Robust portfolio choice with ambiguity and learning about return predictability

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

doi:10.1016/j.jbankfin.2012.05.009

Cited by 85 publications

(25 citation statements)

References 30 publications

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“…The same assumption on predictors, but in a utility maximization framework, was made by Branger et al (2013) who model an ambiguous investor assuming constant interest rates and stock return volatility. 1 By relaxing the assumption of constant interest rates, we show that there are significant welfare benefits from using bonds as a hedge against stock return predictors.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Furthermore, we demonstrate that learning about the unobserved component of the expected stock 1 Modelling an ambiguous investor is beyond the scope of this paper. However, according to the studies of Maenhout (2006), Branger et al (2013), Flor and Larsen (2014), Munk and Rubtsov (2014) among others, who also consider a setting with stochastic investment opportunities, ambiguity about traded assets usually results in less aggressive trading strategies, significant utility losses when ambiguity is ignored, and an additional hedge term appearing in the optimal portfolio. Thus, we expect ambiguity to have similar effects, if modelled in the context of our model, without changing the main findings of our paper.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…This setting is similar to that of van Bisbergen and Koijen (2010) and Branger et al (2013) who use optimal filtering to estimate one of the predictors for stock returns. However, van Bisbergen and Koijen (2010) do not work within a utility maximization framework and Branger et al (2013) models an ambiguous investor assuming constant interest rates and constant stock return volatility.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…Boudoukh et al (2007) provide empirical evidence that the net payout yield can predict expected stock returns and Paye (2012) shows that it can also be used to predict stock return volatility. For this specification we borrow the parameter values from Branger et al (2013) and Munk and Sørensen (2010). The second set of parameters is based on estimates from Aït-Sahalia and Kimmel (2007) for stochastic volatility models because, as it was already mentioned, our general model for the stock price (6) nests stochastic volatility models as special cases.…”

Section: Numerical Examplementioning

confidence: 99%

“…In Table 1 we specify the parameter values borrowed from Branger et al (2013) and Munk and Sørensen (2010). 11 Since Branger et al (2013) model the expected excess return, not the risk premium, as an affine function of the predictors, we divide their estimates of p λ and p β by the stock return volatility σ S .…”

Section: Net Payout Yield As An Observed Predictormentioning

confidence: 99%

See 4 more Smart Citations

Portfolio choice with stochastic interest rates and learning about stock return predictability

Escobar

Ferrando

Rubtsov

2016

International Review of Economics & Finance

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Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

Section: Net Payout Yield As An Observed Predictormentioning

confidence: 99%

See 3 more Smart Citations

Portfolio choice with stochastic interest rates and learning about stock return predictability

Escobar

Ferrando

Rubtsov

2016

International Review of Economics & Finance

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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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Optimal Pension Fund Management Under Risk and Uncertainty: The Case Study of Poland

Baltas

Szczepański

Dopierała

et al. 2021

Springer Proceedings in Mathematics &Amp; Statistics

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Robust portfolio choice with ambiguity and learning about return predictability

Cited by 85 publications

References 30 publications

Portfolio choice with stochastic interest rates and learning about stock return predictability

Portfolio choice with stochastic interest rates and learning about stock return predictability

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

Optimal Pension Fund Management Under Risk and Uncertainty: The Case Study of Poland

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