On Formulating and Solving Portfolio Decision and Asset Pricing Problems

Chen, Yu; Cosimano, Thomas F.; Himonas, A. Alexandrou

doi:10.1016/b978-0-444-52980-0.00004-9

Cited by 4 publications

(2 citation statements)

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“…They were first introduced by physicists and engineers to solve partial differential equations, but they can be used to solve the types of fixed-point equations that arise in economics. (See Judd (1992) for an introduction or Chen, Cosimano, and Himonas (2014) for a brief overview of how to apply projection methods to asset pricing models.) We now provide a detailed description of projection methods and how they can be applied to solve the equilibrium conditions (2) and (6).…”

Section: Appendix A: Computational Methods For Asset Pricing Models Wmentioning

confidence: 99%

Higher Order Effects in Asset Pricing Models with Long‐Run Risks

Pohl¹,

Schmedders²,

Wilms

2018

The Journal of Finance

114

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This paper shows that the latest generation of asset pricing models with long‐run risk exhibit economically significant nonlinearities, and thus the ubiquitous Campbell‐Shiller log‐linearization can generate large numerical errors. These errors translate in turn to considerable errors in the model predictions, for example, for the magnitude of the equity premium or return predictability. We demonstrate that these nonlinearities arise from the presence of multiple highly persistent processes, which cause the exogenous states to attain values far away from their long‐run means with nonnegligible probability. These extreme values have a significant impact on asset price dynamics.

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Section: Appendix A: Computational Methods For Asset Pricing Models Wmentioning

confidence: 99%

Higher Order Effects in Asset Pricing Models with Long‐Run Risks

Pohl¹,

Schmedders²,

Wilms

2018

The Journal of Finance

114

View full text Add to dashboard Cite

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“…3 These second-order terms do not appear in Campbell and Vuolteenaho's (2004) model, which is derived under the assumption of conditional log-normality. When this assumption is relaxed, the nonlinearity of the Epstein-Zin (1991) pricing kernel implies that second-order components capture states in which pricing kernel realizations deviate substantially from their mean (Chen, Cosimano, and Himonas (2013)). 4 Our model implies that investors are willing to pay a premium for assets that hedge an increase in the stand-alone variations of cash-flow news and discount-rate news.…”

Section: Introductionmentioning

confidence: 99%

Coskewness Risk Decomposition, Covariation Risk, and Intertemporal Asset Pricing

Kalev¹,

Saxena²,

Zolotoy³

2018

J. Financ. Quant. Anal.

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We develop an intertemporal asset pricing model where cash-flow news, discount-rate news, and their second moments are priced by the market. This model generalizes the market-return decomposition framework, showing that intertemporal considerations imply a decomposition of squared market returns (coskewness risk). Our model accounts for 68% of the return variation across portfolios sorted by size, book-to-market ratio, momentum, investment, and profitability for a modern U.S. sample period. Further, our findings highlight the importance of covariation risk, that is, the risk of simultaneous unfavorable shocks to cash flows and discount rates, in understanding equity risk premia.

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