The paper, “Price Volatility: An Evaluation of the Indian Stock Market During Global Financial Crisis”, by D Joseph Anbarasu and S Srinivasan, tests the Random Walk (RW) model—one of the earliest models proposed for stock price behavior, which states that future stock prices cannot be predicted on the basis of past price movements in the context of the Indian market. They find that the serial correlation of daily returns of 11 shares and returns of three main indices is very low over different lag lengths. In all the cases under observation, no significant correlation coefficients were observed. This shows that the historical information embedded in the series is less influential in determining the future prices of shares and indices. This also suggests that the Indian market has a system which facilitates the early dissemination of global information. It is also presumed from this study that the trend projection has almost lost its grip over the Indian share market. A distribution in which the ratio of the fourth moment to the square of the second moment is greater than 3—which is the value for a normal distribution—appears to be more heavily concentrated about the mean, or more peaked, than a normal distribution. Therefore, the share prices of the Indian market during the period of current crisis yields a typical leptokurtic normal distribution. It means that there exists fatter tails and greater risks of extreme outcomes. It tells one about the high volatility of the present Indian share market. It also proves that no market in the world is insulated from externalities as it is advocated in the decoupling theories of today.

The second paper “Price Exploration and Financial Market Efficiency” by Diganta Mukherjee, Jyotiska Bhattacharjee and Suraj Dey has come up with a rigorous definition of market efficiency, considering two financial assets. Through simulation and graphical verification, they show that the market is efficient in the longrun, albeit in a simplistic setting. They have also explored alternative value generating processes by considering models with discrete jumps, heavy-tailed probability models and a regime switching model. Their contribution is important as efficiency has been a very important issue for financial markets all over the world, ever since trading has started taking place in the stock exchanges. Most ardent market observers believe that although short-term inefficiencies remain, financial markets are efficient in the longrun. However, this question has not yet been investigated in terms of a theoretical model.

The third paper by Chebbi Tarek titled, “Default, Liquidity and Credit Spread: Empirical Evidence from Structural Model” explores the role of liquidity risks in the pricing of corporate bonds. This liquidity risk is a priced factor for the yield spread of risky corporate bonds and the associated liquidity risk premia helps to explain the credit spread puzzle. Also, amongst the important issues related to credit risk are the factors which affect yield spreads of corporate bonds. In recent literature, the yield spread is regarded as a measure of a comprehensive risk premium to compensate investors for a number of risks associated with corporate bonds. Using a first passage model in which, the default occurs when corporate asset values hit a predefined default barrier, the author concludes that the credit spreads associated with Tunisian bonds are highly defined by default risks. It is to be noticed that residual spreads are sensitive to the dynamics of default barrier, depending on the drift and volatility of a firm’s assets values.

The last paper “Multiscale Carhart Four-Factor Pricing Model: Application to the French Market” by Anyssa Trimech and Hedi Kortas puts forward a methodology aimed at analyzing the Carhart Multifactor Model, over various time horizons in the French stock market. The suggested approach exploits the decomposition scheme inherent to the wavelet-based Multiresolution Analysis, allowing investigation of the time-scale relationships between stock returns and risk factors. The empirical results show that the explanatory power of the wavelet-based four factor model is scale-sensitive. The excess market return factor is highly significant over the range of time scales and is of positive effect at intermediate and long-term investment horizons. Besides, the size factor is found to be negative for the portfolios constructed by small capitalization assets. The size risk also becomes negative for big portfolios at the largest time scale. The value proxy HML, which is rejected for the unitary (single) scale model, is however, significant over a large array of resolution levels (investment periods). Finally, it is found that the momentum factor, within the multiscale framework, has a significant impact on the expected stock returns.

- - Nupur Hetamsaria
Consulting Editor