Constrained Dynamic Optimality and Binomial Terminal Wealth

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Constrained Dynamic Optimality and Binomial Terminal Wealth. / Pedersen, J. L.; Peskir, G.

In: SIAM Journal on Control and Optimization, Vol. 56, No. 2, 2018, p. 1342-1357.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Pedersen, JL & Peskir, G 2018, 'Constrained Dynamic Optimality and Binomial Terminal Wealth', SIAM Journal on Control and Optimization, vol. 56, no. 2, pp. 1342-1357. https://doi.org/10.1137/16M1085097

APA

Pedersen, J. L., & Peskir, G. (2018). Constrained Dynamic Optimality and Binomial Terminal Wealth. SIAM Journal on Control and Optimization, 56(2), 1342-1357. https://doi.org/10.1137/16M1085097

Vancouver

Pedersen JL, Peskir G. Constrained Dynamic Optimality and Binomial Terminal Wealth. SIAM Journal on Control and Optimization. 2018;56(2):1342-1357. https://doi.org/10.1137/16M1085097

Author

Pedersen, J. L. ; Peskir, G. / Constrained Dynamic Optimality and Binomial Terminal Wealth. In: SIAM Journal on Control and Optimization. 2018 ; Vol. 56, No. 2. pp. 1342-1357.

Bibtex

@article{0d57c086baa84e0fab2ff846dbd1d70f,
title = "Constrained Dynamic Optimality and Binomial Terminal Wealth",
abstract = "We assume that the wealth process $X^u$ is self-financing and generated from the initial wealth by holding a fraction $u$ of $X^u$ in a risky stock (whose price follows a geometric Brownian motion) and the remaining fraction $1-u$ of $X^u$ in a riskless bond (whose price compounds exponentially with interest rate $r \in {R}$). Letting $P_{t,x}$ denote a probability measure under which $X^u$ takes value $x$ at time $t,$ we study the dynamic version of the nonlinear optimal control problem $\inf_u\, Var{t,X_t^u}(X_T^u)$ where the infimum is taken over admissible controls $u$ subject to $X_t^u \ge e^{-r(T-t)} g$ and $E{t,X_t^u}(X_T^u) \ge \beta$ for $ t \in [0,T]$. The two constants $g$ and $\beta$ are assumed to be given exogenously and fixed. By conditioning on the expected terminal wealth value, we show that the nonlinear problem can be reduced to a family of linear problems. Solving the latter using a martingale method combined with Lagrange multipliers, we derive the dynamically optimal control $u_*^d$ in closed form and prove that the dynamically optimal terminal wealth $X_T^d$ can only take two values $g$ and $\beta$. This binomial nature of the dynamically optimal strategy stands in sharp contrast with other known portfolio selection strategies encountered in the literature. A direct comparison shows that the dynamically optimal (time-consistent) strategy outperforms the statically optimal (time-inconsistent) strategy in the problem.",
keywords = "constrained nonlinear optimal control, dynamic optimality, static optimality, mean variance analysis, martingale, Lagrange multiplier, geometric Brownian motion, Markov process",
author = "Pedersen, {J. L.} and G. Peskir",
year = "2018",
doi = "10.1137/16M1085097",
language = "English",
volume = "56",
pages = "1342--1357",
journal = "SIAM Journal on Control and Optimization",
issn = "0363-0129",
publisher = "Society for Industrial and Applied Mathematics",
number = "2",

}

RIS

TY - JOUR

T1 - Constrained Dynamic Optimality and Binomial Terminal Wealth

AU - Pedersen, J. L.

AU - Peskir, G.

PY - 2018

Y1 - 2018

N2 - We assume that the wealth process $X^u$ is self-financing and generated from the initial wealth by holding a fraction $u$ of $X^u$ in a risky stock (whose price follows a geometric Brownian motion) and the remaining fraction $1-u$ of $X^u$ in a riskless bond (whose price compounds exponentially with interest rate $r \in {R}$). Letting $P_{t,x}$ denote a probability measure under which $X^u$ takes value $x$ at time $t,$ we study the dynamic version of the nonlinear optimal control problem $\inf_u\, Var{t,X_t^u}(X_T^u)$ where the infimum is taken over admissible controls $u$ subject to $X_t^u \ge e^{-r(T-t)} g$ and $E{t,X_t^u}(X_T^u) \ge \beta$ for $ t \in [0,T]$. The two constants $g$ and $\beta$ are assumed to be given exogenously and fixed. By conditioning on the expected terminal wealth value, we show that the nonlinear problem can be reduced to a family of linear problems. Solving the latter using a martingale method combined with Lagrange multipliers, we derive the dynamically optimal control $u_*^d$ in closed form and prove that the dynamically optimal terminal wealth $X_T^d$ can only take two values $g$ and $\beta$. This binomial nature of the dynamically optimal strategy stands in sharp contrast with other known portfolio selection strategies encountered in the literature. A direct comparison shows that the dynamically optimal (time-consistent) strategy outperforms the statically optimal (time-inconsistent) strategy in the problem.

AB - We assume that the wealth process $X^u$ is self-financing and generated from the initial wealth by holding a fraction $u$ of $X^u$ in a risky stock (whose price follows a geometric Brownian motion) and the remaining fraction $1-u$ of $X^u$ in a riskless bond (whose price compounds exponentially with interest rate $r \in {R}$). Letting $P_{t,x}$ denote a probability measure under which $X^u$ takes value $x$ at time $t,$ we study the dynamic version of the nonlinear optimal control problem $\inf_u\, Var{t,X_t^u}(X_T^u)$ where the infimum is taken over admissible controls $u$ subject to $X_t^u \ge e^{-r(T-t)} g$ and $E{t,X_t^u}(X_T^u) \ge \beta$ for $ t \in [0,T]$. The two constants $g$ and $\beta$ are assumed to be given exogenously and fixed. By conditioning on the expected terminal wealth value, we show that the nonlinear problem can be reduced to a family of linear problems. Solving the latter using a martingale method combined with Lagrange multipliers, we derive the dynamically optimal control $u_*^d$ in closed form and prove that the dynamically optimal terminal wealth $X_T^d$ can only take two values $g$ and $\beta$. This binomial nature of the dynamically optimal strategy stands in sharp contrast with other known portfolio selection strategies encountered in the literature. A direct comparison shows that the dynamically optimal (time-consistent) strategy outperforms the statically optimal (time-inconsistent) strategy in the problem.

KW - constrained nonlinear optimal control

KW - dynamic optimality

KW - static optimality

KW - mean variance analysis

KW - martingale

KW - Lagrange multiplier

KW - geometric Brownian motion

KW - Markov process

U2 - 10.1137/16M1085097

DO - 10.1137/16M1085097

M3 - Journal article

VL - 56

SP - 1342

EP - 1357

JO - SIAM Journal on Control and Optimization

JF - SIAM Journal on Control and Optimization

SN - 0363-0129

IS - 2

ER -

ID: 196437737