Market Sector Indices Research Articles

Dynamic linkages and contagion effects: Analyzing the linkages between crude oil prices, US market sector indices and energy markets

This paper aims to assess the dynamic linkages between crude oil futures contracts, renewable energy indices, carbon credit futures indices and several US sector market indices by applying the DECO-GARCH model and the connectedness index of Diebold and Yilmaz (2012). The analysis is conducted on a daily data sample from August 2014 to February 2024 and performed at both the aggregated and disaggregated levels. The disaggregated analysis revealed that the correlation between the variables was lower before periods of stress, such as the COVID-19 crisis and the Russian–Ukrainian conflict as well as a complex correlation structure with a diverse mix of positive and negative values between different pairs of variables. Moreover, the static connectedness results in terms of returns underscore a significant degree of interconnection and transmission of shocks between oil prices and sector markets, renewable energy, and carbon credit futures indices. In addition, the results highlight the responsiveness of the clean energy and carbon credit sectors to global circumstances and economic conditions. The study concludes with a dynamic connectedness analysis, highlighting once again the intricate connections and interactions between all the variables, which are exacerbated during periods of market instability and key events. The net interconnectedness analysis demonstrated that changes in crude oil prices have a significant impact on most of the variables analyzed. The findings of this study have clear implications for a wide range of market participants, policy-makers, and individuals managing portfolios, particularly in terms of diversification opportunities.

Comparative Analysis of Gold, Bitcoin and Gold-backed Cryptocurrencies as Safe Havens During Global Crises: A Focus on G7 Stock Market and Banking Sector Indices

In this article, we attempt to analyze and compare the safe-haven features of gold, Bitcoin and gold-backed cryptocurrency against the stock and banking indices of G7 countries during the outbreak of adverse events. To do so, we examine dynamic relationships between different assets and we compute optimal hedge ratios for different couples using the corrected Asymmetric Dynamic Conditional Correlation-Exponential Generalized Autoregressive Conditional Heteroscedasticity and corrected Asymmetric Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity models. We clearly show that gold and gold-backed cryptocurrency maintain higher weights in optimal portfolios compared to Bitcoin. We also report that shocks due to unexpected events increasingly affect dynamic correlations, asset weights and hedge ratios. This underscores the need for regular demand for rebalancing the hedge positions and effective risk management. We thereafter show that the relationship between Bitcoin (gold-backed cryptocurrency) and G7 indices is highly affected by the outbreak of COVID-19 pandemic. Such findings highlight the hedging and safe-haven features of different asset classes against stock markets. They could have insightful implications for investors who want to minimize investment risks and policymakers who are worried about the financial consequences of different unexpected events.

Does carbon price volatility affect European stock market sectors? A connectedness network analysis

We investigate how the volatility of carbon emission allowance (EUA) prices affects European stock market sectors. We employ a connectedness network analysis on prices of EUA futures and FTSE stock market sector indices and find that the EUA is mostly a net receiver of volatility connectedness and significantly receives volatility across most sectors during the recent European energy crisis.

Herding behaviour in Turkish stock market sector indices: the effect of COVID-19 outbreak

Herding behaviour in Turkish stock market sector indices: the effect of COVID-19 outbreak

Hedging UK stock portfolios with gold and oil: The impact of Brexit

The purpose of this paper is to examine dynamic co-movements and portfolio management strategies between UK stock indices (aggregate market index and sector indices) and each of gold and crude oil futures markets, during the Brexit referendum (2016) and the Brexit day (2021). Using multivariate GARCH models, the time-varying conditional correlations between the focal markets are extracted. Then, portfolio weights, hedge ratios, hedging effectiveness, and downside risk reductions are determined. The results reveal that, during the Brexit referendum period, gold and crude oil provide a diversification opportunity for the UK stock portfolios. Specifically, investors should give more weight to gold than stocks in their gold–stock portfolios, whereas they should allocate more funds into stocks than oil to minimize risk in their oil–stock portfolios. Based on hedging analysis results, crude oil appears more effective than gold in reducing stock portfolio exposure to downside risk and in improving the hedging effectiveness. Further analysis involving an event study considers the potential shift in the correlations between the UK stock market index (sector indices) and each of gold and crude oil during short-term event widows (one day, one day; five days, five days; and ten days, ten days) around the announcement day of the initial, transmission and actual Brexit periods or during each of these sub-periods. The results show that early news about Brexit led to a substantial change in the means of correlation coefficients in comparison to the tranquil period, where the initial UK stock price movements were driven by fears of a cyclical downturn following the referendum.

The Australian Stock Market’s Reaction to the First Wave of the COVID-19 Pandemic and Black Summer Bushfires: A Sectoral Analysis

In this study, we investigated the impact of the first wave of the COVID-19 pandemic on various sectors of the Australian stock market. Market capitalization and equally weighted indices were formed for eleven Australian sectors to examine the influence of the pandemic on them. First, we examined the financial contagion between the Chinese stock market and Australian sector indices through the dynamic conditional correlation fractionally integrated generalized autoregressive conditional heteroskedasticity (DCC-FIGARCH) model. We found high time-varying correlations between the Chinese stock market and most of the Australian sector indices, with the financial, health care, information technology, and utility sectors displaying a decrease in co-movements during the pandemic. The Modified Iterative Cumulative Sum of Squares (MICSS) analysis results indicated the presence of structural breaks in the volatilities of most of the sector indices around the end of February 2020, but consumer staples, industry, information technology and real estate indices did not display any break. Markov regime-switching regression analysis depicted that the pandemic has mainly affected three sectors: consumer staples, industry, and real estate. When we considered the firm size, we found that smaller companies in the energy sector exhibited gradual deterioration, whereas small firms in the consumer staples sector experienced the largest positive impact from the pandemic.

Herding behaviour in Turkish stock market sector indices: the effect of COVID-19 outbreak

Herding behaviour in Turkish stock market sector indices: the effect of COVID-19 outbreak

The Unprecedented Australian Stock Market Reaction to the Black Summer Bushfires and the COVID-19 Pandemic Outbreak: A Sectoral Analysis

In this study, we investigated the impact of the first wave of the COVID-19 pandemic on various sectors of the Australian stock market. Market capitalization and equally weighted indices were formed for eleven Australian sectors to examine the influence of the pandemic on them. First, we examined the financial contagion between the Chinese stock market and Australian sector indices through the dynamic conditional correlation fractionally integrated generalized autoregressive conditional heteroskedasticity (DCC-FIGARCH) model. We found high time-varying correlations between the Chinese stock market and most of the Australian sector indices, with the financial, health care, information technology, and utility sectors displaying a decrease in co-movements during the pandemic. The Modified Iterative Cumulative Sum of Squares (MICSS) analysis results indicated the presence of structural breaks in the volatilities of most of the sector indices around the end of February 2020, but consumer staples, industry, information technology and real estate indices did not display any break. Markov regime-switching regression analysis depicted that the pandemic has mainly affected three sectors: consumer staples, industry, and real estate. When we considered the firm size, we found that smaller companies in the energy sector exhibited gradual deterioration, whereas small firms in the consumer staples sector experienced the largest positive impact from the pandemic.

Looking through systemic credit risk: Determinants, stress testing and market value

We provide a methodology to estimate a Global Credit Risk Factor (GCRF) from CDS spreads using the information provided by the default-related component of observed spreads. These are previously estimated using Pan and Singleton (2008) methodology. The estimated factor contains higher explanatory power on CDS spread fluctuations across sectors than standard credit indices like iTraxx or CDX. We find a positive association between GCRF and implied volatility variables, and a negative association with MSCI stock market sector indices as well as with interest rates and with the slope and the curvature of the term structure. Such correlations provide useful insights for risk management as well as for the hedging of credit portfolios. Indeed, we present a synthetic factor regression model for GCRF that we apply in a stress testing methodology for credit portfolios as well as to evaluate future credit risk scenarios. Finally, we show evidence suggesting that the exposure to systemic credit risk was priced in the market during the 2006–2015 period.

Sektörel Güven Endeksleri ve BİST Sektör Endeksleri Arasındaki İlişkilerin Simetrik ve Asimetrik Nedensellik Analizi

Bu çalışmada Ocak 2011-Haziran 2019 dönemi için, güven ve borsa endeksleri arasındaki sektörel bazlı ilişkiler araştırılmaktadır. Finansal hizmet güven endeksi, hizmet sektörü güven endeksi, inşaat sektörü güven endeksi, perakende ticaret sektörü güven ve borsa endeksleri değişkenlerine ait aylık veriler, ilgili dönemde ilk olarak, Hacker ve Hatemi-J (2006) testiyle simetrik nedensellik, ikinci olarak, Hatemi-J (2012) testiyle asimetrik nedensellik ilişkisi açılarından incelenmiştir. Yapılan testler sonucunda edinilen bulgulara göre; Türkiye’de sektörel güven endeksleri ile ilgili Borsa İstanbul (BİST) sektör endeksleri arasında simetrik bir nedensellik ilişkinin varlığı tespit edilememiştir. Buna karşın, bu değişkenler arasında asimetrik bir nedensellik ilişkisi saptanmıştır. Diğer bir ifadeyle, sektörel güven endeksleri ile borsa sektör endeksleri arasında doğrudan bir nedensellik ilişkisi bulunmazken, bu değişkenlerin bileşenleri üzerinden bir nedensellik ilişkisi bulunmaktadır. Anahtar Kelimeler: Güven Endeksi, Borsa Sektör Endeksi, Simetrik Nedensellik, Asimetrik Nedensellik JEL Sınıflandırması: G19, G23, G40

Dynamic Relationship Between Stock Market Sector Indices and Macroeconomic Variables in India

This study investigates the long-term and short-term relationships between selected macroeconomic variables and the selected Indian stock market sector indices over the period of 2010 to 2017. The Johansen Co-integration Test, the Vector error correction model (VECM), is applied to calculate the long-term and short-term relationship between sector indices and macroeconomic variables. It is found that stock prices are exposed to macroeconomic factors, but the level of sensitivity is different in different sectors. Out of five sectors taken in the study, it is found that only the realty sector has long run relationship with macroeconomic variables. Other sectors have no long run relationship with macroeconomic variables. Along with this, it is also found that the Auto index has a significant short-term positive relationship with gold prices and the FMCG sector index has a significant short-term positive relationship with industrial production. The consumer price index and exchange rate have significant short run relationship with realty sector index.

Directional predictability from stock market sector indices to gold: A cross-quantilogram analysis

We address the safe haven properties of gold relative to US stock market sector indices using the bivariate cross-quantilogram of Han et al. (2016). Splitting our sample into pre- and post-crisis periods, our results show that the safe haven properties of gold have a changing nature. Before and after the financial crisis, we find only limited quantile dependence and that gold can be considered a safe haven for most of the sectors, except Industrials. On a full sample (1999–2016), there are only three sectors – Healthcare, IT, and Telecommunication services – for which gold can be considered a safe haven.

Impact of Government Budget Announcement on Stock Market Sector Indices: Evidence from Colombo Stock Exchange

This study attempts to investigate the impact of Sri Lankan government budget announcements on five sector indexes of Colombo Stock Exchange, namely banks, finance & insurance; beverage, food & tobacco; manufacturing; motor; diversified holdings. The Event study methodology was used to analyze sector wise stock price behavior during the sample period of 2002 to 2013. Results revealed that although the overall trend of the average returns of each sector around budget announcements are same the magnitude of the reaction is different from sector to sector. Motor was the mostaffected sector and diversified holdings sector was the least affected.

Volatility Spillovers between World Oil Market and Sectors of BIST

Nowadays, enormous increase of production and service sectors leads to increase in demand for energy consumption. Therefore, energy and oil consumption in a variety of countries are considerably effected by energy and oil prices. International oil prices are crucial for both oil exporting countries and capital market investors as a means of volatility spillovers. This paper aims to analyze whether volatility spillovers exist between world oil market and several sector indices operating in Borsa Istanbul (BIST) 100 including energy, non-metal mineral products, and transportation using bivariate GARCH (1, 1) model. Estimation results suggest that except for non-metal mineral products sector, there are interactions between oil returns and the underlying sectors in terms of both shocks and conditional variance.

Stock Market and Economic Growth in the U.S. & France: Evidence from Stock Market Sector Indices

Many scientists have analyzed a relation among economic and financial market variables. One of the purposes of these researches, and probably the most important one, is to find a better prediction of future economic changes. Economic theory suggests a strong link between stock market and economic activity. The question is whether the stock market is a predictor of future economic activity measured as growth of country’s gross domestic product. In this paper we analyze which economic sectors, represented by stock market sector indices, could have most impact on GDP. The aim of this paper is to analyze whether some sectors are more important than others while analyzing GDP change. The study uses data for the period 2000 Q2 – 2012 Q1 of the U.S. seasonally adjusted GDP and Dow Jones indices and data for the period 2001 Q1 – 2012 Q1 of France seasonally adjusted GDP and Euronext CAC indices. In order to find a relation between selected stock market sector indices and GDP, we will use cross correlation analysis. Our findings support the theory that stock market is a leading indicator for economic growth. In France stock market appears to be a stronger indicator for economic growth compared to the U.S. The results revealed that seasonally adjusted GDP growth lagged behind changes in stock market indices four quarters in France and three quarter in the U.S. In the U.S. worst predictive capabilities for GDP growth come from utilities, oil and gas sectors. Industrial and financial sectors gave the best cross correlation results with GDP growth. In France telecommunication, utilities sectors have the worst predictive capabilities, and consumer services and health care sectors gave the best cross correlation results on GDP growth, compared with others. DOI: http://dx.doi.org/10.5755/j01.ee.25.1.1985

Foreign Institutional Investment and Stock Market Returns: Evidence from Indian Capital Market

This study investigated the effect of foreign institutional investment (FII) on the returns of Indian stock market using monthly data collected over the period from January 1993 to May 2012. By applying augmented Dickey-Fuller unit root test, Johansen cointegration test, Granger causality test and vector error correction model (VECM), this study found that FII and Sensex are level and first difference stationary series, respectively. It also found positive long-run equilibrium relationship between stock market index and FII. The study also revealed bidirectional causality between stock market index and FII. This study formulated VECM, which can be used for the estimation purpose. The major implication that this study derived is that foreign investments play a beneficial role in the development of the Indian economy and, therefore, the Indian government should try to maximise net foreign investment. The results obtained in this study were based on the monthly data of Sensex and FII; therefore, one needs to be cautious before generalising the results. For future research, one may consider other Indian stock market indices, sector indices and individual companies. Even more robust analysis can be done by reducing the frequency of data, which will help in validating the result of this study.

Sector Indices Correlation Analysis in China's Stock Market

China's stock market has experienced a big bull market in 2007 and a bear market in 2008. Since then, with the influence of the global economy, China's stock market exhibits ups and downs as usual. We examine the relationship between the stock market sector indices from the meso level, and divide the periods into two stages. One stage represents the drastic shock periods in 2007 and 2008, and the other represents the general ups and downs periods. In the first stage when the market experiences drastic ups and downs, the sector indices tend to rise or fall together, and exhibit very close correlations between each other. In the second stage, however, much smaller correlations appear, and the stock price indices reflect the cyclical characteristics of the real sector economy.

On the interdependence structure of market sector indices: the case of Qatar Exchange

Purpose – The purpose of this paper is to investigate the interrelationships amongst the sector‐specific indices of the Qatar Exchange (QE) (i.e. Banking and Financial Institutions (BFI), Industrial (IND), Insurance (INS), and Services (SER)). More specifically, three key issues are explored in this study. First, the long‐run relationships amongst the sectors. Second, the short‐run causal relationships amongst them; and third, the relative degree of endogeneity/exogeneity of each sector.

Day-of-the-week effect: Evidence from the nine economic sectors of the JSE

ABSTRACTThis study is the first in South Africa to test directly for the day-of-the-week effect on skewness and kurtosis on the nine listed economic stock market sector indices of the JSE. The empirical results of this study show no evidence of the day-of-the-week effect on skewness and kurtosis for eight of the nine JSE stock market sectors. However, the Monday effect was detected for the basic materials sector only. The study also finds the JSE to be weak-form efficient.

Macroeconomic Effects on Emerging Market Sector Indices

This article supplies empirical evidence of the dynamic interactions between international macroeconomic determinants and 10 emerging market sector indices. As so far empirical research has followed the scientific approach of examining each country separately, this article conducts a supranational sector analysis instead. Due to several economic and monetary crises, we find structural breaks in 9 of the 10 sectors. Taking into account the consequential sub-periods, the sensibility of each sector index to macroeconomic influences is detected by the VECM findings which allow conclusions to be drawn about the sector-specific distinctions in the emerging markets. Contrary to theoretical expectations, the majority of the examined sectors benefited from increasing commodity prices during the examination sample. This finding, therefore, affirms that the ongoing catching-up process and the consequential growth in affluence in the emerging countries is largely driven by growing commodity prices and exports.