The working young in urban India exhibit inferior financial knowledge, inferior financial attitude, and superior financial behavior compared to their counterparts elsewhere. While both men and women require intervention to enhance their financial knowledge, focused intervention is needed to improve the financial attitude of men and the financial behavior of women. Living in a joint family impacts financial literacy negatively and consultative decision-making in families impacts it positively. The influence of these key aspects of Indian family life indicates the need to involve family members in financial literacy programs to improve financial decision making of families.

We compute the Fama-French and momentum factor returns for the Indian equity market for the October 1993 -December 2013 period using data from CMIE Prowess. We differ from the previous studies on this topic, in the Indian market, in several significant ways. First, we cover a greater number of firms relative to the existing studies. Second, we exclude illiquid firms to ensure that the portfolios are investible.Third, we have classified firms into small and big using a more appropriate cut-off considering the distribution of firm size. Fourth, as there are many instances of vanishing of public companies in India, we have computed the returns with a correction for the survival bias. During the period from January 1994 to December 2014, the average annual return of the momentum factor was 21.9%; the average annual return on the value portfolio (HML) was 15.3%; that of the size factor (SMB ) nearly 0%; and the average annual excess return on the market factor (MRP ) was 11.5%. This is a revised version of our earlier paper on this topic. The revision is carried out to primarily accommodate the data of firms which are retrospectively added to the prowess database by CMIE. The time series of daily, monthly and yearly returns on the factors and the underlying portfolios are made available at an online data library. The authors would update the library on a monthly basis.

The main objective of the Working Paper series of IIMA is to help faculty members, research staff, and doctoral students to speedily share their research findings with professional colleagues and to test out their research findings at the pre-publication stage.

This paper examines the estimation and forecasting performance of range-based volatility estimators for stocks, with two-scales realized volatility as the benchmark. There is evidence that the daily range-based estimators provide an efficient and low-bias alternative to the return-based estimators. These are not downwardly biased in the presence of negative autocorrelation and low liquidity, as generally suspected. The drift is a major cause of the poor performance of Parkinson's estimator. The forecasts of volatility with these estimators are about as efficient as those with the benchmark itself but are more biased. The forecasts based on realized range are only marginally better on the criterion of bias and are about as efficient. Considering their simplicity and lower data requirement, the daily range-based estimators appear to be more desirable. These results are particularly relevant for the option valuation and the risk management of derivative markets.

This study evaluates the forecasting performance of extreme-value volatility estimators for the equity-based Nifty Index using two-scale realized volatility. This benchmark mitigates the effect of microstructure noise in the realized volatility. Extreme-value estimates with relatively simple forecasting methods provide substantially better short-term and long-term forecasts, compared to historical volatility. The higher efficiency of extreme-value estimators is primarily responsible for this improvement. The extent of possible improvement in forecasts is likely to be economically significant for applications like options pricing. By including extremevalue estimators, the forecasting performance of generalized autoregressive conditional heteroscedasticity (GARCH) can also be improved.

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SYNOPSIS This study examines fee premiums earned by Big 4 auditors in India and identifies the primary reason for such fee premiums. There are three primary drivers of Big 4 fee premiums. Big 4 auditors charge a fee premium for their reputation, for providing a superior quality of audit, and for indemnifying losses for a company's stakeholders. Since the risk of auditor litigation in India is relatively low, Big 4 premiums in India would not be driven by the need for auditors to indemnify losses. The results indicate that Big 4 auditors earn significantly higher fees in India and also that their clients enjoy significantly higher earnings response coefficients compared to non-Big 4 clients. However, there is no difference in the quality of audit provided by Big 4 and non-Big 4 auditors as measured by the magnitude of reported discretionary accruals.

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