Data & Code
Generates derived bid-ask spreads for any asset class with Open, High, Low and Close (OHLC) data following the methodology in Corwin and Schultz (2012) and Abdi and Ranaldo (2017).
R Code to extract "information theoretic" SDFs from Asset Pricing Theory. This repository is designed to enable students (and practitioners) to extract "information theoretic" stochastic discount factors (SDFs) from asset pricing theory. The current iteration of the code extracts the SDF using the exponential tilting method of Kitamura and Stutzer (1997). This method is operationalised empirically by Julliard, Ghosh and Taylor (2019) and is augmented with an l1-penalty by Qui and Otsu (2020). The code allows for the l1-penalty.