Volatility, commonly represented by standard deviation of stock returns, is perhaps one of the most useful statistic because of its importance in numerous real-world applications. For examples, volatility is used in portfolio optimization; it is used in valuing options; and in asset pricing models. Several recent papers have examined three streams related to volatility: 1) what is the best method for estimating volatility since true volatility is unknown, 2) does idiosyncratic volatility matters for future returns, 3) since it is future volatility which is of interest for expected stock returns, how future volatilities can be estimated from current volatilities by examining various specifications of the GARCH model and using ARIMA models. Although there is overwhelming support for the idea that idiosyncratic risk matters not just in the US but also in other international markets, the other two questions have not been settled to satisfaction. In this paper I aim to answer the two questions by using individual stock data from the Indian market.

Dr. Singhal is the professor of finance and the chair of department of accounting and finance at the Oakland University in Michigan, USA which he joined in 2003 after completing his PhD from the University of Utah.. Dr. Singhal’s research interests lie at the intersection of accounting & finance and he has published in top journals in both the areas. He has also spent time in corporate America when he took leave for a year and worked with Moody’s Analytics, then a subsidiary of Moody’s Investors Services, a global rating agency. Before deciding to pursue a PhD, Dr. Singhal completed his MBA from the Indian Institute of Management and engineering degree from Birla Institute of Technology and Science.