Narrow Results

The purpose of this study is to apply polynomial goal programming to establish a new portfolio selection model that considers the tradeoffs between expected return and Value-at-Risk (VaR) of portfolios and the flexibility of incorporating investor's preferences. The historical data of 10 international stock markets of Pacific Rim countries were used in the empirical analysis. The results showed that the proposed model demonstrated the ability to resolve the problems of a traditional asset allocation model. The validity and fitness of the proposed model were confirmed.

In this paper, we analyze the downside risk of alternative investment funds (AIFs) in a cross-country setting over a period of 2015–2021, using popular Value-at-Risk (VaR henceforth) models, and test the efficacy of such models in capturing the volatility posed by these funds. We estimate VaR in the presence of three error distributions, including normal distribution, Student’s t distribution, and GED distribution. Using weekly return data of 991 AIFs from 28 countries, over the period 2015–2021, we find that most of the funds, irrespective of country representation, exhibited a significant proportion of their weekly returns to be negative. To statistically validate our findings we use three backtesting approaches, i.e., Jorion’s failure rate, Kupiec’s proportion of failure (POF) tests and Christoffersen’s independence test. Our findings indicate the presence of significant downside risk for AIFs, which these popular VaR models are unable to capture. These findings highlight the need for investors and fund managers to be cautious when relying solely on popular VaR models to manage downside risk in AIF and suggest that further research is needed to develop more effective risk management strategies for AIF, particularly in light of their complex structure and asymmetric return distributions.