QARMII_project

Quantitative Asset and Risk Management II
Prof. Fabio Alessandrini
HEC Lausanne

The aim of this project is to create a CTA style cross asset trend-following strategy with two side objectives:

1. Avoid portfolio crash during momentum reversal.
2. Work in high correlation period.

Indeed, academic research has shown that trend-following strategies tend to under-perform on high correlation regimes between assets. Moreover, obviously, when market drop or rebound fast, the signal may take a while to change sign and therefore perform poorly.

Signals

We used many signals in order to asses their performance.

1. Basic Signals

• Momentum with varying length, espcially 90 and 252 days as well as the return of the month 9 to 12.

• Moving Average Crossover with varying length

2. Advanced Signals

• Weighted normalized EWMA Crossover (based on this article from Baz. & al. 2015)

• Singular Sprectal Analysis based signal

• Support Vector Machine classification based signal

Weighting schemes

We combined these signals with three different weighting schemes :

• Equally Weighted
• Volatility Parity (inverse volatility, naive parity)
• Risk parity

Constant Volatility

We also used a leverage to attain a constant volatility, allowing the strategy to be more easily compared.

Implementation

The implementation is performed on matlab, for each strategies we created a function that takes on the data and parameters, and compute the signals, weights and leverage at each rebalancing.

Author and Acknowledgment

• Maxime Borel

• Benjamin Souane

Thanks to Fabio Alessandrini for the help and to Kevin Sheppard for the amazing MFE toolbox.