COVID-19 Pandemic and Indices Volatility:Evidence from GARCH Models

This study examines the impact of volatility on the returns of nine National Stock Exchange (NSE) indices before, during, and after the COVID-19 pandemic. The study employed generalized autoregressive conditional heteroskedasticity (GARCH) modelling to analyse investor risk and the impact of volatility on returns. The study makes several contributions to the existing literature. First, it uses advanced volatility forecasting models, such as ARCH and GARCH, to improve volatility estimates and anticipate future volatility. Second, it enhances the analysis of index return volatility. The study found that the COVID-19 period outperformed the pre-COVID-19 and overall periods. Since the Nifty Realty Index is the most volatile, Nifty Bank, Metal, and Information Technology (IT) investors reaped greater returns during COVID-19 than before. The study provides a comprehensive review of the volatility and risk of nine NSE indices. Volatility forecasting techniques can help investors to understand index volatility and mitigate risk while navigating these dynamic indices.</p

Relationship between Crude Oil Price Changes and Airlines Stock Price: The Case of Indian Aviation Industry

The present study investigated the relationship between airline stock price and crude oil price. For this study, six airlines such as Air India, Go Air, IndiGo, Jet Lite, Jet Airways and Spice Jet and three crude oil markets, WTI-West Texas Intermediate and Texas Light Sweet, Brent -North Sea Brent Crude, and Dubai-Dubai Crude, were selected Based on their market capitalization. According to empirical results, a Crude oil price triggered fluctuations in most of the airline stock returns. Moreover, Air India, IndiGo, Jet Airways and Spice Jet experienced statistically significant relationships between their stock returns and crude oil price but did not correlate with them during the study period from 1st January 2007 to 30th November 2018. The findings of present study would be useful for individual and institutional investors and policy makers. Keywords: Crude Oil Price, Airline Stock Return, Descriptive Statistics, Unit Root Test, Correlation Matrix, Granger Causality Test. JEL Classifications: G11, G14, G15, Q43, R40 DOI: https://doi.org/10.32479/ijeep.796

Return and Volatility Spillovers of Asian Pacific Stock Markets’ Energy Indices

The aim of the study was to investigate the presence of volatility among the Energy Indices of Asia Pacific Stock Markets. To test the volatility among the daily returns of Energy Indices of Asia Pacific Stock Markets, the study selected five sample Asian Pacific stock markets’ Energy Indices on the basis of availability of data. The findings of descriptive statistics and the ADF Test revealed, that the daily returns of the sample energy indices of Asian Pacific stock markets were not normally distributed and achieved stationarity at level difference, over the research period. Hence the data may be used for additional analysis. The data were then analysed, by using the GARCH (1,1) model to assess the considerable volatility of daily returns of sample energy indices and the study, which revealed that during the study period, all of the sample energy indices were volatile

The Effects of Crude Oil Price Surprises on National Income: Evidence from India

The goal of this study is to look into how changes in crude oil prices affect GDP per capita and exchange rate fluctuations.to investigate the influence of crude oil price shocks on GDP per capita and exchange rate movements. This research employed yearly time series data for the price of crude oil, exchange rate (USD/INR), and GDP per capita, from 1990 to 2020. Arithmetical tools such as Descriptive, Unit Root, Granger Causality Test, and OLS Model were applied. The present study discovered a strong bi-directional Granger causality effect of Dubai crude oil prices on exchange rates, as well as a bi-directional Granger influence of exchange rates on WTI crude oil prices. The diagnostic tests were successfully passed by the estimated models. According to the OLS model, the exchange rate was driven only by the price of Dubai crude oil, although the price of WTI crude oil influenced both the GDP per capita and the exchange rate over the research period. The key policy recommendation derived from this analysis is that the Reserve Bank of India (RBI) must depreciate the rupee, first to restore much-needed exchange rate stability, then to stimulate domestic manufacturers, and finally, to attract foreign capital inflows

Effect of weather on stock market: A literature review and research agenda

The paper presents a systematic review of the research work, published on the topic of weather effects and stock market behavior. The objectives of the study were to examine the current status of research, by collecting the literature, in the area of weather effects and stock market behavior. In the process, the study would reveal the current status to the budding researchers. In this study, the authors critically assessed and examined fifty one research studies, published from 1993 and 2019, in different regions across the globe. A systematic literature review for this study has been made using Google Scholar. The present study found that number of research works on the weather effects and stock market behavior had increased marginally during the recent time period, especially from the beginning of Twenty First Century. Among the different weather factors, temperature was wildly used for research. Finally, this paper reveals some significant research gap to advance the research agenda for future research

The Dynamic Impact of Financial Technology and Energy Consumption on Environmental Sustainability

This research investigates the dynamic interplay between financial technology, information and communication technology, energy consumption, and economic growth on environmental sustainability within Emerging and Growth-Leading Economies (EAGLEs) from 2005 to 2020. Utilizing advanced econometric techniques, such as Fully Modified Least Squares (FMOLS) and Vector Autoregressive Error Correction Model (VECM), the investigation scrutinizes the hypothesized relationships among these variables. Panel unit root tests were deployed to assess stationarity, while panel least squares methodology was employed to determine the presence of co-integration among the variables under study. The analysis reveals that internet usage, GDP, and renewable energy consumption exhibit a notable influence in diminishing CO2 emissions within EAGLE economies. Additionally, the findings substantiate the existence of long-term causality originating from these variables and impacting CO2 emissions. Conversely, the role of ATM networks in CO2 emissions remains ambiguous, implying that financial technology’s influence on environmental sustainability is inconclusive. Consequently, the research posits that environmental sustainability in EAGLE economies is chiefly determined by factors such as internet usage, economic expansion, and renewable energy consumption, with financial technology demonstrating no discernable impact. In light of these findings, the study advocates for the reevaluation and adaptation of existing policies and strategies to account for shifting climatic conditions. By doing so, decision-makers can better align their efforts with the pursuit of environmental sustainability in the context of rapidly evolving economies

On the relationship between weather and Agricultural Commodity Index in India: a study with reference to Dhaanya of NCDEX

This paper proposes to investigate the Co Movement and Causal Relationship, among the three weather factors (temperature, humidity, and wind speed) and the returns of the Agriculture Commodity Index called Dhaanya, in India. The study employed the second- ary daily data of weather in five sample cities (Chennai, Mumbai, Delhi, Kolkata and Hyderabad), and Agriculture Commodity Index called Dhaanya, in India. Statistical tools like Descriptive Statistics, Unit Root, Correlation Matrix, and Granger Causality Test were employed. This study found that the temperature and wind speed influenced the inves- tors’ mood in Chennai and Mumbai, in respect of Agriculture Commodity Index, namely Dhaanya. The findings of this study would help the investors in making investment deci- sions rationally, on the basis of weather condition

Big data analytics-application of artificial neural network in forecasting stock price trends in India

The world has become data driven, which highly accentuated the utilization of information technology. The movements of stock markets are influenced, by both the micro as well as macro economic variables including the legal framework and taxation policies of the respective economies. The crux of the issue lies in exactly forecasting the future stock price movements of individual firms and stock indices, based on historical past prices. The accuracy, in forecasting the market trend, has become difficult due to the prevalence of stochastic behaviour and volatility in the stock prices and index movements. This paper analyses the non- linear movement pattern of the most volatile, top three stocks in terms of market capitalization, listed in the Bombay Stock Exchange (BSE) in India, namely Reliance Industries Limited (RIL), Tata Consultancy Services (TCS) Limited and HDFC Bank Limited, using the Artificial Neural Network (ANN) for the study period from 2008 to 2017. The findings of the study would help the investors, to make rational, well informed investment decisions, to optimize the stock returns by investing in the most valuable stocks

An empirical investigation of the interlinkages of stock returns and the weather at the Indian Stock Exchange

This paper investigated the effect of three weather factors (temperature, humidity and wind speed), on the returns of the Indian stock indices (BSE Sensex and S&P CNX Nifty). This study examined how weather affected the Movement and relationship of top stock market indices in India. The study used the monthly data of weather, in five sample cities (Chennai, Mumbai, Delhi, Kolkata and Hyderabad), in India. Statistical tools like Descriptive Statistics, Correlation Matrix and Granger Causality Test were used for the analysis. This study found that the temperature influenced the investors’ mood in Bangalore, in respect of BSE Sensex and Kolkata & Mumbai, in respect of CNX Nifty and Humidity influenced Mumbai, in respect of CNX Nifty