Sectoral Indices Research Articles

Analysis of the impact of COVID-19 on the dynamic volatility spillover network in the Saudi stock market

This study investigates the risk spillover among the COVID-19-related news Panic Index, the insurance industry, the Tadawul All-Share Index, and sector indices in Saudi stock market. Volatility spillovers serve as an accurate indicator for the transmission of risk across various sectors. Our empirical findings show that, prior to the outbreak of COVID-19, the Tadawul All-Share stock market played a dominant role as a net transmitter of risk for sector indices, including insurance, media, pharma, retail, transport, and utilities. Furthermore, our analysis demonstrates that the COVID-19 Panic Index exerts significant influence on the net pairwise connectedness, particularly impacting the Tadawul All-Share Index, energy, banking, development, materials, real estate, transport, and utilities sectors. Additionally, our results indicate that the insurance sector acts as a net receiver of spillovers from the utilities, media, pharma, and retail sectors throughout the sample period, affirming the heightened impact of the pandemic on these sectors. The pandemic, being a health-related issue, led to a notable surge in the volatility of sectors associated with health, given their pivotal role in effectively addressing the crisis. These insights carry substantial implications for investors, portfolio managers, and policymakers, shedding light on the potential repercussions of the pandemic on the stock market.

Volatility Spill-Over and Financial Contagion Effect of Conventional Stock on Islamic Equity in Nigeria: Evidence from Covid-19

This study empirically investigated volatility spill-over and financial contagion effect between conventional stocks and shariah compliant equities before and during covid-19 pandemic in Nigeria. Banking and insurance sectoral stocks indices were selected to represent conventional equities while Nigerian stock exchange lotus islamic index represent islamic stocks. Daily closing stock price data from 02-01-2018 to 26-02-2020 for pre-covid and 27-02-2020 to 31-12-2021 for pre-covid and during covid were considered. DCC-GARCH model (Dynamic conditional correlation- Generalized Autoregressive Conditional Heteroskedasticity Model) was deployed to examine the spill-over effect while t-test was applied to check the consistency of the DCC coefficient before and during the pandemic. The result of the multivariate GARCH show that both banking and insurance stocks have long run volatility spillover effect on lotus islamic index, the result also revealed no significant increase in conditional correlation during covid-19 pandemic which indicate absence of financial contagion from the conventional to islamic stocks. The results provide important information to researchers, investors, and policymakers

Riding the waves: A study of return spillovers and inter-sector linkages in US equity markets during the COVID-19 pandemic

In times of crisis, stock markets experience a significant increase in return volatility, which leads to spillovers across equity sectors. The purpose of this study is to investigate the asymmetric spillovers across ten U.S. equity sectors, representing different industries. Daily prices of sector indices were collected from 02 January 2018 to 22 October 2021 for the analysis. In addition, the study applied Diebold and Yilmaz's (2012) dynamic spillover methodology, along with the static and rolling windows phenomena, to examine the daily returns spillovers across sector indices. The results indicate that 82 % of volatility forecast error variance in U.S sector indices is due to the spillover effect. Moreover, both industrials and financials exhibit the highest gross spillovers to other sectors, while they also receive the highest spillovers from other sectors. Furthermore, the oil and gas sector and utilities sector receive the highest net returns spillovers. These empirical findings provide crucial information regarding the interdependence of U.S. sector indices during the COVID-19 pandemic, which is relevant for investors and practitioners.

Climate Change and Volatility Forecasting: Novel Insights from Sectoral Indices

This study investigates the impact of climate policy uncertainty (CPU) on sectoral indices and clean energy exchange-traded funds (ETFs) by using a GARCH-MIDAS model. The stock indices include renewable energy (NEX), transportation, mining, industrial, real estate, green economy US, green economy Europe, green economy Asia index, and ETFs clean energy (PBW), global clean energy (PBD). The research also evaluates the forecasting power of uncertainty factors, including equity market volatility (EMV), economy policy uncertainty (EPU), trade policy uncertainty (TPU), fiscal policy uncertainty (FPU), global economy policy uncertainty (GEPU), and geopolitical risk (GPR), to predict the volatility. We obtained some notable results. First, the out-of-sample findings show that CPU index information is useful to predict the volatility of the NEX renewable energy, green economy US, transportation, energy index for the US, and clean energy (PBW), global clean energy (PBD) ETFs. Second, EMV, EPU, and GPR also contain valuable information for the real estate, industrial, energy, and NEX renewable energy index. Additionally, we find evidence during low and high volatility and upheaval of the COVID-19 pandemic. The Roos2 square and model confidence set (MCS) tests verify each model's out-of-sample forecasting performance.

Crude Oil Prices and the Egyptian Economy Evidence from the Stock Market

This paper investigates two important things; the role of crude oil prices in explaining the Egyptian stock market return, and what factors derive from Egyptian crude oil prices. Using 6 log difference time series variables this paper finds that multiple regression is an inappropriate model to test the two goals mentioned above. On the other hand, using Vector Autoregression (VAR Model) is much more profitable in achieving the paper’s goals. Although the VAR model results are more reliable, crude oil price fails to explain the Egyptian stock market return because Egypt is not a big oil exporter. Furthermore, the VAR model shows that the Energy sector index, stock world index (S&P 500 the proxy), stock exchange index, exchange rate, and the global financial crisis are all factors that derive and determine the Egyptian crude oil price.

Does investor sentiment affect the Indian stock market? Evidence from Nifty 500 and other selected sectoral indices

Does investor sentiment affect the Indian stock market? Evidence from Nifty 500 and other selected sectoral indices

Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings.

This study presents a novel approach to predicting price fluctuations for U.S. sector index ETFs. By leveraging information-theoretic measures like mutual information and transfer entropy, we constructed threshold networks highlighting nonlinear dependencies between log returns and trading volume rate changes. We derived centrality measures and node embeddings from these networks, offering unique insights into the ETFs' dynamics. By integrating these features into gradient-boosting algorithm-based models, we significantly enhanced the predictive accuracy. Our approach offers improved forecast performance for U.S. sector index futures and adds a layer of explainability to the existing literature.

Modeling the Volatility of Daily Listed Real Estate Returns during Economic Crises: Evidence from Generalized Autoregressive Conditional Heteroscedasticity Models

In this paper, we focus on the dynamic volatility behavior of the daily Swedish Real Estate Sector Index and analyze the existence and degree of a long-range dependence or asymmetric news effect since 2003. More specifically, we give extra attention to the 2007–2008 financial crisis, the 2009–2012 European debt crisis, and the first two years of the global COVID-19 pandemic era (2020–2021). We examine changes in volatility during these extreme events. We apply standard GARCH models, asymmetric GARCH models, and long-memory GARCH models with various error distributions to identify the most accurate volatility models of the daily returns of the Swedish Real Estate Sector Index for the full sample period, January 2003 to June 2021. Our results show that the volatility of the Swedish Real Estate Sector Index is time-varying and highly volatile. The impacts of the global financial crisis, European debt crisis, and COVID-19 pandemic are noticeable. Moreover, the volatility pattern during COVID-19 displays significant time-varying long-range dependence and an asymmetrical news impact, which lead to market inefficiency. Finally, the volatility pattern shows a tendency towards increasing leverage effects and less persistent behavior, indicating that the market stakeholders are highly sensitive to negative returns and becoming quicker to respond to market changes.

On sectoral market efficiency

A multi-fractional Brownian approach is used to measure the level of sectoral market efficiency through the Hurst exponent, using S&P 500 and sectoral indices data between 2002 and 2022. Our results show that each sector has a particular level of market efficiency, and it cannot be statistically represented by the aggregate market efficiency. However, there are long and short-term relationships between the efficiency of each sector and the level of market efficiency, which tend to vary from one sector to another. Besides, during periods of crisis, market efficiency by sector decreases sharply, and the cross-correlation of efficiency between sectors tends to increase. On the other hand, during the bull periods, the market efficiency could be considered a good hypothesis for the different sectors.

XGBoost-LSTM for Feature Selection and Predictions for the S&P 500 Financial Sector

Financial industry researchers have long been committed to identifying factors that can predict trends in the financial sector of S&P 500, despite these factors often being difficult to discover. This article, through the combination of the Xgboost regressor and the shap summary plot, has mined and continuously optimized a potential excellent factor combination. Also, by utilizing the Xgboost regressor and LSTM models, it has achieved good prediction accuracy on the test set. This research gets the following results: First, the Xgboost regressor, in combination with Shap, has identified the seven most excellent factors from an initial combination of nine factors. Second, after imparting the final seven features to LSTM, the MSEs of the predictions made by Xgboost regressor and LSTM are 0.0003 and 0.0004, while the running times for Xgboost regressor and LSTM are 27 minutes and 16 minutes. Consequently, these results indicate that in the future predictions of finance sector index, investors may use the Xgboost-LSTM model for selecting effective factors and making accurate predictions efficiently.

An empirical comparison between ESG 100 & sectoral indices with perspective of circular economy

An empirical comparison between ESG 100 & sectoral indices with perspective of circular economy

Sector-Wise Herd Behaviour in the Indian Stock Market during COVID-19

The article examines the herd behaviour at sector level in the National Stock Exchange of India. The novel objective of the paper is to analyse the presence of herd behaviour during the whole, pre- and post-coronavirus disease 2019 (COVID-19) pandemic outbreak period. The popular model proposed by Chang et al. (2000) has been deployed to examine herd formation in each sector. Five major sectors namely auto, bank, fast moving consumer goods, information technology and pharma have been considered along with NIFTY50 consisting daily closing prices of 106 companies comprising 58 from five sectoral indices and 48 from NIFTY50 for the period from 2011 to 2021 considering 1 January 2011–29 January 2020 as before COVID-19 outbreak period and 30 January 2020–31 December 2021 as after COVID-19 outbreak period. Results revealed significant evidence of anti-herd behaviour before COVID19 outbreak. At the same time, significant evidence of the existence of herd behaviour in FMCG, auto and pharma sectors have been detected after the outbreak of COVID-19 pandemic which assumes that investors tend to imitate the investment decisions of other market participants during such adverse macro events around the globe. The study tried to enhance the knowledge of investors to develop their trading strategies in the volatile market conditions by investing vigilantly and keeping such phenomenon in concern.

Spatial risk assessment for climate proofing of economic activities: The case of Belluno Province (North-East Italy)

Recent advancements in spatial risk assessment methodologies, particularly those incorporating GIS and economic evaluations, have significantly enhanced our ability to assess and manage risks associated with natural disasters. Entrepreneurs, investors, and public administrations need information about climate change risks for effective planning and decision making. To move from generic global or national projections about climate change scenarios, towards more actionable information on climate risks for socioeconomic agents, the three dimensions of risk (Hazard, Exposure and Vulnerability) must be quantified and mapped with the involvement of stakeholders. In this study, spatial indicators, tailored to the social and ecological systems of interest and co-designed with the key stakeholders are aggregated into sectoral risk indexes quantified in economic terms. Climate risk indexes were calculated and mapped for the four key economic sectors of the study area of the Belluno Province (Italian Alps): summer tourism, winter sports and events, eyewear industry, and electricity supply. Stakeholders were involved during the assessment to share knowledge, data and needs and to provide expert judgments on intermediate and final results. Outputs include a series of maps and statistical summaries, highlighting future trends of climate related risks, their spatial variability within the area and the estimated levels of uncertainty. Estimates on expected changes of future damages with constant Exposure and Vulnerability, provided socioeconomic agents with simple and clear messages about how their activities could suffer or benefit from climate change in the future.

A Comparative Study of BSE Listed Sectoral Indices of Real Estate and Banking Industry

A Comparative Study of BSE Listed Sectoral Indices of Real Estate and Banking Industry

Do Crude Oil Price Fluctuations Affect the Sectoral Stock Returns: Evidence from India

Crude oil is a vital energy source for industrialised and developing countries. Both investors who trade crude oil derivatives and firms that use oil as raw material for production monitor oil supply and demand. Disruption in the flow of oil in the commodity markets, therefore, leads to oil price volatility that affects major economies worldwide. Often, geopolitical and natural conditions could adversely impact the oil price and the financial markets react to such fluctuations in oil prices. This paper analyses the effects of oil price changes on sectorial stock returns in India. Using daily returns of S&P BSE Sensex, S&P BSE Oil & Gas and S&P BSE-Auto sectoral indices from January 2000 to March 2020, we examined the impact of Brent crude oil price fluctuations on the two major sectorial stock indices and overall market index. The results of the Granger causality test show significant results on S&P BSE-Sensex and S&P BSE-Oil and Gas Index Returns except S&P BSE-Auto Index Returns. Further for contemporaneous association between the oil price changes and sectorial returns, we operationalised the simple regression analysis. The results from the regression analysis show statistically significant impact of crude oil price fluctuations on the sectorial returns.

The impact of COVID-19 pandemic on the stock market volatility in Pakistan; evidence from sectoral indices analysis.

The impact of COVID-19 pandemic on the stock market volatility in Pakistan; evidence from sectoral indices analysis.

Impact of Oil Price Volatility on Selected Sectors of the Polish Stock Market

The commodity market has a significant impact on the global economy. Crude oil is one of the most important energy commodities, used to produce gasoline, diesel fuel, jet fuel and many other products. Changes in oil prices can have several different economic effects, by affecting inflation, trade balance, exchange rate, and many other macro- and micro-economic indicators. The main aim of the research is to identify the impact of oil prices on the Polish capital market. The co-movement of WTI Brent returns in relation to the sector indices of the Warsaw Stock Exchange was examined. The oil price impact assessment considers the period from 2014 to 2023. The research shows that out of fourteen indices, a significant relationship was observed in the case of four sectors: WIG Banks, WIG Chemicals, WIG Mining and WIG Fuel. In the case of most of the remaining indices, the correlation coefficients were positive but low.

Uncertainties and Dynamic Connectedness Among Sectors: A Case of the USA, India, France, Germany and Russia

This study examines the connectedness among the sectoral indices for the USA, India, France, Germany and Russia stock markets pre and post-COVID-19. We use the Diebold and Yilmaz spillover index to examine the study's objectives. This study finds that volatility spillover is higher during COVID-19 than before COVID-19. In addition, the volatility transmission across the sectors demonstrated mixed results regarding net volatility receivers and transmitters. However, the degree of transmission is higher for the net volatility receivers than for the net volatility transmitters. This study will help policymakers draft related policies to immunise their economy and market from spillover contagions of international markets during varying pandemic scenarios. This study would also help potential investors, including foreign institutional investors, diversify their portfolios based on the sectors with net volatility transmitters and receivers.

Impact of Geopolitical Risk on Islamic Sectoral Indices: Evidence from ARDL and NARDL model

Impact of Geopolitical Risk on Islamic Sectoral Indices: Evidence from ARDL and NARDL model

Portfolio Diversification Opportunities On The Asean 5 Stock Market And Sectoral Stock Indexes On The Indonesian Stock Exchange


 This study aims to examine portfolio diversification opportunities on the stock markets of Indonesia, Singapore, Malaysia, Thailand, and the Philippines, as well as sectoral stock price indices on the Indonesian Stock Exchange. The data used is the closing daily price index for the period January 2021 – October 2023. The analysis method uses principal component analysis. The results show that the first forming component includes the Indonesian stock market and its sectoral stock price index. The second component is filled by the stock markets of Singapore, Malaysia, Thailand, and the Philippines. The third component is the consumer goods cyclical and technology sector, then the fourth component is the consumer goods non-cyclical and healthcare sector. Based on the research results, it shows that there are differences in return variance between the five stock markets on ASEAN 5 and the sectoral stock price index on the Indonesia Stock Exchange. Investors can diversify their portfolios into other countries stock markets that have a low correlation with the domestic stock market. This also helps investors outside Indonesia to look for opportunities to diversify into various stock sectors on the Indonesian stock exchange, even though the opportunities are limited.