This article is a primer into the methodology we use for the Portfolio Risk Parity report, which is a part of our Quantpedia Pro offering. We explain three risk parity methodologies – Naive Risk Parity (inverse volatility weighted), Equal Risk Contribution and Maximum Diversification. Quantpedia Pro allows the design of model risk parity portfolios built not just from the passive market factors (commodities, equities, fixed income, etc.) but also from systematic trading strategies and uploaded user’s equity curves.

The time flies very fast, and like every month, I have again a bunch of interesting improvements I would like to present to all of you. We again have two new reports for Quantpedia Pro (the Market Phase Analysis and the Portfolio Risk Parity reports) that I will describe soon.

But first, let’s recapitulate Quantpedia Premium development. Ten new Quantpedia Premium strategies have been added to our database, and twelve new related research papers have been included in existing Premium strategies during the last month. Additionally, we have produced 11 new backtests written in QuantConnect code. Our database currently contains over 430 strategies with out-of-sample backtests/codes.

Additionally, four new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month …

The spring is in the air (at least in the northern hemisphere), and we were not sitting idle in the last month. The most interesting development is two new reports for Quantpedia Pro (the Factor Cycle and Inter-Market Correlation reports) that I will describe soon.

But first, let’s recapitulate Quantpedia Premium development. Nine new Quantpedia Premium strategies have been added to our database, and fifteen new related research papers have been included in existing Premium strategies during the last month. Additionally, we have produced 12 new backtests written in QuantConnect code. Our database currently contains over 420 strategies with out-of-sample backtests/codes.

Additionally, four new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month …

One of the most popular reports in the Portfolio Analysis section of our Quantpedia Pro tool is “Volatility Targeting”. In this article, we will explain some theory behind this portfolio management method. And then, we will go more in-depth, pick several examples and explain some common volatility targeting variants.

The most significant event on our page in February was the introduction of our new Quantpedia Pro platform. We have received positive feedback so far to it; therefore, feel free to revisit our short article describing the main features of this new service and its design and reporting capabilities.

But naturally, we have not forgotten to do our homework for our other services. So, let us recapitulate last month of Quantpedia’s research. Ten new Quantpedia Premium strategies have been added to our database, and ten new related research papers have been included in existing Premium strategies during the last month.

Additionally, we have produced 12 new backtests written in QuantConnect code. Our database currently contains over 410 strategies with out-of-sample backtests/codes.

Also, three new blog posts, that you may find interesting, have been published on our Quantpedia blog:

Practitioners widely use asset pricing models such as CAPM or Fama French models to identify relationships between their portfolios and common factors. Moreover, each asset class has some widely-recognized asset pricing model, from equities through commodities to even cryptocurrencies. 

However, which model can we use if our portfolio is complex and consists of many asset classes? Which factors should we include and which should we omit? (Especially if we have a database that consists of several hundreds of potential factors). Additionally, we know that equities influence bonds, commodities influence equities and vice versa. Hence the question, what about the cross-asset relationships? 

These are the problems and questions we faced when looking for a methodology for our Multi-Factor Analysis report in the Quantpedia Pro platform. This blog post aims to introduce the model, its logic and the method we have decided to use.