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 study the cyclicality of the market risk premium and variance. Although both increase in recessions, we find that the risk premium is less countercyclical than conditional variance, implying that the ratio of risk premium to variance is weakly procyclical, unlike the Sharpe ratio. We document this fact in a broad global equity sample, employing multiple approaches to portfolio formation around recession onsets, and we corroborate the evidence using option markets. We show that the ratio of risk premium to variance pins down the conditional beta in a regression of the stochastic discount factor on the market return, and its cyclicality helps explain key features of the equity term structure. A stylized model reconciles the procyclicality of the price per unit of variance risk with the term structure of Sharpe ratios on dividend claims.
Do Investors Learn From Prices? Evidence From the Securities Lending Market
with Paula Cocoma, August 2025
Using stock-level data from the securities lending market, we develop a new empirical strategy to examine how financial markets process information. A key challenge in this literature is identifying when new—especially private—information arrives. We address this challenge by proxying information arrivals with increases in shorting demand, which captures informed traders’ reaction to new signals. We motivate our method within a theoretical model and validate it empirically using leading measures of informed trading. When shorting demand increases, we observe higher return volatility, higher volume–volatility correlation, and lower price informativeness. These findings align with a differences-of-opinion model in which overconfident investors, acting on their perceived informational advantage, trade so aggressively that market prices become less reflective of fundamentals. Our results hold even after excluding public signals, such as earnings announcements, highlighting the securities lending market’s role in revealing private information.
Are Risk-Neutral Variance and Skewness Good Proxies for their Physical Counterparts?
November 2022
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.