Martingale Method for Utility Maximization

2018 ◽  
pp. 343-364
Author(s):  
Jia-An Yan
Keyword(s):  
Utility Maximization ◽  
Martingale Method

Non-Concave Utility Maximization without the Concavification Principle

2019 ◽  
Author(s):  
Min Dai ◽  
Steven Kou ◽  
Shuaijie Qian ◽  
Xiangwei Wan
Keyword(s):  
Utility Maximization

scholarly journals Implicit incentives for fund managers with partial information

2021 ◽  
Author(s):  
Flavio Angelini ◽  
Katia Colaneri ◽  
Stefano Herzel ◽  
Marco Nicolosi
Keyword(s):  
Asset Allocation ◽  
Partial Information ◽  
Market Price ◽  
Investment Strategy ◽  
Expected Returns ◽  
Martingale Method ◽  
Fund Manager ◽  
Market Price Of Risk ◽  
Fund Managers ◽  
Price Of Risk

AbstractWe study the optimal asset allocation problem for a fund manager whose compensation depends on the performance of her portfolio with respect to a benchmark. The objective of the manager is to maximise the expected utility of her final wealth. The manager observes the prices but not the values of the market price of risk that drives the expected returns. Estimates of the market price of risk get more precise as more observations are available. We formulate the problem as an optimization under partial information. The particular structure of the incentives makes the objective function not concave. Therefore, we solve the problem by combining the martingale method and a concavification procedure and we obtain the optimal wealth and the investment strategy. A numerical example shows the effect of learning on the optimal strategy.

SHORT-RUN TAX-RESPONSE IN A UTILITY-MAXIMIZATION FRAMEWORK

1970 ◽  
Vol 23 (4) ◽  
pp. 365-372
Author(s):  
FREDERICK D. SEBOLD
Keyword(s):  
Utility Maximization ◽  
Short Run

scholarly journals Diffusion approximations for randomly arriving expert opinions in a financial market with Gaussian drift

2021 ◽  
Vol 58 (1) ◽  
pp. 197-216 ◽  
Author(s):  
Jörn Sass ◽  
Dorothee Westphal ◽  
Ralf Wunderlich
Keyword(s):  
Stock Returns ◽  
Financial Market ◽  
Discrete Time ◽  
High Frequency ◽  
Utility Maximization ◽  
Limit Theorems ◽  
Approximate Solutions ◽  
Diffusion Approximations ◽  
Expert Opinions ◽  
Arrival Intensity

AbstractThis paper investigates a financial market where stock returns depend on an unobservable Gaussian mean reverting drift process. Information on the drift is obtained from returns and randomly arriving discrete-time expert opinions. Drift estimates are based on Kalman filter techniques. We study the asymptotic behavior of the filter for high-frequency experts with variances that grow linearly with the arrival intensity. The derived limit theorems state that the information provided by discrete-time expert opinions is asymptotically the same as that from observing a certain diffusion process. These diffusion approximations are extremely helpful for deriving simplified approximate solutions of utility maximization problems.

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