Christian Skov Jensen
University of Copenhagen
Assistant Professor of Financial Economics
Research fellow at the Danish Finance Institute
Bocconi University
Assistant Professor of Finance - on leave since Aug 2025
Main research interests: Asset pricing, Financial Economics, Macro-Finance, Financial Econometrics, Derivatives
CV | SSRN | Google Scholar
Email:
csj [at] econ.ku.dk or christian.jensen [at] unibocconi.it
PUBLICATIONS
We study time variation in the shape of the distribution of stock returns. In a global sample covering 17 countries, returns are more left skewed and fat tailed during good times than during bad times. This pattern creates pro-cyclical variation in conditional tail risk, which is the risk of losing several conditional standard deviations of returns. The variation in higher-order moments is hard to reconcile with the idea that disaster risk is elevated in bad times, which is otherwise a basic premise of leading disaster-based asset pricing models.
Using a new and powerful conditional-risk factor, we document a global effect of time-varying market betas on stock returns. Across 23 developed countries, the major equity risk factors all load on the conditional-risk factor, which means their alpha can partly be explained by time-varying market betas. The conditional-risk factor explains 50% more alpha than traditional methods that use rolling betas to capture conditional risk. Studying the economic driver of the conditional risk, we find evidence that it arises from variation in discount rate betas (not cash flow betas) due to the endogenous effects of arbitrage trading.
Generalized Recovery
Journal of Financial Economics, 133 (1), 154-174, 2019. [code and data]
with David Lando and Lasse Heje Pedersen
We characterize when physical probabilities, marginal utilities, and the discount rate can be recovered from observed state prices for several future time periods. We make no assumptions of the probability distribution, thus generalizing the time-homogeneous stationary model of Ross (2015). Recovery is feasible when the number of maturities with observable prices is higher than the number of states of the economy (or the number of parameters characterizing the pricing kernel). When recovery is feasible, our model is easy to implement, allowing a closed-form linearized solution. We implement our model empirically, testing the predictive power of the recovered expected return and other recovered statistics.
WORKING PAPERS
We decompose market return variance into a component that covaries with the stochastic discount factor (SDF) and one that does not. The SDF-orthogonal component - conditional excess volatility - contributes to variance but cannot earn a risk premium. We show that the ratio of the market risk premium to total market variance reveals the share of market variance orthogonal to the SDF. Using three distinct measures of this ratio across twenty equity markets, we find that it does not increase during recessions and often declines. Because standard models uniformly predict countercyclical risk pricing, this finding requires the excess volatility share to increase substantially in bad times. Our empirical results imply that excess volatility constitutes at least half of the total market volatility in recessions. We show that this mechanism provides a unified explanation for (i) why optimal portfolio weights do not increase in downturns despite rising Sharpe ratios, (ii) why the term structure of Sharpe ratios on dividend claims is downward-sloping, and (iii) why option portfolios with fixed risk quantities earn lower returns at longer horizons.
We revisit the question of whether investors learn from prices by directly conditioning on the arrival of information, including private information. A central challenge in this literature is that information arrivals are typically unobserved, making it difficult to separate learning from prices from reactions to information. We address this challenge using securities lending data to construct a real-time measure that captures informed trading, allowing us to identify the timing of information arrivals at the stock-day level. We find that following information arrivals, price informativeness declines, return volatility increases, and the correlation between trading volume and volatility strengthens. These patterns are inconsistent with noisy rational expectations models and instead support differences-of-opinion models in which investors overweight their own signals and underweight the information in prices. Our results persist outside periods of public news, highlighting the role of private information in financial markets.
Priced Beta, Unpriced Beta
August 2026
Preliminary draft
Market beta treats all market movements alike: it measures how much an asset covaries with the market, regardless of what moved the market. But if some market fluctuations are unrelated to fundamentals relevant for marginal utility, this covariance need not represent compensated risk. I formalize this idea by decomposing market beta into exposure to SDF-relevant (“priced”) and SDF-orthogonal (“unpriced”) market fluctuations. Under a priced-market spanning restriction, only priced beta predicts expected returns, while unpriced beta has a zero structural price. Conventional CAPM pricing errors therefore arise when assets load differently on the two components. Empirically, these exposures differ substantially across stocks. Priced beta predicts average returns, unpriced beta carries little independent premium, and CAPM pricing errors increase with the gap between them. The CAPM’s performance therefore depends not simply on how much the market moves, but on what moves it.
Are Risk-Neutral Variance and Skewness Good Proxies for their Physical Counterparts?
November 2022
Preliminary draft
Many empirical papers use VIX, risk-neutral variance, or risk-neutral skewness obtained from option prices to proxy for the physical moments. I investigate whether risk-neutral moments are good proxies for physical moments in the time series and in the cross-section of stock returns. I find that risk-neutral variance is a good proxy while risk-neutral skewness is a bad proxy in both the time series and cross-section. In the time series, physical skewness is a stronger predictor of business cycle fluctuations than risk-neutral skewness. In the cross-section, a high-minus-low physical skewness portfolio carries a positive premium that is unexplained by risk-neutral skewness.