Cash Flow Simulation in Private Equity : An evaluation and comparison of two models

Detta är en Uppsats för yrkesexamina på avancerad nivå från Umeå universitet/Institutionen för matematik och matematisk statistik; Umeå universitet/Institutionen för matematik och matematisk statistik

Författare: Johannes Forsell; Elias Furenstam; [2018]

Nyckelord: ;

Sammanfattning: The uncertain pattern of cash flows poses a liquidity and risk management challenge for investors of private equity funds. The structure of a private equity investment, where the total committed capital will be paid out in portions at an undetermined schedule, makes it vital for the investor to have sufficient levels of cash in order to meet the called capital from the fund manager. As an investor can hold several investments, it is important to predict future cash flows in order to have effective cash management.   The purpose of this thesis is to increase the supporting institution’s knowledge in cash flow predictions of investments in private equity. To do this, an analysis and evaluation of two models have been executed for cash flow predictions from the view of a limited partner, i.e. the investor. The comparison is done between a deterministic model, the Yale model, that is currently used by the supporting institution to this thesis and a new stochastic model, the Stochastic model, that has been implemented during the work of this thesis.   The evaluation of the models has been done by backtests and with a coefficient of determination test, R2 test, of the Institution’s portfolio. It is hard to make an absolute conclusion on the performance of the two models as they outperform each other on different periods. Overall, the Yale model was better than the Stochastic model on the conducted tests, but the Stochastic model offers desirable attributes from a risk management perspective that the deterministic model lacks. This gives the Stochastic model potential to outperform the Yale model as a better option for cash flow simulation in private equity, provided a better parameter estimation.

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