Equity Index Research Articles

Long-Run Volatility Memory Dynamics and Inter-Market Linkages in GCC Equity Markets: Application of DCC-FIGARCH Models

Abstract The study investigates volatility persistence, long-term memory and time-varying conditional correlations among the stock markets of the Gulf Cooperation Council (GCC) countries. Daily equity index data between 2012 and 2024 were analyzed using univariate fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models to examine long-memory behavior and multivariate dynamic conditional correlation (DCC) models to assess conditional correlations between these markets. For each of the GCC equity markets, the analysis highlighted large degrees of long-memory and volatility persistence. Finally, the DCC model shows that strong and dynamic Intermarket links among the GCC, especially between KSA and UAE, exist and reflect significant volatility spillover from good economic ties. This study fills the gap in the literature by providing a comprehensive understanding of long-run volatility memory and inter-market associations in the GCC stock markets.

On the issue of impact of income inequality on the socio-economic development of territories

The issues of economic inequality and justice were considered in the works of the philosophers of Ancient Greece. Over time, the problem of income inequality has not lost its relevance, which is confirmed by domestic and foreign studies. Until 2015, the capital of the Russian Federation had the highest value of the Gini index, which indicates a high degree of inequality in income distribution in the territory of this subject. Over time, the situation has changed, in particular, by increasing the incomes of the least well-off groups of the population, providing them with subsidies and payments. The purpose of the study is to analyse to what extent these changes have an impact on the socio-economic development of the region. With the help of econometric calculations, the degree of dependence between the equity index and the value of the gross regional product per capita has been determined. It is revealed that there is an inverse relationship between the considered indicators, that is, a reduction in income inequality has an impact on the growth of regional production. The high value of the coefficient of determination indicates the high quality of the constructed model. The possibility of using the simulation results in practice is confirmed by calculations of the average approximation error and the Fisher criterion.

Dynamic Linkages Between U.S and Indian Equity Markets: An Empirical Study

In this study, we investigate the dynamic linkages between US and Indian equity markets by examining the daily Total Return Index values of NIFTY 50 and NASDAQ Composite over a decade ending October 2024. While the indices are not found to be cointegrated, suggesting potential long-term diversification benefits, they exhibit significant short-term correlations. Through various statistical analyses including regression, correlation, and variance tests, we find strong positive associations between the markets, with NASDAQ Composite movements explaining approximately 89% of variations in NIFTY 50 Total Return Index (TRI). The study reveals distinct volatility patterns, with the Indian market showing higher price fluctuations but stronger absolute growth compared to its US counterpart. Despite the strong correlation, both markets maintain unique characteristics influenced by their respective economic fundamentals. The findings demonstrate the evolving nature of market integration between developed and emerging economies, offering valuable insights for international portfolio diversification strategies and risk management in an increasingly interconnected global financial system. Keywords: Market Integration, Volatility Spillover, Portfolio Diversification, Stock Market Returns, Emerging Markets, Cross-market Correlation

ESG and Smart Beta Investing: A Synergistic Approach to Indian Equity Investing using the NIFTY 100 ESG Index and Multi-Factor Weighted Smart Beta Strategies

Smart Beta Investing Strategies have acquired momentum in emerging markets over the past decade; however, they have yet to be investigated in academia, and researchers are unable to provide empirical evidence. Previous research on the Indian equity markets and the clever beta strategies and factors has indicated that there is some optimism in the field. The authors augment the current corpus of research by employing an ESG-filtered index in conjunction with the conventional factors of value, size, and momentum to identify the ten most optimal stocks for each feasible portfolio configuration. A single factor and multi-factor weighting scheme are implemented to determine the ultimate portfolio and return. The Smart Beta portfolio exhibits superior returns when contrasted with its passive counterparts, as evidenced by statistical tools and risk-return characteristics. The absence of empirical evidence and a scarcity of data result in certain limitations for the study.

Development of an older adult Nutrition Equity Index (NEI) and association with the Healthy Eating Index (HEI) in older Black and White U.S. adults

ObjectivesWe developed a novel measure for older U.S. adults called the Nutrition Equity Index (NEI), which includes functional ability. We compared both the NEI and food insufficiency with Healthy Eating Index (HEI) scores. DesignThe Health, Aging, and Body Composition Study is a prospective, longitudinal cohort of Medicare-eligible, community-dwelling Black and White men and women. SettingPittsburgh, PA and Memphis, TN. ParticipantsParticipants (N = 2468, aged 74.7 ± 2.9 years) had baseline (1997−98) and 1-year follow-up NEI data; and 1 year follow-up for food insufficiency, key covariates, and the HEI (diet quality measure) from a 108-item interviewer-administered food frequency questionnaire. MeasurementsFood insufficiency was defined from a modified validated question on ample food amount/variety from the U.S. Department of Agriculture. NEI was constructed from 8 questions in three subdomains: food insecurity (food insufficiency, hunger, anxiety about limited food, limited money for food), food access (groceries), and food acquisition (difficulty shopping, preparing meals, carrying groceries). Final NEI categories were low nutrition equity (0), moderate nutrition equity (1), and high nutrition equity (2). Multivariable linear regression compared both NEI and food insufficiency with HEI, adjusted for sociodemographic, lifestyle and comorbidity factors. ResultsOf participants, 13.5% had food insufficiency, 12.1% had low NEI, and 32.5% had moderate NEI. Participants with food insufficiency (vs. food sufficiency) or low NEI (vs. high NEI) were more likely to be Black and have < high school education (both p < 0.0001). In minimally adjusted models, food insufficiency was associated with 2.2 point lower HEI score (p = 0.001), low NEI with 2.6 point lower HEI score (p = 0.001), and moderate NEI with 1.0 point lower HEI score (p < 0.05). Adjusting for race attenuated NEI associations with HEI to non-significance. After multivariable adjustment, food insufficiency was associated with 1.5 point lower HEI score (p = 0.03). ConclusionFood insufficiency was associated with lower dietary quality in this sample of older Black and White U.S. adults. NEI performed similarly to food insufficiency for HEI associations prior to multivariable adjustment and identified more older adults with nutrition inequity, who may be at risk for poor health outcomes.

Analysis of Portfolio Optimization Performance: Markowitz Model and Index Model in Capital Markets

In the post-pandemic era, marked by economic uncertainty and stock market volatility, investors are turning to Modern Portfolio Theory (MPT) to guide their investment decisions. This study aims to develop a robust mathematical framework for portfolio construction using the Markowitz Model (MM) and the Index Model (IM) with added constraints. The objectives are threefold: (1) to create a portfolio framework that reflects investor preferences using MM and IM with additional constraints, (2) to compare these models against the Gaussian Distribution using Python and Excel, (3) to compare the statistical data and correlation tests of daily logarithmic returns in Python with monthly excess returns in Excel, and (4) to evaluate their performance relative to the traditional Markowitz approach through Monte Carlo simulations.The methodology involves incorporating five additional constraints into the MM and IM models. The analysis uses 20 years of historical daily return data for ten stocks from various sectors, one equity index (S&amp;P 500) as a risk-free rate proxy (1-month Fed Funds rate). Daily logarithmic returns are analyzed using Python, while Excel Solver and Solver Table are employed for monthly excess return data.

Risk factors-adjusted performance and persistence during the post-subprime crisis period: evidence from Indian mutual fund industry

PurposeThe main objective of the paper is to find evidence of abnormal returns and performance persistence of actively managed equity funds in the Indian context on an annual basis during the post-subprime crisis period between 2009 and 10 and 2019 and 2020.Design/methodology/approachThe study is exploratory and empirical, used daily net asset value (NAV) of 180 equity funds for 10 years and applied the risk-adjusted, Jensen's (1968) single-factor, Fama and French's (1993) three-factor model and Carhart's (1997) four-factor model to evaluate the performance. The performance persistence has been tested using cross-section regression (Bollen and Busse, 2005), the non-parametric contingency approach, along with the robustness measure, i.e. Malkiel's (1995) Z-score, Brown and Goetzmann's (1995) cross-product ratio (CPR) and Kahn and Rudd's (1995) χ2 value.FindingsThe results show that the Indian equity funds are unable to generate abnormal returns, and the size, value and momentum strategies applied by the fund managers in generating abnormal returns do not work effectively. However, funds provide strong evidence of significant performance persistence on an annual basis in the short-term, mid-term and long-term periods. Both parametric as well as non-parametric tests provide identical evidence of persistence, and the performance persistence is independent of the choice of models, as all the models (i.e. two, three or four-factor models) provide significant evidence of persistence.Research limitations/implicationsThough the study is comprehensive and covered a longer period, there is a scope for future research by examining the influence of fund characteristics, fund rating and macroeconomic factors on performance and persistence. It can be extended over to a longer period covering the post-COVID-19 period, a larger sample size and a comparative study of Indian and foreign mutual funds (MFs).Practical implicationsThe outcomes of this research paper can help wealth-maximizing investors in the identification of persistent equity funds and can apply the previous period’s performance information as a useful investment strategy to generate higher returns in the future. We believe that these outcomes will have significant ramifications for all MF stakeholders and policymakers, especially for the Indian industry in ensuring and establishing the credibility of MF managers, in providing better returns as well as to make MF investment more attractive to Indian retail investors.Originality/valueDespite the exponential growth in the Indian MF industry, limited evidence is available on performance and persistence covering a large sample size during the post-sub-prime crisis period using different return models and parametric and non-parametric approaches. The study is based on the daily data set of a larger sample size representing all the Asset Management Company (AMC) and the longer period following the post-subprime crises, which affected capital flows significantly. Moreover, the application of all the measures enables us to understand performance persistence in a larger context.

Understanding the Representation of Asians and Asian Americans Within Academic Otolaryngology Leadership.

Elucidate the representation of Asian and Asian Americans in academic otolaryngology and the influence of race on promotion and leadership opportunities. Retrospective analysis of the Association of American Medical Colleges Faculty Administrative Management Online User System. Full-time otolaryngology faculty from all US medical schools from 2020 to 2023. Faculty demographics, tenure, and rank were collected. Descriptive statistics, Fischer's exact test, Rank Equity Index (REI), and multivariable logistic and ordinal regressions were used to characterize our cohort and assess the impact of race on academic advancement and leadership, defined as promotion to tenure or full professorship. Asians comprised 20.53% of 9056 faculty over 4 years. Asians were most likely to hold tenure-eligible positions (n = 600, 30.74%) but were significantly less likely than non-Asians to be tenured (43.00% vs 48.65%, P = .015). Asians were slightly above parity in promotion from assistant to associate professor (REI = 1.09) but below parity in promotion from associate professor to professor (REI = 0.78). Relative to whites, Hispanics, and African Americans, Asians reported the lowest associate/professor and assistant/professor REIs. On multivariable regressions, Asian race was not associated with decreased odds of tenure-eligible positions but was associated with decreased odds of tenure (odds ratio [OR] = 0.77, 95% confidence interval [CI] = [0.64-0.93]) and rank promotion (OR = 0.82, 95% CI = [0.74-0.90]). Despite strong overall representation in otolaryngology, Asians are less likely to receive promotion, tenure, or full professorship relative to other racial groups. Future efforts should emphasize equitable advancement opportunities to ensure a diverse otolaryngology leadership.

Detecting market bubbles: A generalized LPPLS neural network model

To enhance bubble detection capabilities, we introduce two significant improvements to the Log-Periodic Power Law Singularity (LPPLS) model: (1) a novel fitting approach, which yields more accurate predictions of critical price distributions within a single sample window; (2) a restructured neural network approach further enhances the estimations of the probability distributions of the critical points across both time and price dimensions, and it can be fine-tuned with real-world data. The simulation and practical applications to typical asset price bubbles in cryptocurrencies, commodities, and equity indices demonstrate that our refined model, the Generalized-LPPLS Neural Network (G-LPPLS-NN), outperforms all other models we examined in terms of predictive accuracy for critical point distributions.

Time-varying aggregate tail risk and cross-section of stock returns: Indian evidence

This article examines whether the time-varying aggregate tail risk is priced in the cross-section of average returns in the Indian equity market. The results show that the tail risk beta sorted portfolio returns increase monotonically with an increase in their tail risk sensitivity. More importantly, the Indian stocks provide economically and statistically significant risk premium for the tail beta risk factor. The findings remain robust after controlling for the Fama–French and momentum risk factors, and other individual characteristics governing tail behaviour of stocks. The study also provides evidence that institutional ownership and firm size affect tail risk premium.

Land Use Optimization from the Perspective of Multiple Stakeholder Groups: A Case Study in Yongsheng County, Yunnan Province, China

With China’s rapid economic development in recent years, enhancing the sense of well-being among citizens has become a critical objective. However, the interests of various stakeholder groups are often overlooked in decision-making surrounding land use. In this study, Yongsheng County, Yunnan Province serves as a case study for land use scenario simulations. The equivalent factor method is combined with Participatory Rural Appraisal (PRA) to investigate the relationship between ecosystem multifunctionality (EMF) and the equity index of multiple stakeholder groups in various land use scenarios. We also explore whether an optimal combination of land use types exists. The results indicate that (1) The current ecosystem service value in Yongsheng County is primarily driven by climate regulation and biodiversity conservation, with a relatively high functional value index but a comparatively low equity index; (2) Different stakeholder groups mainly prioritize food production and ecosystem services impacting food production, such as water resource provision and climate regulation; (3) A land use allocation pattern of 20% farmland, 4% water bodies, 21% mixed forest, 20% coniferous forest, and 35% grassland appears to provide the optimal EMF index while simultaneously achieving the optimal equity index across stakeholder groups. This research may offer valuable insights for optimizing land use planning while taking into account the well-being of diverse stakeholder groups. It also may have practical implications for the formulation of innovative land use management strategies.

Assessing the Impact of the ECB’s Unconventional Monetary Policy on the European Stock Markets

This study assesses the effects of the European Central Bank’s (ECB) unconventional monetary policy (UMP) on the prices of selected European stock market indices during the European sovereign debt (2010–2012) and the COVID-19 pandemic (2020–2022) crises interventions. This research employs the instrumental variables (IV) two-stage least squares (2SLS) model approach to evaluate the effects of changes in the size of the ECB’s balance sheet on the pricing of key equity market indices in Europe. The results of this study suggest that the ECB’s asset value expansion had the opposite statistically significant effects on the European stock market indices’ prices between the interventions. That is, an increase in the ECB’s balance sheet size was associated with a decrease in the prices of the indices during the sovereign debt crisis and with a rise during the COVID-19 pandemic. This research pinpoints the price sensitivity of each of the European equity indices to the ECB’s UMP and determines the different outcomes of the ECB’s quantitative easing policy between the interventions.

Duration-Based Valuation of Corporate Bonds

Abstract We decompose corporate bond and equity index returns into duration-matched government bond returns and the excess returns over this duration-matched counterfactual, which we term duration-adjusted returns. Compared with previously used excess return definitions (ie, returns in excess of Treasury bills), our decomposition leads to markedly different return patterns and asset pricing implications. In particular, we find that investment-grade bonds earn a small credit risk premium, comparable in magnitude to the convenience yield, and that duration adjustment resolves the CAPM’s failure to price corporate bonds. These findings highlight the importance of adjusting for nonstationary interest rate environments in asset pricing tests. (JEL G10, G12)

Conditional volatility targeting strategy considering jump effects: Evidence from sustainable ESG equity index

This paper considers the sustainable ESG equity index in the proposed conditional volatility targeting strategy. The research first detects jump risk using a jump test and then extends Bongaerts et al. (2020) by addressing jump risk and employing different volatility models to project volatilities under the conditional volatility targeting strategy. To capture consideration of the fact that index return dynamics, we propose an ARMA-GARCH jump model that can capture the characteristics of jump persistence, autocorrelation, and volatility clustering according to the return of the sustainable equity index. Our numerical analyses reveal that the portfolio allocation using a sustainable equity index to predict volatility, combined with a conditional volatility target strategy, can achieve higher performance. Furthermore, the proposed ARMA-GARCH jump model can enhance the performance with conditional volatility targeting strategy.

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.

Patterns in the Chaos: The Moving Hurst Indicator and Its Role in Indian Market Volatility

Estimating the impact of volatility in financial markets is challenging due to complex dynamics, including random fluctuations involving white noise and trend components involving brown noise. In this study, we explore the potential of leveraging the chaotic properties of time series data for improved accuracy. Specifically, we introduce a novel trading strategy based on a technical indicator, Moving Hurst (MH). MH utilizes the Hurst exponent which characterizes the chaotic properties of time series. We hypothesize and then prove empirically that MH outperforms traditional indicators like Moving Averages (MA) in analyzing Indian equity indices and capturing profitable trading opportunities while mitigating the impact of volatility.

Horizontal Inequality in Healthcare Utilisation in Rural Sabah, Malaysia

Ensuring universal access to healthcare is essential for societal equity. However, significant inequalities exist within global healthcare systems, resulting in unequal access to services and divergent health outcomes. Socioeconomic inequity, poor living conditions, and inadequate healthcare infrastructure perpetuate poor health among poor communities. Despite greater healthcare needs, economically disadvantaged populations face substantial barriers to obtaining necessary services. This research evaluates the equitable aspect of healthcare utilisation in Sabah’s rural areas to guide policy formulation. Nabawan, a rural area in Sabah with high poverty rates, was selected to assess income-related inequality and inequity in healthcare utilisation. Using the horizontal equity index and decomposition analysis, it was found that economic status significantly influences healthcare distribution, typically favouring the affluent. Central to this analysis is the concept of “equal treatment for equal need,” ensuring individuals with similar healthcare needs receive similar care regardless of economic status. Results indicate that wealthier individuals are slightly more likely to utilise healthcare services, although the difference is minimal. To address disparities, enhancing targeted financial assistance programs like PeKa B40 could reduce out-of-pocket expenses for low-income households. Additionally, increasing healthcare funding for rural infrastructure and training local healthcare workers can ensure equitable access for all households.

Monetary policy shocks and the high-frequency network connectedness of stock markets

We study the high-frequency impact of monetary policy decisions on the structure of financial market networks, using 1-second return data for 10 industry-level equity indices in the United States. We find that FOMC meetings and policy surprises have a significant influence on network connectedness in returns, volatility, skewness, and kurtosis. The information related to policy decisions instantly shocks and is quickly dissipated throughout the financial network. Network connectedness shocks are positively impacted by the magnitude of interest rate surprises, with asymmetric effects by sign and nonlinear effects being weak. Our results have implications for monetary policy, systemic risk, and asset pricing.

Constructing ESG Equity Index Portfolios: The Impact of ESG Ratings on Risk and Return

Constructing ESG Equity Index Portfolios: The Impact of ESG Ratings on Risk and Return

The risk and return of equity and credit index options

We develop a structural credit risk model, which allows us to price equity/credit indices and their options through the asset dynamics of index constituents. We estimate the model via MLE and find that equity and credit index option prices are well explained out-of-sample. Contrary to recent empirical findings, the two option markets are not inconsistently priced through the lens of our model. Returns on both options, while extreme, do not indicate any evidence of mispricing. Our analysis suggests that jointly addressing the pricing of various instruments requires properly attributing three different sources of systematic risk: asset, variance, and jump risks.