Calculating Risk Neutral Probabilities and Optimal Portfolio Policies in a Dynamic Investment Model with Downside Risk Control

2004 ◽  
Author(s):  
Yonggan Zhao ◽  
William T. Ziemba
2019 ◽  
Vol 8 (1) ◽  
pp. 65-77
Author(s):  
Pierre Chaigneau ◽  
Louis Eeckhoudt

2009 ◽  
Vol 55 (No. 11) ◽  
pp. 541-549 ◽  
Author(s):  
L. Čechura

The paper deals with the theoretical analysis of the impact of credit rationing on farmer’s economic equilibrium. The analysis is carried out based on the derived dynamic optimization model, which is the dynamic investment model with adjustment costs. The credit rationing is introduced by imposing an upper limit on the control variable, which is in this case represented by the investment spending. Then, the optimal control is used to solve the optimization problem in the situation of both with and without credit constraints. Finally, the situations without and with credit rationing are compared. The results show that the occurrence of credit rationing or in general financial constraints significantly determines the capital accumulation and investment decisions of farmers and as a result their supply functions.


2009 ◽  
Vol 12 (07) ◽  
pp. 925-947 ◽  
Author(s):  
RENÉ AÏD ◽  
LUCIANO CAMPI ◽  
ADRIEN NGUYEN HUU ◽  
NIZAR TOUZI

The objective of this paper is to present a model for electricity spot prices and the corresponding forward contracts, which relies on the underlying market of fuels, thus avoiding the electricity non-storability restriction. The structural aspect of our model comes from the fact that the electricity spot prices depend on the dynamics of the electricity demand at the maturity T, and on the random available capacity of each production means. Our model explains, in a stylized fact, how the prices of different fuels together with the demand combine to produce electricity prices. This modeling methodology allows one to transfer to electricity prices the risk-neutral probabilities of the market of fuels and under the hypothesis of independence between demand and outages on one hand, and prices of fuels on the other hand, it provides a regression-type relation between electricity forward prices and forward prices of fuels. Moreover, the model produces, by nature, the well-known peaks observed on electricity market data. In our model, spikes occur when the producer has to switch from one technology to the lowest cost available one. Numerical tests performed on a very crude approximation of the French electricity market using only two fuels (gas and oil) provide an illustration of the potential interest of this model.


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