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• W1577804490 abstract "The thesis explores applications of optimisation in investment management and riskmeasurement. In investment management the information issues are largely concernedwith generating optimal forecasts. It is difficult to get inputs that have the propertiesthey are supposed to have. Thus optimisation is prone to 'Garbage In, Garbage Out', thatleads to substantial biases in portfolio selection, unless forecasts are adjusted suitablyfor estimation error. We consider three case studies where we investigate the impact offorecast error on portfolio performance and examine ways of adjusting for resulting bias.Treynor and Black (1973) first tried to make the best possible use of the informationprovided by security analysis based on Markovitz (1952) portfolio selection. Theyestablished a relationship between the correlation of forecasts, the number of independentsecurities available and the Sharpe ratio which can be obtained. Their analysis was basedon the assumption that the correlation between the forecasts and outcomes is known precisely.In practice, given the low levels of correlation possible, an investor may believehimself to have a different degree of correlation from what he actually has. Using twodifferent metrics we explore how the portfolio performance depends on both the anticipatedand realised correlation when these differ. One measure, the Sharpe ratio, capturesthe efficiency loss, attributed to the change in reward for risk. The other measure, theGeneralised Sharpe Ratio (GSR), introduced by Hodges (1997), quantifies the reductionin the welfare of a particular investor due to adopting an inappropriate risk profile. Weshow that these two metrics, the Sharpe ratio and GSR, complement each other and incombination provide a fair ranking of existing investment opportunities.Using Bayesian adjustment is a popular way of dealing with estimation error in portfolioselection. In a Bayesian implementation, we study how to use non-sample informationto infer optimal scaling of unknown forecasts of asset returns in the presence of uncertaintyabout the quality of our information, and how the efficient use of information affects portfoliodecision. Optimal portfolios, derived under full use of information, differ strikinglyfrom those derived from the sample information only; the latter, unlike the former, arehighly affected by estimation error and favour several (up to ten) times larger holdings.The impact of estimation error in a dynamic setting is particularly severe because of thecomplexity of the setting in which it is necessary to have time varying forecasts. We takeBrennan, Schwartz and Lagnado's structure (1997) as a specific illustration of a genericproblem and investigate the bias in long-term portfolio selection models that comes fromoptimisation with (unadjusted) parameters estimated from historical data. Using a MonteCarlo simulation analysis, we quantify the degree of bias in the optimisation approach ofBrennan, Schwartz and Lagnado. We find that estimated parameters make an investorbelieve in investment opportunities five times larger than they actually are. Also a mild realtime-variation in opportunities inflates wildly when measured with estimated parameters.In the latter part of the thesis we look at slightly less straightforward optimisationapplications in risk measurement, which arise in reporting risk. We ask, what is the mostefficient way of complying with the rules? In other words, we investigate how to reportthe smallest exposure within a rule. For this purpose we develop two optimal efficientalgorithms that calculate the minimal amount of the position risk required, to cover afirm's open positions and obligations, as required by respective rules in the FSA (FinancialSecurities Association) Handbook. Both algorithms lead to interesting generalisations." @default.
• W1577804490 created "2016-06-24" @default.
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• W1577804490 date "2004-04-01" @default.
• W1577804490 modified "2023-09-24" @default.
• W1577804490 title "Information and optimisation in investment and risk measurement" @default.
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