Abstract
There has been a growing interest in CVaR as a financial risk measure in optimal allocation fields. This interest is based many key advantages of CVaR over the most used measures of risk: the Value-at-Risk and the variance. In this paper we develop an asset allocation model that allocates assets by minimizing CVaR subject to a desired expected return and we compare the performance of the resulting optimal portfolios with those resulting from the optimization of mean-variance model. The empirical study uses stocks from the SBF250 index. The purpose of the paper is to highlight the influence of the non-normal characteristics of the return distribution on the optimal asset allocation and test the superiority of the mean- CVaR approach over the mean-variance approach.
Highlights
In today’s increasingly turbulence and volatility on every major stock exchange, it is evident that controlling the risks in one’s investment strategies is an important issue
In this study we examined the relevance of the MCVaR model and we explored the impact of non-normal features on its performance
In a first time we used the empirical distribution to compare the performance of this model with the performance the tradition MV model; The Conditional Value-at-Risk (CVaR) optimal portfolio seems to perform better in term of mean returns and the return-to-CVaR ratio but not in terms of the Sharpe ratio
Summary
In today’s increasingly turbulence and volatility on every major stock exchange, it is evident that controlling the risks in one’s investment strategies is an important issue. The seminal work of Markowitz (1952) demonstrated that financial decision-making is essentially a question of achieving an optimal trade-off between return and risk measured by variance. The main idea underlying this model is that variance of the return distribution is all what we need to describe the risk of the portfolio. When investors do not have quadratic utility and returns are not normally distributed, variance is no longer an appropriate measure of risk since it ignores the higher moments of the return distribution. The central idea behind the use of VaR is to summarize into a single number all the information about the possible portfolio losses implied by the left hand side tail of the return distribution in the case when this distribution is not normal. VaR lacks the property of subadditivity; it does not provide the investors with an incentive to diversify their investment
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
