Shock and Volatility Spillovers Among Equity Sectors of the National Stock Exchange in India

2017 ◽  
Vol 19 (1) ◽  
pp. 227-240 ◽  
Author(s):  
Sayantan Bandhu Majumder ◽  
Ranjanendra Narayan Nag
Keyword(s):  
Global Financial Crisis ◽  
Stock Exchange ◽  
Debt Crisis ◽  
Portfolio Allocation ◽  
Volatility Spillover ◽  
Hedging Strategy ◽  
Volatility Transmission ◽  
Volatility Spillovers ◽  
Daily Data ◽  
Indian Stock Market

The basic thrust of this article is to examine how shocks and volatility are transmitted across sector indices. This article employs the autoregressive asymmetric BEKK-GARCH model. The study is based on daily data from the National Stock Exchange (NSE) of India from January 2004 to January 2014. Volatility spillover was found to be bidirectional among the two pro-cyclical sectors: Finance and IT. But, there was a unidirectional shock and volatility spillover from the non-cyclical FMCG sector to both the pro-cyclical sectors. The FMCG sector has remained almost unaffected by the spillover from the other sectors. Moreover, the evidence of asymmetric spillover has been found to be present in most of the case. Second, correlations between the sectors were found to be higher during the period of global financial crisis. But no such evidence was found in the context of the Euro zone debt crisis. Understanding the dynamics of shocks and volatility transmission is necessary for risk management in general and for optimal portfolio allocation and hedging strategy in particular. To the best of our knowledge, this is the first study on Indian stock market which has analysed the dynamics of shock and volatility transmission across sector indices.

scholarly journals Volatility Spillover Effect in Indian Stock Market

2017 ◽  
Vol 5 ◽  
pp. 83-101 ◽  
Author(s):  
Surya Bahadur G. C ◽  
Ranjana Kothari ◽  
Rajesh Kumar Thagurathi
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Spillover Effect ◽  
Stock Index ◽  
Global Markets ◽  
Series Data ◽  
Volatility Spillover ◽  
Volatility Transmission ◽  
Indian Stock Market

The study aims to empirically examine the transmission of volatility from global stock markets to Indian stock market. The study is based on time series data comprising of daily closing stock market indices from National Stock Exchange (NSE), India and major foreign stock exchange of the three countries one each from America, Europe and Asia making the highest portfolio investment in Indian stock market. The study period covers 11 years from 1st January, 2005 to 31st December, 2015 comprising a total of 2731 observations. The Indian stock index used is CNX Nifty 50 and the foreign indices are S & P 500 from USA, FTSE 100 from UK, and Nikkei 225 from Japan. The results reveal that the Indian stock market return is co-integrated with market returns of US, UK and Japanese stock markets. Therefore, the return and hence volatility of Indian stock market is associated with global markets which depicts that it is getting integrated with global financial markets. The results provide empirical evidence for volatility transmission or volatility spillover in the Indian stock market from global markets. There exists inbound volatility transmission from US market to Indian stock market. The Indian and UK stock market have bi-directional volatility transmission. However, there exists presence of only outbound volatility transmission from Indian stock market to Japanese stock market. The volatility transmission from global markets to India is rapid with the spillover effect existing for up to three days only.Janapriya Journal of Interdisciplinary Studies, Vol. 5 (December 2016), page: 83-101

Returns and volatility spillovers among cryptocurrency portfolios

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ismail Olaleke Fasanya ◽  
Oluwatomisin Oyewole ◽  
Temitope Odudu
Keyword(s):  
Design Methodology ◽  
Asset Returns ◽  
Volatility Spillover ◽  
Content Type ◽  
Vector Autoregressive ◽  
Volatility Spillovers ◽  
Dynamic Relationship ◽  
Major Market ◽  
Daily Data ◽  
Variance Decompositions

PurposeThis paper examines the return and volatility spillovers among major cryptocurrency using daily data from 10/08/2015 to 15/04/2018.Design/methodology/approachThe authors employ the Dielbold and Yilmaz (2012) spillover approach and rolling sample analysis to capture the inherent secular and cyclical movements in the cryptocurrency market.FindingsThe authors show that there is substantial difference between the behaviour of the cryptocurrency portfolios return and volatility spillover indices over time. The authors find evidence of interdependence among cryptocurrency portfolios given the spillover indices. While the return spillover index reveals increased integration among the currency portfolios, the volatility spillover index experiences significant bursts during major market crises. Interestingly, return and volatility spillovers exhibit both trends and bursts respectively.Originality/valueThis study makes a methodological contribution by adopting Dielbold and Yilmaz (2012) approach to quantify the returns and volatility transmissions among cryptocurrencies. To the best of our knowledge, little or no study has adopted the Dielbold and Yilmaz (2012) methodology to investigate this dynamic relationship in the cryptocurrencies market. The Dielbold and Yilmaz (2012) approach provides a simple and intuitive measure of interdependence of asset returns and volatilities by exploiting the generalized vector autoregressive framework, which produces variance decompositions that are unaffected by ordering.

An investigation of price discovery and volatility spillovers in India’s foreign exchange market

2015 ◽  
Vol 42 (2) ◽  
pp. 261-284 ◽  
Author(s):  
Sanjay Sehgal ◽  
Wasim Ahmad ◽  
Florent Deisting
Keyword(s):  
Price Discovery ◽  
Stock Exchange ◽  
Futures Market ◽  
Volatility Spillover ◽  
Futures Prices ◽  
Content Type ◽  
Volatility Spillovers ◽  
Currency Market ◽  
Short Run

Purpose – The purpose of this paper is to examine the price discovery and volatility spillovers in spot and futures prices of four currencies (namely, USD/INR, EURO/INR, GBP/INR and JPY/INR) and between futures prices of both stock exchanges namely, Multi-Commodity Stock Exchange (MCX-SX) and National Stock Exchange (NSE) in India. Design/methodology/approach – The study applies cointegration test of Johansen’s along with VECM to investigate the price discovery. GARCH-BEKK model is used to examine the volatility spillover between spot and futures and between futures prices. The other two models namely, constant conditional correlation and dynamic conditional correlation are used to demonstrate the constant and time-varying correlations. In order to confirm the volatility spillover results, the study also applies test of directional spillovers suggested by Diebold and Yilmaz (2009, 2012). Findings – The results of the study show that there is long-term equilibrium relationship between spot and futures and between futures markets. Between futures and spot prices, futures price appears to lead the spot price in the short-run. Volatility spillover results indicate that the movement of volatility spillover takes place from futures to spot in the short-run while spot to futures found in the long-run. However, the results of between futures markets exhibit the dominance of MCX-SX over NSE in terms of volatility spillovers. By and large, the findings of the study indicate the important role of futures market in price discovery as well as volatility spillovers in India’s currency market. Practical implications – The results highlight the role of futures market in the information transmission process as it appears to assimilate new information quicker than spot market. Hence, policymakers in emerging markets such as India should focus on the development of necessary institutional and fiscal architecture, as well as regulatory reforms, so that the currency market trading platforms can achieve greater liquidity and efficiency. Originality/value – Due to recent development of currency futures market, there is dearth of literature on this subject. With the apparent importance of currency market in recent time, this study attempts to study the efficient behavior of currency market by way of examining the price discovery and volatility spillovers between spot and futures and between futures prices of four currencies traded on two platforms. The study has strong implications for India’s stock market especially at the time when its currency is under great strain owing to the adverse impact of global financial crisis.

scholarly journals Dynamic Interrelationship and Volatility Spillover among Sustainability Stock Markets, Major European Conventional Indices, and International Crude Oil

2020 ◽  
Vol 12 (9) ◽  
pp. 3908 ◽  
Author(s):  
Basel Maraqa ◽  
Murad Bein
Keyword(s):  
Crude Oil ◽  
Stock Returns ◽  
Global Financial Crisis ◽  
Oil Prices ◽  
Volatility Spillover ◽  
Socially Responsible ◽  
Crude Oil Prices ◽  
Daily Data ◽  
Oil Markets ◽  
The Global Financial Crisis

This study examines the dynamic interrelationship and volatility spillover among stainability stock indices (SSIs), international crude oil prices and major stock returns of European oil-importing countries (UK, Germany, France, Italy, Switzerland and The Netherlands) and oil-exporting countries (Norway and Russia). We employ the DCC-MGARCH model and use daily data for the sample period from 28 September 2001 to 10 January 2020. We find that the dynamic interrelationship between SSIs, stock returns of European oil importing/exporting countries and oil markets is different. There is higher correlation between SSIs and oil-importing countries, while oil-exporting countries have higher correlation with the oil market. Notably, the correlation between oil and stock returns became higher during and after the global financial crisis. This study also reveals the existence of significant volatility spillover between sustainability stock returns, international oil prices and the major indices of oil importing/exporting countries. These results have important implications for investors who are seeking to hedge and diversify their assets and for socially responsible investors.

scholarly journals The performance of the Indian stock market during COVID-19

2020 ◽  
Vol 17 (3) ◽  
pp. 133-147
Author(s):  
Rashmi Chaudhary ◽  
Priti Bakhshi ◽  
Hemendra Gupta
Keyword(s):  
Standard Deviation ◽  
Stock Market ◽  
Stock Exchange ◽  
Health Care Information ◽  
Crisis Period ◽  
Daily Data ◽  
Composite Indices ◽  
Health Care Information Technology ◽  
Indian Stock Market ◽  
The Impact

The current empirical study attempts to analyze the impact of COVID-19 on the performance of the Indian stock market concerning two composite indices (BSE 500 and BSE Sensex) and eight sectoral indices of Bombay Stock Exchange (BSE) (Auto, Bankex, Consumer Durables, Capital Goods, Fast Moving Consumer Goods, Health Care, Information Technology, and Realty) of India, and compare the composite indices of India with three global indexes S&P 500, Nikkei 225, and FTSE 100. The daily data from January 2019 to May 2020 have been considered in this study. GLS regression has been applied to assess the impact of COVID-19 on the multiple measures of volatility, namely standard deviation, skewness, and kurtosis of all indices. All indices’ key findings show lower mean daily return than specific, negative returns in the crisis period compared to the pre-crisis period. The standard deviation of all the indices has gone up, the skewness has become negative, and the kurtosis values are exceptionally large. The relation between indices has increased during the crisis period. The Indian stock market depicts roughly the same standard deviation as the global markets but has higher negative skewness and higher positive kurtosis of returns, making the market seem more volatile.

scholarly journals Diffusion Entropy Analysis: Stability of Indian Stock Market

2019 ◽  
Vol 8 (4) ◽  
pp. 9358-9362
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Stock Exchange ◽  
Significant Drop ◽  
Bear Market ◽  
Diffusion Entropy ◽  
The Usa ◽  
The Stability ◽  
Indian Stock Market

The large amount of available data of stock markets becomes very beneficial when it is transformed to valuable information. The analysis of this huge data is essential to extract out the useful information. In the present work, we employ the method of diffusion entropy to study time series of different indexes of Indian stock market. We analyze the stability of Nifty50 index of National Stock Exchange (NSE) India and SENSEX index of Bombay Stock Exchange (BSE), India in the vicinity of global financial crisis of 2008. We also apply the technique of diffusion entropy to analyze the stability of Dow Jones Industrial Average (DJIA) index of USA. We compare the results of Indian Stock market with the USA stock market (DJIA index). We conduct an empirical analysis of the stability of Nifty50, Sensex and DJIA indexes. We find significant drop in the value of diffusion entropy of Nifty50, Sensex and DJIA during the period of crisis. Both Indian and USA stock markets show bull market effects in the pre-crisis and post-crisis periods and bear market effect during the period of crisis. Our findings reveal that diffusion entropy technique can replicate the price fluctuations as well as critical events of the stock market.

Return and volatility spillover effects between the Turkey and the Russia stock market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Melih Kutlu ◽  
Aykut Karakaya
Keyword(s):  
Stock Exchange ◽  
Spillover Effects ◽  
Stock Exchanges ◽  
Volatility Spillover ◽  
Full Sample ◽  
Content Type ◽  
Crisis Period ◽  
Volatility Spillovers ◽  
Volatility Analysis ◽  
Economic Developments

PurposeThis study aimed to investigate return and volatility spillover between the Borsa Istanbul (BIST) and the Moscow Stock Exchange (RTS).Design/methodology/approachThis study used generalized autoregressive conditionally heteroscedasticity (GARCH) model for volatility and the Aggregate Shock (AS) model for return and volatility spillover. The data are divided into six sub-periods. Period events take place between Turkey and Russia.FindingsBIST investors considered the return and volatility of the RTS, it is observed that Moscow Stock Exchange investors considered only the return of BIST at the full sample. It is only a return spillover from BIST to RTS and neither the return nor the volatility of the RTS is spillover to BIST in the pre-crisis period. No evidence of return and volatility spillover between the BIST and the RTS in the post-crisis period. The returns and volatility spillovers between Russia and Turkey are mutual feedback in the jet crisis period.Practical implicationsEconomic developments between Turkey and Russia is growing rapidly in recent years. The return and volatility analysis between the stock exchanges of these two countries is important for investment decisions.Originality/valueThere are many studies in the literature about emerging markets. There are also Turkish and Russian stock exchanges in these studies. However, this study only examined return and volatility spillover analysis between the Turkish and Russian stock exchanges and prevents the results from being overlooked among other countries.

scholarly journals Volatility Spillovers in Capesize Forward Freight Agreement Markets

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaoxing Gong ◽  
Jing Lu
Keyword(s):  
Goodness Of Fit ◽  
Global Financial Crisis ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Spillover Effects ◽  
Volatility Spillover ◽  
Research Subjects ◽  
Volatility Spillovers ◽  
Before And After ◽  
The Global Financial Crisis

This paper is to investigate spillovers in the Capesize forward freight agreements (FFAs) markets before and after the global financial crisis. The paper chooses four Capesize voyage routes FFAs (C3, C4, C5, and C7), two time-charter routes FFAs (BCIT/C average, BPI T/C average), and spot rates as research subjects, covering the periods 3 January 2006 to 24 December 2015. This paper applies Volatility Spillover Multivariate Stochastic Volatility (VS-MSV) model to analyze volatility spillover effects and estimates the parameters via software of Bayesian inference using Gibbs Sampling (BUGS), the deviance information criterion (DIC) used for goodness-of-fit model. The results suggest that there are volatility spillover effects in certain Capesize FFAs routes, and the effects from spot rates to FFAs take place before crisis, yet they are bilateral after crisis. With the development of shipping markets, the correlations between FFAs and spot rate are enhanced, and it seems that the effects depend on market information and traders’ behavior. So practitioners could make decisions according to the spillovers.

scholarly journals Investigating Spillover Effects between Foreign Exchange Rate Volatility and Commodity Price Volatility in Uganda

Economies ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 1 ◽  
Author(s):  
Lorna Katusiime
Keyword(s):  
Financial Crisis ◽  
Global Financial Crisis ◽  
Exchange Rate Volatility ◽  
Price Volatility ◽  
Spillover Effects ◽  
Commodity Price ◽  
Volatility Spillover ◽  
Conditional Correlation ◽  
Volatility Spillovers ◽  
The Global Financial Crisis

This study investigates the impact of commodity price volatility spillovers on financial sector stability. Specifically, the study investigates the spillover effects between oil and food price volatility and the volatility of a key macroeconomic indicator of importance to financial stability: the nominal Uganda shilling per United States dollar (UGX/USD) exchange rate. Volatility spillover is examined using the Generalized Vector Autoregressive (GVAR) approach and Multivariate Generalized Autoregressive Conditional Heteroskedasticity (MGARCH) techniques, namely the dynamic conditional correlation (DCC), constant conditional correlation (CCC), and varying conditional correlation (VCC) models. Overall, the results of both the GVAR and MGARCH techniques indicate low levels of volatility spillover and market interconnectedness except during crisis periods, at which point cross-market volatility spillovers and market interconnectedness sharply and markedly increased. Specifically, the results of the MGARCH analysis show that the DCC model produces the best results. The obtained results point to an amplification of dynamic conditional correlations during and after the global financial crisis (GFC), suggesting an increase in volatility spillovers and interdependence between these markets following the global financial crisis. This is also confirmed by the results of the total spillover index based on the GVAR analysis, which shows low but time-varying volatility spillover that intensified during periods of high uncertainty and market crises, particularly during the global financial crisis and sovereign debt crisis periods.

scholarly journals Volatility Spillover Analysis Post Implementation of AEC 2015 Agreement: Empirical Study on ASEAN-5 Stock Market

2018 ◽  
Vol 9 (2) ◽  
pp. 105
Author(s):  
Yunia Panjaitan ◽  
Siti Saadah
Keyword(s):  
Stock Market ◽  
Stock Return ◽  
Financial Integration ◽  
Stock Exchange ◽  
The Philippines ◽  
Stock Exchanges ◽  
Volatility Spillover ◽  
Asean Member ◽  
Daily Data ◽  
Asean Economic Community

Efforts to improve financial integration that continue to be implemented after the implementation of the Asean Economic Community 2015 agreement, can encourage increased integration of capital markets in countries within the region. This study was conducted to investigate the spillover of volatility between stock markets that accompanied the ongoing efforts of financial integration carried out by ASEAN member countries. Investigation of volatility spillover is done by applying Exponential GARCH method on time series daily data of stock return of ASEAN-5 countries period September 2016 - December 20, 2017. If previous studies found significant spillover of volatility from Singapore, Malaysia, Thailand and Philippines, the results of this study show that only Singapore's stock exchanges consistently have a significant impact on the Indonesian stock market. The turmoil in the Singapore stock market will be consistently transmitted to the Indonesian stock market. However, efforts to improve the financial integration carried out by ASEAN member countries have not consistently caused the turmoil in Malaysia, Thailand and the Philippines stock exchange to be transmitted to the Indonesian stock market.

Sign in / Sign up

Export Citation Format

Share Document