A Recovery that We Can Trust? Deducing and Testing the Restrictions of the Recovery Theorem

Bakshi, Gurdip; Chabi-Yo, Fousseni; Bakshi, Xiaohui Gao

doi:10.2139/ssrn.2872909

Cited by 8 publications

(4 citation statements)

References 53 publications

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“…. This form of SDF can be traced to a minimum discrepancy problem described by Borovička et al (2016), Almeida and Garcia (2017), and Bakshi et al (2018), ] = 0.1207 is greater than LB m 2 in 8 out of 9 instances.…”

Section: A Building An Empirical Case For Considering L[mmentioning

confidence: 99%

New Entropy Restrictions and the Quest for Better-Specified Asset-Pricing Models

Bakshi¹,

Chabi-Yo²

2018

J. Financ. Quant. Anal.

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This article proposes the entropy of m2 (m is the stochastic discount factor) as a metric to evaluate asset-pricing models. We develop a bound on the entropy of m2 when m correctly prices a finite number of returns and consider models that pass the lower bound on m, yet fail the lower bound on m2. Interpreting our results, we elaborate on the distinction between the entropy of m2 versus the entropy of m. We further show that the entropy of m2 represents an upper bound on the expected excess (log) return of the security with the payoff of m.

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Section: A Building An Empirical Case For Considering L[mmentioning

confidence: 99%

New Entropy Restrictions and the Quest for Better-Specified Asset-Pricing Models

Bakshi¹,

Chabi-Yo²

2018

J. Financ. Quant. Anal.

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“…Several papers study the theoretical conditions under which Ross recovery is possible, see, Carr and Yu (2012), Park (2016), Qin and Linetsky (2016), Walden (2017) and Jensen, Lando, and Pedersen (2019), extending Ross's original approach in various directions. 1 A more empirically oriented literature focuses on whether Ross recovery works in practice, with mixed results, see Borovička, Hansen, and Scheinkman (2016), Audrino, Huitema, and Ludwig (2014), Tran and Xia (2014), Bakshi, Chabo-Yo, and Gao (2018), Massacci, Williams, and Zhang (2016), Backwell (2015), and Schneider and Trojani (2016).…”

Section: Introductionmentioning

confidence: 99%

Numerical Ross Recovery for Diffusion Processes Using a PDE Approach

Sydow

Waldén

2020

Applied Mathematical Finance

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We develop and analyse a numerical method for solving the Ross recovery problem for a diffusion problem with unbounded support, with a transition independent pricing kernel. Asset prices are assumed to only be available on a bounded subinterval B ¼ ½ÀN; N. Theoretical error bounds on the recovered pricing kernel are derived, relating the convergence rate as a function of N to the rate of mean reversion of the diffusion process. Our suggested numerical method for finding the pricing kernel employs finite differences, and we apply Sturm-Liouville theory to make use of inverse iteration on the resulting discretized eigenvalue problem. We numerically verify the derived error bounds on a test bench of three model problems.

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“…Var  is the risk-neutral conditional variance at time t. Martin (2017) points out that, if the relative risk aversion and the elasticity of 1 See the follow up papers by Bakshi, Chabi-Yo, and Gao (2017), Borovicka, Hansen, and Scheinkman (2016), Jackwerth and Menner (2018), Jensen, Lando, and Pedersen (2019), and Schneider and Trajani (2019).…”

Section: Introductionmentioning

confidence: 99%

Extracting expected stock risk premia from option prices and the information contained in non-parametric-out-of-sample stochastic discount factors

González‐Urteaga

Nieto

Rubio

2020

Quantitative Finance

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This paper analyzes the factor structure and cross-sectional variability of a set of expected excess returns extracted from option prices and a non-parametric and out-of-sample stochastic discount factor. We argue that the existing potential segmentation between the equity and option markets makes advisable to avoid using only option prices to extract expected equity risk premia. This set of expected risk premia forecast significantly future realized returns, and the first two principal components explain 94.1% of the variability of expected returns. A multi-factor model with the market, quality, funding illiquidity, the default premium and the market-wide variance risk premium as factors explain significantly the cross-sectional variability of expected excess returns. The (asymptotically) different from zero adjusted cross-sectional R-squared statistic is 83.6%.

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A Recovery that We Can Trust? Deducing and Testing the Restrictions of the Recovery Theorem

Cited by 8 publications

References 53 publications

New Entropy Restrictions and the Quest for Better-Specified Asset-Pricing Models

New Entropy Restrictions and the Quest for Better-Specified Asset-Pricing Models

Numerical Ross Recovery for Diffusion Processes Using a PDE Approach

Extracting expected stock risk premia from option prices and the information contained in non-parametric-out-of-sample stochastic discount factors

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