Integration of emerging stock markets with global stock markets

Financial integration for emerging economies should be seen as a long-term objective. In this paper, we examine stock market integration among five selected emerging stock markets (Brazil, China, Mexico, Russia and Turkey) and developed markets of the US, UK and Germany. The bounds testing approach to cointegration and error-correction modeling are used on monthly data from January 2001 to December 2014 to determine the short-run and long-run relationship between emerging stock market returns and the returns of the developed stock markets. The results show evidence of the existence of short-run integration among stock markets in emerging countries and the developed markets. However, the long-run coefficients for stock market returns in all emerging countries show a significant relationship only with Germany stock market return. The empirical findings in this study have important implications for academicians, international investors, and policymakers in emerging markets.     

An empirical analysis of herd behavior in global stock markets

This paper examines herding behavior in global markets. By applying daily data for 18 countries from May 25, 1988, through April 24, 2009, we find evidence of herding in advanced stock markets (except the US) and in Asian markets. No evidence of herding is found in Latin American markets. Evidence suggests that stock return dispersions in the US play a significant role in explaining the non-US market’s herding activity. With the exceptions of the US and Latin American markets, herding is present in both up and down markets, although herding asymmetry is more profound in Asian markets during rising markets. Evidence suggests that crisis triggers herding activity in the crisis country of origin and then produces a contagion effect, which spreads the crisis to neighboring countries. During crisis periods, we find supportive evidence for herding formation in the US and Latin American markets.     

The asymmetric relationship between returns and implied volatility: Evidence from global stock markets

We investigate the asymmetric relationship between returns and implied volatility for 20 developed and emerging international markets. In particular we examine how the sign and size of return innovations affect the expectations of daily changes in volatility. Our empirical findings indicate that the conditional contemporaneous return-volatility relationship varies not only based on the sign of the expected returns but also upon their magnitude, according to recent results from the behavioral finance literature. We find evidence of an asymmetric and reverse return-volatility relationship in many advanced, Asian, Latin-American, European and South African markets. We show that the US market displays the highest reaction to price falls, Asian markets present the lowest sensitivity to volatility expectations, while the Euro area is characterized by a homogeneous response both in terms of direction and impact. These results may be safely attributed to cultural and societal characteristics. An extensive quantile regression analysis demonstrates that the detected asymmetric pattern varies particularly across the extreme distribution tails i.e., in the highest/lowest quantile ranges. Indeed, the classical feedback and leverage hypotheses appear not plausible, whilst behavioral theories emerge as the new paradigm in real-world applications.     

Stochastic correlation across international stock markets

This paper examines the correlation across a number of international stock market indices. As correlation is not observable, we assume it to be a latent variable whose dynamics must be estimated using data on observables. To do so, we use filtering methods to extract stochastic correlation from returns data. We find evidence that the estimated correlation structure is dynamically changing over time. We also investigate the link between stochastic correlation and volatility. In general, stochastic correlation tends to increase in response to higher volatility but the effect is by no means consistent. These results have important implications for portfolio theory as well as risk management.     

Time-varying pattern causality inference in global stock markets

Causality analysis can reveal the intrinsic interactions in financial markets. Though Granger causality test and transfer entropy method have successfully determined positive and negative causal interactions, they fail to reveal a more complex causal interaction, dark causality. Moreover, the causal relationship between variables may be time-varying. Thus, in this work, we are dedicated to determining the nature of causal interaction and explore the time-varying causality in global stock markets. To achieve this goal, pattern causality (PC) theory, cross-convergent mapping (CCM) theory, the sliding window method and complex networks are applied. By them, three causal interactions with different strength are revealed in global stock markets, and the causal strength is time-varying in different periods both in simulated systems and financial markets. While the dominant causal interaction is stable except for some stock pairs in frontier and emerging markets. In total, we determine the positive dominant causality in global stock markets; that is, the overall consistent trend among stocks can be explored. Additionally, we discover some exceptions that show negative dominant causality, where the reverse trend can be revealed among them; moreover, their dominant causality is time-varying. These uncertainties should receive great attention from investors and government managers.     

Local and global price memory of international stock markets

The prime focus of this paper is on the impact of the world’s leading markets (USA, Japan, Hong Kong, UK, France, Switzerland and Germany) on the returns of the small Nordic markets (Denmark, Finland, Norway and Sweden). The order and the degree of processing both ‘local’ and ‘global’ information are uncovered using a combination of cointegration analysis and structural VAR modeling utilizing daily index returns. The results indicate that the US price changes, conditioned on the same day changes on the other markets, have an impact on all other markets during the following day, including the US market itself. Price changes on the Asian–Pacific markets are completely absorbed in price changes in Europe and do not have any direct effect on US prices. Finally, a cointegration relationship between Sweden and Norway is found, which affects also Finland.     

Volatility and cross correlation across major stock markets

Several papers have documented the fact that correlations across major stock markets are higher when markets are more volatile—this is done by comparing unconditional correlations over sub-periods or by using conditional correlations that are time varying. In this paper we examine the relation between correlation and variance in a conditional time and state varying framework. We use a switching ARCH (SWARCH) technique that does two things. One, it enables us to model variance as state varying. Two, a bivariate SWARCH model allows us to go from conditional variance to state varying covariances and correlations and hence test for differences in correlations across variance regimes. We find that the correlations between the U.S. and other world markets are on average 2 to 3.5 times higher when the U.S. market is in a high variance state as compared to a low variance regime. We also find that, compared to a GARCH framework, the portfolio choices resulting from our SWARCH model lead to higher Sharpe ratios.     

Contagion as a domino effect in global stock markets

This paper shows that stock market contagion occurs as a domino effect, where confined local crashes evolve into more widespread crashes. Using a novel framework based on ordered logit regressions we model the occurrence of local, regional and global crashes as a function of their past occurrences and financial variables. We find significant evidence that global crashes do not occur abruptly but are preceded by local and regional crashes. Besides this form of contagion, interdependence shows up by the effect of interest rates, bond returns and stock market volatility on crash probabilities. When it comes to forecasting global crashes, our model outperforms a binomial model for global crashes only.     

Modelling return and conditional volatility exposures in global stock markets

This article empirically investigates the exposure of country-level conditional stock return volatilities to conditional global stock return volatility. It provides evidence that conditional stock market return volatilities have a contemporaneous association with global return volatilities. While all the countries included in the study exhibited a significant and positive relationship to global volatility, emerging market volatility exposures were considerably higher than developed market exposures. JEL Classification G12     

Returns synchronization and daily correlation dynamics between international stock markets

The use of close-to-close returns underestimates returns correlation because international stock markets have different trading hours. With the availability of 16:00 (London time) stock market series, we find dynamics of daily correlation and covariance, estimated using two non-synchroneity adjustment procedures, to be substantially different from their synchronous counterparts. Conditional correlation may have different signs depending on the model and data type used. Other findings include volatility spillover from the US to the UK (and France), and a reverse spillover which is not documented before. Also, unlike previous findings, we found the increase in daily correlation is prominent only under extremely adverse conditions when a large negative return has been registered.     

The recovery of global stock markets indices after impacts due to pandemics

The COVID-19 brings back the debate about the impact of disease outbreaks in economies and financial markets. The error correction terms (ECT) and cointegration processing tools have been applied in studies for identifying possible transmission mechanisms between distinct time series. This paper adopts the vector error correction model (VECM) to investigate the dynamic coupling between the pandemics (e.g., the COVID-19, EBOLA, MERS and SARS) and the evolution of key stocks exchange indices (e.g., Dow-Jones, S&P 500, EuroStoxx, DAX, CAC, Nikkei, HSI, Kospi, S&P ASX, Nifty and Ibov). The results show that the shocks caused by the diseases significantly affected the markets. Nonetheless, except for the COVID-19, the stock exchange indices reveal a sustained and fast recovering when an identical length time window of 79 days is analyzed. In addition, our findings contribute to point a higher volatility for all financial indices during the COVID-19, a strong impact over the Ibov-Brazil and its poor recover when compared to the other indices.     

Links and interdependence of developed stock markets under global economic crisis conditions

The scope of this article is to determine whether global stock markets behave differently under conditions of economic crisis by studying the interdependence among the price indices of 10 markets, including Dow Jones (DJ), DAX and NIKKEI. The stock markets under examination are those of the USA, Belgium, France, Germany, Greece, Italy, The Netherlands, Spain, the United Kingdom and Japan. The sample includes the logarithmic daily closing prices from 1 January 2000 to 20 February 2009, with a total of approximately 2.385 observations analyzed. The empirical findings suggest that the recent deep crisis has increased dramatically their correlation, thus tightening the existing links. Causality also seems to be affected by the crisis, as DJ and DAX cease to exert a dominant influence on the other stock indices. However, in all the other periods, the findings of previous studies (suggesting that DJ and DAX seriously affect the other indices) were verified, independent of the prevailing bear or bull market conditions.     

Interconnected multilayer networks: Quantifying connectedness among global stock and foreign exchange markets

This paper proposes a novel interconnected multilayer network framework based on variance decomposition and block aggregation technique, which can be further served as a tool of linking and measuring cross-market and within-market contagion. We apply it to quantifying connectedness among global stock and foreign exchange (forex) markets, and demonstrate that measuring volatility spillovers of both stock and forex markets simultaneously could support a more comprehensive view for financial risk contagion. We find that (i) stock markets transmit the larger spillovers to forex markets, (ii) the French stock market is the largest risk transmitter in multilayer networks, while some Asian stock markets and most forex markets are net risk receivers, and (iii) interconnected multilayer networks could signal the financial instability during the global financial crisis and the COVID-19 crisis. Our work provides a new perspective and method for studying the cross-market risk contagion.     

Integration of 22 emerging stock markets: A three-dimensional analysis

We apply the three-dimensional analysis of wavelet coherency to examine the integration of 22 emerging stock markets with the U.S. market. We find a high degree of co-movement at relatively lower frequencies between the U.S. and the 22 individual emerging markets. Our results show that the strength of co-movement, however, differs by country. For example, we report a high degree of co-movement between the U.S. and Brazil, Mexico and Korea, but low co-movement with and Egypt and Morocco. Our analyses also document a general change in the pattern of the market relationship after 2006, where we detect co-movements at relatively higher frequencies. Co-movement at the highest frequencies is, however, weak for fluctuations with duration less than a year. Our findings imply that investing selectively in emerging markets may provide significant diversification benefits which, invariably, depend on the investment horizon.     

An analysis of trading strategies in eleven European stock markets

Abstract

In recent years, the validity of the weak form efficient market hypothesis (EMH) has been called into question as several studies have uncovered evidence that technical trading rules have predictive ability with respect to both developed and emerging stock market indices. This study analyses the forecasting power of 2 of the most popular trading rules using index data for a selection of 11 European stock markets over the January 1991 to December 2000 period. The findings indicate that the emerging markets included in this paper are informationally inefficient; these markets displayed some degree of predictability in their share returns, although the developed markets did not. Furthermore, the results point to large differences in the performance of the rules examined; while small size filters consistently outperformed the buy-and-hold strategy in the emerging markets examined even after the consideration of transaction costs, the performance of the moving average rules was erratic and varied dramatically from market to market.     

Non-random topology of stock markets

We have analysed the cross correlations of daily fluctuations for [iopmath latex="$N=6358$"] N = 6358 [/iopmath] US stock prices during the year 1999. From those [iopmath latex="$N(N-1)/2$"] N(N-1)/2 [/iopmath] correlation coefficients, the minimum spanning tree (MST) has been built. We have investigated the topology exhibited by the MST. Even though the average coordination number of stocks is [iopmath latex="$langle n rangleapprox 2$"] n2 [/iopmath], the variance [iopmath latex="$sigma$"] [/iopmath] of the topological distribution [iopmath latex="$f(n)$"] f(n) [/iopmath] diverges! More precisely, we have found that [iopmath latex="$f(n) sim n^{-2.2}$"] f(n)~n ^-2.2 [/iopmath] holds over two decades. We have studied the topological correlations for neighbouring nodes: an extremely broad set of local configurations exists, confirming the divergence of [iopmath latex="$sigma$"] [/iopmath].     

Price limit bands,asymmetric volatility and stock market anomalies: Evidence from emerging markets

I investigate the effects of imposing different bands of price limits on stock returns and volatility in the Egyptian (EGX), Thai (SET) and Korean (KRX) stock exchanges. In addition, the paper examines whether the switch from narrow price limits (NPL) to wider price limits (WPL) structurally alters volatility and the day of the week anomaly. Using the extended EGARCH and PARCH asymmetric volatility models, I found that the switch from NPL to WPL structurally altered both asymmetric volatility and the day of the week anomaly in the EGX, SET and KRX. I argue that the price discovery mechanism is disrupted due to the switch as closing prices do not fully reflect all information arrived in the market when prices hit the limits and that is reflected on volatility and market efficiency.     

Volatility and correlation in international stock markets and the role of exchange rate fluctuations

This paper develops a direct, explicit model for the role of exchange rate fluctuations in international stock markets and examines how and to what extent volatility and correlations in equity markets are influenced by exchange rate fluctuations. Evidence presented in this paper indicates that a higher foreign exchange rate variability mostly increases local stock market volatility but decreases volatility for the US stock market. The extent to which stock market volatility is influenced by foreign exchange variability is greater for local markets than for the US market, due to the fact that exchange rate changes are more strongly correlated with local equity market returns than the US market returns. We find that a higher exchange rate fluctuation marginally decreases the US/local equity market correlation. While exchange rate fluctuations held a relatively large fraction of the variation in local stock market returns, there was no significant influence on the US/local equity market correlation.     

Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach

Measuring the systemic risk contribution (SRC) of country-level stock markets helps understand the rise of extreme risks in the worldwide stock system to prevent potential financial crises. This paper proposes a novel SRC measurement based on quantifying tail risk propagation's domino effect using ΔCoVaR and the cascading failure network model. While ΔCoVaR captures the tail dependency structure among stock markets, the cascading failure network model captures the nonlinear dynamic characteristics of tail risk contagion to mimic tail risk propagation. As an illustration, we analyze 73 markets' SRCs using a daily closing price dataset from 1990.12.19 to 2020.9.8. The validity test demonstrates that our method outperforms seven classic methods as it helps early warning global financial crises and correlates to many systemic risk determinants, e.g., the market liquidity, leverage, inflation, and fluctuation. The empirical results identify that Southeast European markets have higher SRCs with time-varying and momentum features corresponding to significant financial crisis events. Besides, it needs attention that South American and African markets have displayed increasing risk contributions since 2018. Overall, our results highlight that considering tail risk contagion's dynamic characteristics helps avoid underestimating SRC and supplement a “too cascading impactive to fail” perspective to improve financial crisis prevention.