Mean Variance Analysis By Harry Markowitz Essay

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When disaster strikes, two responses exist: lose hope, or find an inner strength to rise above. “Werner” is an essay where the author, Jo Ann Beard, presents the idea of rediscovering yourself, rebuilding a life after loss, and rising above adversity. Werner, Beard’s main character, finds that the only way to truly move on after a tragedy is to take a leap into what is unfamiliar. After a fire burns down everything Werner has, he is forced to grow and become a new man, leaving his old life behind. Throughout the essay, Beard illustrates a man who faces challenges to his sense of self, and who sequentially must change and become someone new to find who he is again. Beard’s use of the third person, candid diction, and conflict resolution compose an elaborate work that focuses on the concept of becoming a new and better person after a traumatic event.

With Reference to this statement, describe, discuss and illustrate the principles of portfolio theory. Your essay should include coverage of the Markowitz Efficient Frontier and the Capital Market Line.

(b) To determine Policy Portfolio by using mean-variance analysis. Given a variable number of expected portfolio return, we would choose our strategy from a set of minimum-variance portfolio basing on our tolerance for risk.

(a) The mean excess return, standard deviation, and portfolio weights for the minimum variance portfolio.

The prices of stocks are taken on daily basis and daily returns are calculated from it. The daily variances between these returns come out and the value at risk calculated from it with the percentile is applied on it. The value at risk tells you about the riskiness a price has on a portfolio. The positive outcome of this approach is the simplicity in implementation. The benefits of this method are its simplicity to implement and its negative aspect is that it requires a large amount of data for calculation which is extensive to calculate.

B. can take on negative values C. is related to the covariance of a share D. All of the given answers. 11. Portfolio risk is heavily based on: A. a simple average of the variance of the stocks in the portfolio B. a weighted average of the variance of the stocks in the portfolio C. a weighted average of the covariance of the stocks in the portfolio D. the standard deviation of the stocks 12. When an investor alters the mix of their portfolio to reflect market changes, this is called _____ asset allocation A. market timing B. passive

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CAPM is a model that describes the relationship between risk and expected return, and the formula itself measures the expected return of the portfolio. Mathematically, when beta is higher, meaning the portfolio has more systematic risk (in comparison to the market portfolio), the formula yields a higher expected return for the portfolio (since it is multiplied by the risk premium and is added to the risk free interest rate). This makes sense because the portfolio needs to

When we measure risk per unit of return, Collections, with its low expected return, becomes the most risky stock. The CV is a better measure of an asset’s stand-alone risk than because CV considers both the expected value and the dispersion of a distribution—a security with a low expected return and a low standard deviation could have a higher chance of a loss than one with a high but a high .

In order to find the optimal portfolio allocation, the group needs to find the portfolio structured with lowest risk under a given return. This can be achieved by applying Mean-Variance Theory and Markowitz model find the efficient frontier, which yields the most optimal portfolio under given returns. It can be expressed in mathematical terms and solved by quadratic programming. [Appendix A]

CAPM results can be compared to the best expected rate of return that investor can possibly earn in other investments with similar risks, which is the cost of capital. Under the CAPM, the market portfolio is a well-diversified, efficient portfolio representing the non-diversifiable risk in the economy. Therefore, investments have similar risk if they have the same sensitivity to market risk, as measured by their beta with the market portfolio.

To reduce a firm’s specific risk or residual risk a portfolio should have negative covariance or rather it should have no variance at all, for large portfolios however calculating variance requires greater and sophisticated computing power. As such, Index models greatly decrease the computations needed to calculate the optimum portfolio. The use of such Index models also eliminates illogical or rather absurd results. The Single Index model (SIM) and the Capital Asset Pricing Model (CAPM) are such models used to calculate the optimum portfolio.

Even though there are flaws in the CAPM for empirical study, the approach of the linearity of expected return and risk is readily relevant. As Fama & French (2004:20) stated “… Markowitz’s portfolio model … is nevertheless a theoretical tour de force.” It could be seen that the study of this paper may possibly justify Fama & French’s study that stated the CAPM is insufficient in interpreting the expected return with respect to risk. This is due to the failure of considering the other market factors that would affect the stock price.

If the assets exist to help meet a liability, the liability should be considered in the process; 3. Basing one’s decision solely on an asset allocation’s mean and variance is insufficient to base one’s decisions, in a world in which asset class returns are not normally distributed; and, 4. Most investors have multi-period objectives and the mean-variance framework is a single period model. These potential shortcomings are the likely reasons that practitioners have not fully embraced meanvariance optimization. For a number of practitioners, mean-variance optimization creates the illusion of quantitative sophistication; yet, in practice, asset allocations are developed using judgmental, ad hoc approaches. Recent advances significantly improve the quality of typical mean-variance optimizationbased asset allocations that should allow a far wider audience to realize the benefits of the Markowitz paradigm, or at least the intent of the paradigm. In this article, we focus on the first issue: the lack of diversification that can result from traditional meanvariance optimization. We begin with two examples in which traditional mean-variance optimization