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Value-at-Risk (VaR) is widely used as a tool for measuring the market risk of asset portfolios. However, alternative VaR implementations are known to yield fairly different VaR forecasts. Hence, every use of VaR requires choosing among alternative forecasting models. This paper undertakes two case studies in model selection, for the S&P 500 index and India's NSE-50 index, at the 95% and 99% levels. We employ a two-stage model selection procedure. In the first stage we test a class of models for statistical accuracy. If multiple models survive rejection with the tests, we perform a second stage filtering of the surviving models using subjective loss functions. This two-stage model selection procedure does prove to be useful in choosing a VaR model, while only incompletely addressing the problem. These case studies give us some evidence about the strengths and limitations of present knowledge on estimation and testing for VaR. Copyright © 2003 John Wiley & Sons, Ltd.

This paper revisits the role of leverage in price discovery, using one of the most liquid single‐stock futures (SSFs) markets in the world. Price discovery is analysed as a dynamic intraday process. We find that the information share of the SSFs is 55% during news arrivals. It increases to 61%, when the news is negative and the futures is preferred because of short‐sales restrictions on the spot. A partial equilibrium analysis predicts that the trade‐off between leverage and market liquidity determines price discovery across securities. These predictions are validated by empirical evidence.

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Regulation of retail finance has been the subject of policy interventions in several countries, including India. Much of the regulatory change in India has been carried out with little support of empirical evidence. This paper is motivated by questions of the evidence of losses due to mis-sales of financial products. It constructs two measures of the loss to customers due to mis-selling of life insurance policies. The first is calculated using the value of lapsed policies, and the second uses the persistence of premium payments. Both arrive at estimates of around USD 28 billion lost between 2004 and 2011.