As we have promised, we present a short article as an introduction into the methodology of the Quantpedia Pro CPPI reports. Quantpedia Pro clients can use the model portfolio built in the Portfolio Manager as a risky asset to test various variants – Basic CPPI, Drawdown Based CPPI and Dynamic Multiplier CPPI.
We present a short article as an insight into the methodology of the Quantpedia Pro report – this time for the Markowitz Portfolio Optimization. As usually, Quantpedia Pro allows the optimization of model portfolios built from the passive market factors (commodities, equities, fixed income, etc.), systematic trading strategies and uploaded user’s equity curves. The current report helps with the calculation of the efficient frontier portfolios based on the various constraints and during various predefined historical periods. The backtests of the periodically rebalanced Minimum-Variance, Maximum Sharpe Ratio and Tangency portfolios will be available at the beginning of July.
Additionally, there is a Case Study dedicated to this Quantpedia Pro tool.
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.
Thanks to quantitative easing, we see record-low interest rates. While yields for short to intermediate maturities in the US are lower than the inflation but still positive, other developed markets such as Japan or European countries even have bond yields negative. Still, it does not implicate that investors have withdrawn from the fixed income markets. Both individual and institutional investors still participate in bond trading. However, the critical question is how these conditions influence the investors. Does their behavior change? Do they reach for yield and prefer riskier bonds in the search for (positive) real yields? In this blog post, we present three novel research papers that offer insights into this topic.
Do not put all your eggs in one basket is a common phrase that resonates among investors worldwide. The errand of such a famous saying is simple, diversify! However, how to diversify, if in the crisis, everything seems to be highly correlated? Last week, we wrote a blog about the Macro Factor Risk Parity, but it certainly is not the only option. Real assets such as REITs, various commodities, and the ever-popular gold are commonly added into portfolios as diversifiers. However, Parikh and Zhan (2019) research examine a much bigger set of real assets than the aforementioned evergreens. Real assets like Timberland, Farmland, Infrastructure, Natural Resources and many others are presented in the paper. All those assets have different sensitivities to inflation, GDP growth, equities or bonds. Therefore, real assets could have a value in the portfolios to protect an investor from inflation, stagnation, or simply distributing the eggs mentioned above in many baskets. All these strategies are presented in the paper and compared to equities, bonds and traditional 60/40.
Risk and diversification are critical interests of every investor, especially when things go south since the correlations across assets tend to rise during stressful times. Therefore, in the asset allocation, the risk parity allocation is one of the key topics. Factors are commonly known as underlying sources of both risk and returns, and it is assumed that they can be utilized to achieve superior risk-adjusted returns and diversification. However, there seems to be a lack of research that would be related to the macro factors. This gap is quite striking since there is a general consent that macro factors (for example, inflation) largely influence the broad set of assets. Amato and Lohre (2020) research paper fills the gap and studies the usage of macro factors as diversifiers in asset allocation.
The authors divide the macro factors to two groups, where the first consists of TERM, MARKET, USD, OIL and DEF (default risk), and the second group consists of CLI (a measure of output by OECD), G7.INFLATION, G7.Short.Rate and VIX. The research shows, that when the diversification matters the most, only the second group improves both the risk and returns, acting as a successful diversification during various economic regimes and particularly, during high economic uncertainty. Overall, the paper offers exciting insights into diversification and macro factors, accompanied by more complex mathematical models definitely worth looking into.
Be first to know, when we publish new content
