#### Publications

**Nonparametric Filtering of Conditional State-Price Densities***Journal of Econometrics* (2020), 214(2):295-325

This paper studies the use of noisy high-frequency data to estimate the time-varying state-price density implicit in European option prices. A dynamic kernel estimator of the conditional pricing function and its derivatives is proposed that can be used for model-free risk measurement. Infill asymptotic theory is derived that applies when the pricing function is either smoothly varying or driven by diffusive state variables. Trading times and moneyness levels are modeled by marked point processes that capture intraday trading patterns. A simulation study investigates the performance of the estimator using a varying plug-in bandwidth in various scenarios. Empirical analysis using S&P 500 E-mini European option quotes reveals significant time-variation at intraday frequencies. An application towards delta- and minimum variance-hedging further illustrates the use of the estimator.

#### Working Papers

**Semiparametric Estimation of Latent Variable Asset Pricing Models** (December 2022)

Revise and Resubmit,* Journal of Econometrics*

This paper studies semiparametric identification and estimation of consumption-based asset pricing models with latent state variables. We model consumption, dividends, and prices via unknown functions of Markovian state variables describing aggregate output growth. Subsequently, we identify state-dependent components in stochastic discount factor models based on the Euler equation. We develop tractable algorithms for filtering, smoothing, and sieve maximum likelihood estimation, and establish its consistency. Empirically, we find sizable nonlinearities in the impacts of expected growth and volatility on the price-dividend ratio and the discount factor.

**Efficient Estimation of Pricing Kernels and Market-Implied Densities **(May 2021)

This paper studies the nonparametric identification and estimation of projected pricing kernels implicit in European option prices and underlying asset returns using conditional moment restrictions. The proposed series estimator avoids computing ratios of estimated risk-neutral and physical densities. Instead, we consider efficient estimation based on the conditional Euclidean empirical likelihood or continuously-updated GMM criterion, which takes into account the informativeness of option prices of varying strike prices beyond observed conditioning variables. In a second step, we convert the implied probabilities into predictive densities by matching the informative part of cross-sections of option prices. Empirically, pricing kernels tend to be U-shaped in the S&P 500 index return given high levels of the VIX, and call and ATM options are more informative about their payoff than put and OTM options.

#### Work in Progress

**Evaluating Option-Implied Density Forecasts with Demand Curves** (with Oliver Linton)