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111
Implications of rational inattention
 JOURNAL OF MONETARY ECONOMICS
, 2002
"... A constraint that actions can depend on observations only through a communication channel with finite Shannon capacity is shown to be able to play a role very similar to that of a signal extraction problem or an adjustment cost in standard control problems. The resulting theory looks enough like fa ..."
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Cited by 525 (11 self)
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A constraint that actions can depend on observations only through a communication channel with finite Shannon capacity is shown to be able to play a role very similar to that of a signal extraction problem or an adjustment cost in standard control problems. The resulting theory looks enough like familiar dynamic rational expectations theories to suggest that it might be useful and practical, while the implications for policy are different enough to be interesting.
Recursive multiplepriors
, 2003
"... This paper axiomatizes an intertemporal version of multiplepriors utility.A central axiom is dynamic consistency, which leads to a recursive structure for utility, to ‘rectangular ’ sets of priors and to priorbyprior Bayesian updating as the updating rule for such sets of priors.It is argued that ..."
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Cited by 167 (27 self)
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This paper axiomatizes an intertemporal version of multiplepriors utility.A central axiom is dynamic consistency, which leads to a recursive structure for utility, to ‘rectangular ’ sets of priors and to priorbyprior Bayesian updating as the updating rule for such sets of priors.It is argued that dynamic consistency is intuitive in a wide range of situations and that the model is consistent with a rich set of possibilities for dynamic behavior under ambiguity.
ROBUST PORTFOLIO SELECTION PROBLEMS
, 2003
"... In this paper we show how to formulate and solve robust portfolio selection problems. The objective of these robust formulations is to systematically combat the sensitivity of the optimal portfolio to statistical and modeling errors in the estimates of the relevant market parameters. We introduce “u ..."
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Cited by 160 (8 self)
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In this paper we show how to formulate and solve robust portfolio selection problems. The objective of these robust formulations is to systematically combat the sensitivity of the optimal portfolio to statistical and modeling errors in the estimates of the relevant market parameters. We introduce “uncertainty structures” for the market parameters and show that the robust portfolio selection problems corresponding to these uncertainty structures can be reformulated as secondorder cone programs and, therefore, the computational effort required to solve them is comparable to that required for solving convex quadratic programs. Moreover, we show that these uncertainty structures correspond to confidence regions associated with the statistical procedures employed to estimate the market parameters. Finally, we demonstrate a simple recipe for efficiently computing robust portfolios given raw market data and a desired level of confidence.
Theory and applications of Robust Optimization
, 2007
"... In this paper we survey the primary research, both theoretical and applied, in the field of Robust Optimization (RO). Our focus will be on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying the most pr ..."
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Cited by 110 (16 self)
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In this paper we survey the primary research, both theoretical and applied, in the field of Robust Optimization (RO). Our focus will be on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying the most prominent theoretical results of RO over the past decade, we will also present some recent results linking RO to adaptable models for multistage decisionmaking problems. Finally, we will highlight successful applications of RO across a wide spectrum of domains, including, but not limited to, finance, statistics, learning, and engineering.
Robust utility maximization in a stochastic factor model
, 2006
"... We give an explicit PDE characterization for the solution of a robust utility maximization problem in an incomplete market model, whose volatility, interest rate process, and longterm trend are driven by an external stochastic factor process. The robust utility functional is defined in terms of a ..."
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Cited by 77 (6 self)
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We give an explicit PDE characterization for the solution of a robust utility maximization problem in an incomplete market model, whose volatility, interest rate process, and longterm trend are driven by an external stochastic factor process. The robust utility functional is defined in terms of a HARA utility function with negative risk aversion and a dynamically consistent coherent risk measure, which allows for model uncertainty in the distributions of both the asset price dynamics and the factor process. Our method combines two recent advances in the theory of optimal investments: the general duality theory for robust utility maximization and the stochastic control approach to the dual problem of determining optimal martingale measures.
Robust Dynamic Programming
 Math. Oper. Res
, 2004
"... In this paper we propose a robust formulation for discrete time dynamic programming (DP). The objective of the robust formulation is to systematically mitigate the sensitivity of the DP optimal policy to ambiguity in the underlying transition probabilities. The ambiguity is modeled by associating ..."
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Cited by 70 (1 self)
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In this paper we propose a robust formulation for discrete time dynamic programming (DP). The objective of the robust formulation is to systematically mitigate the sensitivity of the DP optimal policy to ambiguity in the underlying transition probabilities. The ambiguity is modeled by associating a set of conditional measures with each stateaction pair. Consequently, in the robust formulation each policy has a set of measures associated with it. We prove that when this set of measures has a certain "Rectangularity" property all the main results for finite and infinite horizon DP extend to natural robust counterparts. We identify families of sets of conditional measures for which the computational complexity of solving the robust DP is only modestly larger than solving the DP, typically logarithmic in the size of the state space. These families of sets are constructed from the confidence regions associated with density estimation, and therefore, can be chosen to guarantee any desired level of confidence in the robust optimal policy. Moreover, the sets can be easily parameterized from historical data. We contrast the performance of robust and nonrobust DP on small numerical examples.
Robust control of forwardlooking models
 Journal of Monetary Economics
, 2003
"... This paper shows how to formulate and compute robust Ramsey (aka Stackelberg) plans for linear models with forward looking private agents. The leader and the followers share a common approximating model and both have preferences for robust decision rules because both doubt the model. Since their pre ..."
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Cited by 60 (0 self)
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This paper shows how to formulate and compute robust Ramsey (aka Stackelberg) plans for linear models with forward looking private agents. The leader and the followers share a common approximating model and both have preferences for robust decision rules because both doubt the model. Since their preferences differ, the leader’s and followers ’ decision rules are fragile to different misspecifications of the approximating model. We define a Stackelberg equilibrium with robust decision makers in which the leader and follower have different worstcase models despite sharing a common approximating model. To compute a Stackelberg equilibrium we formulate a Bellman equation that is associated with an artificial singleagent robust control problem. The artificial Bellman equation contains a description of implementability constraints that include Euler equations that describe the worstcase analysis of the followers. As an example, the paper analyzes a model of a monopoly facing a competitive fringe.
Information Immobility and the Home Bias Puzzle
 Journal of Finance
, 2009
"... Many papers have argued that home bias arises because home investors can predict payoffs of their home assets more accurately than foreigners can. But why does this information advantage exist in a world where investors can learn foreign information? We model investors who are endowed with a small h ..."
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Cited by 60 (3 self)
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Many papers have argued that home bias arises because home investors can predict payoffs of their home assets more accurately than foreigners can. But why does this information advantage exist in a world where investors can learn foreign information? We model investors who are endowed with a small home information advantage. They can choose what information to learn before they invest in many risky assets. Surprisingly, even when home investors can learn what foreigners know, they choose not to. The reason is that investors profit more from knowing information that others do not know. Allowing investors to learn amplifies their initial information asymmetry. The model explains local and industry bias as well as observed patterns of foreign investments, portfolio outperformance and asset prices. Finally, we outline new avenues for empirical research.
Portfolio selection with monotone meanvariance preferences. ICER Working paper 27
, 2004
"... # 0136556). †The views expressed in the article are those of the author and do not involve the responsibility of the bank. 1 We propose a portfolio selection model based on a class of preferences that coincide with meanvariance preferences on their domain of monotonicity, but differ where meanvar ..."
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Cited by 54 (5 self)
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# 0136556). †The views expressed in the article are those of the author and do not involve the responsibility of the bank. 1 We propose a portfolio selection model based on a class of preferences that coincide with meanvariance preferences on their domain of monotonicity, but differ where meanvariance preferences fail to be monotone and are therefore not economically meaningful. The functional associated to this new class of preferences is the best approximation of the meanvariance functional among those which are monotonic. We solve the portfolio selection problem and we show that the most important property of meanvariance optimal portfolios, namely the two fund separation property, still holds in our framework. 2 1
Ambiguous Chance Constrained Problems And Robust Optimization
 Mathematical Programming
, 2004
"... In this paper we study ambiguous chance constrained problems where the distributions of the random parameters in the problem are themselves uncertain. We primarily focus on the special case where the uncertainty set Q of the distributions is of the form Q = {Q : # p (Q, Q 0 ) # #}, where # p denote ..."
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Cited by 40 (1 self)
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In this paper we study ambiguous chance constrained problems where the distributions of the random parameters in the problem are themselves uncertain. We primarily focus on the special case where the uncertainty set Q of the distributions is of the form Q = {Q : # p (Q, Q 0 ) # #}, where # p denotes the Prohorov metric. The ambiguous chance constrained problem is approximated by a robust sampled problem where each constraint is a robust constraint centered at a sample drawn according to the central measure Q 0 . The main contribution of this paper is to show that the robust sampled problem is a good approximation for the ambiguous chance constrained problem with high probability. This result is established using the StrassenDudley Representation Theorem that states that when the distributions of two random variables are close in the Prohorov metric one can construct a coupling of the random variables such that the samples are close with high probability. We also show that the robust sampled problem can be solved e#ciently both in theory and in practice. 1