Sectoral Indices Research Articles

Quantifying the volatility spillover dynamics between financial stress and US financial sectors: Evidence from QVAR connectedness

This study uses quantile vector-autoregressive to examine volatility connectedness among a global financial stress index (including five categories: credit, equity valuation, funding, safe assets, and volatility) and US financial sectors under low, moderate, and extreme volatility conditions. The dataset includes the special periods covering the global financial crisis, China crisis, COVID-19 pandemic, Russian–Ukrainian war, Silicon Valley Bank failure, and Credit Suisse bank crisis. The findings imply that spillover effects among the series are higher during extreme volatility than during low and moderate volatility periods. During periods of low volatility, the credit category of the financial stress index and the US financial sector indices are net shock transmitters, but during extreme volatility periods, the US financial sectors become net shock receivers alongside the credit and funding categories of the financial stress indices. US financial sectors also exhibit net shock recipient roles at extreme volatility levels during those special periods.

Boom or bust? A Comparative Analysis of Monetary Policy and Stock Market Performance in India Across Economic Cycles

This study delves into the intricate relationship between monetary policy instruments and various sectors of the Indian stock market from 2011 to 2022. By Analyzing data encompassing significant policy shifts and economic events, including the Reserve Bank of India's (RBI) transition towards inflation targeting and flexible exchange rates, the study aims to uncover the nuanced impacts of monetary policy on sectoral indices. Utilizing a Vector Autoregression (VAR) model, the study examines the dynamic interplay between key monetary policy instruments—Cash Reserve Ratio (CRR), Marginal Standing Facility Rate (MSF), Repo Rate, Reverse Repo Rate, and Treasury Bills—and ten diverse sectors represented in the Bombay Stock Exchange (BSE) indices. The findings reveal intricate sectorspecific responses to monetary policy adjustments, reflecting the complex interplay of economic dynamics and policy measures. While some sectors exhibit positive correlations with certain monetary instruments, others display negative associations, underscoring the diverse impacts of monetary policy on sectoral performance. The study highlights the significance of understanding these nuanced relationships for policymakers, investors, and market participants in navigating India's evolving economic landscape.

Tail risk spillovers between Islamic sectoral equities and bond markets: a time-frequency domain approach

ABSTRACT This study examines the tail risk spillover between Islamic sectoral stock indices and country specific investable Islamic bonds in time and frequency domain and provides useful implications for portfolio management. For the analyses, Conditional Autoregressive ValueatRisk (CAViaR), Quantile Connectedness, and DCCGARCH t-Copula models are estimated utilizing daily data from 15 countries. The findings show that the connectedness and spillover of risks are much stronger at tails than at median, and in the short-term than in long-term. Whereas median risk connectedness surges during COVID-19 pandemic, the connectedness between tail risks is usually elevated even before the COVID-19 pandemic with industrial sector being consistently a significant net transmitter of shocks. Moreover, all the sectoral stock market indices are significant and consistent transmitter of left tail shocks indicating a much stronger role of sectoral stock markets in transmitting large negative shocks. A heightened connectedness is also observed during Russia-Ukraine war mainly in the short-term frequency. The portfolio analysis shows that long positions in sectoral stocks can usually be hedged by taking short positions in Islamic bonds. Hedging effectiveness and optimal portfolio weights are also calculated to provide market participants with further information.

The Effect of Interest and Inflation Rates on Stock Returns: Quantile Regression Analysis for Türkiye

According to financial theory, there is an inverse relationship between interest rate and stock returns and prices. In the framework of the Liquidity Preference Theory, the rise in interest rates causes the money to move towards higher interest rates, so demand for stock market decreases and returns fall. The behaviour of stock prices on interest rates and inflation has been investigated by various time series analysis methods. However, in this study, the subject is handled in a different approach by using the Quantile Regression (QR) method. In the study, nominal interest rate is decomposed into real interest rate and inflation components and effects on stock prices are examined for the period January 2013 - July 2023 separately for services, financial, industrial and technology sector indices. When it comes to the impact of nominal and real interest rates on returns in QR estimates, it is seen that the service and industrial sectors differ in terms of both tail and sign. The similarities between Least Squares estimates and QR estimates are striking. However, the effect of volatility in variables on the differentiation in relationships is also an issue that should be emphasized.

Multivariate Bayesian Time-Series Model with Multi-temporal Convolution Network for Forecasting Stock Market During COVID-19 Pandemic

The paper proposes a hybrid algorithm for forecasting multiple correlated time-series data, which consists of two main steps. First, it employs a multivariate Bayesian structural time series (MBSTS) approach as a base step. This method allows for the incorporation of potentially high-dimensional regression components, and it utilizes spike and slab priors to identify a parsimonious model. Second, the algorithm includes a post-model fitting diagnostic step where the residuals from the MBSTS step are processed through a multi-input/output temporal convolutional network (M-TCN) with multiple time scale feature learning. This step serves as an alternative to traditional subjective residual-based diagnostic procedures in time-series analysis, with the aim of improving forecasting accuracy. The key advantage of the M-TCN is its ability to capture sequential information efficiently. The M-TCN expands the field of convolution kernel without increasing the number of parameters, thus enhancing the capacity of model to capture complex sequential patterns. The paper presents two applications showcasing the effectiveness of the proposed hybrid algorithm. First, it utilizes pre-lockdown data from eleven Nifty stock sectoral indices to predict stock price movements, including the initial post-lockdown upturn. In the second application, it focuses on stock market data from pharmaceutical companies involved in manufacturing COVID-19 vaccines. In both cases, sentiment data sourced from newspapers and social media serve as the regression component. Through rigorous analysis, the paper demonstrates that the hybrid model outperforms various benchmark models, including LSTM, Bidirectional Encoder Representations from Transformers (BERT)-based LSTM, Deep Transformer Model, and GRU, among others, in terms of forecasting accuracy. This underscores the utility of the hybrid algorithm, particularly in predicting stock market trends during the COVID-19 pandemic period and its associated market dynamics.

PANDEMIC’S IMPACT ON BURSA MALAYSIA SECTORS: A COMPREHENSIVE STUDY UTILIZING OLSM AND ESM

The pandemic has the potential to induce significant stock market volatility as a consequence of investor decisions, while movement restrictions may force the closure of many small-to-medium enterprises (SMEs). Despite this, there is a noticeable absence of comprehensive perspectives on the pandemic's impact across all sectors in Bursa Malaysia. Consequently, this paper endeavors to scrutinize the sector within Bursa Malaysia most profoundly affected by Covid-19. Employing the Ordinary Least Squares method (OLSM) method and the Event Study Method (ESM), the study compares stock returns and risks. The independent variables encompass daily counts of Covid-19 cases and deaths in three countries—Malaysia, the United States, and China—as well as the Brent oil price. The study focuses on 13 sectorial indices, including construction, consumer products, energy, finance, healthcare, industrial products, plantations, property, real estate investment trusts, technology, telecommunications and media, transportation and logistics, and utilities. Utilizing the ESM, the study investigates the daily average return, abnormal return, and expected return of each sector to gauge the impact of Covid-19. Historical data from March 18 to May 3 over three years (2019 to 2021) is employed. Results indicate that an increase in Covid-19 cases in Malaysia correlates with a decrease in the performance of these 13 sectors. Consequently, this study contributes to enhancing investor comprehension of sectorial indices' volatility and significance during the pandemic, aiding them in monitoring sectoral performance in the securities market.

COVID-19 and persistence in the stock market: a study on a leading emerging market

AbstractIn this study, we examine how sectors of the National Stock Exchange from India respond to the uncertainties introduced by the COVID-19 pandemic. By examining the synchronization between the sector-specific and overall market index (NIFTY 50) reaction to COVID-19, we contribute to the inconclusive ongoing academic literature regarding the impact of COVID-19 on the stock market, especially in the context of persistence in an emerging market. To analyze the persistence of sectoral indices, we apply multifractal detrended fluctuation analysis (MFDFA). We use the generalized Hurst exponent and singularity spectrum as indicators for persistence and spectral width as a measure of volatility. Our analysis shows that the sample sectoral indices are persistent before and after the announcement of COVID-19; however, volatility in some sectors reduces post-announcement of COVID-19. The findings will enrich the academic literature on the relationship between sector-specific and overall market indexes. In practice, the paper will guide investors to organize their portfolios, especially during future economic uncertainty.

Sectoral performance of Indian stock market during 1st and 2nd wave of COVID-19 pandemic: an event study analysis

PurposeCapital markets are the backbone of an economy. COVID-19 has created an unacceptable and unpredictable economic environment worldwide, resulting in a significant setback for securities exchanges. India also experienced two waves of this pandemic, which led to a significant downturn in the capital market.Design/Methodology/ApproachResearchers have endeavored to study the impact of the first and second waves of COVID-19 on the performance of various sectoral stocks in India. The performance of selected sectoral indices of the Bombay Stock Exchange was compared with the market performance of the S&P BSE 100. An event study was conducted to analyze the normal return, abnormal return (A.R.), and t-statistics of A.R. for various sectoral stocks. In addition, the abnormal returns of sectoral stocks between the first and second waves of COVID-19 in India were comparedFindingsThe results of the tests showed heterogeneous A.R. between different sectors in both the first and second waves of COVID-19 in India. Positive investor outlook and government financial support programs for various sectors helped them recover from the second wave of COVID-19.Research limitations/implicationsThe study analyzed the impact of the peak of the first and second waves of COVID-19 on selected sectoral indices. There may be several reasons for the performance of this particular stock index. However, we have tried to analyze the best possible reasons for this turbulence in the performance of stocks of selected sectoral indices. The study can be further analyzed to examine the long-term impact of such a pandemic on other sectors.Originality/valueThe study is based on the panic behavior of investors during such a pandemic. No one was prepared for this and expected this pandemic to last this long. This pandemic has taught so many lessons to everyone involved. Investors need to be prepared and cautious for such unforeseen disasters before making any investment decision. They need to analyze which industry can survive under such circumstances, and then they should invest there. Industries and enterprises must adapt and improve by honestly looking at their weaknesses and trying to meet investors' expectations.

An empirical investigation of return spillover and volatility dynamics of Indian sectoral indices

ABSTRACT This research delves into the interconnectedness and volatility dynamics within sectoral indices amid the COVID-19 pandemic, to determine the most appropriate model for elucidating these dynamics. It addresses the burgeoning interest among investors in comprehending sectoral dynamics within India’s stock markets. Leveraging five years of daily closing price data (April 2017 to December 2021) sourced from the National Stock Exchange’s official website for 16 sectoral indices and the Nifty50 index, the study employs the Diebold-Yilmaz Spillover 2012 framework alongside four GARCH models (E-GARCH, T-GARCH, FIE-GARCH, and FE-GARCH). Empirical analysis underscores that the mid-small financial sector is the riskiest sector among others, advocating for the inclusion of the pharmaceutical sector to mitigate portfolio risks. Furthermore, the research underscores the persistence of volatility across sectors and identifies the TGARCH model as the appropriate model to capture the volatility for most sectoral indices.

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.

Risk spillover mechanism among commercial banks and FinTech institutions throughout public health emergencies

While FinTech has contributed to the improvement of efficiency and cost reduction for commercial banks, it has also brought about risks for them. Especially during the outbreak of major public emergencies, the degree of risk spillover among financial institutions increased significantly. In this paper, we constructed a high-dimensional risk spillover network using the elastic net shrinkage technique to investigate the impact of public health emergencies on the risk spillovers between FinTech institutions and commercial banks. The total spillover index was utilized to access the overall coupling between FinTech institutions and commercial banks. Furthermore, we employed the sectoral spillover index and institutional centrality index to examine the spillover intensity across different sectors and institutions. Additionally, we analyzed the changes in the risk spillover network structure and institutional risk role during emergencies, aiming to uncover the impact mechanism of public health emergencies on risk spillovers. The results reveal that (1) public health emergencies, such as the COVID-19 pandemic, have intensified the industry correlation between the FinTech and banking sectors, and the primary risks of the system have shifted from intraindustry risks to interindustry risks. (2) Public health emergencies have changed the risk transmission roles of FinTechs and banks. FinTechs transitioned from being risk recipients to risk contributors, while banks shifted from being risk contributors to risk recipients. (3) FinTechs play a crucial role in facilitating indirect risk transmission within the system, acting as influential adjacent institutions that bridge the gap between critical institutions. (4) Compared with large commercial banks, small and medium-sized banks are more sensitive to FinTech risks. This study provides supporting evidence for regulatory agencies to enhance risk management in financial innovation during public health emergencies and in the post-pandemic era.

Asymmetric Return Connectedness Among Indian Equity Sectors: Insights from Recent Global Disruptions

This article aims to study asymmetric shock transmission among India’s National Stock Exchange sectoral indexes before and amid the COVID-19 crisis and the Russian–Ukraine feud using an asymmetric time-varying parameter vector autoregressive connectedness technique. The daily closing price of equity sector indexes from 1 January 2018 to 31 March 2023 were used. The research reveals strong sectoral return interconnectedness, with negative returns spillover dominating the assessment period. COVID-19 substantially impacted connectedness more than the Russian–Ukraine feud. The sectors such as bank, private bank and financial services were net transmitters of risk, and information technology was the net recipient of risk throughout the assessment period, indicating these sectors are not much affected by these crises. Sector return spillover patterns were similar during the epidemic and the Russian–Ukraine dispute, except for the oil and gas and metal sectors, which shifted roles. These findings suggest that policymakers must recognize and assess the peculiarities of industries that are high-risk transmitters when formulating strategies to prevent market failure or mitigate risk transmission to others. Investors may benefit from recognizing industries that operate as risk transmitters and receivers to create effective risk-mitigation investment strategies amid turbulent times.

Do bitcoin shocks truly Cointegrate with financial and commodity markets?

This study examines the long-run relationships between Bitcoin and various financial and commodity markets. Utilizing a novel methodology termed the Implicit Asymmetric Combined Cointegration Test (IACC), an augmented variant of the Bayer Hanck combined cointegration method (BH), this research applies ten-minute frequency time series data to test asymmetric shocks associated with Bitcoin, stock markets, futures indices, sectoral stock indices, Islamic stocks, commodities, and foreign exchange markets. The principal finding reveals a hidden cointegration between negative Bitcoin shocks and both negative and positive shocks in almost all examined financial instruments, indicating an absence of decoupling in the connections between Bitcoin shocks and other financial instrument shocks. The study demonstrates Bitcoin's centrality in financial investments and establishes long-run relationships between Bitcoin price shocks and those of other financial instruments. The findings suggest caution for participants in both financial and commodity markets, as Bitcoin emerges as a major source of the recent volatility observed in these instruments' prices.

A comparative study of the CAPM and extended models in the Indian stock market

To explain the cross section of asset returns, there are numerous asset pricing models being used which require ideal conditions in the markets around the world. An attempt has been made in the present study to estimate and compare the different versions of capital asset princing model (CAPM) using the data of sectoral indices listed in S&P BSE in the Indian stock market. The two widely used approaches i.e. Fama-MacBeth (1973) and Pettengil et al. (1995) for conditional versions have been adopted in the study. The models were compared on the basis of Akaike Information Criterion (AIC) and it was obtained that the AIC value of the Unconditional higher moment CAPM was obtained minimum among other models. Hence it can be said as the better model than other models estimated.

The S&P 500 sectoral indices responses to economic news sentiment

AbstractThis study explores the dynamic relationship between economic news sentiment and the US stock market using a non‐linear empirical framework. The analysis focuses on both sectoral indices and the aggregate stock market index from November 2011 to November 2021. Using causality tests and a rolling window detrended cross‐correlation coefficient, the study reveals several key findings. First, the causal effect of investor sentiment on sectoral returns varies over time, with each sector responding differently. Second, while no evidence of dependence exists for time scales less than 2 months, a positive relationship emerges for time scales greater than 6 months, except for the utilities sector, which is found to be negative. Third, the study shows that the relationships between all pairs of variables are time‐dependent. Finally, economic news sentiment might have a varying impact on market inefficiency over different periods, making it challenging to predict market behaviour based on sentiment data.

WTI, Brent or implied volatility index: Perspective of volatility spillover from oil market to Chinese stock market.

This study investigates the impact of oil market uncertainty on the volatility of Chinese sector indexes. We utilize commonly used realized volatility of WTI and Brent oil price along with the CBOE crude oil volatility index (OVX) to embody the oil market uncertainty. Based on the sample span from Mar 16, 2011 to Dec 31, 2019, this study utilizes vector autoregression (VAR) model to derive the impacts of the three different uncertainty indicators on Chinese stock volatilities. The empirical results show, for all sectors, the impact of OVX on sectors volatilities are more economically and statistically significant than that of realized volatility of both WTI and Brent oil prices, especially after the Chinese refined oil pricing reform of March 27, 2013. That implies OVX is more informative than traditional WTI and Brent oil prices with respect to volatility spillover from oil market to Chinese stock market. This study could provide some important implications for the participants in Chinese stock market.

Heterogeneity effect of positive and negative jumps on the realized volatility: Evidence from China

The existing literature overlooks the different responses of the stock market to jump risk during high and low volatility periods. We examine the impact of positive and negative jumps on China's composite index and sector index volatilities across various volatility regimes from January 4, 2010 to June 30, 2023 based on 5-min high-frequency data and quantile regression. The results indicate that the asymmetry of positive and negative jumps in the emerging sectors is not significant in the high quantiles, but is significant in the middle and low quantiles, compared with the composite index and traditional sectors. The volatility change driven by the negative jump of most emerging sectors indices exhibits a clear ‘J’ shaped distribution. Moreover, COVID-19 has weakened the positive relationship between negative jumps and volatility in traditional sectors. This work alerts risk managers to focus on negative jump risks when markets are in periods of high volatility.

Climate policy uncertainty, media coverage of climate change, and energy markets: New evidence from time-varying causality analysis

Climate change is one of the most critical issues in the last decade, making investigating climate change risks' effects on the economy vital. Employing a novel time-varying Granger causality approach, we test causality from climate policy uncertainty (CPU) to the index returns of clean energy and nonrenewable energy sectors between November 2009 and December 2021. Our analysis generally reveals a significant causality from CPU to S&P clean energy sector index return throughout the sample period. In contrast, very limited significant causality is observed running from the CPU to the S&P nonrenewable energy index return. This result implies that investments in clean energy firms are affected by the CPU due to the cost of reversing the decisions in uncertain environments. On the other hand, the U.S. newspaper media coverage of climate change has a significant impact not only on the clean energy index returns but also on non-renewable energy index returns, implying an increase in the media coverage regarding climate change influences the awareness of investors on climate change, which affects their trading strategies.

Portfolio management of ESG-labeled energy companies based on PTV and ESG factors

This paper evaluates monthly quartile portfolios of ESG-labeled companies constructed based on their Prospect Theory Value (PTV) and ESG scores in the closely monitored energy sector. Investing in ESG-labeled energy stocks can outperform a value-weighted global energy sector index, according to several out-of-sample performance analyses. The PTV strategy stands out over a sample period of more than twelve years. This strategy performs similarly to a fully diversified world market index and consistently outperforms a world energy index. Over the last five years, the simple strategy based on ESG scores performs similarly to the PTV strategy.

Dynamic connectedness among market volatilities: a perspective of COVID-19 and Russia-Ukraine conflict

PurposeThis paper aims to investigate the dynamic volatility connectedness among 13 G20 countries by using the volatility indices.Design/methodology/approachThe connectedness approach based on the time-varying parameter vector autoregression model has been used to investigate the linkage. The period of study is from 1 January 2014 to 20 April 2023.FindingsThis analysis revealed that volatility connectedness among the countries during COVID-19 and Russia–Ukraine conflict had increased significantly. Furthermore, analysis has indicated that investors had not anticipated the World Health Organization announcement of COVID-19 as a global pandemic. Contrarily, investors had anticipated the Russian invasion of Ukraine, evident in a significant rise in volatility before and after the invasion. In addition, the transmission of volatility is from developed to developing countries. Developed countries are NET volatility transmitters, whereas developing countries are NET volatility receivers. Finally, the ordinary least square regression result suggests that the volatility connectedness index is informative of stock market dynamics.Originality/valueThe connectedness approach has been widely used to estimate the dynamic connectedness among market indices, cryptocurrencies, sectoral indices, enegy commodities and metals. To the best of the authors’ knowledge, none of the previous studies have directly used the volatility indices to measure the volatility connectedness. Hence, this study is the first of its kind that has used volatility indices to measure the volatility connectedness among the countries.