What investors do not know, and what it costs them

Standard asset-pricing models assume that investors face known distributions of future returns. In practice, they do not. The mean and covariance matrix are estimated with error; the data-generating process shifts; the probabilities attached to discrete outcomes are themselves unknown; and the macro-financial environment sometimes slips out of the regime in which historical estimates were calibrated. This research agenda treats these various flavours of ignorance as first-class objects of study rather than as nuisance parameters.

Each flavour — parameter uncertainty, structural breaks, ambiguity, event risk, and macro-financial turbulence — has its own measurement problem and its own pricing implication. The through-line across more than a decade of work is a preference for measures that are intuitive, statistically grounded, and testable against both aggregate and cross-sectional evidence.

Measurement: from Mahalanobis distance to PRIX, Turbulence, and Break-age

The measurement thread begins with Financial Applications of the Mahalanobis Distance, which catalogues how a multivariate distance can be used to detect departures from historical return behaviour. PRIX operationalises this idea into a universal, asset-class-independent risk index for the portfolio of a representative global private investor, and shows that the index reacts sensibly to well-known market events and forecasts both risk and returns-to-risk.

Parameter Uncertainty, Financial Turbulence and Aggregate Stock Returns turns the measurement exercise into an asset-pricing test: an intuitive, test-based measure of time-varying parameter uncertainty predicts the equity premium and, combined with the portfolio-choice model of Garlappi, Uppal, and Wang (2007), delivers out-of-sample performance competitive with the strongest known predictors. The companion paper Turbulence in the Cross-Section: Predicting Factor Premia extends the logic to factor portfolios, showing that within-cross-section turbulence forecasts six of seven Fama–French factor premia. Breaking Bad pushes the thread into the cross-section of individual stocks: using unsupervised machine learning to detect structural breaks, it constructs a novel break-age proxy for parameter uncertainty and shows that it is priced in the cross-section of stock returns.

Transmission and macro-uncertainty

Credit Intermediation and the Transmission of Macro-Financial Uncertainty is the macro counterpart to the asset-pricing story. It constructs a global measure of macro-financial uncertainty and documents state-dependent transmission to real activity across 24 OECD countries, with the amplification driven by the state of the domestic banking sector. The measurement philosophy is shared with the aggregate-return work; the dependent variable is GDP growth rather than the equity premium.

Ambiguity and event risk

Event Risk Premia and Non-Convex Volatility Smiles develops closed-form event risk premia under quadratic and power utility, for four combinations of deterministic and stochastic conditional returns and outcome probabilities. Because investors are ambiguous about both the payoff distribution and the probability with which each outcome realises, the model rationalises concave (non-convex) implied-volatility curves around scheduled events. The identification strategy — pricing effects tied to a known event date — connects this pillar to the Political Finance research agenda.

Ongoing work

The single-author Parameter Uncertainty paper is currently undergoing substantial revision. Adjacent extensions in the pipeline include ambiguity-averse portfolio choice, predictive-regression methods robust to distribution shifts, and uncertainty-adjusted loss functions for forecast evaluation.

Collaborators

Michael Hanke, Martin Angerer (University of Liechtenstein); Martin Gächter, Martin Geiger (FMA Liechtenstein / Innsbruck / Liechtenstein Institute); Lukas Salcher (University of Liechtenstein); Wolfgang Schadner (formerly HBS / University of Liechtenstein); Alex Weissensteiner (Free University of Bozen-Bolzano).