Browsing by Author "Andongwisye, John"

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    Asset liability management for Tanzania pension funds.
    (University of Dar es Salaam, 2018) Andongwisye, John
    This thesis presents a long-term asset liability management for Tanzania pension funds. Two kinds of pension benefits are considered; a commuted (at retirement) and a monthly (old age) pension. A decision factor in the analysis is the increased life expectancy of the members of Tanzania pension funds. As an application, data from NSSF are used. The presentation is divided into two parts. First is a 50 years demographic projection of the fund using a fixed and relatively low return on asset value. Basing on the number of members in 2015, a projection of members and retirees is done. The corresponding amount of contributions, asset values, benefit payouts, and liabilities are also projected. The evaluation of some possible reforms of the fund is done. Then, the growth of asset values using different asset returns is studied. The projection shows that the fund will not be fully sustainable in a long future due to the increase in life expectancy of its members. Second is a risk management based on stochastic programming. The model is based on work by Kouwenberg in $2001$ and includes some features from Tanzania pension system. In contrast with most asset liability management models for pension funds by stochastic programming, liabilities are modeled by a number of years of life expectancy. Scenario trees are generated by using Monte Carlo simulation. Numerical results suggest that, in order to improve a long-term sustainability of the Tanzania pension fund system, it is necessary to make reforms concerning the contribution rate, investment guidelines and formulate target levels (funding ratios) to characterize the pension funds' solvency situation.
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    Optimal time to sell an asset whose price is mean reverting
    (University of Dar es Salaam,, 2012) Andongwisye, John
    In this work, we have solved the stopping time problem under uncertainty using the continuous time theory. We used the mean reverting model to find the time that gave the optimal expected reward. The mean reverting model uses the past information to predict the future price. In a real market past information is very important To present the optimal expected reward g*(x), we used Dynkin’s supermeanval¬ued major ant characterization of the value function in which we found the first exit time r*j. We used the continuation region U which is open and is optimal to continue running the process as well as when it enters a closed region D in which it is optimal to terminate the process and receive the reward. To set up a free bound¬ary problem that can be solved, an additional condition is needed. In this work the principle of smooth fit provided the condition. The explicit solution to a free boundary problem was determined. This helped us to find the maximum asset price x*. Our result is divided into two cases. When the optimal price x* is less than the present price Xq = x then the better decision is to sell immediately. But when x* > x, the best decision is to sell later and the optimal price depends on the model parameters. Finally we determined the maximum expected reward g*(x) which depends on the optimal price