Chap 8 Flashcards
ESG INTEGRATED PORTFOLIO CONSTRUCTION AND MANAGEMENT
ESG integration occurs on various different investment levels, each necessitating its own framework for analysis and implementation. Where previous chapters have described ESG integration at the underlying security level, this chapter examines different approaches, research and methodologies for integrating it, starting at strategic asset allocation, and moving on to portfolio construction and management.
Much of the existing evidence supporting ESG integration draws upon single security and issuer case studies. The fact that ESG integration is comparatively less developed as investors elevate the decision-making process to
higher levels – asset allocation, fund manager selection and portfolio investment – makes it an exciting area for innovation. This is particularly true as investors build more robust ESG capabilities outside of the traditional equities focus of ESG; these areas include:
▶ mixed asset;
▶ real assets; and
▶ sovereign debt.
Nonetheless, investors should recognise the trade-offs – both explicit and implicit – to risk-adjusted returns when integrating ESG screening approaches.
Accordingly, this chapter draws upon portfolio management theory complemented with examples of investment best practices to:
▶ discuss research, approaches and challenges to embedding ESG investing risk into global asset allocation models;
▶ examine how ESG investing can be applied to approaches across asset classes and different strategy types;
▶ consider how ESG can leverage quantitative research methods to understand risk exposure and performance return dynamics in portfolios; and
▶ differentiate between actively and passively managed ESG strategies.
INTRODUCING ESG INTEGRATION WITHIN PORTFOLIO CONSTRUCTION AND MANAGEMENT
Describe approaches for integrating ESG into the portfolio management process.
8.1.3
Explain approaches for how internal and external ESG research and analysis is used by portfolio managers to make investment decisions.
Approaches to integrating ESG
As the earlier chapters demonstrate, there is a rich diversity of approaches for integrating ESG at the individual securities level.
This heterogeneity is now carrying over to portfolio construction and management, where new methodologies and frameworks are leveraging ESG data sets with innovations that drive fundamental and quantitative, as well as active and passive, investment strategies.
The endgame for ESG integration is the combination of underlying ESG analysis to produce a more complete picture of ESG exposure and risk at the portfolio construction and management levels.
In this respect, ESG integration within
portfolio management
requires a different manner of explanatory power than integration at the individual security level:
it should embed ESG considerations into:
*portfolio exposure;
*risk management;
performance attribution;
and
at the highest level,
**asset allocation decisions.
New methodologies are now working to combine multiple portfolios to produce a picture of ESG risk and exposure at the asset allocation level.
Nonetheless, it is important to acknowledge the notable misperceptions when applying ESG at the portfolio level.
Accordingly, this chapter will attempt to address the following questions:
How can we characterise interest and demand for investment strategies in this area?
What exactly do we mean by ‘ESG strategies’, and how can we understand this approach within the broader language of responsible investment?
What are the assumptions underlying the claim for alpha generation and how can we extend them to include portfolio management and construction more generally?
Perhaps because of its acronym form, ‘ESG’ has emerged as a catch-all term characterising responsible investment strategies.
However, this usage combines many distinct approaches that are often fundamentally different.
Hence, it is useful to understand that ‘responsible investment’ as a term provides a broader, more appropriate characterisation to describe the diversity of investment strategies this chapter addresses.
An unfortunate consequence is that investors and the wider market often conflate the terminology of ‘responsible investment’.
Although a wide diversity of approaches and methodologies co-exist under this umbrella term, ‘responsible investment’ generally characterises three dominant approaches, which we will look at in further detail in this chapter:
- exclusions-based investing;
- ESG investing; and
- impact investing.
Exclusions-based investing
• Exclusionary / negative screening • Prioritises norms, values and
faith-based
• Non-engagement
ESG investing • Prioritises measurability and scoring • Positive screening • Reinforces engagement activities • Reporting capabilities
Impact investing
- Prioritises intentionality and additionality
- Socio-environmental impact focused
- Framework-oriented (UN SDGs)
- Measurable objectives and outcomes
Statistics published by the Principles for Responsible Investment (PRI) are often used to frame investor activities in ESG integration. But what do these statistics really reveal about ESG integration at the portfolio level?
Data compiled by Mercer Consulting (see Table 8.1), one of the largest global institutional investment advisers, suggests that progress in ESG integration is marked by a high degree of variation depending on asset class and investment strategy type.
What is perhaps more interesting, though, is that this data reinforces the notion that integration is broadly more advanced across managers despite being slower to manifest itself through formal- and dedicated sustainability-themed strategies.
the role of portfolio manager is distinct from that of an investment research analyst.
The role of analysts
Analysts (particularly fundamental analysts) present and justify their views in ‘a story’ or ‘investment thesis’ of a security, which generally entails incorporating different factors. These factors often include:
the intrinsic value of the security;
credit analysis;
the potential for a rerating or derating in valuation;
potential risks;
short-term and long-term catalysts; and
an expectation on the security’s earnings growth and cash flow profile.
ESG is an increasingly recognised element within securities analysis and, if material enough, may likely carry meaningful implications that help the investment thesis.
The role of portfolio managers
The role of portfolio managers, on the other hand, is of much broader scope.
A portfolio manager constructs and manages a portfolio through a careful process that aggregates all of the individual, underlying risks.
And while portfolio managers often form their own views for a given security, their primary role is to weigh security- specific conviction against:
▶ macro- and micro-economic data;
▶ portfolio exposure; and
▶ sensitivities to potential shocks.
The treatment of ESG in a portfolio context – if properly and systematically integrated, regardless of whether in active or passive portfolio management – should be considered in the same light as these other factors.
The challenge that portfolio managers face is how to widen the focus of research and datasets largely optimised for security analysis into tools that can better inform portfolio and asset allocation analysis and decision-making, particularly in understanding where and how ESG contributes to risk-adjusted returns.
To this end, the ESG framework should illustrate a continuity from micro- to macro-forms of analysis, including:
▶ the organising principles and methodologies for ESG analysis;
▶ the identification and analysis of financial and non-financial (ESG) materiality at the individual security level;
▶ the approaches to build a composite picture of risks and exposure at a single portfolio level; and
▶ the representation of ESG risks and exposure that informs a mixed asset strategy which may include many different, underlying strategies.
In addition, ESG integration should be considered in light of two approaches: discretionary and quantitative investment strategies.
Discretionary ESG investment strategies most commonly take the form of a fundamental portfolio approach.
A portfolio manager would work to complement bottom-up financial analysis alongside the consideration of ESG factors to reinforce the investment thesis of a particular holding.
The portfolio manager would then work to understand the aggregate risk at the portfolio level across all factors to understand correlation and event risks, and potential shocks to the portfolio.
Approaches may assume several forms when integrating ESG.
Traditionally, passive or index-based strategies have been the most popular investment vehicles.
These impose a custom index, typically with exclusion criteria.
However, quantitative approaches are now becoming more sophisticated and rigorous when integrated into ESG, from beta-plus funds to single and multi-factor ESG models.
ESG integration can focus on risks as well as opportunities.
A bias towards looking at one of these can lead to different return profiles at the portfolio level as the emphasis can shift from downside protection to upside participation.
Developing a policy that reflects ESG-integrated portfolio management
As a matter of definition – to the market, clients and stakeholders –
an ESG policy should formally outline the investment approach and degree of ESG integration within a firm.
Particularly, asset managers should have ESG policies for asset classes and approach used.
The PRI provides guidance and templates to develop ESG policies.
→
There are well-established resources for developing a comprehensive ESG policy, though these have traditionally catered to the long-only equities and fixed income strategies.4 It is worth noting that investor organisations are now addressing policy development in alternative investment areas, including hedge funds.
Broadly speaking, ESG external research and analysis can be categorised between
academic research and practitioner research.
Each of these resources offers their own unique advantages and disadvantages for investors.
While meta-analyses surveying more than 2,000 academic studies indicate an overall positive bias in the linkage between ESG and investment returns (see Figure 8.3), academic studies on an individual basis often end up disconnected from practice and are not widely or generally applicable.
While certainly additive to the overall discussion, these are often unhelpful for practitioners who tend to search for cross-regional and -temporal factors or frameworks that can be universally or generally applied to portfolios.
Practitioner research, on the other hand, is often less rigorous than academic work, and tends to be less conservative in its assertion to correlate ESG with investment returns, sometimes ignoring other causal factors at play.
As the ESG industry matures, institutional investors are finding an increasingly diverse universe of external research resources.
These resources now include not only ESG-specific research content, but also new quantitative techniques such as natural language processing, machine learning and even artificial intelligence to organise ESG data.
Indeed, the market for ESG content and indices is expected to grow from US$300 million in 2016 to almost US$1 billion by 2021.
These complement internal investment research as well as providing internal quantitative and performance analytics teams the opportunity to refine methodologies for managing ESG risk.
Just as external providers are innovating ESG datasets and producing research, so too are investors developing in-house capabilities to differentiate themselves across asset classes as well as investment strategy types.
For most investors, the sheer breadth and diversity of external ESG research represents a difficult resource to replicate by internal research analysts. While research (such as ESG ratings from third-party data providers) comes at a cost, many of these other resources are openly available.
The list of practitioner resources, though by no means exhaustive, includes:
▶ sell-side research and analysis;
▶ academic studies;
▶ investment consultant research;
▶ third-party ESG data provider research;
▶ ESG-integrated fund distribution platforms;
▶ asset owner and asset manager white papers;
▶ investor initiative research;
▶ non-governmental organisations (NGOs) research;
▶ governmental agencies and central banks; and
▶ multilateral institutions and agencies.
ecommendations by the Task Force on Climate-related Financial Disclosures (TCFD) provide an important example, for both a move towards ESG standards convergence and in elevating risk exposure metrics to the portfolio level from the underlying asset level.
Where carbon intensity was previously determined in the form of carbon footprint on a per company or per asset basis, portfolio managers may now treat carbon exposure on a portfolio-weighted basis.
Weighted-average carbon intensity measures a portfolio’s exposure to carbon- intensive companies on a position-weighted carbon exposure.
Calculated as the carbon intensity (Scope 1 + 2 Emissions ÷ US$ million revenues) weighted for each position within a portfolio, this metric can be employed by investors to
***tilt or overlay portfolios towards lower-carbon exposure.
OK
Investors should recognise the need to differentiate themselves irrespective of their approach to ESG integration.
Asset owners continue to rebase their expectations for the quality of proprietary ESG research that asset managers and consultants can provide to them.
In turn, investors complement external, off-the-shelf research and data analytics with internal, proprietary ESG research.
third-party ESG data provider
online platforms.
Whilst these platforms vary in sophistication, they do offer the first composite picture of a portfolio’s stock-specific risks on a number of potential ESG metrics.
Many of these platforms are capable of:
▶ illustrating a portfolio’s mean exposure and weighting towards low-, mid- or high-scoring companies on ESG metrics;
▶ producing a picture of the portfolio’s environmental and carbon exposure on an absolute-value basis (for instance, expressed as weighted-average carbon intensity; and
▶approximating an overall controversy or risk score for the portfolio).
Asset owners and managers increasingly recognise the limitations of third-party ESG platforms, and the need to develop more sophisticated ESG analytics platforms that combine third-party and proprietary capabilities.
The rationale stems not only in the interest of safeguarding portfolio holdings – particularly with regard to clients’ segregated investment mandates – but also in demonstrating a differentiated approach to understanding and reporting portfolio data.
Given the subjectivity and divergence among ESG ratings providers, developing an approach that incorporates both third party and proprietary ESG data lowers an **overreliance to a single provider and creates greater context for discussion when reviewing the risk profile of a portfolio.
For example, a portfolio ESG analytics tool employed by an asset manager may aggregate a number of different data streams from ESG providers to produce a picture of ‘consensus’, rankings-oriented ESG scores and their variance alongside an internally-produced ‘proprietary’ ESG score in addition to a view of absolute values- based environmental fund metrics and exposures.
These analytics tools allow investment teams to decompose both their portfolios and benchmark indices, sort by ratings and understand the distribution curves across a number of ESG metrics.
They often provide drill-down capabilities that illustrate a more detailed picture of ESG characteristics on an underlying basis for positions.
Portfolio tools provide investors with the ability to stress test a portfolio against different ESG criteria (such as a sudden, hypothetical increase in the price of carbon emissions) to understand the sensitivity of the portfolio.
This exercise is no different to how current portfolio tools provide the means to stress test portfolios against simulations, such as interest rate or oil shocks.
THE EVOLUTION OF ESG INTEGRATION AND ITS APPLICATION TO INDICES AND BENCHMARKING
it is possible to organise exclusions across four basis categories:
▶ universal;
▶ conduct-related;
▶ faith-based; and
▶ idiosyncratic exclusions.
Universal exclusions
Universal exclusions represent exclusions supported by global norms and conventions like those from the UN and the World Health Organization (WHO). It could be argued that controversial arms and munitions (cluster munitions and anti-personnel mines), nuclear weapons, tobacco and varying degrees of exposure to coal-based power generation or extraction all qualify as universally accepted given normative support and the growing asset owner AUM they represent.
Arms and munitions exclusions
Exclusions governing investment in controversial arms and munitions are supported by multilateral treaties, conventions and national legislation:
Ottawa Treaty (1997), which prohibits the use, stockpiling, production and transfer of anti- personnel mines;
UN Convention on Cluster Munitions (2008), which prohibits the use, stockpiling, production and transfer of cluster munitions;
UN Chemical Weapons Convention (1997), which prohibits the use, stockpiling, production and transfer of chemical weapons;
UN Biological Weapons Convention (1975), which prohibits the use, stockpiling, production and transfer of biological weapons;
Treaty on the Non-Proliferation of Nuclear Weapons (1968), which limits the spread of nuclear weapons to the group of so-called Nuclear-Weapons States (USA, Russia, UK, France and China);
Dutch Act on Financial Supervision ‘Besluit marktmisbruik’ art. 21 a. 3. The Belgian Loi Mahoux, the ban on uranium weapons; and
UN Global Compact announced the decision (2017) to exclude controversial weapons sectors from participating in the initiative.
Tobacco exclusions
Although tobacco does not exhibit the same degree of universal acceptance that the exclusion over controversial arms and munitions does, it provides another example which can be said to be supported by:
WHO Framework Convention (2003) on Tobacco Control with 181 parties committing to implementing a broad range of tobacco control measures;
UN Global Compact (UNGC) announced the decision (2017) to exclude tobacco companies from participating in the initiative, as tobacco products are fundamentally misaligned with UNGC’s commitment to advancing business action towards SDG 3 and in direct conflict with the right to public health;
UN SDGs (2015) drive a collection of 17 global goals to eradicate poverty, protect the planet and improve prosperity; many of the goals touch on tobacco as an impediment to improved social and environmental outcomes.
Conduct-related exclusions
Conduct-related exclusions are generally company or country specific, and often not a statement against the nature of the business itself. Labour infractions in the form of violations against the International Labour Organization principles are often cited.
Faith-based exclusions
Faith-based exclusions are specific to religious institutional or individual investors.
Mean-variance optimisation (MVO)
MVO results in the construction of an efficient frontier that represents a mix of assets that produces the minimum standard deviation (as a proxy for risk) for the maximum level of expected return. It is based on defined asset class buckets and long- term expected returns, risks and correlations.
MVO is highly sensitive
to baseline assumptions, making it imperative
to fully understand any revised assumptions due to ESG considerations. MVO is highly dependent on historical data as the baseline with adjustments made to reflect future expectations. Volatility as a proxy for risk does not work well in cases of fat tail risk and large market swings.
ESG issues could impact on assumptions regarding expected return, volatility and correlation at the asset and sub-asset class level. ESG issues also have the potential to expand the regional and asset class mix and to add new sub- asset classes to align with the pursuit of positive real- world impact.
Factor risk allocation
Factor risk frameworks seek to build a diversified portfolio based on
sources of risk. Typically includes factors such as fundamental risks (GDP, interest rates and inflation) as well as market risks (equity risk premium, illiquidity and volatility).
The macroeconomic links to ESG issues are more difficult to quantify with precision from a purely top-down perspective. Market risk factors can be built from the bottom-up using asset and sector level analysis.
ESG issues could require a change to baseline factor risk assumptions. It offers the potential to build
in new ESG-related risk factors (such as climate change) to improve diversification (particularly across market risk factors).
Total portfolio analysis (TPA)
Similar to factor risk allocation, TPA allows for closer review and interplay between the strategy setting process and alignment of investment goals. Based on an
agreed risk budget, asset allocations are made on expected risk exposures and are less constrained by asset class ‘buckets’
as traditional MVO approaches.
TPA is relevant to consider ESG issues that require the interplay between judgment about the future and quantitative analysis. TPA requires specialist knowledge to make informed judgments about future risk.
TPA’s emphasis on risk budgeting and allocation of capital to opportunities within that budget (bringing alignment between top-down
and bottom-up) would provide greater flexibility to capture the potential winners and losers in scenario analysis that also incorporate ESG-related issues.
Dynamic asset allocation (DAA)26
DAA is driven by changes in risk tolerance, typically induced by cumulative performance relative to investment goals or an approaching investment horizon.
DAA could introduce
an additional source of estimation errors due to the need for dynamic rebalancing.
DAA has the potential to reflect changes in baseline assumptions over different time horizons.
Liability driven asset allocation
LDI seeks to find the
most efficient asset class mix driven by a fund’s liabilities. Simultaneously concerned with the return of the assets, the change in value of the liabilities, and how assets and liabilities interact to determine the overall portfolio value.
LDI encounters the same limitations as MVO, with high sensitivity to baseline assumptions.
Some ESG issues could potentially impact on inflation and alter liability assumptions.
Regime switching models28
Regime switching approaches model abrupt and persistent changes
in financial variables due to shifts in regulations, policies and other secular changes. Captures fat tails, skewness and time-varying correlations.
Regime switching approaches are relevant for considering ESG issues where an abrupt shift is expected over time. It is also typically based more on forward looking rather than historical data.
These approaches have the potential to capture dramatic shifts in the investment environment. Models are not yet widely utilised by investment practitioners.
MODEL /
FEATURES /
POTENTIAL LINK TO ESG ISSUES/
OUTPUTS TO REFLECT ESG ISSUES
Mean-variance optimisation (MVO)
MVO results in the construction of an efficient frontier that represents a mix of assets that produces the minimum standard deviation (as a proxy for risk) for the maximum level of expected return. It is based on defined asset class buckets and long- term expected returns, risks and correlations.
MVO is highly sensitive to baseline assumptions, making it imperative to fully understand any revised assumptions due to ESG considerations. MVO is highly dependent on historical data as the baseline with adjustments made to reflect future expectations.
**Volatility as a proxy for risk does not work well in cases of fat tail risk and large market swings.
ESG issues could impact on assumptions regarding expected return, volatility and correlation at the asset and sub-asset class level.
ESG issues also have the potential to expand the regional and asset class mix and to add new sub- asset classes to align with the pursuit of positive real- world impact.
Factor risk allocation
Factor risk frameworks seek to build a diversified portfolio based on sources of risk.
Typically includes factors such as fundamental risks (GDP, interest rates and inflation) as well as market risks (equity risk premium, illiquidity and volatility).
The macroeconomic links to ESG issues are more difficult to quantify with precision from a purely top-down perspective. Market risk factors can be built from the bottom-up using asset and sector level analysis.
ESG issues could require a change to baseline factor risk assumptions. It offers the potential to build
in new ESG-related risk factors (such as climate change) to improve diversification (particularly across market risk factors).
Total portfolio analysis (TPA)
Similar to factor risk allocation, TPA allows for closer review and interplay between the strategy setting process and alignment of investment goals. Based on an
agreed risk budget, asset allocations are made on expected risk exposures and are less constrained by asset class ‘buckets’ as traditional MVO approaches.
TPA is relevant to consider ESG issues that require the interplay between judgment about the future and quantitative analysis. TPA requires specialist knowledge to make informed judgments about future risk.
TPA’s emphasis on risk budgeting and allocation of capital to opportunities within that budget (bringing alignment between top-down
and bottom-up) would provide greater flexibility to capture the potential winners and losers in scenario analysis that also incorporate ESG-related issues.
Dynamic asset allocation (DAA)26
DAA is driven by changes in risk tolerance, typically induced by cumulative performance relative to investment goals or an approaching investment horizon.
DAA could introduce an additional source of estimation errors due to the need for dynamic rebalancing.
DAA has the potential to reflect changes in baseline assumptions over different time horizons.
Liability driven asset allocation
LDI seeks to find the
most efficient asset class mix driven by a fund’s liabilities. Simultaneously concerned with the return of the assets, the change in value of the liabilities, and how assets and liabilities interact to determine the overall portfolio value.
LDI encounters the same limitations as MVO, with high sensitivity to baseline assumptions.
Some ESG issues could potentially impact on inflation and alter liability assumptions.
Regime switching models
Regime switching approaches model abrupt and persistent changes
in financial variables due to shifts in regulations, policies and other secular changes.
Captures fat tails, skewness and time-varying correlations.
Regime switching approaches are relevant for considering ESG issues where an abrupt shift is expected over time. It is also typically based more on forward looking rather than historical data.
These approaches have the potential to capture dramatic shifts in the investment environment. Models are not yet widely utilised by investment practitioners.
Within the asset allocation framework shown in Table 8.4, one of the most promising approaches may well be the Black-Litterman asset allocation model (BLM).
While the Markowitz-derived MVO approach has garnered significant academic support, mean variance theory faces a number of limitations. For it to function, MVO råequires estimates for asset returns across each asset class, which makes the model incredibly input-dependent and sensitive. Any adjustments (even minor ones) to these return estimates will produce a dramatic change in allocation output, so investors may find the model hard to practically implement.
By comparison, BLM represents a more intuitive approach. Anchored by the global equilibrium market and not requiring return estimates for each asset class, it can arguably better accommodate areas like pricing climate risk.3
Within the asset allocation framework shown in Table 8.4, one of the most promising approaches may well be the Black-Litterman asset allocation model (BLM).
While the Markowitz-derived MVO approach has garnered significant academic support, mean variance theory faces a number of limitations. For it to function, MVO råequires estimates for asset returns across each asset class, which makes the model incredibly input-dependent and sensitive. Any adjustments (even minor ones) to these return estimates will produce a dramatic change in allocation output, so investors may find the model hard to practically implement.
By comparison, BLM represents a more intuitive approach. Anchored by the global equilibrium market and not requiring return estimates for each asset class, it can arguably better accommodate areas like pricing climate risk.
Despite the academic work supporting ESG’s effect on risk-adjusted returns, introducing ESG into the asset allocation process will undoubtedly carry exposure and weighting implications that must be considered relative to a standard, non-ESG asset mix strategy.
In other words, integrating a given ESG methodology (e.g. positive screening that tilts the overall assets mix to a higher than mean ESG rating) will introduce some diversification effect or skewness.
To be sure, this effect may well be intended.
In theory, managing a mixed- asset portfolio according to a carbon constraint or desired exposure level should reduce the risk to a carbon pricing shock through lower commensurate exposure to carbon-intensive, coal-reliant utilities and potential stranded assets.
Figure 8.5 illustrates the trade-offs that investors must consider when allocating to ESG or ‘sustainability’ more broadly.
Portfolio risk can be divided into two portions:
- the isolated risk of the individual asset or individual investment strategy; and
- the correlation risk that emerges from the combination of all the assets and strategies.
Traditionally, institutional investors have managed systemic, macro-economic factors by coupling asset allocation strategies (SAA) alongside asset/liability management (ALM).
Where strategic asset allocation establishes return targets across asset classes (equities, fixed income, real assets, etc.) and investment strategy types (i.e. alternatives), ALM provides investors the tools with which to match the cash flows of assets to payment of liabilities.
For example, both of these elements are vital for the sustainability of a pension funds’ risk-adjusted returns and its ability to pay out pension benefits for its beneficiaries
ASSET CLASS
Alternative investments
SUBTYPES • Real estate investment trusts (REITs); • commodities; • currencies; • private equity, venture capital (VC) funds; and • derivatives, hedge funds.
SAA/ALM IMPLICATIONS
• Attractive for diversification and for low or inverse correlation to market returns; and
• heterogeneous and wide-ranging risk/return profiles.
CLIMATE CHANGE CONSIDERATIONS
• Diversification offered by alternative assets may allow for greater hedging of climate risk; and
• climate risk exposure may be concentrated, opaque or difficult to assess.
EQUITIES: • Sensitive to climate impacts on macro-economic performance.
FIXED INCOME:
• Sensitive to fiscal policy related to climate challenges;
• sensitive to climate- related impacts on issuers’ creditworthiness; and
• many climate impacts fall within the tenor of long-term debt.
It is also clear that climate change represents different risks across asset classes.
Accordingly, portfolio managers must recognise that a company’s capital structure will naturally reflect risk.
For example, carbon- intensive companies like coal-powered utilities without an adaptation strategy will be at risk in the transition to a low-carbon economy.
In such a scenario, equity shareholders (who are subordinate to creditors and bondholders in the capital structure) will be disproportionately impacted.
Hence, asset allocation strategies must recognise asset class sensitivity alongside systemic and company-specific risks.
As well as being one of the key recommendations of the **TCFD framework,
**climate scenario analysis is as important in the wider asset allocation process as it is in understanding the micro, macro and ESG sensitivities within a single investment portfolio.
What might that look like in an asset allocation context?
The asset allocator would work to sensitise the portfolio against different warming scenarios using the 1.5o Celsius (2.7o Fahrenheit) as promoted in the Paris Climate Agreement (2015) as a baseline.
