Lower your contribution to global warming while maximising risk-adjusted returns
Investors commonly rely on a portfolio optimisation approach developed in 1952 by Nobel Prize laureate, Harry Markowitz, to find the best trade-off between risk and return. Nowadays, investors increasingly want to know how their portfolio contributes to global warming. To this end, we created a portfolio optimiser that incorporates all three dimensions: risk, return and contribution to global warming (expressed as a simple “temperature” in degrees Celsius).
This temperature is an assessment of where a company sits on the transition path to net zero relative to a Paris-aligned benchmark, designed to limit global warming to 1.5-2°C. The aggregate portfolio temperature is then calculated as the weighted average temperature of the portfolio’s underlying companies. For example, a 3°C portfolio is a portfolio aligned with a world where global temperatures rise 3°C above pre-industrial levels by 2100. Currently, the world economy is on a 3°C trajectory.
The 3D Climate Optimiser finds the portfolio allocation delivering the best trade-off between expected return, risk and climate impact.
Input your portfolio holdings and define their parameters:
[Inputs can be individual securities, funds, ETFs or managed strategies. For illustrative purposes, we have pre-populated with sample global equity and bond managers. Click the toggle on the right to remove the position from the optimisation process. Click the pencil to update input parameters and click the plus sign to add your own portfolio holdings.]
|(( fund.name ))|
Select a temperature aversion
Select a temperature target
For a temperature target of ((temperature)) °C and ((selected_risk_aversion_title)), the optimal portfolio has an expected return of (( Math.round(expected_return*10)/10 ))% and (( Math.round(volatility*10)/10 ))% volatility.
For a temperature aversion of ((temperature)) and ((selected_risk_aversion_title)), the optimal portfolio has an expected return of (( Math.round(expected_return*10)/10 ))%, a volatility of (( Math.round(volatility*10)/10 ))% and implied temperature rise of (( Math.round(portfolio.realised_temperature*10)/10 ))°C.
Using the optimiser to find the best trade-off between risk, returns and portfolio temperature involves three steps:
1. Specify portfolio holdings' risk, return and temperature
You can either:
You can modify the portfolio holdings’ expected return, risk and temperature.
You can decide to include or not certain portfolio holdings with the toggle .
Specify the expected return, volatility, temperature and correlations with other portfolio holdings.
Click the Save button to store the portfolio holdings' new characteristics.
2. Define your optimisation approach
You can incorporate temperature in the portfolio optimisation process either as a third dimension or as target [see technical appendix for details].
On the bottom right-hand side corner of the chart, you can choose your level of risk aversion: Low, Medium or High [see technical appendix for a definition]. By default, the optimisation tool assumes a medium risk aversion.
3. Visualise your optimal portfolio
The optimiser finds the best trade-off between portfolio risk, return and temperature given your aversion to risk and concern for climate.
The pie chart shows your optimal portfolio (in %). The tool also shows your optimal portfolio’s risk, expected return and temperature.
We use two optimisation strategies:
By default, the optimiser uses four pre-defined managers: Global Equity Manager, Paris-aligned Global Equity Manager, Global Bond Manager and Paris-aligned Global Bond Manager. We assume a temperature for the pre-populated global equity and bond manager of 3°C, which is consistent with the current global warming trajectory. For the pre-populated Paris-aligned global equity and bond manager, we assume a temperature of 1.5°C.