Time-varying Correlation Between Indian Equity Market and Selected Asian and US Stock Markets

2019 ◽  
Vol 21 (6) ◽  
pp. 1354-1375 ◽  
Author(s):  
Neha Seth ◽  
Laxmidhar Panda
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
Stock Markets ◽  
Equity Market ◽  
Leverage Effect ◽  
Time Varying ◽  
Asymmetric Effect ◽  
Conditional Volatility ◽  
Conditional Correlation ◽  
Volatility Persistence

The purpose of this article is to examine the dynamic relationship between the Indian stock market and the selected Asian and US stock markets during the post-crisis period. This article uses univariate GARCH (Generalized Autoregressive Conditional Heteroskedasticity) family models on daily observations from March 2009 to December 2015 to evaluate the volatility persistence and leverage effect on Asian developed (Japan, Singapore and Hong Kong) and emerging markets (India, China, Indonesia, Korea, Malaysia and Taiwan) along with the US stock market. AR (Autoregressive) ( 1 )-GARCH (1, 1)-ADCC (Asymmetric DCC) model is employed to find out the dynamic correlation between the Indian equity market and other selected stock markets. The results of the present study give evidence of the leverage effect in conditional volatility but not in conditional correlation, which implies that the rise in conditional volatility is more due to negative shocks than positive ones. On the other hand, dynamic conditional correlation (DCC) does not support any asymmetric effect for the time-varying correlation. The result of average conditional correlation shows the existence of higher diversification opportunities for Indian investors in Malaysian, Chinese and Japanese stock markets (having lower conditional correlation) than in Hong Kong, Indonesian and South Korean markets. The DCC fluctuates more in the cases of India with Singapore, Hong Kong and Indonesia over the sample period. It indicates that the stability of DCC is less reliable and the coefficient of correlation may not be used as a guide for portfolio decisions. But the cases of India with the USA, Japan and China show more stable conditional correlation coefficients. This article investigates the volatility persistence and time-varying relationship among Asian stock markets during the recent period, 2009–2015. The results of this article may be helpful in international portfolio planning and will contribute towards the literature on asymmetric time-varying relationships among Asian markets.

ARE STOCK MARKETS AND CRYPTOCURRENCIES CONNECTED?

2020 ◽  
pp. 1-16
Author(s):  
MUHAMMAD UMAR ◽  
NGO THAI HUNG ◽  
SHIHUA CHEN ◽  
AMJAD IQBAL ◽  
KHALIL JEBRAN
Keyword(s):  
Operating System ◽  
Stock Market ◽  
Stock Markets ◽  
Stock Exchange ◽  
Composite Index ◽  
Wavelet Coherence ◽  
Time Varying ◽  
Conditional Correlation ◽  
Dynamic Conditional Correlation ◽  
Nasdaq Composite Index

This study explores the connectedness between cryptocurrencies (Bitcoin, Ethereum, Ripple, Bitcoin cash and Ethereum Operating System) and major stock markets (NYSE composite index, NASDAQ composite index, Shanghai Stock Exchange, Nikkei 225 and Euronext NV). Using the asymmetric dynamic conditional correlation (ADCC) and wavelet coherence approaches, we document a significant time-varying conditional correlation between the majority of the cryptocurrencies and stock market indices and that the negative shocks play a more prominent role than the positive shocks of the same magnitude. Overall, our findings explore potential avenues for diversification for investors across cryptocurrencies and major stock markets.

scholarly journals A Time-Varying Multivariate Noncentral Contaminated Normal Copula Model and Its Application to the Visualized Dependence Analysis of Hong Kong Stock Markets

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Zhenyu Xiao ◽  
Jie Wang ◽  
Teng Yuan Cheng ◽  
Kuiran Shi
Keyword(s):  
Hong Kong ◽  
Financial Market ◽  
Stock Markets ◽  
Financial Data ◽  
Copula Model ◽  
Dependence Analysis ◽  
Time Varying ◽  
Conditional Correlation ◽  
Normal Copula ◽  
Dependence Structures

Financial data usually have the features of complexity and interdependence structure, such as asymmetric, tail, and time-varying dependence. This study constructs a new multivariate skewed fat-tailed copula, namely, noncentral contaminated normal (NCCN) copula, to analyze the dependent structure of financial market data. The dynamic conditional correlation (DCC) model is also incorporated into constructing the time-varying NCCN copula model. This study comprehensively examines the effects of the DCC-NCCN copula and related models on fitting dependence structures of Hong Kong stock markets. The results show that the DCC-NCCN copula model can better depict the dependence structures of returns. Considering the flexibility and complexity, the DCC-NCCN copula model is a relatively ideal, time-varying, multivariate skewed fat-tailed copula model.

scholarly journals Empirical Evidence on Time-Varying Hedging Effectiveness of Emissions Allowances under Departures from the Cost-of-Carry Theory

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Kai Chang
Keyword(s):  
Futures Market ◽  
Time Varying ◽  
Conditional Volatility ◽  
Conditional Correlation ◽  
Spot Prices ◽  
Market Participants ◽  
Hedging Effectiveness ◽  
Hedge Ratios ◽  
Cost Of Carry ◽  
The Cost

Under departures from the cost-of-carry theory, traded spot prices and conditional volatility disturbed from futures market have significant impacts on futures price of emissions allowances, and then we propose time-varying hedge ratios and hedging effectiveness estimation using ECM-GARCH model. Our empirical results show that conditional variance, conditional covariance, and their correlation between between spot and futures prices exhibit time-varying trends. Conditional volatility of spot prices, conditional volatility disturbed from futures market, and conditional correlation of market noises implied from spot and futures markets have significant effects on time-varying hedge ratios and hedging effectiveness. In the immature emissions allowances market, market participants optimize portfolio sizes between spot and futures assets using historical market information and then achieve higher risk reduction of assets portfolio revenues; accordingly, we can obtain better hedging effectiveness through time-varying hedge ratios with departures from the cost-of-carry theory.

scholarly journals The Symmetric and Asymmetric Time-Varying Causality Relationships Between the COVID-19 Outbreak and the Stock Exchange: The Case of Selected Countries

Ekonomika ◽  
2021 ◽  
Vol 100 (2) ◽  
pp. 144-170
Author(s):  
Cuma Demirtaş ◽  
Munise Ilıkkan Özgür ◽  
Esra Soyu
Keyword(s):  
Mortality Rate ◽  
Stock Market ◽  
Stock Markets ◽  
Time Varying ◽  
Causality Test ◽  
Causality Tests ◽  
Bed Capacity ◽  
Case Rate ◽  
Asymmetric Causality ◽  
Second Wave

In this study, the effects of COVID-19 (mortality rate, case rate, and bed capacity) on the stock market was examined within the framework of the efficient market hypothesis. Unlike other studies in the literature, we used the variable of bed capacity besides the mortality rate and case rate variables. The relationship between the mentioned variables, using daily data between December 31 of 2019 and November 10 of 2020, has been analyzed with time-varying symmetric and asymmetric causality tests for China, Germany, the USA, and India. Considering that the responses to positive and negative shocks during the pandemic process may be different and that the results may change depending on time, time-varying symmetric and asymmetric causality tests were used. According to the time-varying symmetric causality test, stock markets in all countries were affected in the period when the cases first appeared. A causal relationship between COVID-19 and country stock markets was found. The results showed that the effects of the case rate and bed capacity on the stock market occurred around the same time in Germany and the United States; however, these dates differed in China and India. According to time-varying asymmetric causality test findings, the asymmetric effect of the pandemic on the stock market in countries emerged during the second wave. The findings showed that the period during which positive and negative information about the pandemic intensified coincided with the period during which the second wave occurred; besides, the results show the effect of this information on the stock market differed as positive and negative shocks.

Measuring Dependence Between the USA and the Asian Economies: A Time-varying Copula Approach

2019 ◽  
Vol 20 (4) ◽  
pp. 962-980 ◽  
Author(s):  
Shegorika Rajwani ◽  
Dilip Kumar
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Financial Markets ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Tail Dependence ◽  
Time Varying ◽  
The Us ◽  
Asian Economies ◽  
Copula Approach

During the past few years, many of the financial markets have gone through devastating effects due to the crisis in one or the other economy of the world. The recent global financial crisis has triggered dramatic movements in various stock markets which may arise from interdependence or contagion between the markets. This article attempts to measure the contagion between the equity markets of Asia and the US stock market. The countries considered in the Asian group are China, India, Indonesia, South Korea, Taiwan, Hong Kong, Malaysia and Japan. Most of the Asian economies have experienced drastic higher volatility and uncertainty in the financial markets. If the markets are contagious, then the investors will be unable to reap benefits through international diversification of the portfolio. In such a case, the policymakers will further frame policies so that they can insulate themselves from inflicting heavy damage from various crises. To achieve our goal, we make use of the time-varying copula approach which helps us to study the joint behaviour of the series based on their marginal distribution. Time-varying copula approach can also capture the non-linear dependence in the series and exhibits a rich pattern of tail behaviour. Our findings support the contagion between the Asian stock markets and the US stock market during the global financial crisis. This article also highlights that the increased tail dependence is an important factor for the contagion between the Asian stock markets and the US market.

DCC Analysis of the Two Stock Market Returns by a Threshold Model: Empirical Study of the Stock Markets in Japan and Canada

2012 ◽  
Vol 468-471 ◽  
pp. 181-185
Author(s):  
Wann Jyi Horng ◽  
Tien Chung Hu ◽  
Ming Chi Huang
Keyword(s):  
Stock Market ◽  
Empirical Result ◽  
Stock Markets ◽  
Threshold Model ◽  
Conditional Correlation ◽  
Market Returns ◽  
Stock Market Returns ◽  
Asymmetrical Effect ◽  
Relationship Of ◽  
The Relationship

The empirical results show that the dynamic conditional correlation (DCC) and the bivariate asymmetric-IGARCH (1, 2) model is appropriate in evaluating the relationship of the Japan’s and the Canada’s stock markets. The empirical result also indicates that the Japan and the Canada’s stock markets is a positive relation. The average estimation value of correlation coefficient equals to 0.2514, which implies that the two stock markets is synchronized influence. Besides, the empirical result also shows that the Japan’s and the Canada’s stock markets have an asymmetrical effect, and the variation risks of the Japan’s and the Canada’s stock market returns also receives the influence of the good and bad news, respectively.

The Shanghai-Hong Kong Stock Market

2015 ◽  
Vol 07 (03) ◽  
pp. 36-45
Author(s):  
Jing WAN
Keyword(s):  
Hong Kong ◽  
Stock Market ◽  
Capital Markets ◽  
Capital Market ◽  
Stock Markets ◽  
Market Access ◽  
Mainland China ◽  
Opening Up ◽  
Hong Kong Stock Market ◽  
The Capital Market

The Stock Connect scheme launched on 17 November 2014 was the first mutual market access between mainland China and Hong Kong stock markets. It is the biggest move ever in the opening up of the capital market. Experiences accumulated will be of great value to mainland regulators who will decide on how these experiences could be utilised for China’s future opening up of its capital markets and for accelerating renminbi internationalisation.

Volatility persistence and trading volume in an emerging futures market

2010 ◽  
Vol 11 (3) ◽  
pp. 296-309 ◽  
Author(s):  
Pratap Chandra Pati ◽  
Prabina Rajib
Keyword(s):  
Capital Market ◽  
Trading Volume ◽  
Price Volatility ◽  
Futures Market ◽  
Market Volatility ◽  
Leverage Effect ◽  
Time Varying ◽  
Conditional Volatility ◽  
Content Type ◽  
Futures Contract

PurposeThe purpose of this paper is to estimate time‐varying conditional volatility, and examine the extent to which trading volume, as a proxy for information arrival, explain the persistence of futures market volatility using National Stock Exchange S&P CRISIL NSE Index Nifty index futures.Design/methodology/approachTo estimate the volatility and capture the stylized facts of fat‐tail distribution, volatility clustering, leverage effect, and mean‐reversion in futures returns, appropriate ARMA‐generalized autoregressive conditional heteroscedastic (GARCH) and ARMA‐EGARCH models with generalized error distribution have been used. The ARMA‐EGARCH model is augmented by including contemporaneous and lagged trading volume to determine their contribution to time‐varying conditional volatility.FindingsThe paper finds evidence of leverage effect, which indicates that negative shocks increase the futures market volatility more than positive shocks of the same magnitude. In addition, the results indicate that inclusion of both contemporaneous and lagged trading volume in the GARCH model reduces the persistence in volatility, but contemporaneous volume provides a greater reduction than lagged volume. Nevertheless, the GARCH effect does not completely vanish.Practical implicationsResearch findings have important implications for the traders, regulatory bodies, and practitioners. A positive volume‐price volatility relationship implies that a new futures contract will be successful only to the extent that there is enough price uncertainty associated with the underlying asset. Higher trading volume causes higher volatility; so, it suggests the need for greater regulatory restrictions.Originality/valueEquity derivatives are relatively new phenomena in Indian capital market. This paper extends and updates the existing empirical research on the relationship between futures price volatility and volume in the emerging Indian capital market using improved methodology and recent data set.

TIME-VARYING LONG MEMORIES OF THE CHINESE CURRENCY AND STOCK MARKETS BASED ON THE HURST EXPONENT

2014 ◽  
Vol 13 (01) ◽  
pp. 1450007 ◽  
Author(s):  
CAO GUANGXI ◽  
HAN YAN ◽  
CUI WEIJUN
Keyword(s):  
Exchange Rate ◽  
Stock Market ◽  
Stock Markets ◽  
Hurst Exponent ◽  
Composite Index ◽  
Fluctuation Analysis ◽  
Time Varying ◽  
Chinese Stock Market ◽  
Currency Market ◽  
Cointegration Tests

Based on the daily return and volatility series of the Chinese yuan (RMB)/US dollar (USD) exchange rate and the Shanghai Stock Composite Index, the time-varying long memories of the Chinese currency and stock markets are investigated by comprehensively using the rescaled range (R/S), the modified R/S, and the detrended fluctuation analysis methods. According to the results drawn: (1) the efficiency of the Chinese currency market has not improved significantly, whereas the efficiency of the Chinese stock market has improved steadily, (2) volatility series presents longer memory than return series either in the Chinese currency or stock market and (3) the time-varying Hurst exponent of the Chinese currency market is sensitive to the reform that enhances the flexibility of the RMB exchange rate. Moreover, we find that short-term bidirectional Granger causal relationship exists, but no long-run equilibrium relationship between the time-varying Hurst exponents of the Chinese currency and stock markets was found based on the Granger causality and cointegration tests, respectively.

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