Forward-looking P

37 Pages Posted: 17 Aug 2019

See all articles by Maxim Ulrich

Maxim Ulrich

Karlsruhe Institute of Technology

Simon Walther

Karlsruhe Institute of Technology

Jonas Rothfuss

ETH Zurich

Fabio Ferreira

Karlsruhe Institute of Technology

Date Written: August 14, 2019

Abstract

We present a forward-looking estimator for the time-varying physical return distribution with minimal prior assumptions about the shape of the distribution and no exogenous assumptions about the economy or preferences. Our estimator, which is based on a neural network, derives its forecasts from option-implied measures and predicts the conditional mean and volatility of returns such that profitable trading strategies can be derived. In contrast to backward-looking estimators and alternative forward-looking parametric and non-parametric approaches, its distribution forecasts cannot be rejected in statistical tests and it features lower prediction errors and higher conditional log likelihood values than the alternatives.

Keywords: physical density, density prediction, neural network, option-implied, forward-looking, return forecast, variance forecast, machine learning

JEL Classification: G12, G17, C45, C53

Suggested Citation

Ulrich, Maxim and Walther, Simon and Rothfuss, Jonas and Ferreira, Fabio, Forward-looking P (August 14, 2019). Available at SSRN: https://ssrn.com/abstract=3437281 or http://dx.doi.org/10.2139/ssrn.3437281

Maxim Ulrich

Karlsruhe Institute of Technology ( email )

Bluecherstrasse 17
Karlsruhe, Baden Württemberg 76131
Germany
+4972160844270 (Phone)

HOME PAGE: http://risk.fbv.kit.edu/c-ram

Simon Walther (Contact Author)

Karlsruhe Institute of Technology ( email )

Bluecherstrasse 17
Karlsruhe, Baden Württemberg 76131
Germany

Jonas Rothfuss

ETH Zurich ( email )

Rämistrasse 101
ZUE F7
Zürich, 8092
Switzerland

Fabio Ferreira

Karlsruhe Institute of Technology ( email )

Kaiserstraße 12
Karlsruhe, Baden Württemberg 76131
Germany

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