A Note on Parametric and Nonparametric Regression in the Presence of Endogenous Control Variables This note argues that nonparametric regression not only relaxes functional form assumptions vis-a-vis parametric regression, but that it also permits endogenous control variables. To control for selection bias or to make an exclusion restriction in instrumental variables regression valid, additiona...

Decomposing the Composition Effect This paper proposes a decomposition of the composition effect, i.e. the part of the observed between-group difference in the distribution of some economic outcome that can be explained by differences in the distribution of covariates. Our decomposition contains three types of components: (i) the “direct contributions” of each covariate due to between-group dif...

Generally, determining the size and magnitude of the omitted variable bias (OVB) in regression models is challenging when multiple included and omitted variables are present. Here, I describe a convenient OVB formula for treatment effect models with potentially many included and omitted variables. I show that in these circumstances it is simple to infer the direction, and potentially the magnit...

We propose a novel estimator for the amount of international risk sharing that depends exclusively on asset returns data. In particular, our estimator has a nonparametric flavor in that it makes no parametric assumption on preferences and on the stochastic process that governs the dynamics of asset returns. This is in contrast with the existing estimators in the literature that either assume a ...

In this paper, we describe a general method for constructing the posterior distribution of an option price. Our framework takes as inputs the prior distributions of the parameters of the stochastic process followed by the underlying, as well as the likelihood function implied by the observed price history for the underlying. Our work extends that of Karolyi (1993) and Darsinos and Satchell (200...

This paper uses a functional coefficient regression to estimate time-varying betas and alphas in the conditional capital asset pricing model. Functional coefficient representation relaxes the strict assumptions on the structure of betas and alphas by combining the predictors into an index that best captures time variations in betas and alphas and estimates them nonparametrically. This index in ...

Ornstein-Uhlenbeck models are continuous-time processes which have broad applications in finance as, e.g., volatility processes in stochastic volatility models or spread models in spread options and pairs trading. The paper presents a least squares estimator for the model parameter in a multivariate Ornstein-Uhlenbeck model driven by a multivariate regularly varying Lévy process with infinite v...

In this paper we combine recent advances in marginal modelling for contingency tables with the notion of copula to formulate a class of models for describing how the joint distribution of a set of ordinal response variables depends on exogenous regressors. We derive the main properties of a marginal parameterization, the global interaction copula, whose nature is essentially non parametric, and...

This paper studies belief heterogeneity in a benchmark competitive asset market: a market for Arrow-Debreu securities. We show that differences in agents’ beliefs lead to a systematic pricing pattern, the favorite longshot bias (FLB): securities with a low payout probability are overpriced while securities with high probability payout are underpriced. We apply demand estimation techniques to be...

We address the empirical implementation of the static asset allocation problem by developing a forward-looking approach that uses information from market option prices. To this end, constant maturity S&P 500 implied distributions are extracted and subsequently transformed to the corresponding risk-adjusted ones. Then, we form optimal portfolios consisting of a risky and a risk-free asset and ev...