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The extent of correlation or co-movement among the returns of developed and emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners are analyzed. The dynamic conditional correlation–exponential generalized autoregressive conditional heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibits time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between the JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises. [ABSTRACT FROM AUTHOR]

The extent of correlation or co-movement among the returns of developed and emerging stock markets remains pivotal for efficiently diversifying global portfolios. This correlation is prone to variation over time as a consequence of escalating economic interdependence fostered by international trade and financial markets. In this study, the time-varying correlation and co-movement between the JSE.JO stock market of South Africa and its developed and developing stock market partners are analyzed. The dynamic conditional correlation–exponential generalized autoregressive conditional heteroscedasticity (DCC-EGARCH) methodology is employed with different multivariate distributions to explore the time-varying correlation and volatilities between the JSE.JO stock market and its partners. Based on the conditional correlation results, the JSE.JO stock market is integrated and co-moves with its partners, and the conditional correlation for all markets exhibits time-variant behavior. The conditional volatility results show that the JSE.JO stock market behaves differently from other markets, especially after 2015, indicating a positive sign for investors to diversify between the JSE.JO and its partners. The highest value of conditional volatility for markets was in 2020 during the COVID-19 pandemic, representing the riskiest period that investors should avoid due to the lack of diversification opportunities during crises.

This document is a compilation of various academic articles and research papers that explore the relationship between stock prices and exchange rates in different countries and regions. The articles use various methodologies and approaches to analyze this relationship, including multivariate models, bivariate causality analysis, and panel data analysis. The findings of the studies are diverse, with some suggesting a significant relationship between stock prices and exchange rates, while others find limited or no relationship. The articles provide valuable insights into the complex dynamics between these two variables, but further research is needed to fully understand their relationship. [Extracted from the article]

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This study investigates the role of oil futures price information on forecasting the US stock market volatility using the HAR framework. In-sample results indicate that oil futures intraday information is helpful to increase the predictability. Moreover, compared to the benchmark model, the proposed models improve their predictive ability with the help of oil futures realized volatility. In particular, the multivariate HAR model outperforms the univariate model. Accordingly, considering the contemporaneous connection is useful to predict the US stock market volatility. Furthermore, these findings are consistent across a variety of robust checks.

This paper discusses the volatility spillovers between the Greek debt crisis and the Cypriot financial crisis. Cyprus was in the spotlight of financial markets due to significant problems stemming from the banking sector, which were dealt with by EU regulators with a bail-in on bank deposits. The current analysis aims to shed light on the reasons behind implementing this novel approach to bank distress. The study uses a Dynamic Conditional Correlation model on the returns of the stock markets of the two countries, which shows strong spillover effects during the period leading up to the 2013 Cypriot crisis, but a significant decrease of these effects from then on. The results confirm the close interdependence of the Greek and Cypriot economies before 2013 and show that this interdependence was limited from that point onwards. This would indicate that since the risk of contagion to the Eurozone had diminished, regulators could test the bail-in solution in Cyprus in 2015. The current work contributes to the discussion on the interdependence of European economies. The paper’s findings can also be applied to other emerging European economies.

This study investigates the effect of introducing a fiscally neutral increasing block-rate water budget price structure on residential water demand. We estimate that demand was reduced by at least 18 percent, although the reduction was achieved gradually over more than three years. As intermediate steps the study derives estimates of price and income elasticities that rely only on longitudinal variability. We investigate how different subpopulations responded to the pricing change and find evidence that marginal, rather than average, prices may be driving consumption. Additionally, we derive alternative rate structures that might have been implemented, and assess the estimated demand effects of those rate structures.

This paper focuses on four major aggregate stock price indexes (SP 500, Stock Europe 600, Nikkei 225, Shanghai Composite) and two "safe-haven" assets (Gold, Swiss Franc), and explores their return co-movements during the last two decades. Significant contagion effects on stock markets are documented during almost all financial crises; moreover, in line with the recent literature, the defensive role of gold and the Swiss Franc in asset portfolios is highlighted. Focusing on a new set of macroeconomic and financial series, a significant impact of these variables on stock returns correlations is found, notably in the case of the world equity risk premium. Finally, long-run risks are detected in all asset portfolios including the Chinese stock market index. Overall, this empirical evidence is of interest for researchers, financial risk managers and policy makers. [ABSTRACT FROM AUTHOR]

In the present investigation an attempt has been made to forecast and understand the volatility transmission in onion prices for three vital markets in Maharashtra, viz. Lasalgaon, Pune and Nagpur. The ARIMAX-GARCH model was employed to estimate mean and volatility among the different markets and also examined the nature of dynamic correlation using the DCC model. The quantity arrival of each market was considered as covariate to improve the mean forecast. We have obtained superior results for ARIMAX-GARCH over ARIMAX model in terms of forecasting. Forecasting efficiency of the models was judged in terms of lower RMSE and MAPE values. Presence of volatility was found in and between the markets as well. Lasalgaon market exhibits highest volatility, whereas the combination of Lasalgaon and Nagpur market experiences the largest volatility movement among them. Identification of interdependency of the markets in terms of volatility movement helps the traders as well as policy makers in a large way. The concerned stakeholders can easily anticipate the prices of other dependent market based on the behaviour of one market.

The dependence structure between the main energy markets (such as crude oil, natural gas, and coal) and the main stock index plays a crucial role in the economy of a given country. As the dependence structure between these series is dramatically complex and it appears to change over time, time-varying dependence structure given by a class of dynamic copulas is taken into account.To this end, each pair of time series returns with a dynamic t-Student copula is modelled, which takes as input the time-varying correlation. The correlation evolves with the DCC(1,1) equation developed by Engle.The model is tested through a simulation by employing empirical data issued from the Italian Stock Market and the main connected energy markets. The author considers empirical distributions for each marginal series returns in order to focus on the dependence structure. The model’s parameters are estimated by maximization of the log-likelihood. Also evidence is found that the proposed model fits correctly, for each pair of series, the left tail dependence coefficient and it is then compared with a static copula dependence structure which clearly underperforms the number of joint extreme values at a given confidence level.

Empirical studies show that there is stronger dependency between large losses than large profit in financial market, which undermine the performance of using symmetric distribution for modeling these asymmetric. That is why the assuming normal joint distribution of returns is not suitable because of considering the linier dependence, and can be lead to inappropriate estimate of VaR. Copula theory is basic tool for multivariate modeling, which is defined by using marginal and dependencies between variables joint distribution function. In addition, Copulas are able to explain and describe of complex multiple dependencies structures such as non-linear dependence. Therefore, in this study, by combining symmetric and asymmetric GARCH model for modeling the marginal distribution of variables and Copula functions for modeling financial data and also use of DCC model to determine the dynamic correlation structure between assets, try to estimate the Value at Risk of investment portfolio consists of five active index In Tehran Stock Exchange. The results demonstrate excellence of GJR-GARCH(1,1) with the distribution of t-student for marginal distribution. t-Copula model, estimates the Value at Risk model less than the Gaussian Copula in all cases.

Highlights • We apply a new DCC model incorporating asymmetric spillovers and skew- t errors. • Return transmission is evident among oil and equities of oil-producing countries. • Volatility spillovers are evident among oil and oil-producing countries' equities. • US and Canadian oil equities are more informationally efficient. • Hedging equities with oil is more effective than hedging oil with equities. Abstract This paper examines the return transmission, volatility spillovers, dynamic correlations, and financial risks among oil futures and oil and gas sector equity returns of the US, Canada, Australia, and Russia by applying our new vector autoregressive (VAR) dynamic conditional correlation (DCC) multivariate exponential generalized autoregressive conditional heteroscedasticity (MEGARCH) model incorporating asymmetric spillovers and skew- t errors. We employ bivariate models for oil futures and oil and gas sector equity returns and a four-variate model for oil and gas sector equity returns. Our results reveal mostly unidirectional return transmission between oil futures and oil equities, unidirectional and bidirectional volatility spillovers between oil futures and oil equities, and mostly bidirectional volatility spillovers among oil equities. Interpreting our results from an informational efficiency perspective suggests that US and Canadian oil equities are more informationally efficient than Australian or Russian oil equities, and that oil futures are not always the most informationally efficient. Our results also indicate that downside risk is important in considering volatility spillovers because they are always associated with the well-known leverage effect. Using the conditional variances and covariances from our new model, we further compute more precise time-varying optimal hedge ratios and portfolio weights, and these suggest that hedging equities with oil is more effective than vice versa. This indicates the importance of the hedging function of oil on the back of globalizing and financializing energy markets. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Economics & Administrative Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we attempt to identify the Domestic Systemically Important Banks (D-SIB) in the Tunisian Banking System. Based on the CES measure proposed by Banulescu and Dumitrescu (2014) , we compute the systemic risk of the financial institution during the period 01/06/2008-30/06/2015.The results allow us to classify Tunisian banks as systemically important financial institutions in Tunisian banking system. In this regard, Tunisian public banks (STB, BNA and BH) are the riskiest systemically banking sector. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamic correlations between Chinese stock returns and global markets at both the market and sectoral levels. Statistics suggest that stock-return correlations across markets are time-varying and display structural breaks. An upward shift in stock return correlations is associated with China's adoption of a higher degree of financial liberalization. The evidence indicates that the stock returns of the financial sector exhibit the highest correlation across countries among 10 sectors. Low correlations are present in the Health Care, Telecommunications, and Utilities sectors. The correlations are closely tied to geographic location: the correlation with Hong Kong is the highest, followed by South Korea, Japan, Europe and the US. The time-series stock-return correlations are positively correlated with the conditional variance and variance premiums. [ABSTRACT FROM AUTHOR]

China's economy has maintained a rapid growth rate over the past two decades; however, its stock market has exhibited a very different level of performance during financial crises. In this paper, we try to explain this phenomenon and answer two important questions: Is there financial contagion in China? Can economic integration aggravate financial contagion? We construct a composite index of economic integration by reviewing the incremental reform and opening-up process in China's financial markets. We utilize a dynamic conditional correlation model to capture the correlations between stock returns of China and those of other important markets around the world. The empirical results provide positive evidence for the aforementioned two questions. [ABSTRACT FROM AUTHOR]

Copyright of Econometrics / Ekonometria is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study examines the US interest rate pass-through mechanism and considers the illiquidity shocks upon retail interest rate correlations caused by financial crises between 1986 and 2011. We estimate a bi-variable EGARCH model using a dynamic conditional correlation model developed by Engle (2002) in order to analyze how asymmetric monetary policy influences interest rate pass-through. We test the risks to the dynamic condition and changes in the correlation coefficient. The main empirical results are as follows. First, the long-run interest rate pass-through mechanism is unstable in the US. Second, expected monetary policy impulses are greater than the unexpected ones in the short-run. Finally, according to the one-step and N-step forecast tests, the illiquidity shocks caused by financial crises demonstrate a significant change in retail interest rate risks, but not in correlations between retail interest rates. We conclude that when the interest rate pass-through mechanism is unstable, banks may stop helping each other and will not provide loans to firms and consumers, thereby exhausting the capital of all economic systems. The characteristics of illiquidity enter into the interest rate pass-through mechanism; therefore, the relationship between illiquidity and the interest rate pass-through needs to be investigated. [ABSTRACT FROM AUTHOR]

This article investigates the impact of the Turkish post-2001 stabilization reforms on the conditional correlation between the Turkish stock index (ISE 100) and the four major stock indices (S&P 500, FTSE 100, DAX 30, NIKKEI 225). We evaluate these correlations for the period ranging from January 1997 to April 2011 with Dynamic Conditional Correlation (DCC) models. The results obtained with the Asymmetric Generalized DCC (AGDCC) model lead to two substantial conclusions. First, the conditional correlation between the ISE 100 and the four other stock indices has strengthened permanently since late 2003, when the reforms started to produce the expected results in the Turkish economic and financial situations. Second, during most of the financial crises that occurred over the retained period, the correlation between the ISE 100 and the other indices increased for a short period. These increases could be explained by the ‘flight to quality’ and ‘flight to liquidity’. By contrast, during the Turkish crisis (2000–2001) the correlation between the ISE 100 and the other stock indices decreased due to the outflow of capital. [ABSTRACT FROM PUBLISHER]

This paper examines the stock market linkages within the Asia-Pacific region and between Asian markets and the U.S. market over the period of January 2000 to June 2010, employing the dynamic conditional correlation GARCH model. Our results show that there exist very high correlations among the stock markets during the 2008 financial crisis. Therefore, consistent with the finding in literature, there are no diversification benefits during the financial crisis. However, our results show that there are still substantial opportunities for global investors to improve the risk-return performance between China and other markets during the sample period. In addition, we find evidence that the U.S. market significantly affects the stock markets in the Asia-Pacific region. Using T-GARCH model, there is a strong evidence of an asymmetric effect on conditional variance except stock markets in China and Malaysia. [ABSTRACT FROM AUTHOR]

• The dynamic return correlation show that bitcoin returns are independent of variation in fiat currency return variations. • The detachment is reinforced by the lack of association between the conditional volatility of bitcoin returns with its dynamic correlations with fiat currencies. • Whereas fiat currencies display a strong dependence structure among themselves. This study investigates the dependence structure between bitcoin (BTC) and five leading – leaving the US dollar out – conventional currencies by using Engle's (2002) DCC model. Results show a detachment between changes in BTC returns and fiat currencies. The weak association between conditional BTC volatility and its dynamic pairwise correlations with fiat currencies further endorses this finding. Implying that BTC investing does not offer currency hedging gains in consideration to its lack of return dependence, and association of its conditional volatility, with the integration patterns seen among fist currencies. [ABSTRACT FROM AUTHOR]

This paper quantitatively investigates the return transmission, volatility spillovers, dynamic correlations, and financial risks between corn, soybean, and crude oil futures and US and European equities. Specifically, by applying our newly extended quinquevariate dynamic conditional correlation (DCC) system with asymmetric spillovers and skew- t errors, we obtain the following new evidence. First, during our recent sample period, the nexuses and dynamic interactions between corn, soybean, and crude oil futures and US and European equities have tightened. Second, we find strong return transmission from US equities to corn, soybean, and crude oil futures and European equities. Third, we clarify significant volatility spillovers that are tied to the leverage effect, from US and European equities to corn and crude oil futures, indicating that downside risk in equity markets is important when considering volatility spillovers to fuel and energy markets. Using the conditional variances and covariances from our new system, we further derive more precise time-varying optimal hedge ratios and optimal portfolio weights. These newly find that for the three asset classes of biofuel crops, crude oil, and equities, hedging one asset class with the other asset classes is effective, and well-balanced portfolios constructed using the three asset classes are optimal. [ABSTRACT FROM AUTHOR]