We show that a volatility-managed strategy using equity options provides higher alphas, increases Sharpe ratios, and generates significant utility gains for investors, exceeding those of the statistical volatility-managed counterparts. Return and volatility expectations embedded in options prices are particularly effective during intense market uncertainty. Results are robust out-of-sample and across different portfolios based on firms' leverage, size, credit rating, and industry. Investors' lottery preferences are a data-feature in options markets.

We propose a structural estimation method for the risk aversion and the ability to cope with uncertainty over time of a representative agent in a dynamic market equilibrium. The method exploits the conditional restrictions for nonparametric state variables dynamics that describe the utility model in a Markovian setting. These restrictions are functional equations solved by Approximate Nonparametric Dynamic Programming based on a contraction mapping argument. Therefore, our method can accommodate any preference specification that features contraction, such as those in the Chew-Dekel class, and characterizes the corresponding parameter space. We study a representative agent endowed with Epstein-Zin utility for the U.S. equity and T-bill markets from 1952 to 2019. Our estimates of the preference parameters are plausible values that have not yet been established empirically. We include asymptotic properties of the model parameter estimators and evaluate the finite sample performance with a Monte Carlo experiment using the Bansal-Kiku-Yaron (2012) model.

The paper by Antoine, Proulx, and Renault (2018) (APR) deals with the econometric definition, economic interpretation, and statistical estimation of the pseudo-true stochastic discount factor (SDF) in misspecified conditional asset pricing models. The paper revolves around fundamental issues like the role of conditioning information and omitted risk factors, and has non-trivial interactions with the current debate in the literature on the impact of weak factors (weak identification) for assessing asset pricing models. Building on, and substantially extending, previous contributions in the literature, the approach of the authors to define a pseudo-true SDF relies on the minimizers of econometric criteria based on a conditional version of the Hansen–Jagannathan (HJ) distance, that is, an average across states of squared conditional pricing errors. The authors provide an insightful discussion of the economic interpretation of pseudo-true SDFs. APR advocate the use of a fixed bandwidth (i.e., independent of the sample size) when estimating the conditional pricing errors by kernel regression methods to facilitate statistical analysis. This route leads to bandwidth-dependent pseudo-true SDF parameters and estimators thereof.

We compare non-nested parametric specifications of the Stochastic Discount Factor (SDF) using the conditional Hansen-Jagannathan (HJ-) distance. This distance measures the discrepancy between a parametric model-implied SDF and the admissible SDF's satisfying all the conditional (dynamic) no-arbitrage restrictions, instead of just few unconditional no-arbitrage restrictions for managed portfolios chosen through the instrument selection. We estimate the conditional HJ-distance by a Generalized Method of Moments estimator and establish its large sample properties for model selection purposes. We compare empirically several SDF models including multifactor beta pricing specifications and some recently proposed SDF models that are conditionally linear in consumption growth.

We provide sufficient and necessary conditions for the unique net present valuation of assets with ambiguous cash-flows that are not in a unidirectional causal relationship with the capital structure of the issuing firm. Recent literature has shown that the net present valuation of such assets is not generally unique if trading is discontinuous. Prominent examples are the assets of a firm issuing Contingent Convertible bonds (CoCos) that convert based on the trading prices of the firm's equity. These bonds have been proposed as automatic recapitalization tools for large banks to enhance financial stability. Our policy recommendation to the CoCo regulatory framework is to ensure that investors know the probability of a liquidity-induced change of the firm's capital structure. This results in the uniqueness of the asset net present valuation. Our analysis is solely based on the distinction between net present valuation and trading price and fundamental economic laws of asset demand. We illustrate the uniqueness of the net present valuation in cases of CoCos that are converted when the firm's equity trading price declines to a predetermined level, and for capital structure changes favoring either equity or debt holders.

This paper describes an empirical study of the information content of daily share prices and American put and call option mid-quotes about their generating process. Considering stock return and its volatility as the risk factors and without parameterizing their historical joint dynamics, two results are empirically obtained. First, share prices and option mid-quotes are both necessary to identify at the same time the discount for uncertain stock return and return variance in the asset price formation. Second, constraining the nonparametric estimation procedure of the historical joint dynamics of the risk factors to satisfy an arbitrage-free pricing model is useful to get more stable estimates over time. As an illustration, time series of different estimates of historical conditional correlation of the risk factors, Sharpe ratio of an investment on the stock, return skewness and kurtosis are reported.

We introduce a novel semi-parametric estimator of American option prices in discrete time. The specification is based on a parameterized stochastic discount factor and is nonparametric w.r.t. the historical dynamics of the Markovian state variables. The historical transition density estimator minimizes a distance built on the Kullback–Leibler divergence from a kernel transition density, subject to the no-arbitrage restrictions for a non-defaultable bond, the underlying asset and some American option prices. We use dynamic programming to make explicit the nonlinear restrictions on the Euclidean and functional parameters coming from option data. We study asymptotic and finite sample properties of the estimators.