Generalised Mean-Variance Analysis and Robust Portfolio Diversification
AbstractThis paper presents a new approach to portfolio optimisation that we call generalised mean-variance (GMV) analysis. One important case of this approach is based on the stocks m-tile (or quantile): if m = n, where n is the number of stocks, m-tile membership becomes rank. Our analysis is the rank equivalent of conventional Markowitz Mean Variance analysis. The first stage to generate rank probability statistics using, historic data, Monte Carlo analysis or direct user input. The second stage is optimisation based on those rank statistics to calculate recommended portfolio weights. Our optimisation uses state preference theory to derive an objective function that can be minimised using standard quadratic programming techniques. We deal with some advantages of this method including a more intuitive fully diversified (or minimum risk) position on the efficient frontier with all the portfolio holdings equally weighted.
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Bibliographic InfoPaper provided by Faculty of Economics, University of Cambridge in its series Cambridge Working Papers in Economics with number 0201.
Date of creation: Jan 2002
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Web page: http://www.econ.cam.ac.uk/index.htm
Mean Variance Analysis; Diversification; Portfolio Construction; Forecasts.;
Find related papers by JEL classification:
- G00 - Financial Economics - - General - - - General
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2002-02-10 (All new papers)
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