Sep 2022
The IUP Journal of Financial Risk Management
Venture Capitalists' Investment Criteria as Determinants of Risks and Returns: Evidence from India
Considering the risk-taking behavior of Venture Capitalists (VCs) expecting higher returns, this paper investigates their investment criteria. Two broad hypotheses tested in a structural equation modeling show that Management Team Characteristics (MGTC), Market Characteristics (MKTC), Product Characteristics (PRC), and Entrepreneurial Characteristics (ENTC) positively affect the returns. On the other hand, MKTC and PRC negatively affect the expected risk. Moreover, the expected risk is weakly associated with ENTC and financial considerations. The study concludes that MKTC and PRC are commonly responsible for expected risk and returns. The findings will help VCs to choose the criteria based on their risk appetite or returns expectation. It also helps entrepreneurs to understand VCs and develop their skills according to VCs expectations. The study suggests that VCs should not avoid risky ventures but avoid risks by involving measurement criteria in evaluating the proposals.
Framework of CreditMetrics Methodology for Computing Credit VaR
Financial institutions face many risks which increase the degree of uncertainty about future net returns. Credit risk can result in potential losses for a company and is a function of the credit exposure, the probability of default and the loss in the event of default. For a given credit instrument portfolio of credit obligations such as a portfolio of corporate bonds or swaps, Credit Value-at-Risk (VaR) quantifies how much at most can be lost with a given probability over a specific time horizon. The associated CreditMetrics Methodology, originally introduced in 1997 by JP Morgan, is now considered the industry standard along with Credit VaR for credit risk modeling. The CreditMetrics methodology is used for assessing portfolio risk due to changes in bond or debt value caused by credit quality changes, including credit migration (upgrades and downgrades) as well as default. It measures the uncertainty in forward value of the bond portfolio at the risk horizon caused by such credit events. CreditMetrics offers better understanding of credit risk in terms of diversification benefits and concentration risk compared to standard capital adequacy measures. This paper provides a framework for using this methodology.
Performance of Active and Passive Mutual Fund Schemes in India
The Indian mutual fund industry has been growing at an unprecedented rate, and there are more than 2,000 schemes on offer today. There is a preconceived notion among Indian investors that passive funds that cover the index companies in their portfolio are better than active funds. This research aims at providing effective insights for investors to make better decisions before entering the world of mutual funds. Primarily, active and passive schemes are compared on five popular criteria, namely, tracking error, standard deviation, Sharpe ratio, Treynor ratio and Compound Annual Growth Rate (CAGR). The analysis has been done for a period of 15 years, further subdivided into smaller intervals. It is concluded that active mutual fund schemes outperform passive ones by a considerable margin in the long run. In the short run of three to five years, passive funds are slightly better. Investors who are risk-averse should analyze not only the risks but also various other parameters, as those used here, for a better understanding of schemes.
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