A Comparative Panel Data Study of ESG Impact on Asset

Valuation: Real versus Financial Assets
Abstract
Many research studies confirmed that companies with positive ESG performance outperform others
in managing systematic risk during crisis. Therefore, studying the relationship between ESG
performance and asset valuation became the interest of several scholars. Though, existing literature
emphasized mainly on studying this relationship for the equity asset class while few studies
addressed the fixed income and RIET asset classes with a lack of studies on commodities asset
class. Further, little literature examined the relationship between ESG controversies and asset
valuation. Similarly, limited studies assessed the relationship between ESG performance and asset
price volatility. Moreover, there is a lack of comparative panel data studies that evaluated these
relationships. Thus, this paper will explore the impact of ESG factors and controversies on real
versus financial asset valuation and will also study the effects on asset price volatility. The selected
sample will include the top 10 developed European and Emerging market countries and will use
monthly data covering a period of 10 years. The OLS multiple regression and GARCH-based
quantile regression models will be used to examine the identified research relationships. The paper
will bring in new empirical evidence for the study on ESG impact on asset valuation and provide
relevant policy recommendations to the investment community.
Introduction
Following the financial crisis of 2008, scholars investigated the companies that were more resilient
to systematic risk. It was found that the companies with positive ESG performance were more
effective in reducing systemic risk. The same observation was concluded after COVID-19
pandemic based on number of research studies (Broadstock et al., 2021). Accordingly, the demand
for investment products with favorable ESG performance has grown substantially (Erol, Unal and
Coskun, 2023). Further, many empirical studies have proved that the quality of ESG management
can be a critical value driver (Chmielewska and Kluza, 2024). In fact, ESG indicators have become
an essential component of asset value determination (Yang et al., 2021).
There are many previous studies which examined the impact of ESG performance on stock
valuation. There are some few other studies which explored the effects of ESG performance on
bond credit spread, REIT financial performance and commodity valuation (Kjerstensson and
Nygren, 2019; Erol, Unal and Coskun, 2023; Cagli, Mandaci and Taşkın, 2023;). This paper will
study the impact of ESG performance on the valuation of different investment products including
a mix of long-term real and financial investment products. The following section provides a
preliminary review of literature highlighting the significance of this study and presents a discussion
of hypothesis development. The data and methodology section describes the research sample
selection, variable measurements, model specification and data analysis techniques. The remaining
sections will cover the research ethical considerations, planned timeline and the resources required
to conduct the study.
Literature Review
Many research studies focused on examining the relationship between ESG performance and
corporate valuation. Zheng et al. (2022) explored the impact of ESG performance on corporate
valuation of companies in China and discovered a significant positive relationship. Furthermore,
PhD Candidate: Saida Salih Mohamed Khalifa
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Khan et al.,2024) studied the effects of ESG performance on equity valuation of European
companies and found that positive ESG performance raises the market price relative to true value
indicating that ESG is a friction to market efficiency. Elamer and Boulhaga (2024), on the other
hand, investigated the relationship between ESG controversies and firm performance in EU
countries and concluded a significant negative relationship. Zhou and Zhou,2022) examined the
impact of ESG performance on stock price volatility during COVID-19 pandemic and concluded
that company with positive ESG performance have lower price volatility.
Moreover, a few studies examined the effects of ESG performance on bond valuation and RIET
financial performance. Lian et al. (2023), for instance, investigated the effects of ESG performance
on corporate bonds credit spreads in China and found that companies with strong ESG performance
experience a decrease in their bond credit spreads. Similarly, Li and Adriaens (2024) explored the
effects of ESG performance on US corporate bond credit spreads and discovered that the
performance of social and governance components of ESG significantly affecting the credit spreads
whereas the environment component of ESG has an insignificant effect.
Furthermore, Ho (David), Rengarajan and Lum (2013) studied the impact of green investments
on RIET operational and financial performance in Singapore and discovered that the impact is
significantly positive. Additionally, Morri, Dipierri and Colantoni (2024) assessed the effect of ESG
rating on the return of European Equity REITs and revealed that there is a negative effect.
Contrarily, Lambourne (2022) studied the impact of sustainability on UAE real estate valuation
using a qualitative structured questionnaire and concluded there are number of obstacles for the
recognition of green building value and these include the shortage of market data and lack of
technical skills. Likewise, Babawale and A. Oyalowo, (2011) used surveys to explore the
perceptions of real estate valuers of incorporating sustainability in real estate valuation and found
that there is a good level of awareness among valuers of the need to incorporate sustainability in
real estate valuation; however, there was a lack of perception regarding the definition of
sustainability.
Exploring the commodities market, there is a lack of studies that investigated the impact of
ESG performance on commodity valuation. However, Dutta et al. (2021) explored the effects of
commodity market implied volatility (VIX) on stock volatility of Indian green companies and
confirmed that there is a significant positive relationship particularly during bearish stock market
conditions. Additionally, Cagli, Mandaci and Taşkın (2023) evaluated the volatility spillover
between commodity and companies with positive ESG performance and discovered that ESG
indices are volatility transmitter while all commodity indices excluding crude oil and copper are
volatility receivers.
Significance and Rationale
Based on the preliminary literature review, it can be concluded that the existing literature focuses
mainly on studying the relationship between ESG performance and corporate valuation. Few
studies assessed the relationship between ESG performance and bond and RIET valuation with a
lack of studies studying the impact on commodity valuation. Moreover, very limited studies
incorporated the impact ESG controversies on asset valuation. In addition, little literature examined
the impact of ESG performance on asset price volatility with emphasis mainly on stock price
volatility and little on commodity price volatility. Furthermore, no comparative studies of ESG
impact on asset valuation were found. This study will contribute highly to the existing literature by
providing comparative panel data of the ESG impact on real versus financial asset valuation.
PhD Candidate: Saida Salih Mohamed Khalifa
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Aim and Objectives
The aim of the research is to explore and measure the impact of ESG factors and controversies on
the valuation of various long-term real and financial asset classes including equity, fixed income,
real estate and commodities as well as to analyze the consequent effects on these assets’ price
volatility.
The research objectives
1. To examine the impact of ESG factors and controversies on the valuation of long-term financial
assets (i.e., equity and fixed income).
2. To assess the impact of ESG factors and controversies on the valuation of long-term real assets
(i.e., real estate and commodities).
3. To measure and compare the effects of ESG factors and controversies on long-term financial
versus real assets’ price volatility.
Research Questions and hypothesis
1. What is the impact of ESG factors and controversies on the valuation of long-term financial
assets?
Hypothesis (H1): Contrary to ESG controversies ESG factors can significantly improve the
valuation of long-term financial assets.
2. What is the impact of ESG factors and controversies on the valuation of long-term real assets?
Hypothesis (H2): Contrary to ESG controversies, ESG factors can significantly improve the
valuation of long-term real assets.
3. To what extent do ESG factors and controversies affect long-term financial and real assets price
volatility?
Hypothesis (H3): Contrary to ESG factors, ESG controversies affect long-term financial and
real assets’ price volatility to a greater extent.
Hypothesis (H4): ESG factors and controversies effect on long-term real assets’ price volatility
is greater than their effect on long-term financial asset’s price volatility.
Data and Methodology
Sample Selection: the selected sample in this research will include the top 10 developed European
and Emerging market countries. The results of the research on these two groups of countries will
provide a useful comparison. The research will use monthly data covering a period of 10 years to
allow high statistical precision.
Variable Measurements: to study the impact of ESG factors and controversies on the valuation
and the respective volatility of various long-term real and financial asset classes, the following
variables will be used:
Variable Definition Literature Sources
Independent Variables
E Environment Component of Total ESG Score (Cohen, 2023)
S Social Component of Total ESG Score (Cohen, 2023)
PhD Candidate: Saida Salih Mohamed Khalifa
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G Governance Component of Total ESG Score (Cohen, 2023)
ESG Total ESG Score (Zheng et al., 2022)
ESG CON ESG Controversy Score (Elamer and
Boulhaga, 2024)
HESGL The difference in excess returns
between the top 25% ESG-rated REIT portfolio and the
bottom 25% ESG-rated REIT portfolio.
(Morri, Dipierri and
Colantoni, 2024)
Dependent Variables
PBV Share Price to Book Value Per Share (Chmielewska and
Kluza, 2024)
PE Share Price to Earnings Per Share (Chmielewska and
Kluza, 2024)
PEBIT Share Price to Earnings Before Interest and Taxes (Chmielewska and
Kluza, 2024)
TobinQ Market Value B/Total assets (Zheng et al., 2022)
Spread Bond credit spread is the difference between the bond
yield and the yield of a Treasury bond that is identical
to the corporate bond in all characteristics such as
coupon rate, term to maturity, and payment schedule
(Lian et al., 2023)
FFO/Revenues An indicator of the cash-generating potential of the
firm existing properties which is also a measurement
for the operational performance of REIT.
(Ho (David),
Rengarajan and
Lum, 2013)
Ri – Rf The excess return of REIT company stock which is
calculated by subtracting 3-month interbank offered
rates from the end-of year RIET stock return.
(Erol, Unal and
Coskun, 2023)
Sharpe Ratio
[(Ri-rf)
/volatility]
Risk-adjusted excess return (Erol, Unal and
Coskun, 2023)
Beta Systematic risk (historical local index) (Erol, Unal and
Coskun, 2023)
SpotR The monthly spot price returns for major commodities
such crude oil, gasoline, gold, silver, soybeans, and
wheat.
(Brooks and
Prokopczuk, 2013)
VOL (Zhou and Zhou,
2022)
Commodity
VIX
Implied volatility index for major commodities. (Dutta et al., 2021)
Control Variables
CE Capital Expenditure (Khan et al., 2024)
DPR Dividend Payout Ratio (Khan et al., 2024)
Cash Cash holding ratio = cash/total assets (Zhou and Zhou,
2022)
BondSize The natural logarithm of the bond issuance scale (Lian et al., 2023)
BondTerm The bond issuance period (in years) (Lian et al., 2023)
BondRate Bond rating (Lian et al., 2023)
SIZE The natural logarithm of total assets (Lian et al., 2023)
LEV Leverage ratio is the ratio of total liabilities to total
assets
(Lian et al., 2023)
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LIQ The ratio of current assets to total assets (Lian et al., 2023)
IBR the ratio of the company’s current profit before interest
and tax to interest expenses;
(Lian et al., 2023)
ROA Return on assets (Lian et al., 2023)
GR The growth rate of the company’s sales revenue (Lian et al., 2023)
CPI Consumer price index (Yang et al., 2021)
GDP GDP growth rate (Yang et al., 2021)
M2 Broad money supply (Yang et al., 2021)
PBV Share Price to Book Value Per Share (Ho (David),
Rengarajan and
Lum, 2013)
Operating
Expenses
Operating Performance (Ho (David),
Rengarajan and
Lum, 2013)
DUR Bond Duration (Kjerstensson and
Nygren, 2019)
CON Bond Convexity. Convexity, together with modified
duration (described above), provide a more reliable
estimation of the percentage price change resulting
from a specified change in a bond’s yield,
(Kjerstensson and
Nygren, no date)
SMB The return spread between small- and large-cap stocks,
and high minus low (HML)
(Morri, Dipierri and
Colantoni, 2024)
MKT return of the
stock market index less the risk-free rate
(Morri, Dipierri and
Colantoni, 2024)
HML The return spread between high book-to-market and
low book-to-market stocks.
(Morri, Dipierri and
Colantoni, 2024)
RMW The return spread of firms with high, or robust,
operating profitability, and those with weak, or low,
operating profitability.
(Morri, Dipierri and
Colantoni, 2024)
CMA The return spread between companies that invest
aggressively and those that do so more conservatively.
(Morri, Dipierri and
Colantoni, 2024)
Model Specification: To test H1, the assessment of the impact of ESG factors and controversies
on equity valuation can be modelled using OLS regression in four models:
Model 1: 𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖,𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 + 𝛽𝛽5𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 +
𝛽𝛽6𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽7𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Model 2:𝑃𝑃𝑃𝑃𝑖𝑖,𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 + 𝛽𝛽5𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 +
𝛽𝛽6𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽7𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Model 3: 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖,𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 + 𝛽𝛽5𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 +
𝛽𝛽6𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽7𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Model 4: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖.𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 + 𝛽𝛽5𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 +
𝛽𝛽6𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽7𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Further, the assessment of the impact of ESG factors and controversies on bond valuation can also
be modelled using OLS regression:
PhD Candidate: Saida Salih Mohamed Khalifa
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Model 5: 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖.𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑖𝑖,𝑡𝑡 +
𝛽𝛽5𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑖𝑖,𝑡𝑡 + 𝛽𝛽6𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽7𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽8𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖,𝑡𝑡 + 𝛽𝛽9𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡 + 𝛽𝛽10𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 + 𝛽𝛽11𝐺𝐺𝐺𝐺𝑖𝑖,𝑡𝑡 +
𝛽𝛽12𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛽𝛽13𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖,𝑡𝑡 + 𝛽𝛽14𝑀𝑀2𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Additionally, to test H2, the examination of the impact of ESG factors and controversies on public
real estate (RIET) valuation can be modelled in three models using OLS regression
Model 6 (Fama five factor model): 𝑅𝑅𝑖𝑖− 𝑅𝑅𝑓𝑓𝑖𝑖.𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝑖𝑖,𝑡𝑡 +
𝛽𝛽4𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 + 𝛽𝛽5𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖,𝑡𝑡 + 𝛽𝛽6𝐻𝐻𝐻𝐻𝐻𝐻𝑖𝑖,𝑡𝑡 + 𝛽𝛽7𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡 + 𝛽𝛽8𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Model 7: 𝑆𝑆ℎ𝑎𝑎𝑎𝑎𝑎𝑎 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽5𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖,𝑡𝑡 +
𝛽𝛽6𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽7𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 + 𝛽𝛽8𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖,𝑡𝑡 + 𝛽𝛽9𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛽𝛽10𝑀𝑀2𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Model 8: 𝐹𝐹𝐹𝐹𝐹𝐹/𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖,𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 +
𝛽𝛽5𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Moreover, the examination of the impact of ESG factors and controversies on commodity valuation
can be modelled OLS regression:
Model 9: 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛽𝛽5𝑀𝑀2𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
To test H3 and H4, the effects of ESG factors and controversies on equity’ price volatility can be
measured using OLS regression:
Model 10: 𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 +𝛽𝛽3𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 + 𝛽𝛽5𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 +
𝛽𝛽6𝐶𝐶𝐶𝐶𝐶𝐶ℎ𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Further, the effect of ESG factors and controversies on bond’s price volatility can be measured
using OLS regression:
Model 11: 𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖,𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 +𝛽𝛽3𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝐷𝐷𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡 + 𝛽𝛽5𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 +
+𝛽𝛽6𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽7𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Additionally, the effect of ESG factors and controversies on RIETs price volatility can be measured
using OLS regression:
Model 12: 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑖𝑖,𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + 𝛽𝛽2𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑖𝑖,𝑡𝑡 + + 𝛽𝛽3𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖,𝑡𝑡 + 𝛽𝛽5𝐺𝐺𝐺𝐺𝐺𝐺𝑖𝑖,𝑡𝑡 +
𝛽𝛽6𝐶𝐶𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 + 𝛽𝛽7𝑀𝑀2𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡
Moreover, the effect of ESG factors and controversies on commodity price volatility can be
measured using GARCH-based quantile regression model. Data for Commodity price volatility can
be obtained from the commodity implied volatility index (VIX).
Data Analysis: The econometric models’ results will be used to make useful statistical inferences
on whether ESG factors and controversies can significantly impact the valuation of long-term
financial assets and long-term real assets. Also, the degree of the impact will be also evaluated by
exploring the effects on asset price volatilities. The reliability of the results will be ensured by
checking the robustness of the results using measures such as, alternative measures of dependent
variables, alternative measures of independent variables, lag test for reverse causation, change the
time span and others (Zhou and Zhou, 2022; Zheng et al., 2022; Cai, Geng and Yang, 2024)
PhD Candidate: Saida Salih Mohamed Khalifa
7
Ethical Considerations
This research paper is considered an empirical research study type that is purely based on secondary
data. Therefore, the ethical issues of confidentiality, anonymity, voluntary participation, and
informed consent are of little concern.
The most important ethical consideration is getting approval for the research proposal from the
institutional review board of the intended university. Further, proper citation and referencing will
be ensured when conducting a critical review of the literature to avoid plagiarism. Moreover, the
research design will define clearly the methodology of data collection and analysis to enhance the
reliability of the study. Additionally, special attention will be given to maintaining the accuracy and
integrity of the data throughout the research process
Timeline
Research Phase Timeframe
Literature Review Year 1
Research Design and Methodology Development Year 2
Data Collection Year 3 and 4
Data Analysis Year 5
Discussion of Findings Year 5
Writing up the thesis Year 6
The Viva Year 6
Resources
The resources needed to conduct this research study will be mainly:
1. Access to databases such as Refinitiv or Bloomberg terminal to obtain the relevant market data
required for the empirical study.
2. Use of statistical and econometrics software needed for running and testing the research
empirical model.
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