Evaluating Climate Risk in Liquid Investment Strategies - MATLAB
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      Evaluating Climate Risk in Liquid Investment Strategies

      Maria Palazzi-Nieves, MJ Hudson

      The current climate emergency has led companies and governments to take action to mitigate this crisis and transition toward a low-carbon economy. Financial institutions are not exempt from taking part in such a transition, as they are increasingly pushed to assess their exposure to climate risk and to consider sustainability objectives in their investment strategies.

      Maria Palazzi-Nieves, a research data analyst, introduces MJ Hudson Quantitative Solutions’ carbon screening and temperature scoring tool, developed to evaluate internal security for liquid investment portfolios. Built on SBTi methodology and employing a varied set of MATLAB® toolboxes, the tool enables institutions to evaluate their alignment with current GHG emissions reduction and temperature goals, which can potentially influence their investment decisions.

      Published: 16 Nov 2022

      So before going into the details on the development of how we are developing this primary risk tool, I would like to dedicate a few minutes to introduce a little bit what we do, or who we are. So we are part of a quantitative solutions unit within MJ Hudson, and what we do is to deliver fully-automatic risk and regulatory reporting using our proprietary platform called RiskMonitor. So we produce highly-modular and customizable reports that aim to help asset managers, fund administrators, ManCos, wealth managers, hedge funds and fund service to fulfill their internal marketing, political, or regulatory demands in a highly-transparent way.

      So our process, which is mostly based around automation using modular functionalities, can be described easily within these three steps. It's a first step, once all the data is being delivered from the clients to us via SFTP or email, for which we employ the compiler to check the correctness of the files first using Compile MATLAB code, and then using MATLAB object-oriented code to unify all the different data files from the clients into a unique format. And then it's fitted to our internal database within the database Toolbox.

      So as a second step, once the data is upload in email database, we use the Data Feed Toolbox to connect the different data vehicles to enrich these different data sets and perform many of our analysis and calculations using, for example, the Financial Toolbox, the Financial Economic Instruments Toolbox, and the Econometrics one.

      Final step, once the reports are reproduced and all the report parts and details, they are defined specifically for a particular client, we use the MATLAB Report Generator tool again to fill the holes in the different templates they will produce. And later, the reports are automatically delivered to the clients, again via SFTP or email.

      So which one of the existing core solutions that we have were doing performance reports, which can be used to provide based on historical returns or at the asset position level. We produce as well liquidity reports to test, for example, liquidity requirements under certain stressed conditions, or the ability of funds to fulfill certain . Compliance report as well, to monitor investment-specific investment restriction and in specific with compliance measures like, for example, the value records.

      Finally, we have our RegTech solution, which is used to fill documents exposed with receipts, EMTs, and MPTs. ValuSim solution, that is mostly focused for private firms or private asset clients. And finally, the solution that we are currently developing, which is the main central aspect of this presentation. That is developing this climate risk or, as we call to expand on a bigger umbrella, an ESG solution.

      So why we decided to start with the climate resolution? Because I think we are all aware that we're under a current climate emergency, and we're hearing the screams from different climate scientists. And we also that this climate emergency has enormous potential destabilizing effects for the global economy. Actually, if you read some of the reports, they say that under the current projections, the impacts of climate change on economic are even worse than the financial crisis that we have in 2008.

      So as it was a little bit before in our previous presentation, these impacts from climate and climate change, or the climate emergency, can translate into a financial aspect across two aspects, like, for example, the physical risks that we can be exposed to, whether it's extreme weather, global temperature rise, or the transition risks that can compliment policy changes. For example, that we have to implement to comply to the change in regulations or the changing political pressures they will be subjected to.

      Of course, the financial effects, that can be representing changes in facilities damage, changes in revenue, or even disruption of the supply chains of the operations as of the companies. So with this in mind, where we're at now that with this regulatory pressure increasing with the physical risks are really becoming more and more frequent, the assessment and treatment of these effects will be key for the business success. So this doesn't imply by any means that the other aspects within the ESG, within the umbrella, are less important, but we decided to start with these aspects first.

      So this has not been straightforward at all. So we are facing several challenges, and we are still trying to come up with different ways to alleviate some of the challenges. The first thing that we encountered when we decided to do this implementation were aspects regarding the data. If we see, for example, that regarding GHG emissions, we can see the scarcity of this data. If we focus on the different scopes in which these GHG emissions can be classified, particularly for Scope 3, the emission data for companies, is very rarely disclosed.

      How we are coming to alleviate this aspect? Well, we are starting to collect data from multiple sources. And we are, of course, using estimating models to fill the gaps, which brings us to our second challenge that we face in terms of data, the consistency aspect. When we use different GHG reporting methods or different estimation methodologies, we need to develop a process to compare and reconcile these data.

      And finally, another challenge that we came across in terms of data, which the extraction of the data. This data, for example, coming again to GHG emissions, we will be going to GHG emission reduction targets that can be set by companies. We see that this data sometimes is hard to interpret with it being expressed over different units, time frames, or different scope. So we are going to need to translation this sometimes extra data into more common or a little bit more intuitive metrics.

      So a second challenge that we are facing when we start working on this, we've encountered a quantity of metrics and scenarios, or how I like to call it, a suite of metrics and scenarios. So finding the selection of the appropriate ones has not been trivial. So in terms, for example, selecting carbon foot metrics, we are going over the literature to review results from surveys that have been carried out by other institutions.

      And we are trying to implement the metrics that are being more recommended across different regulatory entities, for example, that are being recommended by the TCFD, or they are the metrics that are being complied within the EEG template that's already coming into-- that has been launched in August recently. Or when it comes to the scenarios, we are looking to cover different scenarios across different contexts. For example, considering the extreme kind of scenarios under current policies and the scenarios that are mostly sector-specific or they are relevant to the different business models of clients.

      And then the last challenge that we are facing is in terms of the regulatory aspects of the solution that we want to develop are that these regulations are, in fact, still evolving. So what to disclose? What to report? These are changing. I need to provide this or that.

      So for this, we are keeping in mind that we are trying to build this framework from the beginning as a unified one. So we want to respond to the different regulatory pressures no matter how we develop these final solutions. So if we are using this particular metric, we are trying to be aware that this can be, as I say again, to produce a TCFD report, an internal report for any more or for any EET.

      So how are we developing this? We're building on three key aspects to working on the solution. So the first tool is what we are calling Carbon Screening and Emissions Reduction Targets Assessment Tool, which is part of the metrics and target pillars if we go to the TCFD recommendation, but can also inform government and strategy and risk management deals.

      A second tool that we are working into our integration is a temperature alignment method that is inspiring science-based methodologies. It can also inform on strategy and risk management pillars. And finally, this is the most recent one that is the most under development yet. That's the scenario modeling part, which also serves to inform on strategy and risk management.

      So let's talk a little bit of, more or less, the things that we're already doing with the tools that we will have in a more mature state. In terms of the carbon screening and carbon assessment tools, we already implemented many different metrics that are aligned within the TCFD or the EET. And for example, are able to provide different types of extra analysis. For example, in some cases, when there are data allowances, we can do a historical analysis to show some trend changes. Or we can provide benchmark analysis comparing a resource with, for example, appropriate indices or peer portfolios.

      One of the things that I mentioned at the beginning on the extraction of the data, sometimes it's hard to interpret it. Or the numbers can mean, if I tell you your carbon intensity goes to this many tons of CO2, how do I interpret that? How can that be useful for me? I can try to provide extra analysis comparing to a benchmark, how are you performing. You're performing well, you're performing within the mean of and distribution of portfolios of peers similar to you, and so on, and so on.

      And in terms of emission reduction targets assessment, this could be a way for the clients to assess how they are aligning with the transitions to defining policies. For example, they can assess the assets in their investment, whether they have or not set emission reduction targets, or whether these targets are SBTi-aligned or not, or, for example, how much of these investments are going to what we call green or carbon-intensive companies.

      So with respect to the temperature alignment tools that we are developing, we are using a temperature scoring method that was developed by the SBTi initiative of citing this target initiative, employing the MATLAB version within a risk monitoring system. We're integrating this MATLAB version of the tool to our RiskMonitor. And what this tool does is basically, it's able to assess the temperature alignment based on the current emissions target that are set by the companies.

      In other words, once you have the different corporate targets that can be either expressed on different units or over different time frame, these tools, applying simple regression models, is able to link these corporate targets into long-term interactive trajectories, providing you with a more intuitive matrix, which is a temperature response.

      So you can provide this to other different time frames, other different corporate levels. And, for example, even if for a particular company, you have different targets, you can aggregate that to have in a single company scores. That could be used, for example, to adjust or to develop more ambitious emission reduction target. Or if you apply this, an aggregated portfolio level, you could use this to security selection or to take different allocation decisions.

      If we go to some of the results that we are implementing for this tool, the temperature alignment tool, we can have other different aggregation methods at the portfolio level. And also the tool is able to provide what is a scenario analysis. So we can, for example, based on the sample portfolio that I'm showing here, which is actually not performing very well. If we take into account the 2 or 1.5 temperature goals, we can see that this portfolio is aligned. But we know that within the what-if scenario, you can recompute the portfolio temperature score, assuming that you have the power to engage some of the companies that contribute most of the temperature score to set or to adjust their carbon emission targets to a more ambitious one. So you can see how this will impact in your current temperature score.

      So you can use this, for example, also at a sector level. We can see here that actually, if we allocate this over sectors, the only sector of the investments for the sample case that is aligned with the current temperature goals is the utilities one. So you can use this to engage companies or to, for example, create a list of assets in which you don't want to invest because these are the ones that are producing more, contributing more to your portfolio scores.

      And finally, the last aspect that we are trying to cover right now is also the scenario modeling to assess better the transitions and physical risks. So for this aim, we're including the MATLAB Climate IAM Explorer tool. We're focusing, at the moment, on the scenarios provided by the NGFS. And what we are trying to do at the moment is to combine the results from the different scenarios considering different values to be able to compute different financial-related quantities, for example shocks in the value of the financial assets, to compute portfolio losses, or to expand our calculations to consider what is called now in the literature the climate VaR.

      So these are some of the examples of the different types of reports that we can produce. As I say again, this is fully-automatized. You can produce these at different levels. You can select the different analysis that you want us to show you in the report or to include in the report. Or you can have different types of report if you want only to focus on carbon screening, or temperature alignment, or scenario alignment, or if you want to have a fully-complete climate report.

      So to conclude, some of the messages that I want to deliver here is, OK, our mission was to provide a transparent, unique, and flexible climate risk regulatory solution, mostly building on MATLAB capabilities. So also, these report solutions are not only used for internal purposes, but the outputs of everything are fully-aligned with the current existing regulatory restrictions. So you can use these for internal purposes to take different investment decisions or to reallocate your current investment, but also, to example, to field your regulatory reports.

      We are also taking into consideration further works. We want to include portfolio optimization with climate variables. For example, since we are employing these temperature alignment metrics, we would like to see how we can relocate the weights on a particular portfolio which come out with a particular temperature score to minimize this, minimize the current score. And we are also, of course, carrying out an expansion to consider a full ESG solution covering the social and governance aspects.

      So as I said, I would like to thank you all for your attention, and I also would like to take here a special mention to from the marketing team, which has been the one who's provided us with a lot of support and help into working with the temperature model and with the climate explore tool.

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