Analysis of surveillance data for 2010–2016 in eastern Ontario, Canada, demonstrates the rapid northward spread of Ixodes scapularis ticks and Borrelia burgdorferi, followed by increasing human Lyme disease incidence. Most spread occurred during 2011–2013. Continued monitoring is essential to identify emerging risk areas in this region.
Seasonality in stock market is a well recognized postulation. The phenomenon stands for a regular or rhythmic pattern, apparent in stock returns. The present study investigates the persistence of such regularities in the form of weekend effect, monthly effect and holidays effect employing twelve-year data from 2000 to 2011 of S&P CNX Nifty. The article examines the survival of seasonalities in Indian stock market through Generalized Autoregressive Conditional Heteroskedasticity (GARCH) (1,1) model. The results indicate the occurrence of weekend effect in long run but reject the hypothesis of positive weekends and negative Mondays. On the contrary, the mean return on Tuesday is negative for the entire period. Instead of March effect, the study comes out with November effect and hence nullifies the ‘Tax-Loss Selling Hypothesis’. On dividing the entire period into three-year lags, anomalies instantaneously disappear confirming the fact that any seasonality takes some time to establish itself. Higher GARCH values validate that the Indian stock market is inefficient in its weak form and does not follow a random walk.
Purpose: The major objective of this research is to investigate the existence of volatility-based anomalies in Indian stock market, which are the result of various behavioural biases. The nature of Indian stock market volatility is investigated employing various volatility models such as spillover effect profoundly acknowledged as herding, leverage effect prominently entitled as low volatility anomaly and persistence of long- and short-term volatility. Design/Methodology/Approach: The present study has employed various autoregressive conditional heteroscedasticity (ARCH) family models such as generalised autoregressive conditional heteroscedasticity (GARCH), exponential GARCH (EGARCH) and component GARCH (CGARCH) to appraise assorted nature of volatility patterns in Indian stock market. Findings: The results empirically validate that herding endures in both bullish and bearish trend, whereas herding has amplified for Nifty and Smallcap in bearish trend. Furthermore, the results divulge the existence of stock market inefficiency due to low volatility anomaly. The outcomes pragmatically verify the persistence of volatility in long and short run. The empirical evidences also assist in acknowledging the degree of subsistence of anomalies and biases in market uptrends and downtrends in order to comprehend the level of rationality of investors in diverse market situations. Practical Implications: Various GARCH models, employed in the study, alleviate a direction for forecasting as regards volatility to assemble optimum portfolio in diverse market situations. Originality/Value: The present study gives a distinctive insight on the existence of volatility-based anomalies using exclusive econometric models. It informs about the market anomalies and states about the most prominent bias.
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