I am an economist working in asset pricing and macro-finance, drawing on tools from statistics and decision theory.
I study how different sources of uncertainty shape investors' decisions and the premia they demand in financial markets.
Working papers
I extend the robust mean-variance portfolio analysis proposed by Maccheroni et al. (2013) by examining how ambiguity prudence affects optimal stock allocation when portfolios are evaluated using the smooth ambiguity model. Ambiguity prudence reflects an aversion to model uncertainty that intensifies as the investor believes unfavorable events to be more likely. I derive a higher-order approximation of the certainty equivalent to disentangle the contributions of preferences and beliefs in payoff valuation. Ambiguity prudence introduces nonlinearities into the investor’s valuation, leading to sizable deviations from the robust mean-variance solution and to underreaction (overreaction) to positive news when concern about downside uncertainty is high (low).
I propose a consumption-based asset pricing model in which the decision maker prices U.S. Treasury zero-coupon bonds and dividend cash flows on the aggregate S&P 500 index with maturities up to 30 years. The decision maker does not know the objective probability generating the data and evaluates a set of models that is twisted to include structured parametric alternatives. I set up a state-space with macroeconomic and aggregate financial variables to measure how the market price of (model) uncertainty contributes to the short- and long-run valuation of payoffs. My analysis replicates prominent features of the data for both asset classes.
With Christian Schlag
We propose a dynamic model that explains a large share of both in-sample and out-of-sample variation in the annual return and growth of fundamentals on the aggregate S&P 500 index. To capture the time variation in investors' beliefs, we rely on a penalized vector autoregressive model and predictors that summarize a substantial portion of the information available in the market. We combine model-implied conditional expectations and present value identities to investigate what drives the variations in the price-to-dividend and price-to-earnings ratios. We find that time-varying expected returns account for most of the movements in the price-to-dividend ratio over the period 1980–2021, but play a smaller role in the price-to-earnings ratio. Notably, over the period 2001–2021, the expected growth of fundamentals explains a significantly larger share of the variation in both valuation ratios.
With Lukas Koerber and Christian Schlag
We evaluate the pricing performance of a robust stochastic discount factor spanned by a broad cross-section of factor returns. Methodologically, we combine kernel principal component analysis, which extracts factors as nonlinear functions of a high-dimensional set of firm characteristics, with novel regularization techniques. Allowing for nonlinearities enhances the model's performance in explaining a wide range of prominent cross-sectional stock-return anomalies and reduces pricing errors. We further decompose the mean-variance efficient portfolio into linear and nonlinear components and study their relative contributions across macro-financial conditions. Out-of-sample, incorporating nonlinearities increases the Sharpe ratio of the mean-variance efficient portfolio by roughly 25% to 2.15.
With Paul Schneider
We attribute the coskewness premium in the cross-section of equity portfolios, the negative variance risk premium, and the downward-sloping implied volatility skew to one common source, risk prudence, the aversion to downside risk. The discount factor in a minimum-divergence economy with skew-normal factor payoffs delivers this dependence through a single parameter. A decomposition into a symmetric and a downside-risk channel yields their prices of risk, estimated on U.S. characteristic-managed portfolios. The estimates have the signs the model predicts in the cross-section and in index and sector-fund options.