Paul Schneider

Summer/Winter School: Digitise, Optimise, Visualise

We are organizing courses on convex optimisation taught this summer and next winter at USI Lugano. Recent educational methods will make it impossible not to understand the basic principles. The courses combine cutting edge theoretical knowledge with state-of-the-art visualisation techniques, and real-world data-intensive problems. Check it out at

Interests and Research

I am interested in finding structure in financial markets. My research focus is on methods to recover information that is not directly observable with the most innocuous assumptions possible. I try to learn every day about old and new techniques from engineering and mathematics, to keep myself informed about politics, and to enjoy and play music as much as possible.


USI Lugano

Via G. Buffi 13

CH-6900 Lugano

Working Papers

On the nature of jump risk premia. with P. Orlowski and F. Trojani

A Theory of Scenario Generation.

Does it Pay to be an Optimist?.

Low Risk Anomalies?. with Christian Wagner and Josef Zechner

Fear Trading. with Fabio Trojani

Refereed Journal Articles

An Anatomy of the Market Return. Journal of Financial Economics (2019) 132, 325-350

(Almost) Model-Free Recovery. Journal of Finance (2019) 74, 323-370, with Fabio Trojani

Divergence and the Price of Uncertainty. Journal of Financial Econometrics (2018) 9, 1-56, with Fabio Trojani

The Economic Value of Predicting Bond Risk Premia: Can Anything Beat the Expectations Hypothesis. Journal of Empirical Finance (2016) 37, 247-267, with Lucio Sarno and Christian Wagner

Generalized Risk Premia. Journal of Financial Economics (2015) 116, 487-504

Asset Pricing with Nonlinear Risk Premia. Journal of Financial Econometrics (2014) 12, 479-506, with Aleksandar Mijatovic

The Skew Risk Premium in the Equity Index Market. Review of Financial Studies (2013) 26, 2174-2203, with Roman Kozhan and Anthony Neuberger

Density Approximations for Multivariate Affine Jump-Diffusion Processes. Journal of Econometrics (2013) 176, 93-111, with Damir Filipovic and Eberhard Mayerhofer

Properties of Foreign Exchange Risk Premia. Journal of Financial Economics (2012) 105, 279-310, with Lucio Sarno and Christian Wagner

Flexing the Default Barrier. Quantitative Finance (2011) 11, 1729-1743, with Gregor Dorfleitner and Tanja Veza

Bayesian Inference of Discretely Sampled Markov Processes with Closed-Form Likelihood Expansions. Journal of Financial Econometrics (2010) 8, 450-480, with Osnat Stramer and Matthew Bognar

Globally Optimal Parameter Estimates for Nonlinear Diffusion Processes with Expected Likelihood. Annals of Statistics (2010) 8, 215-245, with Aleksandar Mijatovic

The Economic Role of Jumps and Recovery Rates in the Market for Corporate Default Risk. Journal of Financial and Quantitative Analysis (2010) 45, 1517-1547, with Leopold Soegner and Tanja Veza

Pricing Options with Green's Functions when Volatility, Interest Rate, and Barriers Depend on Time. Quantitative Finance (2008) 8, 119-133, with Gregor Dorfleitner, Arne Buch and Kurt Hawlitschek

Useful Stuff

Latex Templates

JF2018 A template for preparing an accepted paper for publication in the Journal of Finance. Reuses parts of Richard Stanton's packages. Contains a main text as well as an internet appendix with examples of equations, figures, and references.

JFE2018 My paper 'An Anatomy of the Market Return' which was accepted by the copy editor of the Journal of Financial Economics. Contains a main text as well as an internet appendix. Note that the first submission to the copy editor must be made single page which you can achieve by commenting the first line and un-commenting the second line in the main document JFEmaintext.tex

R Packages

DIconvex, with Liudmila Karagyaur. Ever worked with cross sections of options and wondering whether bid ask spreads admit at least one arbitrage-free path of option prices? Look no further. DIconvex maximizes the number of options you can keep such that at least one set of monotonic and convex prices exists within bid ask spreads. A mixed integer linear program does all the work. Fast, scalable and robust.