Robust Portfolio Choice with Ambiguity and Learning About Return Predictability

Branger, Nicole; Larsen, Linda Sandris; Munk, Claus

doi:10.2139/ssrn.1859916

Cited by 14 publications

(16 citation statements)

References 81 publications

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“…The terms also vanish if the inflation is locally deterministic (σ Z = 0) and if ρ ZS − ρ ZP ρ SP = 0 (for the stock) and ρ ZP − ρ ZS ρ SP = 0 (for the bond). 5 This property of the optimal portfolio is similar to Bensoussan, Keppo and Sethi (2009) where the optimal portfolio includes the hedge against inflation risk if the stock price is correlated with the inflation.…”

Section: Solutionmentioning

confidence: 57%

Portfolio management with stochastic interest rates and inflation ambiguity

Munk

Rubtsov

2013

Ann Finance

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We solve a stock-bond-cash portfolio choice problem for a risk-and ambiguityaverse investor in a setting where the inflation rate and interest rates are stochastic. The expected inflation rate is unobservable, but the investor may learn about it from realized inflation and observed stock and bond prices. The investor is aware that his model for the observed inflation is potentially misspecified, and he seeks an investment strategy that maximizes his expected utility from real terminal wealth and is also robust to inflation model misspecification. We solve the corresponding robust Hamilton-Jacobi-Bellman equation in closed form and derive and illustrate a number of interesting properties of the solution. For example, ambiguity aversion affects the optimal portfolio through the correlation of price level with the stock index, a bond, and the expected inflation rate. Furthermore, unlike other settings with model ambiguity, the optimal portfolio weights are not always decreasing in the degree of ambiguity aversion.

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Section: Solutionmentioning

confidence: 57%

Portfolio management with stochastic interest rates and inflation ambiguity

Munk

Rubtsov

2013

Ann Finance

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“…This technique optimally combines two existing estimators such as the sample estimator (respectively for the expected value or the variance-covariance matrix) and, for instance, a factor model-based estimator. More recently, Garlappi, Uppal, and Wang (2006), Boyle, Garlappi, Uppal, and Wang (2012), and Branger, Larsen, and Munk (2013) utilize a multiprior model to take account of investors' aversion to ambiguity or model misspecifications in the optimal portfolio. From the "downstream" viewpoint, but not so far from the aforementioned Bayesian approach, another stream of literature proposes to focus on the optimization program's objective function and constraint specifications.…”

Section: Introductionmentioning

confidence: 99%

Understanding the interplay between covariance forecasting factor models and risk‐based portfolio allocations in currency carry trades

Ames

Bagnarosa

Peters

et al. 2018

Journal of Forecasting

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With the exception of naive methods for portfolio selection, such as the equal weighted approaches, all other methods of portfolio allocation are more or less sensitive to the quality of the inputs considered in constructing the models and risk measures utilized in the allocation framework. The extensively used factor model proposed initially by Sharpe in 1963 has provided a robust backdrop for development of relevant, micro, macro, and context‐specific or asset specific explanatory variables to be incorporated in a statistical manner as inputs to forecasting models that can then be used to obtain risk measures upon which portfolio allocations are based. However, like all statistical models, a set of statistical assumptions accompany this factor model regression framework, one of which has recently been highlighted as seemingly nonvalidated in financial data. This is, of course, the assumption such factor models make on homoskedasticity or weak‐sense covariance stationarity of the returns processes being modeled. Such factor models, therefore, have typically failed to cope with an important and ubiquitous feature of financial assets data, which often demonstrates heteroskedasticity of the returns variances and covariances. We propose a novel generalized multifactor forecasting structure utilizing a covariance regression model, which allows us to incorporate the required heteroskedasticity effects while also admitting potential dependence in the idiosyncratic error terms. We argue that such a modeling approach allows for more explicit relationships to be interpreted between the driving factors and the conditional responses of the portfolio returns. We then compare the forecasting performances of our model with the multifactor model and the time series dynamic conditional correlation model through a currency portfolio application.

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“…Indeed, as new information comes to light through further observation, the investor updates its posterior distribution for the unobserved parameter, thus changing the investment opportunity set which, in turn, produces an hedging demand due to the possibility of learning from these changes. In this setting, agents may be boundedly rational in that they either do not behave dynamically (i.e., they do not learn from fluctuations in the conditional distribution of the unobserved parameter) but process information properly, we refer to them as myopic investors, 3 or they also lack of competence in 1 In this paper, the expressions "parameter uncertainty", "partial observation", and "partial information" are used interchangeably. 2 Since we are interested in valuing and comparing conditions such as better access to information, or better ability to process it or to behave dynamically, we abstract from transaction costs and others types of market frictions such as limited trading and assume that investors trade continuously at zero costs.…”

Section: Introductionmentioning

confidence: 99%

“…2 Since we are interested in valuing and comparing conditions such as better access to information, or better ability to process it or to behave dynamically, we abstract from transaction costs and others types of market frictions such as limited trading and assume that investors trade continuously at zero costs. 3 Myopic strategies have also been proved to be optimal in certain optimization-based portfolio selection models such as the rational inattention approach proposed in Huang and Liu [13]. Here, we gathering and processing information (i.e., they completely disregard available information provided by prices, thus ignoring predictability of assets returns), we call them unconditional investors.…”

Section: Introductionmentioning

confidence: 99%

“…However, almost all these papers are mainly concerned with portfolio allocation effects of either bounded rationality or full information rather than with the effects on agents' utility and the few of them that consider the latter effects confine their analysis either to special cases in terms of agent's risk aversion or to a single type of sub-optimal strategy (see, for instance, Browne and Whitt [8], Brennan and Xia [7], Cvitanić et al [9]) or just to uncertainty (see, for example, Karatzas and Zhao [14], Brendle [4]). Branger et al [3] analyze, are not concerned with the issue of rationalizing certain, apparently boundedly rational, behaviors but rather to evaluate and compare them. 4 We focus on these sub-optimal behaviors because, by citing De Bondt and Thaler [10] (pp.…”

Section: Introductionmentioning

confidence: 99%

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Welfare effects of information and rationality in portfolio decisions under parameter uncertainty

Longo

Mainini

2018

Quantitative Finance

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We analyze and quantify, in a financial market with parameter uncertainty and for a Constant Relative Risk Aversion investor, the utility effects of two different boundedly rational (i.e., sub-optimal) investment strategies (namely, myopic and unconditional strategies) and compare them between each other and with the utility effect of full information. We show that effects are mainly caused by full information and predictability, being the effect of learning marginal. We also investigate the saver's decision of whether to manage her/his portfolio personally (DIY investor ) or hire, against the payment of a management fee, a professional investor and find that delegation is mainly motivated by the belief that professional advisors are, depending on investment horizon and risk aversion, either better informed ("insiders") or more capable of gathering and processing information rather than their ability of learning from financial data. In particular, for very short investment horizons, delegation is primarily, if not exclusively, motivated by the beliefs that professional investors are better informed.

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Robust Portfolio Choice with Ambiguity and Learning About Return Predictability

Cited by 14 publications

References 81 publications

Portfolio management with stochastic interest rates and inflation ambiguity

Portfolio management with stochastic interest rates and inflation ambiguity

Understanding the interplay between covariance forecasting factor models and risk‐based portfolio allocations in currency carry trades

Welfare effects of information and rationality in portfolio decisions under parameter uncertainty

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