Search results

The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.

This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.

The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.

One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.

The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.

Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.

This study aims to investigate the connectivity among four principal implied volatility (“fear”) markets in the USA.

The empirical analysis relies on daily data (“fear gauge indices”) for the period 2017–2023 and the quantile vector autoregressive (QVAR) approach that allows connectivity (that is, the network topology of interrelated markets) to be quantile-dependent and time-varying.

Extreme increases in fear are transmitted with higher intensity relative to extreme decreases in it. The implied volatility markets for gold and for stocks are the main risk connectors in the network and also net transmitters of shocks to the implied volatility markets for crude oil and for the euro-dollar exchange rate. Major events such as the COVID-19 pandemic and the war in Ukraine increase connectivity; this increase, however, is likely to be more pronounced at the median than the extremes of the joint distribution of the four fear indices.

This is the first work that uses the QVAR approach to implied volatility markets. The empirical results provide useful insights into how fear spreads across stock and commodities markets, something that is important for risk management, option pricing and forecasting.

This study investigates the asymmetric effects of exchange rate and investors' sentiments simultaneously on stock market performance in the United States context. In addition, we have also considered the potential effect of the global financial crisis of 2008 on this nexus.

We have employed the NARDL (nonlinear autoregressive distributed lag) model on monthly data ranging from January-1999 to December-2018 to investigate the asymmetric (short- and long-run) effects of exchange rate and investors' sentiments on stock market performance. We have also broken down the data into two segments, pre and post-crisis periods to capture the effect of the global financial crisis of 2008.

The findings of the study reveal that exchange rate and investors' sentiments simultaneously affect stock market performance and omitting any of these variables can produce misleading results. Results also show that the effect of sentiments is stronger than the exchange rate. There is significant evidence of asymmetric short-run and long-run effects of both explanatory variables. Moreover, we have found different outcomes for pre and post-crisis periods. Specifically, the impact of macroeconomic variables on the stock market has been substantiated in the post-crisis period.

Several studies are available which separately evidence the effects of investors' sentiments and exchange rate on performance of the stock market but they can suffer from the problem of omitted variable bias. This study is conducted to test the said effect simultaneously in a single model. Moreover, this study is considering short-run and long-run asymmetry in analyzing the effects of explanatory variables along with the inclusion of the global financial crisis of 2008.

To investigate the role of domestic and foreign economic policy uncertainty (EPU) in driving the corporate bond yields in emerging markets.

The study utilizes monthly data from January 2008 to June 2023 from the selected emerging economies. The data analysis is conducted using univariate, bivariate and multivariate statistical techniques. The study includes bond market liquidity and global volatility (VIX) as control variables.

Domestic EPU has a significant role in driving corporate bond yields in these markets. The study finds weak evidence to support the role of the USA EPU in influencing corporate bond yields in emerging economies. Domestic EPU holds more weight and influence than the EPU originating from the United States of America.

The findings provide useful insights to policymakers about the potential impact of policy uncertainty on corporate bond yields and enable them to make informed decisions regarding economic policies that maintains financial stability. Understanding the relationship between EPU and corporate bond yields enables investors to optimize their investment decisions in emerging market economies, opens the scope for further research on the interaction between EPU and volatility and other attributes of fixed income markets.

Focuses specifically on the emerging market economies in Asia, providing an in-depth analysis of the dynamics and challenges faced by these countries, Explores the influence of both domestic and the USA EPU on corporate bond yields in emerging markets, offering valuable insights into the transmission channels and impact of EPU from various sources.

The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.

There has been a burgeoning interest in exploring the impact of uncertainty factors on share returns. However, studies on the influence of global financial uncertainties on emerging market sectoral indices are scarce. Thus, there is a need to have a thorough investigation of the connection between global financial uncertainties and emerging market sectoral indices. To fill this gap, using the theoretical framework of international portfolio diversification (IPD) and utilizing data from 2008 to 2021, this study examines the spillover connection between global uncertainty indices (GUIs) and leading sectoral indices of 28 emerging markets.

The authors employ the quantile spillover-based connectedness approach and minimum connectedness portfolio approach to explore the dynamic connectedness among sectoral indices and global uncertainty indices (GUIs) as well as portfolio implication.

The study found high connectedness among all indices, especially at higher and lower quantiles. Among GUIs, the authors find that stock market volatility (VIX) and oil volatility index (OVX) are strongly interconnected with all leading emerging markets' sectoral indices. Among sectoral indices, the linkage between the financial (F-Index), information technology (IT-Index), and consumer discretionary (CD-Index) sectors shows moderate interconnectedness. In contrast, the communication services (CS-Index) sector has low interconnectedness with the system. In terms of spillover effects, the authors find EVZ, OVX, and the IT sectors to be net recipients for the entire period. The authors also explored portfolio diversification benefits by employing a minimum connectedness portfolio approach. The cumulative returns' findings show a slight decline in the portfolio's value after 2010; during 2012, the pattern remained stable; from 2014 to 2020, the portfolio performed negatively, that is, underperformance due to different events in that period, including COVID-19. The Consumer Discretionary sector is found to be significant because of having the largest weight, 51%, in the portfolio during the study period.

The study suggests that investors should invest in the communication services sector as it is the least connected. However, the connectedness increases during COVID-19, which implies that it may be difficult for investors to benefit from IPD in a crisis period. Hence, to obtain the benefits from IPD, the evidence suggests that investors need to consider Consumer Discretionary sector while considering assets for investment.

The study's uniqueness is that the authors have investigated spillover between GUIs and 28 emerging markets sectoral indices by employing a quantile spillover-based connectedness approach and minimum connectedness portfolio approach with a special focus on portfolio implication.

This study aims to systematically reveal the complex interaction between uncertainty and the international commodity market (CRB).

A composite uncertainty index and five categorical uncertainty indices, together with wavelet analysis and detrended cross-correlation analysis, were used. First, in the time-frequency domain, the coherency and lead-lag relationship between uncertainty and the commodity markets were investigated. Furthermore, the transmission direction of the cross-correlation over different lag periods and asymmetry in this cross-correlation under different trends were identified.

First, there is significant coherency between uncertainties and CRB mainly in the short and medium terms, with natural disaster and public health uncertainties tending to lead CRB. Second, uncertainty impacts CRB more markedly over shorter lag periods, whereas the impact of CRB on uncertainty gradually increases with longer lag periods. Third, the cross-correlation is asymmetric and multifractal under different trends. Finally, from the perspective of lag periods and trends, the interaction of uncertainty with the Chinese commodity market is significantly different from its interaction with CRB.

First, this study comprehensively constructs a composite uncertainty index based on five types of uncertainty. Second, this study provides a scientific perspective on examining the core and diverse interactions between uncertainty and CRB, as achieved by investigating the interactions of CRB with five categorical and composite uncertainties. Third, this study provides a new research framework to enable multiscale analysis of the complex interaction between uncertainty and the commodity markets.

This paper aims to explore the impact of cycle superposition on bank liquidity risk under different levels of financial openness so that banks can better manage their liquidity risk. Meanwhile, it can also provide some ideas for banks in other emerging economies to better cope with the shocks of the global financial cycle.

Employing the monthly data of 16 commercial banks in China from 2005 to 2021 and based on the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model, the authors first examine whether the cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. Subsequently, the authors investigate the impact of different levels of financial openness on cycle superposition amplification. Finally, the shock of the financial cycle of the world's major economies on the liquidity risk of Chinese banks is also empirically analyzed.

Cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. However, there are significant differences under different levels of financial openness. Compared with low financial openness, in the period of high financial openness, the magnifying effect of cycle superposition is strengthened in the short term but obviously weakened in the long run. In addition, the authors' findings also demonstrate that although the United States is the main shock country, the influence of other developed economies, such as Japan and Eurozone countries, cannot be ignored.

Firstly, the cycle superposition index is constructed. Secondly, the authors supplement the literature by providing evidence that the association between cycle superposition and bank liquidity risk also depends on financial openness. Finally, the dominant countries of the global financial cycle have been rejudged.

This study aims to investigate the co-volatility patterns between cryptocurrencies and conventional asset classes across global markets, encompassing 26 global indices ranging from equities, commodities, real estate, currencies and bonds.

It used a multivariate factor stochastic volatility model to capture the dynamic changes in covariance and volatility correlation, thus offering empirical insights into the co-volatility dynamics. Unlike conventional research on price or return transmission, this study directly models the time-varying covariance and volatility correlation.

The study uncovers pronounced co-volatility movements between cryptocurrencies and specific indices such as GSCI Energy, GSCI Commodity, Dow Jones 1 month forward and U.S. 10-year TIPS. Notably, these movements surpass those observed with precious metals, industrial metals and global equity indices across various regions. Interestingly, except for Japan, equity indices in the USA, Canada, Australia, France, Germany, India and China exhibit a co-volatility movement. These findings challenge the existing literature on cryptocurrencies and provide intriguing evidence regarding their co-volatility dynamics.

This study significantly contributes to applying asset pricing models in cryptocurrency markets by explicitly addressing price and volatility dynamics aspects. Using the stochastic volatility model, the research adding methodological contribution effectively captures cryptocurrency volatility's inherent fluctuations and time-varying nature. While previous literature has primarily focused on bitcoin and a few other cryptocurrencies, this study examines the stochastic volatility properties of a wide range of cryptocurrency indices. Furthermore, the study expands its scope by examining global asset markets, allowing for a comprehensive analysis considering the broader context in which cryptocurrencies operate. It bridges the gap between traditional asset pricing models and the unique characteristics of cryptocurrencies.

This study aims to empirically explore and compare the dynamic dependency between health-care sector and Islamic industries before, during and after the COVID-19 pandemic.

Time-varying student-t copula is used for before, during and after COVID-19 periods. The data used are the daily frequency price series of the selected markets from February 2017 to October 2023.

Empirical results found strong evidence of significant impact of the COVID-19 pandemic on the dependence structure of the studied indexes: Co-movements between various sectors are certain. The authors assist also in the birth of new dependence structure with the health-care industry in response to the COVID-19 crisis. This reflects the contagion occurrence from the health-care sector to other sectors.

By specifically examining the Islamic industry, this study sheds light on the resilience, challenges and opportunities within this sector, contributing novel perspectives to the broader discourse on pandemic-related impacts on economies and industries. Also, this paper conducts a comprehensive temporal analysis, examining the dynamics before, during and after the COVID-19 lockdown. Such approach enables an understanding of how the relationship between the health-care sector and the Islamic industry evolves over time, accounting for both short-term disruptions and long-term effects. By considering the pre-pandemic context, the paper adopts a longitudinal perspective, enabling a deeper understanding of how historical trends, structural factors and institutional frameworks shape the interplay between the health-care sector and the Islamic industry.