Time Weighted Portfolio Optimisation
نویسندگان
چکیده
In estimating the inputs into the Modern Portfolio Theory (MPT) portfolio optimisation problem it is usual to use equal weighted historic data. Equal weighting of the data, however, does not take account of the current state of the market. Consequently this approach is unlikely to perform well in any subsequent period as the data is still reflecting market conditions that are no longer valid. The need for some return-weighting scheme that gives greater weight to the most recent data would seem desirable. Consequently this study uses returns data which are weighted to give greater weight to the most recent observations to see if such a weighting scheme can offer improved ex ante performance over that based on un-weighted data.
منابع مشابه
A Multi-Criteria Portfolio Analysis of Hedge Fund Strategies
This paper features a tri-criteria analysis of Eurekahedge fund data strategy index data. We use nine Eurekahedge equally weighted main strategy indices for the portfolio analysis. The tri-criteria analysis features three objectives: return, risk and dispersion of risk objectives in a Multi-Criteria Optimisation (MCO) portfolio analysis. We vary the MCO return and risk targets and contrast the ...
متن کاملApplying a global optimisation algorithm to Fund of Hedge Funds portfolio optimisation
Portfolio optimisation for a Fund of Hedge Funds (“FoHF”) has to address the asymmetric, non-Gaussian nature of the underlying returns distributions. Furthermore, the objective functions and constraints are not necessarily convex or even smooth. Therefore traditional portfolio optimisation methods such as mean-variance optimisation are not appropriate for such problems and global search optimis...
متن کاملAdaptive Bayesian Optimisation for Online Portfolio Selection
We present a Bayesian approach for online portfolio selection, a fundamental problem in computational finance. We pose the problem as the global optimisation of an expensive, time-varying, black-box function. As the optimum is itself dynamic, we use a model that allows us to capture time-dependent patterns of the function and to provide sequential decision processes that enable us to select opt...
متن کاملKeyCredit risk, Portfolio credit risk model, Portfolio optimisation, Genetic
This paper proposes a new combination of quantitative models and Genetic Algorithms for the task of optimising credit portfolios. Currently, quantitative portfolio credit risk models are used to calculate portfolio risk figures, e. g. expected losses, unexpected losses and risk contributions. Usually, this information is used for optimising the risk-return profile of the portfolio. We show that...
متن کاملA Hybrid Genetic-quantitative Method for Risk-return Optimisation of Credit Portfolios
This paper proposes a new combination of quantitative models and Genetic Algorithms for the task of optimising credit portfolios. Currently, quantitative portfolio credit risk models are used to calculate portfolio risk figures, e. g. expected losses, unexpected losses and risk contributions. Usually, this information is used for optimising the risk-return profile of the portfolio. We show that...
متن کامل