Results 1  10
of
65
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 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.
Ambiguity, information quality and asset pricing
 2007, J. Finance
, 2004
"... When ambiguity averse investors process news of uncertain quality, they act as if they take a worstcase assessment of quality. As a result, they react more strongly to bad news than to good news. They also dislike assets for which information quality is poor, especially when the underlying fundamen ..."
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Cited by 52 (7 self)
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When ambiguity averse investors process news of uncertain quality, they act as if they take a worstcase assessment of quality. As a result, they react more strongly to bad news than to good news. They also dislike assets for which information quality is poor, especially when the underlying fundamentals are volatile. These effects induce negative skewness in asset returns, increase price volatility and induce ambiguity premia that depend on idiosyncratic risk in fundamentals. Moreover, shocks to information quality can have persistent negative effects on prices even if fundamentals do not change. This helps to explain the reaction of markets to events like 9/11/2001. 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
Vector Expected Utility and Attitudes toward Variation
, 2007
"... This paper analyzes a model of decision under ambiguity, deemed vector expected utility or VEU. According to the proposed model, an act f: Ω → X is evaluated via the functional V (f) = ..."
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Cited by 30 (5 self)
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This paper analyzes a model of decision under ambiguity, deemed vector expected utility or VEU. According to the proposed model, an act f: Ω → X is evaluated via the functional V (f) =
Ambiguity Aversion: Implications for the Uncovered Interest Rate Parity Puzzle
, 2009
"... Empirically, highinterestrate currencies tend to appreciate in the future relative to lowinterestrate currencies instead of depreciating as uncovered interest rate parity (UIP) states. The explanation for the UIP puzzle that I pursue in this paper is that the agents' beliefs are systemati ..."
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Cited by 25 (3 self)
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Empirically, highinterestrate currencies tend to appreciate in the future relative to lowinterestrate currencies instead of depreciating as uncovered interest rate parity (UIP) states. The explanation for the UIP puzzle that I pursue in this paper is that the agents' beliefs are systematically distorted. This perspective receives some support from an extended empirical literature using survey data. I construct a model of exchange rate determination in which ambiguityaverse agents need to solve a filtering problem to form forecasts but face signals about the timevarying hidden state that are of uncertain precision. In the presence of such uncertainty, ambiguityaverse agents take a worstcase evaluation of this precision and respond stronger to bad news than to good news about the payoffs of their investment strategies. Importantly, because of this endogenous systematic underestimation, agents in the next periods will perceive on average positive innovations about the payoffs which will make them reevaluate upwards the profitability of the strategy. As a result, the model's dynamics imply significant expost departures from UIP as equilibrium outcomes. In addition to providing a resolution to the UIP puzzle, the model predicts, consistent with the data, negative skewness and excess kurtosis for currency excess returns and positive average payoffs even for hedged positions.
2009, `Dynamic Asset Allocation with Ambiguous Return Predictability'. Working Paper
"... Abstract We study an investor's optimal consumption and portfolio choice problem when he confronts with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor&apo ..."
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Cited by 23 (3 self)
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Abstract We study an investor's optimal consumption and portfolio choice problem when he confronts with two possibly misspecified submodels of stock returns: one with IID returns and the other with predictability. We adopt a generalized recursive ambiguity model to accommodate the investor's aversion to model uncertainty. The investor deals with specification doubts by slanting his beliefs about submodels of returns pessimistically, causing his investment strategy to be more conservative than the Bayesian strategy. This effect is large for extreme values of the predictive variable. Unlike in the Bayesian framework, model uncertainty induces a hedging demand, which may cause the investor to decrease his stock allocations sharply and then increase with his prior probability of IID returns. Adopting suboptimal investment strategies by ignoring model uncertainty can lead to sizable welfare costs.
Model uncertainty, limited market participation, and asset pricing
, 2002
"... We demonstrate that limited market participation can arise endogenously in the presence of model uncertainty. Our model generates novel predictions on the relation between limited market participation, equity premium, and diversification discount. When the dispersion in investors ’ model uncertainty ..."
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Cited by 20 (1 self)
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We demonstrate that limited market participation can arise endogenously in the presence of model uncertainty. Our model generates novel predictions on the relation between limited market participation, equity premium, and diversification discount. When the dispersion in investors ’ model uncertainty is small, full market participation prevails in equilibrium. In this case, equity premium is unrelated to model uncertainty dispersion and a conglomerate trades at a price equal to the sum of its single segment counterparts. When model uncertainty dispersion is large, however, investors with high uncertainty optimally choose to stay sidelined in equilibrium. In this case, equity premium can decrease with model uncertainty dispersion. This is in sharp contrast to the understanding in the existing literature that limited market participation leads to higher equity premium. Moreover, when limited market participation occurs, a conglomerate trades at a discount relative to its single segment counterparts. The discount increases in model uncertainty dispersion and is positively related to the proportion of investors not participating in the markets.
Ambiguity, Learning, and Asset Returns
, 2007
"... We develop a consumptionbased assetpricing model in which the representative agent is ambiguous about the hidden state in consumption growth. He learns about the hidden state under ambiguity by observing past consumption data. His preferences are represented by the smooth ambiguity model axiomatiz ..."
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Cited by 19 (1 self)
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We develop a consumptionbased assetpricing model in which the representative agent is ambiguous about the hidden state in consumption growth. He learns about the hidden state under ambiguity by observing past consumption data. His preferences are represented by the smooth ambiguity model axiomatized by Klibanoff et al. (2005, 2006). Unlike the standard Bayesian theory, this utility model implies that the posterior of the hidden state and the conditional distribution of the consumption process given a state cannot be reduced to a predictive distribution. By calibrating the ambiguity aversion parameter, the subjective discount factor, and the risk aversion parameter (with the latter two values between zero and one), our model can match the first moments of the equity premium and riskfree rate found in the data. In addition, our model can generate a variety of dynamic asset pricing phenomena, including the procyclical variation of pricedividend ratios, the countercyclical variation of equity premia and equity volatility, and the mean reversion and long horizon predictability of excess returns.
Portfolio Choices and Asset Prices: The Comparative Statics of Ambiguity Aversion
 Review of Economic Studies
, 2011
"... This paper investigates the comparative statics of ”more ambiguity aversion” as defined by Klibanoff, Marinacci and Mukerji (2005). The analysis uses the static twoasset portfolio problem with one safe asset and one uncertain one. While it is intuitive that more ambiguity aversion would reduce dema ..."
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Cited by 19 (0 self)
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This paper investigates the comparative statics of ”more ambiguity aversion” as defined by Klibanoff, Marinacci and Mukerji (2005). The analysis uses the static twoasset portfolio problem with one safe asset and one uncertain one. While it is intuitive that more ambiguity aversion would reduce demand for the uncertain asset, this is not necessarily the case. We derive sufficient conditions for a reduction in the demand for the uncertain asset, and for an increase in the equity premium. An example which meets the sufficient conditions is when the set of plausible distributions for returns on the uncertain asset can be ranked according to their monotone likelihood ratio. It is also shown how ambiguity aversion distorts the price kernel in the alternative portfolio problem with complete markets for contingent claims.