What is it that is keeping the bulls buoyed in the Indian stock markets? The Sensex is touching 20,000 and with the festival season round the corner, is expected to scale greater heights. The volatile nature of the Indian markets can to a large extent be attributed to the larger players in the market, (read Financial Institutions and Foreign Institutional Investors). The first article in this issue, "Investor Confidence in an Underdeveloped Stock Market", by Diganta Mukherjee, models the impact of the presence of a few large traders and their behavior on the stock prices and the price of their derivatives. They model the stock prices by a suitable stochastic differential equation involving a Wiener process and then formally establishing the volatility and sensitivity relationships for the market price with respect to the large traders' behavior.

The second article, "Persistence Characteristics of European Stock Indices", by Joanna M Lipka and Cornelis A Los, discusses a very important issue in portfolio risk management. An important question for regulators and risk managers is to identify which market indices are persistent, and thus inefficient and illiquid, and which can therefore produce abnormal returns. Using daily deviations on eight European stock market indices the paper identifies the ergodicity, stationarity, independence, and persistence (Long Memory) of the eight European index prices and their transforms, or the lack thereof. They find that most of the data analyzed are far from being either ergodic, or stationary or independent. Thus, such series cannot be modeled with ARIMA or GARCH family models that assume stationarity of the final residual series. The article finds that visualization of the time-frequency spectra by wavelet scalograms is a useful way to visualize the important localized characteristics of the high frequency financial time series produced by a stock market.

The third article, "Convergence of Futures and Spot Prices: A Cointegration Analysis", by Naveen Prakash Singh and V Shanmugam, makes an effort to analyze the convergence of futures prices with the spot market prices using cointegration analysis. They prove that the futures and the spot prices converge effectively in the context of the Indian commodities markets, thus asserting that futures markets offer the perfect mechanism for hedging price risk in selected crops. This is an important study because the Indian commodities markets have long been marred by bans on futures and options trading on various commodities from time to time. Hence, whether the futures market have been effective in providing a tool for hedging or not is a question which policy makers will be eager to be answered.

The next article, "A Simulation-Based Approach to Measure Concentration Risk", by Joocheol Kim and Duyeol Lee, focuses on the issue of credit risk of a portfolio. Asymptotic Single Risk Factor (ASRF) model is used to derive the regulatory capital formula of Internal Ratings-Based approach in the new Basel accord (Basel II). One of the important assumptions in ASRF model for credit risk is that the given portfolio is well-diversified so that one can easily calculate the required capital level by focusing only on systematic risk. In real world, however, idiosyncratic risk of a portfolio cannot be fully diversified away, causing the so-called concentration risk problem. In this article, the authors suggest simulation-based approach for measuring concentration risk using the bank capital dynamic model. According to the authors, this approach is especially suitable for a portfolio with relatively small to medium number of obligors and relatively large-sized loans.

The last article in this issue, "Loss Distribution Estimation, External Data and Model Averaging", by Ethan Cohen-Cole and Todd Prono, discusses a proposed method for the estimation of loss distribution using information from a combination of internally derived data and data from external sources. The relevant context for this analysis is the estimation of operational loss distributions used in the calculation of capital adequacy. The authors present a robust, easy-to-implement approach that draws on Bayesian inferential methods. The principal intuition behind the method is to let the data itself determine how they should be incorporated into the loss distribution. This approach avoids the pitfalls of managerial choice on data weighting and cut-off selection and allows for the estimation of a single loss distribution.

Risks associated with the behavior of the investors, inefficiency and illiquidity of the indices, price inefficiency, credit risk and concentration risk and the complexity and the reach of risk are growing by the day. The question to ask is: Which are the risks one is willing to take and which should be avoided, shared, transferred or managed in any other way? Though this question alone would keep most of us busy for long!

- Nupur Hetamsaria
Consulting Editor