Interdependence Between Some Major European Stock Markets - A Wavelet Lead/Lag Analysis

Stock market comovements between developed (represented in the article by markets of Austria, France, Germany, and the UK) and developing stock markets (represented here by three Central and Eastern European (CEE) markets of Slovenia, the Czech Republic, and Hungary) are of great importance for the financial decisions of international investors. From the point of view of portfolio diversification, short-term investors are more interested in the comovements of stock returns at higher frequencies (short-term movements), while long-term investors focus on lower frequencies comovements. As such, one has to resort to a time-frequency domain analysis to obtain insight about comovements at the particular time-frequency (scale) level. The empirical literature on the CEE and developed stock markets interdependence predominantly apply simple (Pearsons) correlation analysis, Granger causality tests, cointegration analysis, and GARCH modeling. None of the existent empirical studies examine time-scale comovements between CEE and developed stock market returns. By applying a maximal overlap discrete wavelet transform correlation estimator and a running correlation technique, we investigated the dynamics of stock market return comovements between individual Central and Eastern European countries and developed European stock markets in the period from 1997-2010. By analyzing the time-varying dynamics of stock market comovements on a scale-by-scale basis, we also examined how major events (financial crises in the investigated time period and entrance to the European Union) affected the comovement of CEE stock markets with developed European stock markets. The results of the unconditional correlation analysis show that the developed European stock markets of France, the UK, Germany and Austria were more interdependent in the observed period than the CEEs stock markets. The later group of countries exhibited a lower degree of comovement between themselves as well as with the developed European stock markets during all the observed time period. The Slovenian stock market was the least correlated with other stock markets. By using the rolling wavelet correlation technique, we wanted to answer the question as to how the correlation between CEE and developed stock markets changed over the observed period. In particular, we wanted to examine whether major economic (financial) and political events in the world and European economies (the Russian financial crisis, the dot-com financial crisis, the attack on the WTC, the CEE countries joining the European union, and the recent global financial crisis) have influenced the dynamics of CEE stock market comovements with developed European stock markets. The results show that stock market return comovements between CEE and developed European stock markets varied over time scales and time. At all scales and during the entire observed time period the Hungarian and Czech stock markets were more interconnected to developed European stock markets than the Slovenian stock market was. The highest comovement between the investigated CEE and developed European stock market returns was normally observed at the highest scales (scale 5, corresponding to stock market return dynamics over 32-64 days, and scale 6, corresponding to stock market return dynamics over 32-64 and 64-128 days). At all scales the Hungarian and Czech stock markets were more connected to developed European stock markets than the Slovenian stock market. We found that European integration lead to increased comovement between CEE and developed stock markets, while the financial crises in the observed period led only to short-term increases in stock market return comovements.DOI: http://dx.doi.org/10.5755/j01.ee.23.1.1221

W hen stock market risk, or volatility, increases, risk-averse investors tend to reduce their holding of equities relative to safe assets such as Treasury bills. Thus, to induce investors to hold a broadly measured stock market index, the expected excess stock market return—the difference between the return on the stock market index and a risk-free rate—has to rise. Such a positive relation between stock market volatility and returns is an important prediction of the widely accepted capital asset pricing model. The level of volatility tends to persist over time, and, hence, we expect that past volatility should provide some indication of future stock market returns. Several studies, however, have found that volatility, by itself, explains little of the variation of stock market returns. We replicate this result in the accompanying table. Quarterly realized vola tility is measured by the sum of the squared daily stock market returns in a quarter. We regress quarterly excess returns, measured by the difference between the return on the Standard & Poor’s 500 index and the yield on three-month Treasury bills, on this measure of volatility. Row 1 shows that the adjusted R2 from this regression is 0.01, indicating that volatility accounts for only about 1 percent of the variation of the one-quarter-ahead excess stock market return. The weak estimated relationship between stock market volatility and returns may reflect the fact that other factors also affect stock prices. For example, Guo (2000) shows that, in addition to the risk premium, a liquidity premium is also an important component of excess stock market returns.1 Intuitively, if investors have excess liquidity, they might be willing to hold stocks when expected return is low, even though expected volatility is high. However, the theory still stipulates a positive relation between stock market volatility and returns after taking into account the liquidity premium. Although the liquidity premium is not directly observable in data, the consumption-to-wealth ratio is a suitable proxy for it. Row 2 shows that the one-quarter-lagged consumption-to-wealth ratio has predictive power for excess stock market returns.2 Moreover, when this proxy for liquidity is included in the regression, we find that past volatility explains a significant portion of excess stock returns (row 3). Thus, theory and empirical evidence both indicate that stock market returns increase when volatility rises.

This thesis consists of three empirical studies on what drives stock market dynamics. The first empirical study explores the effect of crude oil price changes on the stock market returns of oil-exporting countries and oil-importing countries as well as those of a number of global stock indices. Using the Ordinary Least Squares (OLS) approach as well as the more robust Quantile Regression (QR) approach to explore the relationship between crude oil and stock market dynamics. The empirical findings suggest that the QR approach provides further insights compared to the OLS approach. For instance, the QR approach is able to identify specific quantiles where a significant relation exists. In particular crude oil price increases tend to have a negative impact on the stock market returns for some oil-exporting countries (such as Mexico, Iraq, Ecuador, and Venezuela) and a positive effect for other oil-exporting countries (such as Brazil and Algeria). However, the OLS approach suggests that these relationships are insignificant at the level of the mean. Overall, the empirical findings confirm that the QR approach can reveal more information about the relationship between crude oil price changes and stock market return across different quantiles of their distribution.The second study explores the extent to which implied volatility extracted from commodity markets and developed stock markets can predict the implied volatility of stock markets in BRICS countries. Using daily data from 2011 to 2016 and employing the newly developed Bayesian Graphical Vector Autoregressive (BGVAR) model of Ahelegbey et al. (2016) which does not suffer from over-parameterization and the identification problems associated with traditional VAR frameworks, this study finds that implied volatilities extracted from global and regional stock markets have a significant predictive power over the implied volatilities in BRICS stock markets. However, the predictive power of implied volatility from commodity markets are significant only in the case of South Africa.The third empirical study analyses the relationship between illiquidity and stock market returns in the G7 and BRICS countries. More specifically, this study explore the extent to which the Amihud (2002) illiquidity measure can improve the explanatory power of three commonly used asset pricing models, namely the Capital Asset Pricing Model (CAPM), the Fama-French three-factor model and the Carhart four-factor model. The empirical analysis is based on 15 years of monthly data on the returns of seven stock portfolios: 100 largest companies (Largest100), small value (S/V), small neutral (S/N), small growth (S/G) stocks, big value (B/V) stocks, big neutral (B/N) stocks, and big growth (B/G) stocks. The findings suggest that incorporating illiquidity as an additional factor results in a significant improvement in the explanatory power of these asset pricing models across several of the sample countries (8 countries in the case of the CAPM and Carhart four-factor model, and 6 countries in the case of the Fama-French three-factor model). For example, in the US adding illiquidity to the CAPM leads to an increase of the goodness of fit by 2.6% in the B/V portfolio, and for the Fama-French three-factor model the goodness of fit increases by up to 3% in all portfolios Moreover, the goodness of fit increases in all portfolios in the US by adding illiquidity to the Carhart four-factor model, with an up to 36% increase in the B/N portfolio.

Stock market return predictability has long been one of the key and unsolved areas of research in finance. Although the stock market has been argued to follow a random walk, researchers have struggled to improve the accuracy of predicting stock market returns through extensively examining forecasting variables such as financial ratios, economic indicators, and behaviour factors. Pollet and Wilson (2010) have recently developed a new predicator and claimed that average correlation reveals the movement of the systematic component of the market return and it predicts the stock market returns. This thesis uses the newly developed predictor, average correlation, to predict stock market returns, both in the US and across a number of developed countries and emerging countries. Three interrelated studies are sequentially undertaken to examine the predictive power of average correlation for future stock market returns. The first study uses the average correlation of the 48 Fama-French industry portfolio returns in the US stock market to predict the US stock market returns. To juxtapose average correlation with conventional predictors, a number of forecasting variables, including term spread, default spared, dividend price ratio, the cyclically adjusted price-to-earnings ratio and investor sentiment, are incorporated in the model. The second study uses 27 non-US financial markets and extends the analysis to the relatively less explored area relating to the predictability of the international stock market returns. The average correlation of industry portfolio returns in each financial market, including more forecasting variables such as industrial production, gross domestic production and financial crisis dummies, is used to predict the stock returns of the financial markets under study. The third study further extends the analysis and uses both the US average correlation from the first study and the local average correlation from the second study as predictors for the stock market returns of each financial market. The US average correlation is posited as capturing the global influence on a particular financial market, while the local average correlation is used to represent the domestic influence within that financial market. The key findings of the thesis are summarised as follows. First, average correlation is a significant predictor for the US stock market returns at a two-month lag and for the returns of other stock markets with a one-month lag. Second, average correlation outperforms all predictors conventionally used in the US stock market, as well as in most other international stock markets. Third, the US and local average correlations predict the local stock market returns, indicating that the global influence has an impact on the local stock market returns and that the US average correlation successfully captures such an influence. The research findings suggest that the average correlation is closely related to stock market returns. The findings of the thesis would be of interest to policymakers as well as stock market practitioners who wish to formulate effective trading strategies.

In this article we investigate comovement of the three Central and Eastern European (CEE) stock markets (Slovenia, the Czech Republic and Hungary) with certain developed European stock markets (Austria, France, Germany and the United Kingdom) through the novel approach of maximal overlap discrete wavelet transform (MODWT). We use two features of MODWT to explore energy decomposition of stock market returns at different time scales and to apply methodology of [29] to study comovement between investigated stock markets. We show that most of the energy (variability) of stock market return series is captured by scale 1 (which correspond to 2–4 days return dynamics) and scale 2 (which correspond to 4–8 days return dynamics) MODWT coefficients. MODWT details are used to show that comovement between stock markets is scale- dependent and declines from raw (daily) return series to first- and second-scale reconstructed return series. The findings of the survey then have important implications for foreign financial investors who already hold international portfolios that exactly replicate those of non-Czech or non- Hungarian stock markets: international investing in the Czech or Hungarian stock markets with investment horizons corresponding to scale 2 (4 to 8 days) brings greater international diversification benefits than shorter (2 to 4 day horizon) international trading diversification strategies. The Slovenian stock market differs from the Czech and Hungarian markets also in this respect, as when the scale is increased the benefits of diversification are reduced. We also find that the volatility of Slovenian stock index returns is less synchronized with other observed stock return series. Interestingly, the Czech and Slovenian stock markets seem to comove with the Austrian stock market to a greater extent than with other developed stock markets.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country's economic development.

The COVID-19 pandemic has emerged as a significant event of the current century, introducing substantial transformations in economic and social activities worldwide. The primary objective of this study is to investigate the relationship between daily COVID-19 cases and Pakistan stock market (PSX) return volatility. To assess the relationship between daily COVID-19 cases and the PSX return volatility, we collected secondary data from the World Health Organization (WHO) and the PSX website, specifically focusing on the PSX 100 index, spanning from March 15, 2020, to March 31, 2021. We used the GARCH family models for measuring the volatility and the COVID-19 impact on the stock market performance. Our E-GARCH findings show that there is long-term persistence in the return volatility of the stock market of Pakistan in the period of the COVID-19 timeline because ARCH alpha (ω1) and GARCH beta (ω2) are significant. Moreover, is asymmetrical effect is found in the stock market of Pakistan during the COVID-19 period due to Gamma (ѱ) being significant for PSX. Our DCC-GARCH results show that the COVID-19 active cases have a long-term spillover impact on the Pakistan stock market. Therefore, the need of strong planning and alternative platform should be needed in the distress period to promote the stock market and investor should advised to make diversified international portfolio by investing in high and low volatility stock market to save their income. This study advocated the implications for investors to invest in low volatility stock especially during the period of pandemics to protect their return on investment. Moreover, policy makers and the regulators can make effective policies to maintain financial stability during pandemics that is very important for the country’s economic development.

This paper studies the association and the model construction of exchange rate, Japanese and U.S. stock markets, the data period is from January 1998 to June 2006. In this paper also uses the heavy-tailed Student's t distribution to analysis the proposed model. Empirical results show that the relationships of exchange rate, Japanese and U.S stock markets, we can construct a trivariate GJR-GARCH (1,2)-M model to evaluate them, and the exchange rate and U.S. stock markets do have the asymmetrical effects. The empirical results also show that the U.S. and the Japanese stock market returns have the positive relations, namely the two stock market returns' volatility are mutually affect, and they are synchronized each other. And the Japanese stock market return volatility receives the previous 1 period influence of the U.S. stock market return volatility. The exchange rate and the Japanese stock market return have the negative relations, namely the two market volatility are the reverse influence, and the Japanese stock market return volatility also receives the previous 1 period influence of the exchange rate volatility. The exchange rate and the U.S. stock market return also have the negative relations, namely two market volatility are the reverse influence, also exchange rate does not have receive the lags' influence of the Japanese and the U.S. stock price market return volatility. Besides, under the bad news in U.S. Stock market, the variation risk increases in the U.S. stock market. Under the bad news in Japan's exchange rate market, the variation risk increases in the Japan's exchange rate market. As such the error correction and GJR-GARCH-M model has the best explanatory ability as compared to the model of the error correction and GARCH-M. Of these evidences may suggest, for example, the stock market investors or international fund managers, on the decision-making before of the investment stock or the evaluation stock market, they must considers the risk and the relatedness of the exchange rate volatility and the stock price return volatility.

The spread of COVID-19 has caused severe damage to human lives and the global economy. The stock markets around the world have plummeted to their lowest levels since the 2008 Global Financial Crisis. This paper attempts to examine the joint dynamics of gold and stock market returns during unprecedented times of health and financial shock due to COVID-19 between January 2020 and May 2020 using granger test, ARMA model, and symmetric and asymmetric GARCH models to improve the understanding of the microstructure of investment scenario in India. The period considered in the study helps to evaluate the impact of lockdown due to coronavirus on Gold and Nifty index return. Results based on GARCH and E-GARCH models indicate a significant negative impact of gold on nifty returns during the sample period. The results also indicate investors' perception of gold as a safe-haven asset during periods of elevated uncertainty. Thus, the study is expected to enhance the understanding of market asymmetry, the behavior of investors towards these avenues of investments, and information processing.

This paper uses the Singapore and the Thailand's stock prices of material from January 4, 2000 to July 20, 2007, discussing the model construction and their associations of between Singapore and Thailand's stock markets, and also uses Student's t distribution to analyze the proposed model. The empirical results show that the mutual affects of the Singapore and the Thailand's stock markets may construct in bivariate IGARCH (1, 1) model with a DCC. The empirical result also shows that between Singapore and Thailand's stock market returns exists the positive relations- namely two stock market return's volatility are synchronized influence, the average estimation value of the DCC coefficient of two stock market returns equals to 0.3876. Also, Singapore and Thailand's stock markets do not have the asymmetrical effect in the research data period. These evidences may suggest stock market investors or international fund managers- before investing in Singapore must consider the Thailand stock price return's volatility risk and its connection. Therefore, in the stock market, investors and managers may not neglect the influence of the foreign country's stock market return volatility behavior; otherwise, his decision will not achieve the anticipated effect.

(Study on the Impact of Us Stock Market's Shock Due to Global Financial Crisis on Korean Financial Markets)

This paper examines the impact of oil price fluctuations on a large set of stock market returns in net-oil importer countries and net-oil exporter countries. It applies multivariate cDCC-GARCH model, which has greater flexibilities, allowing the conditional variance covariance matrix of stock market returns to vary over time. Daily data spanning from January 2005 to February 2016 is used to obtain dynamic correlations between crude oil and stock market returns. Moreover, it employs the commonly recognized vector auto regression (VAR) specification and the corresponding Granger causality test in order to examine the linear relationship between crude oil and stock market volatility within each country, revealing whether there is a causal relationship between the variables in terms of time precedence. The influence of bullish and bearish market conditions is also measured by dividing the sample period into two sub-periods: Global Financial Crisis Period (2007–2010) and Post-Crisis Period (2010–2016). Main findings of this research indicate time-varying correlation of oil and stock prices for oil-importing countries is more pronounced than that for oil-exporting countries. This result shows that the correlation between the volatilities of stock market and oil price returns varies depending on the net position of the country in global oil market.

Purpose/Relevance: The main goal of this study is to find whether the cryptocurrency market does significantly impact the stock market prices and returns. Conspicuously, understanding this impact is quite interesting to clarify whether cryptocurrency market and stock market are acting as substitute or complement markets for investors. Design/Methodology: We compile stock market daily data obtained from the historical indices of the Gulf countries with cryptocurrency market dataset obtained from bitcoincharts.com on a daily basis over the period 2014-2019. Properties of Panel Cointegration tests (Kao, Pedroni, Westerlund), Panel Causality tests as well as Generalized Method of Moments with Instrumental Variable (IV-GMM) have been implemented to fulfill the objective of the paper. Findings: The results of this paper show that the Stock market and cryptocurrency market are acting as substitutes in Gulf countries. Each 10 percent increase in the cryptocurrency returns, the stock market returns in Gulf countries shrink 0.17 percent. Cryptocurrency market does significantly hamper the stock market indices in the Gulf countries. Originality/Value: Having agreed upon in the literature that stock market is affected by fundamental factors, market sentiment, technical factors as well as anomalies, this study offers robust evidence that cryptocurrency should be introduced, nowadays, as one of the main determinants of stock market prices and returns.

The main purpose of this study is to investigate the causal response of the stock market returns to COVID-19 related economic support in 19 emerging countries by using the Maximal Overlap Discrete Wavelet Transform (MODWT) and Fourier Toda-Yamamoto Causality Test (FTYCT). With the help of MODWT, we identify the instant, short-term, mid-term and long-term reactions of stock market returns and COVID-19 related economic support to each other. Implementing FTYCT, we determine the existence of the causal relationships running from COVID-19 related economic support to stock returns. We obtain two major results. First, the COVID-19 related economic support have significant effects on stock market returns in the short-, medium-, and long-term, except in China. Second, the results of the causality tests vary across countries based on the different time scales. Some emerging markets show an immediate reaction to the Economic Support, while most stock market reactions occur over the medium- and long-term. Since economic support will created unintended effects on stock market returns, the way that these support policies are implemented should be reconsidered. Also, their effectiveness should be evaluated carefully.

This paper uses the Thailand and the Singapore's stock prices of material from January, 2001 to December, 2009, discussing the model construction and their associations of between Thailand and Singapore's stock markets. The empirical results show that the mutual affects of the Thailand and the Singapore's stock markets may construct in bivariate IGARCH (1, 1) model with a DCC. The empirical result also shows that between Thailand and Singapore's stock market returns exists the positive relation- namely these two stock market return's volatility is synchronized influence, the average estimation value of the DCC coefficient of two stock market returns amounts to 0.4632. Also, Thailand and Singapore's stock markets do not have the asymmetrical effect in the research data period. The variation risk of the Singapore stock market truly receives the influence of the Hong Kong stock market. But the variation risk of the Singapore stock market does not receive the influence of the Japan stock market. The variation risk of the Thailand stock market does not receive the influence of the Japan and the Hong Kong stock markets.