Correlation and volatility in an Indian stock market: A random matrix approach

Kulkarni, Varsha S.; Deo, Nivedita

doi:10.1140/epjb/e2007-00322-1

Cited by 39 publications

(20 citation statements)

References 15 publications

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“…For UT, it is a extremely-high-volatility segment that coincides with the high-volatility segment in other sectors. the nontrivial cross-correlations between different financial time series using random matrix theory [122][123][124][125][126][127][128][129]. In all these studies, the cross-correlations were computed either over the entire data period, or over sliding windows.…”

Section: Tablementioning

confidence: 99%

Will the US economy recover in 2010? A minimal spanning tree study

Zhang

Lee

Wong

et al. 2011

Physica A: Statistical Mechanics and its Applications

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a b s t r a c tWe calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period and also over 11 segments within the present financial crisis, to construct minimal spanning trees (MSTs) of the US economy at the sector level. In all MSTs, a core-fringe structure is found, with consumer goods, consumer services, and the industrials consistently making up the core, and basic materials, oil & gas, healthcare, telecommunications, and utilities residing predominantly on the fringe. More importantly, we find that the MSTs can be classified into two distinct, statistically robust, topologies: (i) star-like, with the industrials at the center, associated with low-volatility economic growth; and (ii) chain-like, associated with high-volatility economic crisis. Finally, we present statistical evidence, based on the emergence of a starlike MST in Sep 2009, and the MST staying robustly star-like throughout the Greek Debt Crisis, that the US economy is on track to a recovery.

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Section: Tablementioning

confidence: 99%

Will the US economy recover in 2010? A minimal spanning tree study

Zhang

Lee

Wong

et al. 2011

Physica A: Statistical Mechanics and its Applications

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“…Various volatility estimates and diagnostic tests indicated volatility clustering, i.e., shocks to the volatility process persist and the response to news arrival was asymmetrical, meaning that the impact of good and bad news is not the same. Kulkarni and Deo (2005) observed the volatility of an Indian stock market in terms of aspects like participation, synchronization of stocks and quantification of volatility using the random matrix approach. Pandey (2005) compared the empirical performance of various unconditional volatility estimators and conditional volatility models (GARCH and EGARCH) using time-series data of S&PCNX Nifty, a value-weighted index of 50 stocks traded on the National Stock Exchange (NSE), Mumbai Estimates computed by various estimators and conditional volatility models over non overlapping one-day, five-day and one-month periods are compared with the "Realized Volatility" measured over the same period.…”

Section: Literature Reviewmentioning

confidence: 99%

A study of volatility behaviour of S&P BSE BANKEX return in India: A pragmatic approach using GARCH model

Khan¹,

Javed

2017

Int. j. adv. appl. sci

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The purpose of this study is to know that how the National and International market, namely (S&P BSE SENSEX) (NASDAQ) (SSE) (FTSE) can influence the volatility of (S&P BSE BANKEX) return in India and the factors affecting the volatility for the same. However, the previous studies mostly considered the volatility of stock in the Indian capital market. But the present study mainly focuses on the Bankex return volatility. Here the researcher identified and estimated the mean and variance components of the daily Bankex return using Garch (1, 1) model by explaining the volatility structure of the residuals obtained under the best-suited model for the used data series. The method ML -ARCH (Marquardt) -Normal distribution has satisfied the criterion of model selection based on the three assumptions. These Null Hypotheses deals with the problems firstly, no serial correlation, secondly, the presence of no ARCH effect, thirdly, residual are normally distributed. We have chosen daily data period from 03 rd May, 2012 to 08th January 2016, nearly 914 working days of all markets for estimating Arch-Garch model. The study shows the significant result of ARCH and GARCH effect. The Bankex Return is also significantly affected by endogenous variable (SENSEX return). The NASDAQ composite and SSE composite Index are also statistically significant, In sum-up, the foreign market return volatility or outside shock can influence the volatility of BANKEX return. However, the FTSE 100 was not found statistically significant, revealing that volatility in FTSE return cannot transmit to S&P BSE BANKEX return in India.

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“…Tulino described the application of the basic constraints of wireless communication channels on the basis of stochastic matrix theory [4]. Varsha Kulkarni analyzed the volatility of the Indian stock market based on stochastic matrix theory [5]. Based on the knowledge of stochastic matrix, Meng and Xie used absorption rate, linear regression and clustering methods to study the correlation degree and division of the American real estate market, and found that the risk of the American estate market was very high and the whole market was unstable [6,7].…”

Section: Literature Review Of Stochastic Matricesmentioning

confidence: 99%

The Spatiotemporal Dynamics of Natural Gas Imports in OECD Countries

Yang

et al. 2017

Sustainability

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The natural gas receives universal attention and priority use around the world because of its high quality and cleanness. The Organisation for Economic Co-operation and Development (OECD) countries' data on imports of natural gas are rich in market information and have foresight. We quantify the role of the market in resource allocation. Based on the random matrix method, this article studies the correlation coefficient matrix of OECD countries' natural gas imports by average correlation coefficients and explores the information that the natural gas imports market contains. At the same time, this article analyzes the risk and correlation in the OECD countries' natural gas imports market by eigenvalues, regression analysis and absorption. Finally, combined with the clustering method, we synchronize and regionalize the OECD countries' natural gas import market. We can find that the OECD countries' natural gas imports are related by analyzing the correlation coefficient and the risk. Correlations in OECD countries' natural gas imports market are high. We find that different eigenvalues have different effects on the OECD countries' natural gas imports market by researching on the entropy of the eigenvalues. We can correctly predict the turning points of OECD countries' natural gas imports and find the correlation coefficient matrix contains advanced market information. Through the method of absorption rate, the risk of the gas import market in OECD countries is further studied. It is found that the import of natural gas in OECD countries is large and the whole market is unstable. Through the method of dynamic clustering, it is found that the whole OECD countries have high synchronism in the market of natural gas import, except for Germany.

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Correlation and volatility in an Indian stock market: A random matrix approach

Cited by 39 publications

References 15 publications

Will the US economy recover in 2010? A minimal spanning tree study

Will the US economy recover in 2010? A minimal spanning tree study

A study of volatility behaviour of S&P BSE BANKEX return in India: A pragmatic approach using GARCH model

The Spatiotemporal Dynamics of Natural Gas Imports in OECD Countries

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