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This study aims to investigate the dynamic relationships between oil price shocks and Indian stock market.

The study used daily data for the period starting from January 2001 to March 2013. In this study, Johansen's cointegration test, vector error correction model (VECM), Granger causality test, impulse response functions (IRFs) and variance decompositions (VDCs) test have been applied to exhibit the long-run and short-run relationship between them.

The cointegration result indicates the existence of long-term relationship. Further, the error correction term of VECM shows a long-run causality moves from Indian stock market to oil price but not the vice versa. The results of the Granger causality test under the VECM framework confirm that no short-run causality between the variables exists. The VDCs analysis revealed that the Indian stock markets and crude oil prices are strongly exogenous. Finally, from the IRFs, analysis revealed that a positive shock in oil price has a small but persistence and growing positive impact on Indian stock markets in short run.

The study would enhance the understandings of the interaction between oil price volatilities and emerging stock market performances. Further, the study would enable foreign investors who are interested in Indian stock market helps in understanding the conditional relationship between the variables.

India is the birthplace of four major religions which are Hinduism, Jainism, Buddhism, and Sikhism. It is a country where people of all religions live in peace and harmony. Many tourists experience different forms of harassment during their pilgrimage journey, for example, fleecing, extortion of money, harassment by beggars, persistence by vendors and priests, fraud, sexual harassment, and other unacceptable behaviors. In order to appreciate the extent of harassment encountered by tourists, an in-depth study was conducted on the reviews provided by tourists on TripAdvisor's (Indian) website. This study characterizes harassments through ethnographic research approach of published reviews. A total of 260 reviews of 28 top Hindu temples are considered for all the states and union territories where the top Hindu pilgrim centers are located, (excluding Nagaland) according to TripAdvisor. The concerned reviews are categorized and further investigated through a primary data collection in proportion with the reviews received in respective temple sites in the study. through structural equation modeling (SEM). Important factors have been identified for future policy issues and recommendations in these most crowded places with unique mass tourism practices.

This study aims to explore the linkage between fluctuations in the global crude oil price and equity market in fast emerging economies of India and China.

The present research uses wavelet decomposition and maximal overlap discrete wavelet transform (MODWT), which decompose the time series into various frequencies of short, medium and long-term nature. The paper further uses continuous and cross wavelet transform to analyze the variance among the variables and wavelet coherence analysis and wavelet-based Granger causality analysis to examine the direction of causality between the variables.

The continuous wavelet transform indicates strong variance in WTIR (return series of West Texas Instrument crude oil price) in short, medium and long run at various time periods. The variance in CNX Nifty is observed in the short and medium run at various time periods. The Chinese stock index, i.e. SCIR, experiences very little variance in short run and significant variance in the long and medium run. The causality between the changes in crude oil price and CNX Nifty is insignificant and there exists a bi-directional causality between global crude oil price fluctuations and the Chinese equity market.

To the best of the authors’ knowledge, very limited work has been done where the researchers have analyzed the linkage between the equity market and crude oil price fluctuations under the framework of discrete wavelet transform, which overlooks the bottleneck of non-stationarity nature of the time series. To bridge this gap, the present research uses wavelet decomposition and MODWT, which decompose the time series into various frequencies of short, medium and long-term nature.