Long run commonality in Indian stocks: empirical evidence from national stock exchange of India
20 May 2020-Journal of Indian Business Research (Emerald Publishing Limited)-Vol. 12, Iss: 4, pp 441-458
TL;DR: In this paper, the authors investigate long-run commonality in liquidity using multiple proxies computed from limited order book data of NIFTY50 stocks and find strong evidence in support of long run commonality across three liquidity measures.
Abstract: The purpose of this paper is to investigate long-run commonality in liquidity using multiple proxies computed from limited order book data of NIFTY50 stocks. The findings indicate the existence of systematic liquidity or commonality on NIFTY50 market and comprising industries.,The sample comprises all intraday transactions corresponding to NIFTY 50 stocks for April 2015. The study runs firm by firm time series regressions to test the concept of long-run commonality, while controlling other effects.,Strong evidence is found in support of long-run commonality across three liquidity measures. On the basis of significance (10%) of long-run commonality beta (βLR), the strength of long-run commonality is found to be highest in natural resources and infrastructure sector. Portfolios having greater exposure to these sectors will face diversification risk to a great extent.,Knowledge of long-run commonality helps portfolio managers in formulating diversification strategies and reshuffling the portfolio over the period. Commonality risk being non-diversifiable is a policy concern for regulators and central bankers. Its empirical evidence will assist in managing exchange organization and thus preventing market crashes because of sudden liquidity evaporation.,Although there are recent studies documenting commonality in short run, little empirical work has been done on commonality in the long run and in emerging markets such as India. This research contributes to the literature by testing concept of commonality in long-run on NIFTY50 stocks using detailed transaction data from National Stock Exchange.
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01 Jan 2012
TL;DR: In this paper, a simple equilibrium model with liquidity risk is proposed, where a security's required return depends on its expected liquidity as well as on the covariances of its own return and liquidity with the market return.
Abstract: This paper solves explicitly a simple equilibrium model with liquidity risk. In our liquidityadjusted capital asset pricing model, a security s required return depends on its expected liquidity as well as on the covariances of its own return and liquidity with the market return and liquidity. In addition, a persistent negative shock to a security s liquidity results in low contemporaneous returns and high predicted future returns. The model provides a unified framework for understanding the various channels through which liquidity risk may affect asset prices. Our empirical results shed light on the total and relative economic significance of these channels and provide evidence of flight to liquidity. r 2005 Elsevier B.V. All rights reserved.
1,156 citations
TL;DR: In this paper , the authors examined the volatility of Indian stock market from 2008 to 2021 using GARCH 1, 1 (generalized autoregressive conditional heteroskedasticity) and FIGARCH (fractionally integrated GARCH).
Abstract: Background: In this study, we examined the volatility of the Indian stock market from 2008 to 2021. Owing to the financial crisis, volatility forecasting of the Indian stock market has become crucial for economic and financial analysts. An empirical study of the returns of the NSE indices revealed an autoregressive conditional heteroskedastic trend in the Indian stock market. Methods: Using GARCH 1, 1 (generalized autoregressive conditional heteroskedasticity) and FIGARCH (fractionally integrated GARCH), we examine investor behaviour and the persistence of long-term volatility. Results: The empirical findings of the estimated models suggest that shocks persist for a long time in NSE returns. Furthermore, bad news has a greater impact on stock volatility than good news. The return on assets is stable but highly volatile, even though the Indian economy has experienced the global crash to some extent. Conclusions: Models of volatility derived from the GARCH equation provide accurate forecasts and are useful for portfolio allocation, performance measurement, and option valuation.
TL;DR: In this article , the authors explored the trends and causes of established and emerging nations' stock market integration with India and investigated the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States.
Abstract: Background: The purpose of this study is to explore the trends and causes of established and emerging nations' stock market integration with India. The National Stock Exchange (NSE) indices act as a counterweight to international market indices. This study investigates the sustained interest of foreign investors in the Indian stock market in the wake of capital market reforms, as well as whether it moves in tandem with other markets in Asia and the United States. Methods: Our study examined the possibility of cross-country cointegration between the largest economies and indices around the world using multiple financial econometric models, such as Augmented Dickey-Fuller, Unit Root, Correlation, and Johansen Cointegration. Results: The findings of this study significantly support the notion that Indian and international financial markets are highly integrated. Vector error correction model indicates that the Indian market (NSE) is highly cointegrated with the US market (National Association of Securities Dealers Automated Quotations) and increased volatility signifies global contagion. Conclusion: A cursory examination of the data reveals distinct investment and portfolio diversification options for global investors. This could assist regulators in formulating more effective rules regarding price discovery processes.
TL;DR: In this paper , the authors examined the volatility of Indian stock market from 2008 to 2021 using GARCH 1, 1 (generalized autoregressive conditional heteroskedasticity) and FIGARCH (fractionally integrated GARCH).
Abstract: Background: In this study, we examined the volatility of the Indian stock market from 2008 to 2021. Owing to the financial crisis, volatility forecasting of the Indian stock market has become crucial for economic and financial analysts. An empirical study of the returns of the NSE indices revealed an autoregressive conditional heteroskedastic trend in the Indian stock market. Methods: Using GARCH 1, 1 (generalized autoregressive conditional heteroskedasticity) and FIGARCH (fractionally integrated GARCH), we examine investor behaviour and the persistence of long-term volatility. Results: The empirical findings of the estimated models suggest that shocks persist for a long time in NSE returns. Furthermore, bad news has a greater impact on stock volatility than good news. The return on assets is stable but highly volatile, even though the Indian economy has experienced the global crash to some extent. Conclusions: Models of volatility derived from the GARCH equation provide accurate forecasts and are useful for portfolio allocation, performance measurement, and option valuation.
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01 Jan 1986
TL;DR: In this article, the effect of the bid-ask spread on asset pricing was studied and it was shown that market-observed expexted return is an increasing and concave function of the spread.
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