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The primary objective of the paper is to examine the short and long run price linkages between Malaysian unit trust funds and the stock market index as proxied by the Kuala Lumpur composite index (KLCI) over the period 1996‐2000.

Cointegration analyses are used to identify the long run relationship between unit trust funds and the stock market index while Granger causality tests are used to measure the short run price linkages.

Cointegration results show that the long run pricing performance of the unit trust funds differs significantly from that of the KLCI. Interestingly, the findings also reveal that two index funds are found not to be cointegrated with the stock market index. In the short run, one‐way Granger causality test shows that changes in the KLCI Granger causes changes in the unit trust funds. This suggests that fund managers are responding to the past changes in the stock market index over the short run.

The findings of non‐cointegration between passively managed funds and the KLCI are restricted to only two index funds in the sample among other actively managed funds. Since there were not enough index funds available over the study period, future research should include more index funds in the analysis.

In the short run, investors may gather information on the changes in their portfolio composition by observing the movement in the KLCI.

The paper represents the first evidence on the pricing relationships between unit trust funds and the local stock market index and the findings are important to investors in terms of their investment strategies.

The various stock market indexes are interrelated due to the similar fundamentals which determine the movement in the respective markets. Applying the efficient market hypothesis, an investor should not be able to predict the movement of one index based on the past movement of another index. If the stock markets are efficient, then no long term comovement should exist between stock market indexes. The existence of a long term relation can be tested by use of cointegration tests and common serial correlation feature tests. If no cointegration exists and if no common serial correlation feature exists, then we would not be rejecting efficiency of the stock markets. Using the S&P 500 stock index, the Wilshire 5000 index, and the NASDAQ index, the Hang Seng index, the Footsie index, and the Nikkei index to proxy world stock market indexes, the empirical results of the cointegration and common feature test support the efficiency of the stock markets in most instances. However, the Footsie index consistently demonstrated a relation with the three US stock market indexes included in the study.

The purpose of this paper is to examine the effects of the current global crisis on the integration and co‐movements of selected stock index futures markets.

Time series techniques of cointegration and weekly data covering the period from January 2001 to December 2009 were used in this study. The period of analysis was divided into two periods, namely the pre‐crisis period (January 2001‐July 2007) and during crisis period (August 2007‐December 2009).

No evidence was found of cointegration among the stock index futures markets in both periods. Accordingly, the 2007 subprime crisis does not seem to affect the long‐run co‐movements among the stock index futures markets.

The stock index futures markets provide opportunity for the potential benefits from international portfolio diversification and hedging strategies even after the subprime crisis. The stock index futures significantly extended the variety of investment and risk management strategies available to investors.

Examining the effects of the US subprime crisis on the stock index futures markets integration, to the best of the authors' knowledge, goes clearly beyond the existing literature on the subject matter.

This study aims to identify calendar anomalies that can affect stock returns and asymmetric volatility. Thus, the objective of this study is twofold: on the one hand, it examines the impact of calendar anomalies on the returns of both conventional and Islamic indices in Indonesia, and on the other hand, it analyzes the impact of these anomalies on return volatility and whether this impact differs between the two indices.

The authors apply the GJR-generalized autoregressive conditional heteroskedasticity model to daily data of the Jakarta Composite Index (JCI) and the Jakarta Islamic Index for the period ranging from October 6, 2000 to March 4, 2022.

The authors provide evidence that the turn-of-the-month (TOM) effect is present in both conventional and Islamic indices, whereas the January effect is present only for the conventional index and the Monday effect is present only for the Islamic index. The month of Ramadan exhibits a positive effect for the Islamic index and a negative effect for the conventional index. Conversely, the crisis effect seems to be the same for the two indices. Overall, the results suggest that the impact of market anomalies on returns and volatility differs significantly between conventional and Islamic indices.

This study provides useful information for understanding the characteristics of the Indonesian stock market and can help investors to make their choice between Islamic and conventional equities. Given the presence of some calendar anomalies in the Indonesia stock market, investors could obtain abnormal returns by optimizing an investment strategy based on seasonal return patterns. Regarding the day-of-the-week effect, it is found that Friday’s mean returns are the highest among the weekdays for both indices which implies that investors in the Indonesian stock market should trade more on Fridays. Similarly, the TOM effect is significantly positive for both indices, suggesting that for investors are called to concentrate their transactions from the last day of the month to the fourth day of the following month. The January effect is positive and statistically significant only for the conventional index (JCI) which implies that it is more beneficial for investors to invest only in conventional assets. In contrast, it seems that it is more advantageous for investors to invest only in Islamic assets during Ramadan. In addition, the findings reveal that the two indices exhibit lower returns and higher volatility, which implies that it is recommended for investors to find other assets that can serve as a safe refuge during turbulent periods. Overall, the existence of these calendar anomalies implies that policymakers are called to implement the required measures to increase market efficiency.

The existing literature on calendar anomalies is abundant, but it is mostly focused on conventional stocks and has not been sufficiently extended to address the presence of these anomalies in Shariah-compliant stocks. To the best of the authors’ knowledge, no study to date has examined the presence of calendar anomalies and asymmetric volatility in both Islamic and conventional stock indices in Indonesia.

This paper explores some institutional aspects of the Athens Stock Exchange (ASE) and investigates the time‐series properties of three major ASE stock indices. The results depict that future returns on these indices are difficult to predict. However, volatility in these indices can be predictable using the GARCH models. Various models for predicting volatility patterns are presented.

The chapter describes the recent history of Sustainability Indices in three Latin American countries: Brazil, Mexico, and Chile. In these countries, local Stock Exchanges have been recently launching their own Sustainability Indices. This ongoing trend may indicate a particular way of addressing Socially Responsible Investment (SRI) in the region. The chapter relies on secondary data, mainly documents published by the Stock Exchanges themselves, and on some selected academic and practitioner oriented articles. All three countries present some common features. In all cases, local stock markets launched Sustainability Indices, and their composition has been publicly available from the beginning. Consequently, SRI is now developing in the region in a different way from that of developed markets. The chapter is based on secondary data only. Further research may involve interviews and surveys with different stakeholders (i.e., investors, quoted companies, public officials). The illustration of a different way of developing an SRI market may help public officials and investors from other countries, either in Latin America or elsewhere, who intend to promote SRI. There are few studies on SRI in Latin America, and comparative research between different countries in the region is still rare.

The aim of this study is to compare volatility persistence with daily volatility and to analyze the asymmetry effect of volatilities in stock markets of emerging economies. Using daily observations of stock market indices of selected major emerging countries during the period of January 1, 2002 to December 31, 2018, the authors estimate the persistence, the half-life measure of volatility and the daily volatility of the return series using the GARCH model application. The authors also examine the leverage effect on stock market returns using the EGARCH model estimation. In addition, the authors investigate the impact of the 2008 global financial crisis on various volatility measures and the leverage effect of emerging stock market returns. The authors then examine and compare the different speeds of mean reversion, volatility persistence and leverage effects in the national stock market indices during the pre-crisis, crisis, and post-crisis periods. The authors hereby present evidence that the effects of negative shocks are significantly larger than those of positive shocks in emerging stock markets throughout their different sample periods.

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).

This chapter examines the impact of price fluctuations in foreign stock markets on the stock prices of domestic banks in Korea, Malaysia, Singapore, and Thailand. Some studies have argued that the 2007–2009 global financial crisis (GFC) affected domestic banks less in East Asia, even though the supporting evidence is rather limited. Employing a multinomial logit model, we estimate how changes in the United States and Japanese stock markets affected the banking sectors in the sampled countries before the 1997 Asian financial crisis, and before and during the more recent GFC. We interpret the number of banks in a given country that experienced a large price shock on the same day (or “coexceedance”) as shocks to the domestic banking sector. The results suggest that fluctuations in foreign stock market indices exerted a larger impact on the prices of East Asian banking stocks during the 2000s than during the 1990s. In addition, although the shocks brought about by the deterioration of foreign stock markets were significant before the GFC, both increases and decreases in foreign stock prices significantly affected the banking sectors of the respective countries during the crisis. Lastly, we conclude that increasing foreign capital flows and foreign assets and liabilities greatly influenced domestic banking systems in East Asia during the 2000s.

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.