Cristi Spulbar

This paper investigates volatility spillovers in the stock market in Japan during the COVID-19 pandemic by using GARCH family models. The empirical analysis is focused on the dynamics of the NIKKEI 225 stock market index during the sample period from July 30, 1998, to January 24, 2022. In other words, the sample period covers both the period of the global financial crisis (GFC) and the COVID-19 pandemic. The econometrics includes GARCH (1,1), GJR (1,1), and EGARCH (1,1) models. By applying GARCH family models, this empirical study also examines the long-term behavior of the Japanese stock market.
The Japanese stock market is much more stable and efficient than emerging or frontier markets characterized by higher volatility and lower liquidity. The paper establishes that NIKKEI 225 index dynamics is different in intensity in the case of the two most recent extreme events analyzed, namely the global financial crisis (GFC)of 2007–2008 and the COVID-19 pandemic. The findings confirmed the presence of the leverage effect during the sample period. Moreover, the empirical results identified the presence of high volatility in the sample returns of the selected stock market. Nevertheless, the econometric framework showed that the negative implications of the GFC were much more severe and caused more significant contractions compared to the COVID-19 pandemic for the Japanese stock market. This study contributes to the existing literature by providing additional empirical evidence on the long-term behavior of the stock market in Japan, especially in the context of extreme events.

Many investors in order to predict stock prices use various techniques like fundamental analysis and technical analysis and sometimes rely on the discussions provided by various stock market analysts. ARIMA is a part of time-series analysis under prediction algorithms, and this paper attempts to predict the share prices of selected pharmaceutical companies in India, listed under NIFTY100, using the ARIMA model. A sample size of 782 time-series observations from January 1, 2017 to December 31, 2019 for each selected pharmaceutical firm has been considered to frame the ARIMA model. ADF test is used to verify whether the data are stationary or not. For ARIMA model estimation, significant spikes in the correlogram of ACF and PACF have been observed, and many models have been framed taking different AR and MA terms for each selected company. After that, 5 best models have been selected, and necessary inculcation of various AR and MA terms has been made to adjust the models and choose the best adjusted ARIMA model for each firm based on Volatility, adjusted R-squared, and Akaike Information Criterion. The results could be used to analyze the stock prices and their prediction in-depth in future research efforts.