(Almost) Model‐Free Recovery

Abstract

Under mild assumptions, we recover the model‐free conditional minimum variance projection of the pricing kernel on various tradeable realized moments of market returns. Recovered conditional moments predict future realizations and give insight into the cyclicality of equity premia, variance risk premia, and the highest attainable Sharpe ratios under the minimum variance probability. The pricing kernel projections are often U‐shaped and give rise to optimal conditional portfolio strategies with plausible market timing properties, moderate countercyclical exposures to higher realized moments, and favorable out‐of‐sample Sharpe ratios.

Bakshi, Kapadia, and Madan (2003) risk‐neutral moment estimators: An affine jump‐diffusion approach

“…Using this more advanced model along with the exact specification of the benchmark overcomes the drawbacks of past research. The double-jump model is more comprehensive and is widely used in the literature, for example, Cao et al (2020), Christoffersen et al (2009), Eraker et al (2003), Luo and Zhang (2012), Schneider and Trojani (2019), and Zhen and Zhang (2020), to name a few. Clearly, the double-jump model supersedes the SVJ model.…”

Derivatives Pricing via Machine Learning

“…In [100], the authors propose a model-free recovery theorem, based on a series expansion of higher order conditional moments of asset returns. Their work inspires us to exploit the ANN-factor models to represent the higher order conditional moments of the asset returns and therefore validating the recovery theorem proposed there-in.…”

Constrained Polynomial Likelihood

“…In economics, Renner and Schmedders (2015) solve linearrational expected utility problems using the same principles as this paper. Ryu and Boyd (2015), Schneider and Trojani (2019) and Schneider (2019) exploit the relation between positive polynomials and the truncated moment problem to find the minimal support for numerical quadrature and scenario analysis.…”