blind to carbon risks? an assessment of stock market

Blind to carbon risks? An assessment of stock marketreaction to the Paris Agreement

Luca De Angelis (joint with Irene Monasterolo)

Department of Economics, University of Bologna, Italy

UZH Sustainable Finance 2020 ConferenceZurich, 16-17 January 2020

(Forthcoming in Ecological Economics)

Luca De Angelis (University of Bologna) Blind to carbon risks? UZH Sustainable Finance 2020 1 / 18

Introduction

The Paris Agreement (PA - 12/12/2015) is considered as a landmarkstep for global climate action and it came at surprise

Achieving the PA climate targets requires both scaling up oflow-carbon investments and divestments from carbon-intensive assets(HLEG 2018, NGFS 2018)

Growing investors’ appetite for “green” assets (e.g. ESG, greenbonds) but lack of standardized classification generates uncertainty

Existing literature does not agree on whether/how markets react toclimate announcements (policy, science) and whether/how they pricethem

This represents a main knowledge gap for investors (portfoliomanagement strategies) and for financial regulators (analysis ofsystemic financial implications of climate risks)


Contribution

We contribute to fill this gap by developing empirical analysis of theperformance of low-carbon and carbon-intensive stock market indicesbefore and after the PA

We apply ‘state of the art’ financial econometric models to assess:

If the market is pricing the risk of “staying brown” (larger beta/smallerportfolio weights for carbon-intensive indices) and the opportunity of“going green” (smaller beta/larger portfolio weights for low-carbonindices)

If the difference in beta/portfolio weights between low-carbon andcarbon-intensive indices could be explained as reaction to the PA

We focus on stock markets because

there are very few contributions in the literature on climate change

usually react faster to announcements and shocks than debt securities


Main results and implications

Our results show potential mispricing of climate risks and opportunities inEU, US, and global stock markets:

Systematic risk of low-carbon assets significantly reduced after PA

Mild market reaction to PA for carbon-intensive assets

Correlations between low-carbon and carbon-intensive indices dropafter PA

Main implications and contributions to sustainable finance:

In contrast from traditional theory, stock market not good at pricingclimate policy risk (yet): why? (Some reasons in Battiston et al.2019)

Mispricing of climate risks leads to incomplete markets, with relevantimplications on portfolio risk management strategies and financialstability (Battiston & Monasterolo 2019)


Review of the state of the art

Growing research attention on markets’ pricing of climate physical andtransition risk but no conclusive evidence so far:

Trump’s election: Ramelli et al. (2018) find that markets rewardedcompanies in high-emissions industries after Trump’s election but alsothat investors rewarded companies with more responsible climatestrategies

US withdrawing from PA: Sterner & Mukanjari (2018) find nounique evidence of portfolios’ response

Green indices’ performance: Singh & Shrimali (2018) find thatS&P Clean Energy does not add value to portfolios


Methodology I: (Extended) market and Fama-French5-factor models

We consider a linear model akin to the market model within theCAPM framework (Sharpe 1964) and the F-F five-factor model(Fama & French 2015)

Our models derive the linear relation between an asset’s expectedreturn and its beta (systematic risk)

We extend standard model specifications by including an interactionvariable to capture the possible impact of PA on the level ofsystematic risk and the risk-return profile of an asset

In the spirit of the Chow test (Chow 1960) we assess the presence ofa structural break in the slope parameter at a given time, i.e. abruptchange in the level of systematic risk due to PA announcement


Market model: Index performance and sensitivity to PA

ri ,t = αi + βi rm,t + γi rm,tdt + ε i ,t

where

ri ,t and rm,t are (log-)return at time t of index i and market index m

dt = 1 after PA (2015/12/12) and dt = 0 before PA

ε i ,t ∼ i .i .d . with E (ε i ,t) = 0 and E (ε i ,trm,t) = 0

Rejection of the null hypothesis H0 : γi = 0 provides evidence of a changein the index’s systematic risk level after PA


(Extended) Fama-French 5-factor model

ri ,t − rf ,t = αi + βi (rm,t − rf ,t) + γi (rm,t − rf ,t)dt

+ςiSMBt + ξiHMLt + ρiRMWt + ζiCMAt + ε i ,t

where

rf ,t is risk-free rate

SMB (Small Minus Big) is the size factor

HML (High Minus Low) is the value factor

RMW (Robust Minus Weak) is the profitability factor

CMA (Conservative Minus Aggressive) is the investment factor

Rejection of the null hypothesis H0 : γi = 0 provides evidence of a changein the index’s systematic risk level after PA


Methodology II: Portfolio optimization

Markowitz (1952)’s portfolio optimization performed before and after PA

argminw

w ′Σw

subject to

R ′w = µn

∑i=1

wi = 1

using Lagrange multipliers, where

w is a vector of portfolio weights

Σ is the covariance matrix for the returns on the indices in theportfolio

R is the vector of indices’ expected returns

µ is the expected return objective


Portfolio optimization before and after PA

We consider low-carbon and carbon-intensive indices to construct anoptimal portfolio

We perform Markowitz’s portfolio optimization before and after PAand compare the (cumulated) weights for the low-carbon andcarbon-intensive components of the portfolios


Data

We consider the following set of equity indices from US, European andglobal markets at daily and monthly frequencies (low-carbon,carbon-intensive, and market indices)

Index Mkt

Sample lengthFrom To

NASDAQ CLEAN EDGE GREEN ENERGY US 20/11/2006 26/11/2018WILDERHILL CLEAN ENERGY World 29/12/2000 26/11/2018S&P GLOBAL CLEAN ENERGY World 24/11/2003 26/11/2018

WORLD RENEWABLE ENERGY (RENIXX) World 03/01/2002 26/11/2018STOXX GLOBAL ESG ENV LEADERS World 05/04/2011 26/11/2018

STOXX EUROPE 600 OIL & GAS EU 30/12/1999 26/11/2018S&P 500 INTEGRATED OIL & GAS US 30/12/1999 26/11/2018

FTSE USA OIL & GAS US 30/12/1999 26/11/2018FTSE WORLD OIL & GAS World 30/12/1999 26/11/2018

MSCI WORLD World 30/12/1999 26/11/2018STOXX EUROPE 600 EU 30/12/1999 26/11/2018S&P500 ES ENERGY US 30/12/1999 26/11/2018


Preliminary analysis on daily data

Index

Before PA After PAMean Std Dev Mean Std Dev

NASDAQ CLEAN EDGE GREEN ENERGY -0.0097 2.210 0.0317 1.207WILDERHILL CLEAN ENERGY -0.0374 2.074 0.0100 1.270S&P GLOBAL CLEAN ENERGY -0.0142 1.810 -0.0117 1.012

WORLD RENEWABLE ENERGY (RENIXX) -0.0256 2.555 -0.0006 1.087STOXX GLOBAL ESG ENV LEADERS 0.0257 0.915 0.0175 0.767

STOXX EUROPE 600 OIL & GAS -0.0049 1.656 0.0029 0.893S&P 500 INTEGRATED OIL & GAS 0.0174 1.555 0.0127 1.084

FTSE USA OIL & GAS 0.0208 1.633 0.0137 1.230FTSE WORLD OIL & GAS 0.0113 1.431 0.0196 1.120

MSCI WORLD 0.0035 1.040 0.0264 0.662STOXX EUROPE 600 -0.0016 1.249 0.0059 0.955S&P500 ES ENERGY 0.0178 1.673 0.0084 1.237


Correlation analysis on monthly data


Market model: results for daily and monthly data

HAC robust standard errors***, **, * denote significance at 1%, 5% and 10% levels, respectively


Market model: results for daily and monthly data



F-F 5-factor model: results for monthly data



Portfolio optimization: results for monthly data

Optimal weights w for µ ∈ {0.35, 0.5}

Pre-PA Post-PA Pre-PA Post-PA

σ = 3.60 σ = 3.01 σ = 3.48 σ = 3.04


Conclusions

Overall the performance of low-carbon and carbon-intensive indices isstatistically different before/after the PA

After the PA the systematic risk associated to low-carbon assets andindices tends to decrease while the level of systematic risk associatedto carbon-intensive assets and indices tends to increase (but only atdaily frequency)

Markowitz’s portfolio optimization shows that the weights oflow-carbon indices increase after the PA

Our results have implications for investors’ portfolio risk managementstrategies in the low-carbon transition and financial supervisors’ rolein preserving financial stability


blind to carbon risks? an assessment of stock market

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