Risk Appetite in Practice: Vulgaris Mathematica

--Bertrand K Hassani

The ultimate goal of risk management is 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 is assessing its coupled risk-return. But this task may be difficult as banks face various types of risks, for instance, operational, market, credit, and liquidity, and these cannot be evaluated on a standalone basis; interaction and contagion effects should be taken into account. In this paper, methodologies to evaluate banks’ exposures are presented along with their management implications, as the purpose of the risk appetite evaluation process is the transformation of risk metrics into effective management decisions.

© 2015 IUP. All Rights Reserved.

Portfolio Attribution of Large Cap Companies

--Latha Sreeram and Ankita Sarin

Portfolio managers strive to achieve their strategic goals and maximize their return on investments. This study is an analysis of 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 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 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. Then, factor volatilities are calculated and specific return and risk are determined. 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 assets 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.

© 2015 IUP. All Rights Reserved.

Risk Anomaly – Empirical Evidence from Indian Stock Market

--Nehal Joshipura

Finance theory suggests that higher return comes with higher risk. However, several studies have reported the 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 present study aims to investigate the presence of low-risk anomaly in Indian stock market by using all constituent stocks of S&P CNX 200 index of NSE for the period from January 2004 to August 2013. The CNX 200 index represents about 88.75% of the freefloat market capitalization of the stocks listed on NSE as on June 28, 2013. 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.

© 2015 IUP. All Rights Reserved.

Modeling and Forecasting of Time-Varying Conditional Volatility of the Indian Stock Market

--P Srinivasan

Volatility forecasting is an important area of research in financial markets and immense effort has been expended in improving volatility models since better forecasts translate themselves into better pricing of options and better risk management. In this direction, the present paper attempts to model and forecast the volatility (conditional variance) of the S&P CNX Nifty index returns of Indian stock market, using daily data for the period from January 1, 1996 to January 29, 2010. The forecasting models that are considered in this study range from the simple GARCH(1, 1) model to relatively complex GARCH models, including the 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. The findings are consistent with those of Banerjee and Sarkar (2006) that relatively asymmetric GARCH models are superior in forecasting the conditional variance of Indian stock market returns rather than the parsimonious symmetric GARCH models.

© 2015 IUP. All Rights Reserved.