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In this chapter the author studies the capital market efficiency hypothesis and checks whether the stock price adjustment dynamics is instantaneous, continuous, and linear or not. In particular, the author proposes to analyze the stock price evolution while taking into account the presence of transaction costs, the coexistence of heterogeneous investors, and the interdependence between stock markets. On the one hand, he provides strong evidence to suggest that the efficiency hypothesis is rejected. On the other hand, he proves that the stock index adjustment is rather discontinuous, asymmetrical, and nonlinear. Using threshold cointegration techniques, he proposes a new nonlinear modeling to reproduce the CAC40 adjustment dynamics that not only replicates the French market adjustment dynamics in the presence of market frictions but also captures the interdependence between the French and American stock markets, highlighting the reaction of French shareholders in relation to the changes in the behaviour of American speculators.

We propose a novel dynamic factor model to characterise comovements between returns on securities from different asset classes from different countries. We apply a global-class-country latent factor model and allow time-varying loadings. We are able to separate contagion (asset exposure driven) and excess interdependence (factor volatility driven). Using data from 1999 to 2012, we find evidence of contagion from the US stock market during the 2007–2009 financial crisis, and of excess interdependence during the European debt crisis from May 2010 onwards. Neither contagion nor excess interdependence is found when the average measure of model implied comovements is used.

The purpose of this chapter is to analyze the possible portfolio diversification opportunities between Asian Islamic market and other regions’ Islamic markets; namely USA, Europe, and BRIC. This study makes the initial attempt to fill in the gaps of previous studies by focusing on the proxies of global Islamic markets to identify the correlations among those selected markets by employing the recent econometric methodologies such as multivariate generalized autoregressive conditional heteroscedastic–dynamic conditional correlations (MGARCH–DCC), maximum overlap discrete wavelet transform (MODWT), and the continuous wavelet transform (CWT). By utilizing the MGARCH-DCC, this chapter tries to identify the strength of the time-varying correlation among the markets. However, to see the time-scale-dependent nature of these mentioned correlations, the authors utilized CWT. For robustness, the authors have applied MODWT methodology as well. The findings tend to indicate that the Asian investors have better portfolio diversification opportunities with the US markets, followed by the European markets. BRIC markets do not offer any portfolio diversification benefits, which may be explained partly by the fact that the Asian markets cover partially the same countries of BRIC markets, namely India and China. Considering the time horizon dimension, the results narrow down the portfolio diversification opportunities only to the short-term investment horizons. The very short-run investors (up to eight days only) can benefit through portfolio diversification, especially in the US and European markets. The above-mentioned results have policy implications for the Asian Islamic investors (e.g., Portfolio Management and Strategic Investment Management).

The study investigates the interdependence of the stock markets between the following countries Hong Kong, Japan, Korea, Taiwan, Indonesia, Malaysia, Philippines, Singapore, Thailand and the advanced stock markets of Australia, Germany, United Kingdom and the United States. Using data from 1994 to 2003 the paper employs both correlation, causality and cointegration analysis to describe the behaviour of the above stock market indices over the period pre and post the 1997 Asian Financial Crises. The paper investigates both the short- and long-run relationships between the Asian markets and the markets of selected advanced industrial countries.

Capital markets thrive on information, and the information revolution has transformed these markets all over the world. Investors can now keep track of the movements of capital markets in real-time and they react to the flow of information from around the world. One of the concerns of stock market investors is whether the markets operate efficiently, independently, and with sound fundamentals. However, real market movements tend to exhibit a link as is evident from recent market movements across the world.

The assessment of interdependence between stock markets is an important aspect of international portfolio management. The aim of this chapter is to examine the shock and volatility spillover between the Standard and Poor’s 500 (S&P500) index from the United States (US) Stock Exchange and the Istanbul Stock Exchange 100 (BIST100) index from the Stock Exchange Istanbul.

S&P500 index, which is the most important index representing US markets, and BIST100 index, which is the index representing the Turkish market, were used as variables in this study. In the analysis, the causality in variance test was applied to determine the volatility spillover between these two markets. Later, multivariate GARCH (MGARCH) models were used to measure the volatility spillover in the markets. VAR(1)-GARCH (1,1)-Diagonal BEKK model was applied to the daily data to determine the shock and volatility spillover in the markets.

As a result of the variance causality test, it was found that there is a bi-directional volatility spillover between S&P500 index and BIST100 index. When the return spillover between the markets is examined, a one-way spillover from the S&P500 index to the BIST100 index emerged. Diagonal BEKK model results show that each market is affected by its own news (unexpected shocks) and volatility. Furthermore, the volatility is persistent for both markets. These findings demonstrate that the US market and the Turkish market interact with each other.

This chapter examines dynamic connectedness among emerging Asian equity markets as well as explores their linkages vis-à-vis other major global markets. We find that international equity markets are tightly integrated. Measuring connectedness based on a generalized Vector Autoregressive (VAR) model, more than half of all total forecast error variance in equity return and volatility shocks come from other markets as opposed to country own shocks. When examining the degree of connectedness over time, we find that international stock markets have become increasingly connected, with a gentle upward trend since the Asian financial crisis (AFC) but with a rapid burst during the global financial crisis (GFC). Despite the growing importance of Asian emerging markets in the world economy, we find that their influence on advanced economies are still relatively small, with no significant increase over time. During the past decade, advanced markets have been consistently net transmitters of shocks while emerging Asian markets act as net receivers. Based on the nature of equity shock spillovers, we also find that advanced countries are still tightly connected among themselves while intraregional connectedness within Asia remains strong. By investigating whether uncertainty plays an important role in explaining the degree of stock market connectedness, we find that economic policy uncertainty (EPU) from the US is an important source of financial shock spillover for the majority of international equity markets. In contrast, US financial market uncertainty as proxied by the VIX index drives equity market spillovers only among advanced economies.

The study evaluated the interlinkages and diversification opportunities in the context of emerging bond markets from 2007:1 to 2020:5, using the vector autoregressive (VAR) model and sub‐period analyses to compare BRIC (2007:1–2010:11) and BRICS (2010:12–2020:5) regimes. As indicated by the breaking unit‐root test, dummies for the global financial crisis and COVID‐19 were incorporated in the analyses. VAR results showed that the Indian bond market responds positively to the previous change in the Chinese bond market during the BRIC era while BRICS bond markets are mostly uninfluenced by prior behavior patterns of one another. These suggested that the diversification opportunity has been increased following the admission of South Africa to the league. In addition, variance decomposition and impulse response provide proofs to suggest that BRICS bond markets are more exogenous and independent compared to what is obtained during the BRIC period. Consequently, the authors concluded that the BRICS bloc has provided greater diversification opportunities for emerging markets’ bondholders in the recent past.

This chapter proposes augmenting a simple income stock price model with spatial structures to evaluate the significance of real and financial linkages in instigating stock market contagion. The treatment is premised upon the clustering of excessive returns and volatilities during the Subprime crisis envisaged from our regime switching analysis over a long time span, and the presence of spatial autocorrelation in the baseline income stock model. With the channel factors manifested as spatial weights, this chapter explores specifications of explicit interrelated stock price returns and implicit spatial autocorrelation in the error term for the 3-year period from 2007 to 2009. Model validity is authenticated by way of model choice and spatial weight selection. The finding shows that spatial dependence in either specification is not too sizable indicating that contagion is not spreading fast in the sample period. Of the various factors considered, non-performing loans, market liquidity, and credit to deposit ratio turn out to be the most important transmission factors. Current account balance, net FDI flows, and size of GDP are among the least significant media. In sum, these suggest that financial linkages could play a more important role in facilitating shock transmission when compared to real linkages such as trade.

The COVID-19 pandemic has recently had an impact on the stock market all over the globe. A thorough review of the literature that included the most cited articles and articles from well-known databases revealed that earlier research in the field had not specifically addressed how the BRIC stock markets responded to the COVID-19 pandemic. The data regarding COVID-19 were collected from the World Health Organization (WHO) website, and the stock market data were collected from Yahoo Finance and the respective country’s stock exchange. A random forest regression algorithm takes the closing price of respective stock indices as target variables and COVID-19 variables as input variables. Using this algorithm, a model is fit to the data and is visualised using line plots. This study’s findings highlight a relationship between the COVID-19 variables and stock market indices. In addition, the stock market of BRIC countries showed a high correlation, especially with the Shanghai Composite Stock Index with a correlation value of 0.7 and above. Brazil took the worst hit in the studied duration by declining approximately 45.99%, followed by India by 37.76%. Finally, the data set’s model fit, which employed the random forest machine learning method, produced R² values of 0.972, 0.005, 0.997, and 0.983 and mean percentage errors of 1.4, 0.8, 0.9, and 0.8 for Brazil, Russia, India, and China (BRIC), respectively. Even now, two years after the coronavirus pandemic started, the Brazilian stock index has not yet returned to its pre-pandemic level.

In recent decades, interconnections between countries have increased substantially worldwide as the process of integration and globalisation intensifies, with a positive impact in terms of economic development, but, also with a vulnerability to external shocks, such as the financial contagion phenomenon. The analysis of this research field becomes even more relevant in the context of a new major exogenous shock, but which, this time, has different specificities, being a sanitary crisis. Thus, the chapter aims to investigate the impact of crises on capital market volatility for the period of 1995–2021, using the bibliometric analysis highlighting the dynamics of the literature and potential future research directions through a science mapping that enables investigating scientific knowledge. In order to explore the development of the research field in terms of publications, author impact, affiliated institutions and countries, citation patterns, trending topics, relationship between keywords–authors–journals, abstracts’ analysis, authors and documents clustering by coupling, multiple correspondence analysis of major research themes, keyword analysis, co-citation analysis and authors, institutions and countries collaboration analysis have been applied. Hence, almost 500 publications from Web of Science database covering the period 1995–2021 have been extracted. The empirical findings emphasise the conceptual structure, with clusters focussing mainly on long-term receivables, market efficiency, volatility, dynamic conditional correlation (DCC)-GARCH models, asymmetric effects. According to the intellectual structure of the field, Lambertides N., Zopiatis A., McAleer M. or Savva C. S. are the most representative authors for the sub-area of volatility topic; whilst Balcerzak A. P., Pietrzak M. B., Zinecker M., Meluzin T. and Faldzinski M. are the reference names for the whole spectrum of DCC-GARCH models’ topic. Jayasekera R., Lundblad C., Choundhry T., Gupta R. and Demirer R. are the authors mostly associated with asymmetric effects’ topic, whilst Thorp S., Bouchaud J. P. and Dungey M. with the quantitative finance. The Journal of Banking & Finance, the Journal of International Money and Finance and the International Review of Financial Analysis as well as Economic Modelling, Research in International Business and Finance and the International Journal of Finance & Economics are the most prolific journals in the field of capital flow and financial crises. This chapter’s main contribution is to build a structure of knowledge for the impact of crises on capital market volatility, elaborate and classify empirical research into relevant dimensions that can be used as a reference for comprehensively developing research. Finally, the bibliometric analysis results may provide insight into future research prospects. Our conclusions offer some recommendations for market practitioners and policy-making.