Estimating Policy Functions Implicit in Asset Prices
– This version: April 2018
I study the estimation and identification of a semiparametric asset pricing model in which consumption and dividend policy depends nonlinearly on unobserved but affine state variables. The stock pricing functions identify the shape of the policy functions separately from that of the stochastic discount factor. Polynomial approximations of the unknown functions leads to closed-form price-dividend ratios, that are used to efficiently recover the state variables from cross-sections of asset prices and/or volatility proxies. In the empirical application uncertainty and risk aversion are separately identified from the heterogeneous impact of uncertainty on dividend policy across small and large firms. I find an asymmetric and convex response in consumption (-) and dividend growth (+) towards uncertainty shocks, which together with moderate uncertainty aversion, can generate large leverage effects and divergence between macroeconomic and stock market volatility.
Nonparametric Filtering of Conditional State-Price Densities
– Revise and Resubmit, Journal of Econometrics
– This version: May 2018
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 modelled by marked point processes to capture intraday trading patterns. A simulation study investigates the performance of the estimator using an iterated plug-in bandwidth in various scenarios. Empirical results using S&P 500 E-mini European option quotes find significant time-variation at intraday frequencies. An application towards delta- and minimum variance-hedging further illustrates the use of the estimator.
Work in Progress
Recent evidence suggests that the market risk premium is highly volatile. I propose a model-free approach to measure how much of this variation can be attributed to changing beliefs versus changing risk preferences. The approach is based on a variance decomposition of forward-looking risk-neutral densities and the conditional pricing kernel for each value of the market return. This is implemented by a nonparametric empirical likelihood procedure which efficiently incorporates the no-arbitrage conditions into the conditional density estimator. Empirical results using S&P 500 index options point towards substantial return predictability at short horizons, and provide new insights on the ability of investors to predict tail events.
The Demand For Risk Sharing in OTC Derivative Markets (with Oliver Linton)