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24
Portfolio selection in stochastic environments, Working Paper
 Review of Financial Studies
, 1999
"... In this article, I explicitly solve dynamic portfolio choice problems, up to the solution of an ordinary differential equation (ODE), when the asset returns are quadratic and the agent has a constant relative risk aversion (CRRA) coefficient. My solution includes as special cases many existing expli ..."
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Cited by 99 (7 self)
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In this article, I explicitly solve dynamic portfolio choice problems, up to the solution of an ordinary differential equation (ODE), when the asset returns are quadratic and the agent has a constant relative risk aversion (CRRA) coefficient. My solution includes as special cases many existing explicit solutions of dynamic portfolio choice problems. I also present three applications that are not in the literature. Application 1 is the bond portfolio selection problem when bond returns are described by ‘‘quadratic term structure models.’ ’ Application 2 is the stock portfolio selection problem when stock return volatility is stochastic as in Heston model. Application 3 is a bond and stock portfolio selection problem when the interest rate is stochastic and stock returns display stochastic volatility. (JEL G11) There is substantial evidence of time variation in interest rates, expected returns, and asset return volatilities. Interest rates change over time, and although expected stock returns are not directly observed, future stock returns seem to be predictable using term structure variables and scaled prices such as dividend yields. 1 Similarly, there is welldocumented evidence
Learning about predictability: the effects of parameter uncertainty on dynamic asset allocation
, 2000
"... This paper examines the effects of uncertainty about the stock return predictability on optimal dynamic portfolio choice in a continuous time setting for a long horizon investor. Uncertainty about the predictive relation affects the optimal portfolio choice through dynamic learning, and leads to a s ..."
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Cited by 70 (3 self)
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This paper examines the effects of uncertainty about the stock return predictability on optimal dynamic portfolio choice in a continuous time setting for a long horizon investor. Uncertainty about the predictive relation affects the optimal portfolio choice through dynamic learning, and leads to a statedependent relation between the optimal portfolio choice and the investment horizon. There is substantial market timing in the optimal hedge demands, which is caused by stochastic covariance between stock return and dynamic learning. The opportunity cost of ignoring predictability or learning is found to be quite substantial.
Liquidity and Expected Returns: Lessons from Emerging Markets
, 2006
"... Given the crosssectional and temporal variation in their liquidity, emerging equity markets provide an ideal setting to examine the impact of liquidity on expected returns. Our main liquidity measure is a transformation of the proportion of zero daily firm returns, averaged over the month. We find ..."
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Cited by 53 (8 self)
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Given the crosssectional and temporal variation in their liquidity, emerging equity markets provide an ideal setting to examine the impact of liquidity on expected returns. Our main liquidity measure is a transformation of the proportion of zero daily firm returns, averaged over the month. We find that it significantly predicts future returns, whereas alternative measures such as turnover do not. Consistent with liquidity being a priced factor, unexpected liquidity shocks are positively correlated with contemporaneous return shocks and negatively correlated with shocks to the dividend yield. We consider a simple asset pricing model with liquidity and the market portfolio as risk factors and transaction costs that are proportional to liquidity. The model differentiates between integrated and segmented countries and time periods. Our results suggest that local market liquidity is an important driver of expected returns in emerging markets, and that the liberalization process has not fully eliminated its impact.
Learning How to Invest when Returns are Uncertain
, 2003
"... Most asset returns are uncertain, not merely risky: investors do not know the probabilities of different possible future returns. A large body of evidence suggests that investors are averse to uncertainty, as well as to risk. This paper builds up an axiomatic foundation for the dynamic portfolio and ..."
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Cited by 16 (2 self)
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Most asset returns are uncertain, not merely risky: investors do not know the probabilities of different possible future returns. A large body of evidence suggests that investors are averse to uncertainty, as well as to risk. This paper builds up an axiomatic foundation for the dynamic portfolio and consumption choices of an uncertaintyaverse (as well as riskaverse) investor who tries to learn from historical data. The theory developed, modelbased multiplepriors, generalizes existing theories of dynamic choice under uncertainty aversion by relaxing the assumption of consequentialism. Examples are given to show that consequentialism, the property that counterfactuals are ignored, can be problematic when combined with uncertainty aversion. An analog of de Finetti’s statistical representation theorem is proven under modelbased multiplepriors, but consequentialism combines with multiple priors to rule out priorbyprior exchangeability. A simple dynamic portfolio choice problem illustrates the contrast between a modelbased multiplepriors investor and a consequentialist multiplepriors investor.
Predictable returns and asset allocation: Should a skeptical investor time the market
 Journal of Econometrics
, 2009
"... are grateful for financial support from the Aronson+Johnson+Ortiz fellowship through the Rodney L. White Center for Financial Research. This manuscript does not reflect the views of the Board of Governors of the Federal Reserve System. Predictable returns and asset allocation: Should a skeptical inv ..."
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Cited by 16 (0 self)
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are grateful for financial support from the Aronson+Johnson+Ortiz fellowship through the Rodney L. White Center for Financial Research. This manuscript does not reflect the views of the Board of Governors of the Federal Reserve System. Predictable returns and asset allocation: Should a skeptical investor time the market? We investigate optimal portfolio choice for an investor who is skeptical about the degree to which excess returns are predictable. Skepticism is modeled as an informative prior over the R 2 of the predictive regression. We find that the evidence is sufficient to convince even an investor with a highly skeptical prior to vary his portfolio on the basis of the dividendprice ratio and the yield spread. The resulting weights are less volatile and deliver superior outofsample performance as compared to the weights implied by an entirely modelbased Are excess returns predictable, and if so, what does this mean for investors? In classic studies of rational valuation (e.g. Samuelson (1965, 1973), Shiller (1981)), risk premia are constant over time and thus excess returns are unpredictable. 1
Liquidity and Price Discovery
, 2003
"... This paper examines the implications of market microstructure for asset pricing. I argue that asset pricing ignores the central fact that asset prices evolve in markets. Markets provide liquidity and price discovery, and I argue that asset pricing models need to be recast in broader terms to incorpo ..."
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Cited by 15 (0 self)
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This paper examines the implications of market microstructure for asset pricing. I argue that asset pricing ignores the central fact that asset prices evolve in markets. Markets provide liquidity and price discovery, and I argue that asset pricing models need to be recast in broader terms to incorporate the transactions costs of liquidity and the risks of price discovery. I argue that symmetric informationbased asset pricing models do not work because they assume that the underlying problems of liquidity and price discovery have been solved. I develop an asymmetric information assetpricing model that incorporates these effects. 2 This paper examines the implications of market microstructure for asset pricing. Both research areas focus on the behavior and evolution of asset prices, but the microstructure implications have been largely missing from the asset pricing literature. Such an omission is unimportant if asset pricing models work well in the sense of explaining the observed behavior of asset prices, but this is not the case. The proliferation of anomalies, momentum, and the changing cast of factors needed to explain even partially the behavior of asset prices, all suggest that success is not yet within our grasp.
Size and Value Anomalies under Regime Shifts ∗
, 2005
"... The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors. Federal Reserve Bank of St. Louis Working Papers are preliminary materials circulated to stimulat ..."
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Cited by 8 (3 self)
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The views expressed are those of the individual authors and do not necessarily reflect official positions of the Federal Reserve Bank of St. Louis, the Federal Reserve System, or the Board of Governors. Federal Reserve Bank of St. Louis Working Papers are preliminary materials circulated to stimulate discussion and critical comment. References in publications to Federal Reserve Bank of St. Louis Working Papers (other than an acknowledgment that the writer has had access to unpublished material) should be cleared with the author or authors.
Dynamic Portfolio Choice with Parameter Uncertainty and Economic Value of Analysts’ Recommendations, working paper
, 2004
"... We derive a closedform solution for the optimal portfolio of a nonmyopic utility maximizer who has incomplete information about the “alphas, ” or abnormal returns of risky securities We show that the hedging component induced by learning about the expected return can be a substantial part of the d ..."
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Cited by 8 (0 self)
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We derive a closedform solution for the optimal portfolio of a nonmyopic utility maximizer who has incomplete information about the “alphas, ” or abnormal returns of risky securities We show that the hedging component induced by learning about the expected return can be a substantial part of the demand. Using our methodology, we perform an “ex ante ” empirical exercise, which shows that the utility gains resulting from optimal allocation are substantial in general, especially for long horizons, and an “ex post ” empirical exercise, which shows that analysts ’ recommendations are not very
Correlation Risk and Optimal Portfolio Choice
, 2007
"... In this paper we solve an intertemporal portfolio problem with correlation risk, using a new approach for the simultaneous modeling of stochastic correlation and volatility. The solutions of the model are in closed form and include an optimal portfolio demand for hedging correlation risk. We calibra ..."
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Cited by 7 (0 self)
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In this paper we solve an intertemporal portfolio problem with correlation risk, using a new approach for the simultaneous modeling of stochastic correlation and volatility. The solutions of the model are in closed form and include an optimal portfolio demand for hedging correlation risk. We calibrate the model and find that the optimal demand to hedge correlation risk is a nonnegligible fraction of the myopic portfolio, which often dominates the pure volatility hedging demand. The hedging demand for correlation risk is larger in settings with high average correlations and correlation variances. Moreover, it is increasing in the number of assets available for investment as the dimension of uncertainty with regard to the correlation structure becomes proportionally more important. JEL classification: D9,E3,E4,G12
Learning and Asset Prices under Ambiguous Information, Forthcoming in Review of Financial Studies
, 2005
"... We propose a new continuous time framework to study asset prices under learning and ambiguity aversion. In a partial information Lucas economy with time additive power utility, a discount for ambiguity arises if and only if the elasticity of intertemporal substitution (EIS) is above one. Then, ambig ..."
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Cited by 7 (1 self)
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We propose a new continuous time framework to study asset prices under learning and ambiguity aversion. In a partial information Lucas economy with time additive power utility, a discount for ambiguity arises if and only if the elasticity of intertemporal substitution (EIS) is above one. Then, ambiguity increases equity premia and volatilities, and lowers interest rates. Very low EIS estimates are consistent with EIS parameters above one, because of a downward bias in Eulerequationsbased least squares regressions. In our setting, ambiguity does not resolve asymptotically and, for high EIS, it is consistent with the equity premium, the low interest rate, and the excess volatility puzzles.