;Kumar and Tse, 2009). However, our results are not consistent with the findings of some of the previous studies in the Indian context (Inani, 2017;Pati and Rajib, 2011;Swaroop Debasish, 2009).…”

Section: Discussioncontrasting

confidence: 99%

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

Kumar

Inani

2017

IJFMD

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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.

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Section: Discussioncontrasting

confidence: 99%

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

Kumar

Inani

2017

IJFMD

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“…Both |RA| and HLR were measured employing daily data. RV was calculated by summing the corresponding 5-minute interval square returns (Andersen & Bollerslev, 1997; Inani, 2017; Paital, 2016; Pati et al, 2017) that is from 9:55 am until 15:30 pm (IST) for 2009 data and 9:15 am to 15:30 pm (IST) for data collected after 2009. This was because the trading time before 2009 was from 9:55 am to 15:30 pm (IST) and from 2010 onward trading time has been changed to 9:15 am to 15:30 pm (IST).…”

Section: The Datamentioning

confidence: 99%

Volatility Forecast Incorporating Investors’ Sentiment and its Application in Options Trading Strategies: A Behavioural Finance Approach at Nifty 50 Index

Mutum

2020

Vision: The Journal of Business Perspective

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The present study was to examine whether the performance of options trading strategies can be improved if volatility forecasting incorporating investors’ sentiment was incorporated in the decision-making process at the Indian options market. The study adopted the multiple-factor model to build the Indian volatility forecasting model. The benchmark forecasting model (BMF) includes absolute daily returns (|RA|), daily high–low range (HLR) and daily realized volatility (RV). The proxies of investors’ sentiment considered in the study were India volatility index (IVIX), advance decline ratio (ADR), put-call open interest (PCOI) and their changes. The results of the causality and regression test indicate that investors’ sentiment and their changes should be included in the forecasting model. Mean absolute percentage error (MAPE) indicates that 15-day holding period shows the minimum error. Straddle strategies were simulated 15 days ahead before the options maturity date base on the direction of the forecast for different volatility forecasting models. The simulation result shows that the options trading performance might be improved if volatility forecasting incorporating investor sentiment, particularly IVIX, was incorporated in the decision-making process at the Indian options market. From the behavioural finance point of view, the study bridges the gap between options trading, volatility forecasting and information content of investors’ sentiment at the Indian financial market.

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Price discovery in Indian stock index futures market: new evidence based on intraday data

Cited by 2 publications

References 22 publications

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

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

Volatility Forecast Incorporating Investors’ Sentiment and its Application in Options Trading Strategies: A Behavioural Finance Approach at Nifty 50 Index

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