Least Squares Monte Carlo Simulation-Based Decision-Making Method for Photovoltaic Investment in Korea

Solar power for clean energy is an important asset that will drive the future of sustainable energy generation. As interest in sustainable energy increases with Korea’s renewable energy expansion plan, a strategy for photovoltaic investment (PV) is important from an investor’s point of view. Previous research primarily focused on assessing and analyzing the impact of the volatility but paid little attention to the modeling decision-making project to obtain the optimal investment timing. This paper utilizes a Least Squares Monte Carlo-based method for determining the timing of PV plant investment. The proposed PV decision-making method is designed to simulate the total PV generation revenue period with all uncertain PV price factors handled before determining the optimal investment time. The numerical studies with nine different scenarios considering system marginal price (SMP) and renewable energy certificate (REC) spot market price in Korea demonstrated how to determine the optimal investment time for different PV capacities. Therefore, the proposed method can be used as a decision-making tool to provide PV investors with information on the best time to invest in the renewable energy market.

A Neural Network Approach to Value R&D Compound American Exchange Option

In this paper we show as the neural network methodology, coupled with the Least Squares Monte Carlo approach, can be very helpful in valuing R&D investment opportunities. As it is well known, R&D projects are made in a phased manner, with the commencement of subsequent phase being dependent on the successful completion of the preceding phase. This is known as a sequential investment and therefore R&D projects can be considered as compound options. In addition, R&D investments often involve considerable cost uncertainty so that they can be viewed as an exchange option, i.e. a swap of an uncertain investment cost for an uncertain gross project value. Finally, the production investment can be realized at any time before the maturity date, after that the effects of R&D disappear. Consequently, an R&D project can be considered as a compound American exchange option. In this context, the Least Squares Monte Carlo method is a powerful and flexible tool for capital budgeting decisions and for valuing American-type options. But, using the simulated values as “targets”, the implementation of a neural network allows to extend the results for any R&D valuation and to abate the waiting time of Least Squares Monte Carlo simulation.

Least-Squares Monte Carlo for Proxy Modeling in Life Insurance: Neural Networks

The least-squares Monte Carlo method has proved to be a suitable approximation technique for the calculation of a life insurer’s solvency capital requirements. We suggest to enhance it by the use of a neural network based approach to construct the proxy function that models the insurer’s loss with respect to the risk factors the insurance business is exposed to. After giving a mathematical introduction to feed forward neural networks and describing the involved hyperparameters, we apply this popular form of neural networks to a slightly disguised data set from a German life insurer. Thereby, we demonstrate all practical aspects, such as the hyperparameter choice, to obtain our candidate neural networks by bruteforce, the calibration (“training”) and validation (“testing”) of the neural networks and judging their approximation performance. Compared to adaptive OLS, GLM, GAM and FGLS regression approaches, an ensemble built of the 10 best derived neural networks shows an excellent performance. Through a comparison with the results obtained by every single neural network, we point out the significance of the ensemble-based approach. Lastly, we comment on the interpretability of neural networks compared to polynomials for sensitivity analyses.