Doubts and Variability: A Robust Perspective on Exotic Consumption Series

Bidder, Rhys; Smith, Matthew

doi:10.24148/wp2013-28

Cited by 10 publications

(18 citation statements)

References 35 publications

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“…While it is difficult to determine what the true value for the default frequency is in the data, it seems to be consensus in the literature that it lies close to 3 percent per year (see footnote 7). These papers and ours replicate this feature of the bond spreads in a general equilib-49 To explain different asset-pricing puzzles, Maenhout (2004), Drechsler (2012), and Bidder and Smith (2013) require a detection error probability in the range between 10 and 12 percent. Barillas, Hansen and Sargent (2009) needs even lower values to reach the Hansen and Jagannathan (1991) bounds.…”

Section: B Simulation Resultssupporting

confidence: 64%

“…57 56 The weight of one-half is arbitrary; see Barillas, Hansen and Sargent (2009) among others. Moreover, as the number of observations increases, the weight becomes less relevant, since the quantities p A,T and p D,T get closer to each other; as shown in Figure 4 57 To make our results for the DEP comparable with those of Barillas, Hansen andSargent (2009) andBidder andSmith (2013), we consider a similar number of periods and thereby T=240 is chosen. If instead T was set to replicate the number of periods used in the calibration, the DEP would be considerably higher for the same probability distortions.…”

Section: A Detection Error Probabilitiesmentioning

confidence: 99%

“…Epstein and Schneider (2008) studies the impact of uncertain information quality on asset prices in a model of learning with investors endowed with recursive multiple-priors utility, axiomatized in Epstein and Schneider (2003). More asset-pricing applications with different formulations of ambiguity aversion are Epstein and Wang (1994), Chen and Epstein (2002), Hansen (2007), Bidder and Smith (2013) and Drechsler (2012). calibrate our model to Argentinean data and present our quantitative results for long-term bonds. Section V disciplines the degree of robustness in our economy using detection error probabilities and a new moment-based uncertainty measure.…”

Section: Introductionmentioning

confidence: 99%

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Sovereign Default Risk and Uncertainty Premia

Pouzo¹,

Presno

2016

American Economic Journal: Macroeconomics

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This paper studies how international investors' concerns about model misspecification affect sovereign bond spreads. We develop a general equilibrium model of sovereign debt with endogenous default wherein investors fear that the probability model of the underlying state of the borrowing economy is misspecified. Consequently, investors demand higher returns on their bond holdings to compensate for the default risk in the context of uncertainty. In contrast with the existing literature on sovereign default, we match the bond spreads dynamics observed in the data together with other business cycle features for Argentina, while preserving the default frequency at historical low levels. JEL: D81, E21, E32, E43, F34.

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Section: B Simulation Resultssupporting

confidence: 64%

Section: A Detection Error Probabilitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sovereign Default Risk and Uncertainty Premia

Pouzo¹,

Presno

2016

American Economic Journal: Macroeconomics

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“…Following Pettenuzzo, Timmermann, and Valkanov (2014), the nonnegativity restriction can 4 Allowing for potential model misspecification could be a natural response of investors to evidence in the literature against certain assumptions or implications of an asset pricing model (e.g., Beeler and Campbell 2012;Yu 2012;Bidder and Smith 2015). 5 We examine versions of the VARs that are estimated without a nonnegativity constraint in the Online Appendix.…”

Section: Predictive Vector Autoregression Estimation Approachmentioning

confidence: 99%

Are Stocks Riskier over the Long Run? Taking Cues from Economic Theory

Avramov

Cederburg

Lučivjanská

2017

The Review of Financial Studies

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We study whether stocks are riskier or safer in the long run from the perspective of Bayesian investors who employ the long-run risk, habit formation, or prospect theory models to form prior beliefs about return dynamics. Economic theory delivers important guidance for long-run investment opportunities. Specifically, incorporating prior information from the habit formation or prospect theory models reinforces beliefs in mean reversion and inferences that stocks are safer over longer horizons. Conversely, investors with long-run risk priors perceive weaker mean reversion and riskier equities. Model-based information is particularly important for inferences about uncertainty in the dividend growth component of returns. (JEL C11, G11, G12) * We thank Stijn Van Nieuwerburgh (the editor), two anonymous referees, 1 the models for long-run return dynamics, their success in matching these aspects of the data lend them credibility in this setting. The underlying assumptions in the long-run risk, habit formation, and prospect theory models about preferences and economic dynamics as well as the resulting return dynamics are quite different, which leads to interesting implications for long-horizon variance. In addition, our analysis incorporates a nonnegativity restriction on the equity premium as proposed by Campbell and Thompson (2008) and Pettenuzzo, Timmermann, and Valkanov (2014). This economic constraint has been shown to improve out-of-sample forecasting performance and sharpen estimates of model parameters. Incorporating a nonnegative equity premium is novel in the context of long-horizon return variance.We study the risk of investing in stocks by examining the predictive return variance over various investment horizons. Predictive variance is typically computed (e.g., Avramov 2002) based on the vector autoregression (VAR) framework of Campbell and Shiller (1988), Kandel and Stambaugh (1996), Campbell and Viceira (1999), and Barberis (2000), among others. Our Bayesian investors consider long-horizon risk in the context of a VAR with stock returns, dividend growth, and model-implied state variables from the long-run risk, habit formation, or prospect theory models. Specifically, the investors form prior beliefs about the VAR parameters based on asset pricing model implications. They also allow for the possibility of model misspecification, such that prior uncertainty about the VAR parameters remains. Finally, the investors combine information from the model-based prior and historical data to make inferences about long-horizon predictive variance.Incorporating model-based prior information can potentially improve inferences about risk over long horizons. As noted earlier, the perceived long-horizon return variance crucially depends on the return dynamics implied by the VAR. It has long been recognized that incorporating prior information into VARs improves the quality of prediction. In the macroeconomics literature, Litterman (1986) and Todd (1984) demonstrate improved predictions by Bayesian VARs with the "Minnesota" ...

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“…She notes that conventional calibrations of the long-run risks model explain a substantially lower fraction of variation in expected returns ( r 2 less than 0.05) and that an estimated version of the model generates even less. Her analysis is based on estimates of a simplified version of the long-run risks model by Bidder and Smith (2015) in which the r 2 of the return predictability regression for the estimated version of the long-run risks model is essentially zero. Our model shows that, in fact, a fully estimated version of the long-run risks model generates more rather than less return predictability than in the calibrations of BY and BKY.…”

mentioning

confidence: 99%