This issue comprises four research papers. The first paper, “Risk Appetite in Practice: Vulgaris Mathematica”, by Bertrand K Hassani, presents the methodologies to evaluate banks’ exposures, along with their management implications. The ultimate goal of risk management is the generation of efficient income. The aim is to generate the maximum return for a unit of risk taken or to minimize the risk taken to generate the return expected, i.e., it is the optimization of a financial institution’s strategy. Therefore, by measuring its exposure against its appetite, a financial institution assesses its coupled risk-return. But this task may be difficult as banks face various types of risks, like operational, market, credit and liquidity, and these cannot be evaluated on a standalone basis. Interaction and contagion effects should be taken into account.

The second paper, “Portfolio Attribution of Large Cap Companies”, by Latha Sreeram and Ankita Sarin, studies portfolio attribution or how the fund manager decides where to invest and how to manage his customers’ funds. This is possible by creating portfolios based on different strategies and further analyzing the portfolio’s risk and returns. The objective of this paper is to gain insights helpful in improving the portfolio management process, its investment decision and strategy from risk and return perspective for achieving the desired investment performance. To attain this objective, the portfolio’s risk is analyzed on the basis of the multifactor model which features economic factors based on market, fundamental or technical data. This allows the portfolio managers to extend the use of the risk forecast from determining the expected level of risk to explaining where it is coming from and what actions should be taken to bring the portfolio into alignment. For a fundamental model the themes that are important in characterizing the behavior of securities are identified and then the asset exposure is determined. The next step in the process is to calculate factor volatilities and determine specific return and risk. With this information, the asset’s risk as a combination of factor-related risk and specific risk is calculated. Factor-related risk is caused due to the asset’s exposure to each factor, the volatility and the correlations between factors. The portfolio risk is calculated in a similar manner by substituting portfolio-level exposures for asset-level exposures. Finally, returns of a portfolio are analyzed based on its sources when compared with its risk.

The third paper, “Risk Anomaly – Empirical Evidence from Indian Stock Market”, by Nehal Joshipura, aims to investigate the presence of low-risk anomaly in Indian stock market. Finance theory suggests that higher return comes with higher risk. However, several studies have reported evidences of low-risk anomaly in the US and other global markets, where portfolio of low volatility stocks delivers superior risk-adjusted returns as compared to market index and high volatility stocks’ portfolio. The study uses all constituent stocks of S&P CNX 200 index of NSE, which represents about 88.75% of the free-float market capitalization of the stocks listed on NSE as on June 28, 2013. Data for the period from January 2004 to August 2013 is used. The study is based on construction of low and high volatility portfolios using volatility of historical monthly returns of stocks and holding portfolios for the next period on iterative basis.

The last paper, “Modeling and Forecasting of Time-Varying Conditional Volatility of the Indian Stock Market”, by P Srinivasan, attempts modeling and forecasting the volatility (conditional variance) of the S&P CNX Nifty index returns of Indian stock market, using daily data covering a period from January 1, 1996 to January 29, 2010. The forecasting models considered in the study range from the simple GARCH(1, 1) model to relatively complex GARCH models [including Exponential GARCH(1, 1) and Threshold GARCH(1, 1) models]. Based on out-of-sample forecasts and a majority of evaluation measures, the results show that the asymmetric GARCH models do perform better in forecasting conditional variance of the Nifty returns rather than the symmetric GARCH model, confirming the presence of leverage effect.

-Nupur Pavan Bang
Consulting Editor