It can be safely said that more volatile times in the financial markets have not been seen before. Whether they are stock markets, forex markets or commodities markets, all of them are displaying huge fluctuations and uncertainity at the same time. This issue reflects the time that we are in.

The first paper, "Stability of Beta: An Empirical Investigation into Indian Stock Market", by Jonali Sarma and Pranita Sarmah, talks about the importance of risk management in case of investment decisions in modern day financial management. They say that though risk cannot be completely eliminated, it can be reduced by precautionary measures. The stability of beta is of great concern, as it is a very important tool for almost all investment decisions and plays a significant role in risk measurement and risk management. This paper studies stability of beta for various stocks that form a part of Bombay Stock Exchange Sensitivity Index (Sensex). Stability of beta is tested using the Chow test and the result shows that betas are unstable over time.

The second paper, "An Analysis of Operational Risk of Banks: Catastrophe Modeling", by Gabor Benedek and Daniel Homolya, assumes greater importance due to the number of banks going bankrupt or on the verge of bankruptcy in the US. The authors point out that due to modern regulations and company's internal considerations, financial institutions pay increasingly careful attention to their risks. The systematic management of operational risks is a relatively recent development. Operational risk is the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events. In this paper, operational risk is analyzed with the help of a simulation model framework developed by the authors. Their approach is based on the analysis of latent risk processes rather than manifest risk processes, which is a highly popular method in risk literature. The latent risk process is modeled by a stochastic risk process, the so-called Ornstein-Uhlenbeck process, with mean-reversion characteristics. In the model framework, the authors define catastrophe as an event, where the process crosses a critical threshold. Based on the analysis of the distributions of catastrophe frequency, severity and the first hitting time of a single process and a dual process, they could not reject the Poisson process character of frequency or the long tailed nature of severity. But the distribution of `first hitting time' requires more sophisticated analysis. The authors also discuss the advantages of simulation-based forecasting.

The third paper, "Protective Put Strategy in the Indian Stock Market: An Empirical Study", by P A K Preetham, Subramanian S and U S Rao, tries to understand the effectiveness of protective put in the Indian context. They examine the performance of the two trading strategies: (1) Having a long position in the S&P CNX Nifty basket; and (2) Having a long position in stocks combined with buying put options on the Nifty Index. Each option strategy is examined over different maturities and option series. The analysis was constructed by assuming a long position in European style options on Nifty from the time they were introduced in India. The returns using put options are compared to the returns on S&P CNX Nifty Index. The analysis revealed significant profitability in investing in the one-month expiry Volume-based Protective Put Strategy.

The last paper, "Joint Interest Rate Risk Management of Balance Sheet and Hedge Portfolio in a Present Value Perspective", by Simone Farinelli and Paolo Vanini, presents a multi-period mean-variance optimization program, which allows for a joint optimization of the balance and off-balance sheet. The authors show that if the best forecast of the interest rates is the forward rate, then it is optimal to mimic the benchmark strategy. They apply the model to UBS data and show that the myopic models are not acceptable for key rate delta profile management. Then, they calculate present value of a portfolio for 2005. It follows that the optimal dynamic portfolio strategy leads to a return of 3.12% compared to 2.6% of the myopic model.

-- Nupur Hetamsaria
Consulting Editor.