Job Market Paper
Estimating Policy Functions Implicit in Asset Prices (Latest version)
I propose a semiparametric asset pricing model to measure how consumption and dividend policy depends on unobserved state variables, such as economic uncertainty and risk aversion. Under a flexible specification of the stochastic discount factor, the state variables are recovered from cross-sections of asset prices and volatility proxies, and the shape of the policy functions is identified from the pricing functions. The model leads to closed-form price-dividend ratios under polynomial approximations of the unknown functions and affine state variable dynamics. The coefficients are estimated by quasi-maximum likelihood, taking account of the transition density in concentrating the state variables. 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 (March 2017)
– Revise and Resubmit, Journal of Econometrics
– Winner of the G-Research PhD Prize in Quantitative Finance 2016
This paper studies the use of noisy high-frequency data to estimate the time-varying state-price density implicit in European option prices. A kernel estimator of the conditional pricing function and its derivatives is proposed that can be used for model-free pricing, hedging, and 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 nonstationary marked point processes to capture intraday trading patterns. The estimator is applied to S&P 500 E-mini European call and put option mid quotes using iterated plug-in bandwidth surfaces. A simulation study investigates the performance of the estimator in various scenarios. An application towards delta- and minimum variance-hedging 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)