Quantocracy’s Daily Wrap for 01/06/2020

This post will be about analyzing SVIXa proposed new short vol ETF that aims to offer the same short vol exposure as XIV used towithout the downside of, well, blowing up in 20 minutes due to positive feedback loops. As Im currently enrolled in a Python bootcamp, this was one of my capstone projects on A/B testing, so, all code will be in Python (again). So, first off, with those not

Variance and covariance are widely accepted risk measures for liquid assets that trade in public markets. Illiquid assets are not part of this framework because of their lack of regular price quotes and thus time variance. Due to the difficulty in using standard risk measures to assess non-traded assets, illiquid assets are often analyzed using different tools and analyzing the risk of these

The difference between the cheapest and most expensive smart beta ETF in the US is 59 bps on average Some smart beta ETFs offer negative factor exposure, which requires explanation Factor scores can be used to identify which smart beta ETFs offer the best ratio of factor exposure per dollar in fees INTRODUCTION Buying a Big Mac at McDonalds in Basel, Switzerland, costs CHF6.50, compared with

Factors play an important role for quantitative portfolio construction. How a factor is defined and how a factor portfolio is constructed play important roles in the results achieved. Naively constructed portfolios such as most academic factors can lead to latent style exposures and potentially large unintended bets. Through numerical techniques, we can seek to develop pure factors

As every year, I checked my analytics so that I can let you know what was popular. This year I have also experimented with a survey where I asked one question at the end of each relevant post. About 120 replies recieved, but the free Survey Monkey account (the survey provider I went with) only lets out the first 100 replies, and no exports*. Here are the results: Partial survey results Looks nice,