Periods Of Volatility Research Articles

Performance Analysis of Reits and Invits in India: A Comparative Study with the Nifty 50

This paper investigates the performance of Real Estate Investment Trusts (REITs) and Infrastructure Investment Trusts (InVITs) in India compared to the Nifty 50 index, using a series of financial models including the Sharpe Ratio, Mann-Whitney U test, Chi-square proportion test and Pearson’s Correlation. The study focuses on whether REITs and InVITs consistently outperform traditional investments like the Nifty 50 in terms of returns and risk-adjusted returns. Despite the significant capital inflow and growing interest in alternative investments, there is limited research focused on the Indian market, where macroeconomic conditions, regulatory changes, and investor sentiment play a crucial role. This paper fills that gap by exploring the risk-return profiles, performance during periods of market volatility, and potential for portfolio diversification provided by these instruments.

Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

This study aims to introduce a novel approach for predicting China’s consumer confidence index (CCI), a key economic indicator that reflects consumers’ confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.

ERM strategies for navigating financial stress: Lessons from US commercial banks

This study explores the Enterprise Risk Management (ERM) strategies employed by U.S. commercial banks during times of financial stress, with a focus on the 2008 financial crisis and the COVID-19 pandemic. It investigates how ERM frameworks enable banks to make risk-adjusted decisions and effectively manage credit risk, ensuring both operational resilience and long-term value preservation. The research highlights the critical role of ERM in guiding banks through periods of market volatility, emphasizing the need for a structured approach to risk identification, assessment, and mitigation. Through a detailed analysis, the study examines how commercial banks have adjusted their risk appetite and governance structures in response to economic disruptions, balancing short-term stability with long-term growth objectives. Key ERM components such as capital adequacy, liquidity management, and stress testing are reviewed to demonstrate their effectiveness in safeguarding financial institutions. Additionally, the research discusses the importance of leadership and governance in enhancing risk oversight and fostering a culture of risk awareness across banking operations. The findings offer valuable lessons for financial institutions on how to navigate future financial stresses by leveraging robust ERM frameworks. By examining these strategies, the paper provides insights into the adaptability and resilience of U.S. commercial banks in maintaining financial stability and shareholder value in the face of uncertainty.. Keywords: Enterprise Risk Management (ERM), Financial Stress, Credit Risk Management, Operational Resilience, U.S. Commercial Banks, Governance Structures.

Leadership Development and Capacity Building in Non-Profit Organizations in Senegal

The purpose of this research is to assess leadership development and capacity enhancement approaches within non-profit organizations (NPOs) of Senegal and evaluate how the application of participative, adaptive and transformational leadership improves the organizational performance of the NPOs in a condition of limited resources. Therefore, the study seeks to establish how leadership practice, culture and operations impact on the capacity and sustainability of NPOs to achieve their missions and goals. A qualitative research approach was used for the study through self-administered questionnaires or telephonic / face-to-face interviews with senior officials from different NPOs in Senegal. The collected data were coded systematically using the NVivo software so essential themes and patterns revealing the leadership and capacity building difficulties and practices were revealed. This type of methodology was chosen because the research was interpretive in nature, thus enabling the researcher to engage in a discussion of contextual practices of leadership in the non-profit. Accordingly, the study indicates that participatory leadership is cultural and operationally relevant in Senegal since it is endorsed by collective decision-making culture of the society. Thus, the concept of adaptive leadership became important to sustain the stability in organisations during the periods of volatility and change; and the concept of transformation leadership was deemed critical to achieve cultural congruence with the strategic, mission-oriented objectives. This paper also gives precaution on the issues connected with these leadership styles such as the fact that participatory leadership requires expert help to facilitate, adaptive leadership may lead to burnout.

Better safe than sorry? The effect of asymmetric cost management on firm resilience in manufacturing firms

PurposeGlobal business practices show that during periods of demand volatility, manufacturing firms often engage in asymmetric cost management (ACM), a behavior in which costs increase faster than they decrease when demand decreases by the same amount. However, whether managing asymmetric costs will enhance or impede firm resilience remains an open question. We aim to investigate the impact of ACM on firm resilience and its boundary conditions.Design/methodology/approachUsing unbalanced panel data of 2,273 Chinese manufacturing listed companies from 2002 to 2021, we conduct an empirical analysis using a double fixed effects model.FindingsOur findings reveal that ACM has a negative effect on firm resilience. This suggests that in coping with external environmental fluctuations, ACM fails to fulfill its expected role effectively. Instead, it manifests as a severe agency problem affecting firm resilience. Further, we find that managerial myopia and digitalization diminish the negative effect, while customer instability exacerbates it.Originality/valueThis study contributes to the literature on the organizational resilience of manufacturing firms by providing an in-depth understanding of cost management and emphasizing the need to consider agency issues carefully when managing asymmetric costs.

FROM CALENDAR TIME TO BUSINESS TIME: THE CASE OF COMMODITY MARKETS

We address the problem of modeling commodity forward curves, while preserving empirical features observed in commodity markets. By letting a commodity market to “ live” in the tempo of a business rather than calendar clock, we create a model with a rich but realistic set of features, such as stochastic volatility, stochastic rate of mean reversion and various shapes of forward curves such as backwardation and contango. The model, when applied to extensive historical datasets of crude oil and natural gas forward curves, shows a remarkably good fit to the observed futures prices, also in periods of high volatility and negative prices. The model is developed in such a way that it can be used for a wide variety of applications, ranging from exotic derivatives pricing to risk management of commodity portfolios.

Saving Honest Entrepreneurs through the Second Chance Policy in Ghana

This paper analyses the existing legal frameworks on insolvency in Ghana and how they sufficiently provide to save honest entrepreneurs during financial reforms. Financial sector reforms are a welcomed mechanism to foster financial sector efficiency for sustainable growth. During the period of decision-making in financial sector reforms, it is expected that some business concerns would have their lifespan terminated. Notwithstanding, recent episodes of financial deregulation have revealed unintended consequences of demobilising the sector which has affected innocent entrepreneurs. The aftermath of the financial sector reforms has passed the Corporate Restructuring and Insolvency Act, 2020 (Act 1015). This paper aims to address the issue of whether or not the existing legal framework benchmarked against the Capital Adequacy, Asset quality, Management, Earnings, Liquidity (CAMEL) framework absolves honest entrepreneurs in such a period as against fraudulent entrepreneurs. The paper adopted the doctrinal legal research approach using distinct research tools including data from primary sources, statutes, journal articles, online resources and other obligatory tools. It finds that recent financial sector reforms did not segregate the fraudulent entrepreneur from honest entrepreneurs to make room for the CAMEL to save the latter. It concludes on how a second chance policy can be developed along with existing statutes to settle and save honest businesses in periods of economic volatility.

Arts Community Resilience in Malaysia during the Time of COVID-19: Five Arts Centre’s Open House Initiative

The intrusion of COVID-19 in Malaysia had resulted in the disruption of many art projects for the whole year of 2020. Due to the country’s countermeasures to combat the disease’s ongoing spread, several art groups and art projects were facing mass postponements, cancellations, and uncertainty—arts communities were vulnerable to economic losses, social and environmental limitations during the volatile period. However, there were few art groups that managed to adapt their practices and work around the pandemic constraints they’ve encountered. Five Arts Centre arts collective was sampled and interviewed for this year-long study which looks at the preliminary impacts and responses of COVID-19 on the arts communities in Kuala Lumpur. The results indicate Malaysian arts communities were able to remain resilient against the pandemic mostly due to their social connectedness across the arts practitioners and institutions within the arts ecosystem. Added with their intuitive instrumentalisation of existing resources to meet the environmental limitations that the COVID-19 presented. The research findings are analysed through the utilisation of an adapted resilience framework to help explain the sampled art groups’ experiences through the pandemic period.

Market resilience in turbulent times: a proactive approach to predicting stock market responses during geopolitical tensions

PurposeThe Indian stock market can be tricky when there's trouble in the world, like wars or big conflicts. It's like trying to read a secret message. We want to figure out what makes investors nervous or happy, because their feelings often affect how they buy and sell stocks. We're building a tool to make prediction that uses both numbers and people's opinions.Design/methodology/approachHybrid approach leverages Twitter sentiment, market data, volatility index (VIX) and momentum indicators like moving average convergence divergence (MACD) and relative strength index (RSI) to deliver accurate market insights for informed investment decisions during uncertainty.FindingsOur study reveals that geopolitical tensions' impact on stock markets is fleeting and confined to the short term. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.47% accuracy in forecasting stock market values during such events.Originality/valueTo the best of the authors' knowledge, this model's originality lies in its focus on short-term impact, novel data fusion and high accuracy. Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of geopolitical tensions on market behavior, a previously under-researched area. Novel data fusion: Combining sentiment analysis with established market indicators like VIX and momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods. Advanced predictive accuracy: Achieving the prediction accuracy (98.47%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

The volatility-liquidity dynamics of single-stock ETFs

Single-stock exchange-traded fund (SSETF) is the latest innovation in the ETF arrays of products. We examine the dynamics of the volatility-liquidity relationship between these SSETFs and their underlying stocks. We find that SSETFs have higher volatility and lower liquidity than the underlying stocks. During periods of higher stock volatility, the liquidity of SSETFs is significantly reduced. We also find evidence that SSETF traders can mitigate the liquidity problem by using intermarket sweep orders and routing orders to lit exchanges.

The time-varying interaction of northbound capital flows and stock market performance in China

This study employs the SV-TVP-SVAR model to investigate the dynamic interactions between northbound capital and stock market performance in China, highlighting the time-varying statistical relationships. The findings reveal that the influence of market returns on northbound capital is predominantly short-term, exhibiting negative feedback, which helps stabilize the market during periods of extreme volatility. However, during market reversals, northbound capital shows positive feedback, correlating with improving stock returns. Regarding predictability, while northbound capital provides some predictive power for stock returns, this influence diminishes quickly. The study further notes that retail investors tend to imitate the high-frequency trading patterns of northbound capital.

Quantile Connectedness Across Socially Responsible Equity Markets of The BRICT Nations

This paper investigates the shock linkages between the socially responsible equity indices of Brazil, Russia, India, China, and Türkiye, by using the quantile connectedness approach that is used by Chatziantoniou et al. (2021), to assess the median-based and tail connectivity, we will analyse daily time series data from April 4, 2018, to March 31, 2023. The outcomes of the static and dynamic analyses can be summarized as follows: for static quantile connectedness, Russia and India are net transmitters of shock at the tails, while China is a net receiver. China and Türkiye are net receivers, whereas Brazil, India, and Russia are net transmitters at the median quantile. Considering the dynamic quantile connectedness assessment, the findings indicate that the magnitude of connectedness significantly increases positive and negative shock connectedness. This suggests that during periods of extreme market volatility, socially responsible equity indices in BRICT nations experience more pronounced shock propagation. This suggests that socially responsible investments are susceptible to contagion and, as a result, provide restricted portfolio diversification advantages during periods of extreme market volatility. The analysis also indicates that there was a substantial rise in the overall dynamic connection during the COVID-19 pandemic and the Russia-Ukraine war.

BRICS Stock Markets Performances During COVID-19: Comparison with Other Economic Crises

The study is directed to assess the downside stock market risk of the BRICS during COVID-19 and compare it with other economic crises. The study further investigates the association between the stock markets among the BRICS nations. As per the results, the stock markets of India, Brazil, and South Africa suffered the worst crisis during the COVID-19. China has shocked the world by recovering very quickly compared to the rest of the world. Additionally, its been observed that there is an increased association between different markets depending on the severity of the crisis.

On the different impact of local and national sources of policy uncertainty on sectoral stock volatility

We analyze the impact of state-level and national-level economic policy uncertainty (EPU) on stock volatility across 17 sectors. Using EPU data from 46 U.S. states, we construct a local uncertainty index through principal component analysis and compare it with national uncertainty using quantile regression. Our findings indicate that banks are influenced by both local and national uncertainty. Consumer-related sectors, such as beverages and transportation, react more to local uncertainty, while healthcare and defense sectors respond to national uncertainty. Sectors like medical, technology, electricity, and chemicals show no significant impact from either source. The effects are more pronounced at the 75th quantile, suggesting greater impact during high volatility periods. Our results provide important evidence that a generalized policy may not be effective as sectors respond differently to various sources of uncertainty. Instead, policymakers should combine national level policies with sector-specific approaches.

A generalization of the Topological Tail Dependence theory: From indices to individual stocks

This study investigates the Topological Tail Dependence (TTD) theory’s applicability to individual stock volatility and high dimensions. Utilizing a comprehensive dataset from the S&P 100, the research employs various methodologies to test the predictions and implications of the TTD theory. The theory’s main prediction of Wasserstein Distance’s predictive utility, particularly in nonlinear models during volatile periods, is confirmed. The research suggests extending the TTD theory’s application to various financial instruments and incorporating dynamic topological features to enhance understanding market dynamics. This study validates the TTD theory for individual stocks and highlights the necessity of topological considerations in financial modeling, promising advancements in financial econometrics and risk management strategies.

Extreme co-movements between CO2 emission allowances and commodity markets and their response to economic policy uncertainty

This study employs the generalized extreme value - autoregressive conditional Fréchet - tail quotient correlation coefficient (GEV-AcF-TQCC) analytical framework to investigate the extreme co-movements between the EU carbon futures market and the commodity futures market. Subsequently, it also examines the response of the dynamic tail quotient correlation coefficient (TQCC) values to economic policy uncertainty stemming from the US using the linear Granger causality test. The empirical results demonstrate that the tail risk indices for both upside and downside trends of the two futures markets experience changes most of the time and show drastic changes during extreme events. More importantly, before the outbreaks of the COVID-19 pandemic (from September 2019 to February 2020) and the Russia-Ukraine conflict (from May 2021 to February 2022), the dynamic TQCC values for the downside trends of the two futures markets remain at relatively high levels. Of note, when examining the dynamic TQCC values between GSCI_down and EUA_down, as well as between GSCI_down and EUA_up, it can be observed that these TQCC values rise to higher levels during periods of extreme volatility. However, they quickly decline as the GSCI futures market experiences significant fluctuations during its downside trend. This pattern indicates a tail risk spillover effect from the GSCI futures market to the EUA futures market. Further analysis using the linear Granger causality test reveals that economic policy uncertainty is a significant driver of the extreme co-movements between the EU carbon and the commodity futures markets in most cases, in addition to the extreme co-movements between the downside trend of the EU carbon futures market and the upside trend of the commodity futures market. The insights gained from this study can provide valuable guidance and enlightenment for policymakers, financial investors, and academic researchers in understanding the dynamics of the extreme relationship between the two markets under discussion, and the impacts of economic policy uncertainty on their extreme co-movements.

The impact of COVID-19 uncertainties on energy market volatility: Evidence from the US markets

This research investigates the influence of the COVID-19 on the returns of energy indexes in the US using daily time series data of WTI and Brent markets for the January 2020 – March 2022 period. The findings reveal compelling evidence of the effects of the COVID-19 on the volatility of energy commodity indexes in the US. Nevertheless, the magnitude of this impact varies across different volatility regimes. Notably, the WTI crude oil exhibits more substantial effects during periods of lower volatility characterised by fewer COVID-19 cases, potentially attributed to market control measures. In contrast, the Brent crude oil displays a more pronounced impact of the COVID-19 during turbulent periods, attributed to the market's openness and the prevalence of speculative participants. The study suggests that participants in the Brent market, driven by a desire to capitalise on price differentials, intensify their activities during turbulent periods, contributing to increased volatility during successive waves of high COVID-19 cases. The observed structural differences between the WTI and Brent commodity markets carry significant policy implications for investors in the energy sector.

Precision forecasting in perilous times: stock market predictions leveraging google trends and momentum indicators during COVID-19

PurposeThis study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and analyze how public sentiment, measured through Google Trends, can predict stock market fluctuations. We propose a novel framework using Google Trends for financial sentiment analysis, aiming to improve understanding and preparedness for future crises.Design/methodology/approachHybrid approach leverages Google Trends as sentiment tool, market data, and momentum indicators like Rate of Change, Average Directional Index and Stochastic Oscillator, to deliver accurate, market insights for informed investment decisions during pandemic.FindingsOur study reveals that the pandemic significantly impacted the Indian financial sector, highlighting its vulnerabilities. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.95% maximum accuracy in forecasting stock market values during such events.Originality/valueTo the best of authors knowledge this model's originality lies in its focus on short-term impact, novel data fusion and methodology, and high accuracy.• Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of COVID-19 on market behavior.• Novel data fusion and framework: A novel framework of sentiment analysis was introduced in the form of Trend Popularity Index. Combining trend popularity index with momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods.• High predictive accuracy: Achieving the prediction accuracy (98.93%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

What drives cryptocurrency pump and dump schemes: Coin versus market factors?

This paper investigates both coin-specific and market-based factors that drive cryptocurrency pump-and-dump schemes. It analyzes a data set comprising 1457 pump events that occurred from January 3, 2018, to January 2, 2022. Empirical findings, derived from binary cross-sectional regression models, reveal several characteristics that increase the likelihood of cryptocurrencies being pumped. These include lower market capitalization, lower trading volume, greater social media popularity, increased developer activity, and fewer exchanges trading them. Furthermore, the study employs count time-series models to examine market-based factors. The results indicate that periods of higher volatility or uncertainty are associated with an increase in pre-announced pump-and-dump activities. Additionally, the analysis shows that macroeconomic factors and specific time-related effects – such as Sundays, certain months, and the COVID-19 period – are significant in explaining the frequency of pump occurrences. Based on these findings, the article discusses several targeted recommendations.

Prevalence and correlates of depression, anxiety, and stress among adolescents in urban and rural areas of Mysuru, South India.

Adolescence, a volatile period of growth between the ages of 10 and 19, is associated with increased vulnerability to mental health problems. Factors such as academic pressure can contribute to these challenges. The current study aimed to evaluate the factors and prevalence of depression, anxiety, and stress among adolescents in the urban and rural areas of Mysuru district. A cross-sectional study was conducted in private high schools in both urban and rural regions. Prior permission and informed consent were obtained from participants and their legal guardians aged 18 years and older. The gender distribution in urban areas was 60.2% female and 39.8% male, while in rural areas, it was 51% male and 49% female. The prevalence of depression was higher in rural (39.3%) than in urban areas (24.2%), while anxiety was more prevalent in urban (50.6%) than in rural areas (49%). Stress was also more common in rural (16.6%) than urban adolescents (14.6%). Factors significantly associated with mental health outcomes included monthly family income, parenting practices, academic pressures, and self-esteem. Key needs identified were mobile mental health applications, online counseling services, and access to school counselors. This study provides insights into the prevalence and correlates of common mental health issues among adolescents in this region of South India. The findings emphasize the necessity of providing mobile applications and offline counseling services to effectively support and meet the needs of adolescents in these settings.