Download Sequential Binary Investment Decisions: A Bayesian Approach by Werner Jammernegg PDF

By Werner Jammernegg

This booklet describes a few types from the idea of funding that are commonly characterised via 3 positive aspects. to begin with, the decision-maker acts in a dynamic setting. Secondly, the distributions of the random variables are just incompletely recognized at first of the making plans technique. this is often termed as decision-making below stipulations of uncer­ tainty. Thirdly, in huge components of the paintings we limit the research to binary determination versions. In a binary version, the decision-maker needs to decide on one among activities. for instance, one choice capacity to adopt the make investments­ ·ment undertaking in a making plans interval, while the opposite selection prescribes to delay the venture for no less than yet another interval. The research of dynamic determination types lower than stipulations of uncertainty isn't a truly universal process in economics. during this framework the op­ timal judgements are just bought by way of the large use of tools from operations study and from information. it's the goal to slim many of the present gaps within the fields of funding and portfolio research during this recognize. this can be performed through combining innovations which have been devel­ oped in funding thought and portfolio choice, in stochastic dynamic programming, and in Bayesian data. The latter box exhibits using Bayes' theorem for the revision of the chance distributions of the random variables over time.

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Additional resources for Sequential Binary Investment Decisions: A Bayesian Approach

Example text

G. by consulting an expert is in verbal form rather than in the form of probability statements. These pieces of advice may contain information reaching from the individual firm and industry level to governmental decisions and international developments (cp. for this subject Winkler (1979)). We now assume that the naive investor considered knows the family of distributions of the return of the risky asset B, but does not know all 48 parameters of this distribution. In the following one parameter is taken as unknown at the beginning of the investment process.

Winkler (1973), Dhingra (1983)). Dynamic Bayesian portfolio models were for the first time considered by Winkler/Barry (1975) and Winkler (1979). These papers only contain a fundamental discussion of the assumptions to the model and write down the value iteration equations of dynamic programming. A detailed analysis is presented by Kischka (1984), chapter 3. As the name of the model already indicates, the proper framework for the analysis is Bayesian dynamic programming (cp. Rieder (1975a)). Essentially the original Bayesian model is reduced to an ordinary stochastic dynamic program of the type used in this work.

2. 7 the optimal investment decisions ft(a;), 1 ~ ~ T, are nondecreasing functions of the initial wealth a;. It is a difficult, if not impossible task to justify condition (3,20): until now we do not know any family of return distributions which satisfies (3,20). e. E(r) ~ 8. The preceding analysis comes to a contrary result compared to the one obtained for utility functions (3,8)-(3,10). 6 shows that the optimal investment decision functions It (a; ), 1 ~ t ~ T, are nonincreasing provided E( r) ~ 8 holds.

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