Analysis : minimax portfolio choice based on pessimistic decision making

Extract

[...] When the mean-variance portfolio is neglected with a daily average return of 0.005 percent and volatility of 0.54 percent, the Minimax strategy is found have the highest Sharp Ration. The minimum variance portfolio is found to have the lowest volatility when out-of-sample is used, which is in accordance with the minimum variance optimization objective. Essentially, a wide diversifying of funds across all classes of assets makes the naïve strategies beat the volatility aspect of the mean-variance portfolio (Schaarschmidt & Schanbacher, 2014). Conclusion The paper concluded that the Minimax strategy is proposed. The strategy has its basis on pessimistic decision-making process. [...]

[...] It particularly comes out as the best strategy for optimizing asset allocation usually for the big pension funds as well as for the investors who go through daily risk reporting practices attributed to mark-to-market accounting or regulatory requirements (Schaarschmidt & Schanbacher, 2014). The strategy is also easy to implement, which also adds up to why it is better than the other portfolios. Reference Schaarschmidt, S., & Schanbacher, P. (2014). Minimax: Portfolio Choice Based on Pessimistic Decision Making. International Journal of Economics and Finance, 23-40. [...]

[...] The fund allocation strategy was to be based on a pessimistic decision making process for the construction of four major classes of assets. This objective was to be achieved through the utilization of data on stocks indexes, bonds, commodities and real estate among others, between January 1990 and December 2010 (Schaarschmidt & Schanbacher, 2014). Techniques and methods used in the computations According to the authors of the paper, taking risk is often accompanied by the obtaining of economic gains (Schaarschmidt & Schanbacher, 2014). [...]

[...] In this case the calculations were done as follows; Let, Random daily returns = Rt = n and t = T). Vector of weights W = and = 1 (with e = ( being a vectors of ones), = argmax W min τ t subject to = 1 and wi 0 Where, the first T returns are considered. In this application, T =250 in estimating the Minimax weights on the basis of a year's daily returns. For each allocation a daily worst outcome of WRτ is noted with τ t T}. [...]