Content uploaded by Mesut Doğan
Author content
All content in this area was uploaded by Mesut Doğan on Dec 26, 2023
Content may be subject to copyright.
ARAŞTIRMA MAKALESİ
RESEARCH ARTICLE
(2023), 13 (2)
Başvuru / Received :18.11.2023
Kabul / Accepted :15.12.2023
https://dergipark.org.tr/tr/pub/jsstr
İstatistik Araştırma Dergisi
Journal of Statistical Research
ISSN: 2791-7616
The Analysis of the Relationship Among Climate Policy Uncertainty, Logistic Firm
Stock Returns and ESG Scores: Evidence from the TVP-VAR Model
Fatma Gül ALTIN
Mehmet Akif Ersoy University / Assoc. Prof.
gulaltin@mehmetakif.edu.tr
Orcid No: 0000-0001-9236-0502
Samet GÜRSOY
Mehmet Akif Ersoy University / Assist. Prof.
sametgursoy@mehmetakif.edu.tr
Orcid No: 0000-0003-1020-7438
Mesut DOĞAN
Bilecik Şeyh Edebali University / Assoc. Prof.
mesutdogan07@gmail.com
Orcid No: 0000-0001-6879-1361
Enes Burak ERGÜNEY
Mehmet Akif Ersoy University / Master Student
enesburakergney@gmail.com
Orcid No: 0000-0002-1538-1489
Abstract
This study examines the relationship between climate policy uncertainty (CPU), China's environmental impact,
social responsibility and corporate governance practices (ESG) leader scores and logistics stocks. China Ocean
Shipping Company (COSCO), one of the pioneers in global markets, was chosen to represent the logistics industry.
The variables were analyzed with the Time-Varying Parameter Vector Autoregressive Model (TVP-VAR) using
monthly data from October 2007 to July 2022. As a result of the analysis, it was determined that the COSCO
logistics sector variable spreads the volatility to the Chinese ESG Leaders and CPU variables. This indicates that
COSCO, one of the leading companies in the global markets, has an impact on the sustainability scores of the
CHINA Stock Exchange. In other words, it has been observed that shock transfer occurs from the COSCO variable
to the China ESG Leader and CPU variables. Finally, it proves that the sustainability scores of companies operating
in the Logistics sector, especially for China, are dominant among all other sector scores.
Keywords: Climate Policy Uncertainty, ESG, Logistics Sector, COSCO
Corresponding Author / Sorumlu Yazar: 1- Mesut DOĞAN, Bilecik Şeyh Edebali University.
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
43
İklim Politikası Belirsizliği, Lojistik Firma Hisse Getirileri ve ESG Puanları Arasındaki
İlişkisinin Analizi: TVP-VAR Modelinden Kanıtlar
Özet
Bu çalışma iklim politikası belirsizliği (CPU), ESG skorları ve lojistik hisse getirileri arasındaki ilişkiyi
incelemektedir. Lojistik sektörünü temsilen China Ocean Shipping Company (COSCO) seçilmiştir. Ekim 2007-
Temmuz 2022 döneminin aylık verileri kullanılarak Zamanla Değişen Parametre Vektörü Otoregresif Modeli
(TVP-VAR) uygulanmıştır. Analiz sonucunda COSCO`nun volatiliteyi Çin ESG ve CPU`ya yaydığı tespit
edilmiştir. Bu durum küresel piyasalarda öncü firmalardan olan COSCO`nun Çin Borsasının sürdürülebilirlik
skorları üzerinde etkili olduğunu işaret etmektedir. Başka bir ifadeyle, COSCO değişkeninden Çin ESG skorları
ve CPU değişkenlerine şok aktarımı gerçekleştirdiği görülmüştür. Son olarak, özellikle Çin için Lojistik
sektöründe faaliyet gösteren firmaların sürdürülebilirlik skorlarının tüm diğer sektör skorları içinde de baskın
olduğunu kanıtlar niteliktedir.
Anahtar sözcükler: İklim Politika Belirsizliği, ESG, Lojistik Sektörü, COSCO
1. Introduction
Global warming and climate change are not only one of the most important problems facing humanity in the 21st
century, but also a problem that changes the future plans of companies (Chen et al. 2023). On the other hand,
policies aimed at combating climate change bring along the efforts of countries around the world to reduce
greenhouse gas emissions for a more sustainable future (Gavriilidis, 2021). Therefore, how to regulate
environmental protection along with economic growth in the face of climate change has become an important issue
all over the world (Ren et al. 2022). The Paris Agreement has brought a new perspective to climate policy, such
as promoting the use of clean energy, carbon emission permits and green bonds. However, there are concerns about
the uncertainties in the implementation of these policies and the macroeconomic effects of these uncertainties (Li,
2022).
Logistics has become a rapidly growing and developing industry around the world, playing a very important role
in global trade and economic growth (Yingfei et al., 2022). The primary purpose for companies is to organize
logistics activities in a way that maximizes profitability. However, in recent times, as a result of growing public
and governmental emphasis on ecological matters, company have faced mounting demands to diminish the
ecological footprint arising from their logistical activities (McKinnon, 2015). The growing trend of globalization,
coupled with the rising prominence of outsourcing and commercial interactions within the logistics industry,
underscores the critical significance of effective supply chain management. However, this heightened connectivity
and economic activity also contribute to environmental challenges. Greenhouse gas emissions stemming from fuel
consumption, escalating use of natural resources, and the mounting volume of packaging and other waste types
present substantial issues, impacting sustainability across environmental, economic, and social dimensions
(Yontar, 2022).
Logistics pertains to the systematic administration of procurement, conveyance, and warehousing of resources,
components, and finalized goods across companies and distribution networks, encompassing the associated
streams of information to meet order requirements (Christopher, 2011). Green logistics is the main sustainability
trend of modern logistics. The notion of eco-friendly logistics pertains to a collection of supply chain management
practices concentrated on the handling of materials, waste disposal, packaging, and transportation. Its objective is
to minimize the environmental and energy impacts associated with the distribution of goods (Seroka-Stolka &
Ociepa-Kubicka, 2019). Maritime transport accounts for more than 80% of world trade, which constitutes a large
area for global logistics applications (Pang et al., 2021:423). Although this situation offers shipping companies
new opportunities in the global economy, it has caused the maritime industry to cope with some new challenges.
The globalization of commercial practices has brought with it climate change and environmental problems caused
by maritime transport (Felicio et al. 2021).
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
44
Supply chain practices, especially logistics practices, are part of the critical practices of companies that consume
more energy and release dangerous gases and wastes into the environment (Agyabeng-Mensah et al., 2020:1).
Logistics is the most dependent on fossil fuels of all sectors. In addition, 37% of CO2 emissions in 2021 originate
from the logistics sector. Although logistics is one of the sectors most affected by the Covid-19 epidemic, this rate
is increasing day by day due to global trade (IEA, 2021). In 2021, global CO2 emissions from the logistics sector
rose 8% to around 7.7 Gt CO2, as pandemic restrictions were lifted and passenger and goods movements resumed
after the big drop in 2020 (IEA Transport Report, 2022). Figure 1 shows the global CO2 emission rates in transport
by mode for 2020. In Figure 1, “shipping” CO2 emissions rank third after “passenger cars” and “medium and heavy
trucks”.
Figure 1. Global CO2 emissions in transport by mode of 2020
Source: IEA, 2022
The possible stagnation of economies in an environment of uncertainty is supported by academic literature. On the
other hand, it is seen that many prediction and calculation methods have been developed for the uncertainties in
global markets. Apart from the known methods, an indexing that has been trying to find a place in the academic
literature in recent years has come to the fore. These indexes, in which economic and political uncertainties are
calculated, appear as a form of calculation that includes political discourses along with financial risk. The climate
policy uncertainty index(CPU) is also an index created using this method (Gürsoy, 2021). Combating
environmental degradation worldwide is of great importance for both developed and developing countries. The
fact that the issue has global as well as local effects forces countries to cooperate in improving environmental
quality in this field. Researchers examine numerous determinants of environmental quality that can help reduce
the growing ecological footprint and achieve sustainable development and make recommendations on how
environmental quality can be improved worldwide. Financial and economic variables can influence environmental
dynamics in various ways, ultimately causing environmental degradation or contributing to environmental
recovery. As a result of both situations, there is an expectation that environmental sustainability has a two-way
interaction, especially with logistics (Shahbaz et al. (2023)
This study explores the correlation spread between CPU, ESG and COSCO stock returns. Previous studies have
shown that CPU index and sustainability were examined together. In particular, the relationship between climate
policy uncertainty and variables such as sustainable financial assets (stock market, bonds), fossil fuel prices,
geopolitical risk, EPU, exchange rate is frequently investigated. The number of studies investigating the
relationship between CPU, ESG and stock market is quite limited. In the study, the share prices of China Ocean
Shipping Company (COSCO) were taken to represent the logistics sector. COSCO is one of the world's leading
conglomerates for container shipping. On the other hand, it is the largest company in China. COSCO is the world's
fourth largest container shipping company in 2022, with operations spread over 40 countries with a fleet of
approximately 480 container ships with a container carrying capacity of 2,932,779 TEU (Marine Insight, 2023).
On the other hand, it is still the fourth largest company in the world with a container transport capacity of 2,888,256
TEU and a market share of 10.8% as of May 19, 2023 (Alphaliner, 2023). In addition, the results of the study are
important for investors and company managers. The results obtained will allow policy makers to develop more
realistic and accurate sustainable environmental policies.
41%
22%
11%
8%
7%
5% 3% 3%
Passenger cars
Medium and heavy trucks
Spipping
Aviation
Buses and minibuses
Light commercial vehicles
Two/three-wheelers
Rail
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
45
The rest of this study is created as follows: In the subsequent section a brief literature review is given. In section
3, detailed explanations are given about the time-varying parameter vector autoregressive (TVP-VAR) model. In
Chapter 4, after giving information about the variables and data set, analyses are made to determine the asymmetric
dynamic spillover relationship and empirical findings are discussed. Finally, the results are evaluated and
suggestions for next studies are presented.
2. Literatür Review
In the last few years, the Climate Policy Uncertainty (CPU) Index, MSCI China ESG Leaders Index (China ESG
Leaders) and the shipping market have been evaluated from different perspectives in the literature using the time-
varying parameter vector autoregressive (TVP-VAR) model. Below is a three-dimensional literature review. First
of all, studies on CPU and TVP-VAR model, and secondly, studies on MSCI Index and TVP-VAR model were
examined. Finally, studies focusing on the shipping market and TVP-VAR model were examined.
- CPU Index and TVP-VAR Model
Yan and Cheung (2023) explored the changing impacts of Central Processing Unit (CPU) and coal price on the
carbon price in China through the implementation of the TVP-VAR model. The research involved the development
of a CPU index specifically for China. The findings of the analysis revealed that both CPU and coal price exhibited
noteworthy time-varying influences on the carbon price. Yu et al. (2023) examined the time-varying effects of
CPU on green bond market volatility using the TVP-VAR model. With short-term overreactions or underreactions
as well as medium and long-term inversions were found from the analyses.
Xiao and Liu (2023) evaluated the effect of uncertainty measures of CPU, geopolitical risk (GPR), economic
policy uncertainty (EPU), and equity market volatility (EMV) on the oil implied volatility index (OVX) using
TVP-VAR model. Empirical results have shown that the CPU is more important to trigger oil market fears since
the last Paris Agreement. During the COVID-19 pandemic, CPU, EPU and EMV instead of GPR play an important
role in increasing the fear of the oil market.
Zhou et al. (2023) investigated a model known as the time-varying parameter vector autoregressive model with
stochastic volatility (TVP-VAR-SV) to ascertain the changing association between CPU, oil prices, and renewable
energy consumption. The outcomes of their examination revealed that the relationship between these variables
fluctuates over time. In a separate study, Guo et al. (2022) examined the nonlinear impacts of CPU, financial
speculation, economic activity, and the US dollar exchange rate on global prices of crude oil and natural gas by
employing TVP-VAR-SV models. The findings underscored the existence of significant nonlinear effects in how
energy prices respond to various shocks.
- MSCI Index and TVP-VAR Model
Polat et al. (2023) investigated the influence of the media coverage index (MCI) related to COVID-19 on the
interconnectedness of return and volatility among five MSCI Climate Changes Indices, namely the USA, Emerging
Markets (EMU), Japan, Europe, and the Asia Pacific. The research employed the TVP-VAR model and the
frequency-dependent connectedness network approach for analysis. Empirical results underscore that the MCI acts
as a recipient of net shocks across all waves, with the highest level of interconnectedness observed in the initial
wave. Similar patterns were observed regarding volatility in the findings. Cepni et al. (2023) assessed the influence
of climate-related uncertainty on the transmission of effects among conventional and environmental, social, and
governance (ESG) financial markets in Europe. The study examined the spillover effects stemming from climate
uncertainty within these markets. TVP-VAR and asymmetric dynamic conditional correlation (ADCC) models
and portfolio analysis were used in the analyses. The results show substantial evidence of climate uncertainty,
important insights into managing climate risk exposures, and the driver of information spillovers across
conventional and ESG assets.
Liu et al. (2023) examined the effect of ESG investment on return and volatility spillover effects in major Chinese
financial markets such as stock, bond, interbank and foreign exchange markets using the TVP-VAR method. In
the study, it was found that sustainability and stability are positively related. Akhtaruzzaman et al. (2022)
investigated the dynamic connectedness between the COVID-19 MCI and the ESG leader indices. The results of
the analysis showed that MCI facilitated the contagion during the pandemic to the developed and emerging equity
markets.
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
46
- Shipping Market and TVP-VAR Model
Xie et al. (2023) analyzed risk spillovers in China's financial and maritime markets using dynamic spillover
measures based on TVP-VAR and generalized forecast error variance decompositions (GFEVD). Unexpectedly,
the study found that bonds, gold, and shipping were safe tools that facilitate portfolio optimization. Samitas et al.
(2022-a) evaluated the dynamic interconnections between fine wine, equities, bonds, crude oil, commodities, gold,
copper, shipping, and real estate markets using the TVP-VAR model. The research investigated the presence of
positive spillovers in terms of volatility among these markets. However, the study revealed that the overall
connectedness is susceptible to external shocks, which reach their highest levels during periods of stress.
Samitas et al. (2022-b) analyzed the transmission of volatility between natural alternative investments (such as
timber and water) and a range of traditional financial instruments (including bonds, crude oil, gold, real estate,
shipping, and currency) using a time-varying spillover methodology. The results indicate that these markets
demonstrate a moderate level of integration, and the overall interconnectedness amplifies during periods of
heightened stress. In a separate study, Chen et al. (2021) explored the nonlinear and dynamic relationship between
the global oil market, the global shipping market, the Chinese stock market, and GDP using the TVP-VAR-SV
model. The findings suggest that the impact intensity of the Baltic Dry Index (BDI) on China's economy triggers
diverse changes, ranging from positive to negative, across various lag periods.
In Table 1, the author, variable, period information and methodology of the studies in the literature are summarized,
respectively.
Table 1. Literature Summary
CPU Index and TVP-VAR Model
Yan and Cheung (2023)
- CPU Index
- Coal Price
- Carbon Emission
02.01.2019-
05.05.2022 (daily)
- TVP-VAR Model
Yu, Zhang, Liu and Wang
(2023)
- CPU Index
- Green Bond Index
- Green bond volatility
01.01.2010 -
31.12.2021 (monthly)
- TVP-VAR Model
Xiao and Liu (2023)
- CPU Index
- OVX Index
- GPR Index
- EPU Index
- EMV Index
01.05.2007 -
31.10.2021 (monthly)
- TVP-VAR Model
Zhou, Siddik, Guo and Li
(2023)
- CPU Index
- Oil Prices
- Renewable Energy
Consumption
01.01.2005 -
30.04.2021 (monthly)
- TVP-VAR-SV Model
Guo, Long and Luo (2022)
- CPU Index
- Oil Prices
- Natural Gas Prices
- Baltic Dry Index
- US dollar exchange rate
- T-index (WT)
01.01.2000 -
31.03.2021 (monthly)
- TVP-VAR-SV Model
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
47
Table 1. Literature Summary (Continuing)
MSCI Index and TVP-VAR Model
Polat, Khoury, Alshater and
Yoon (2023)
- MSCI Climate Change
Index
- Media Coverage Index
11.03.2020 -
19.01.2023 (daily)
- TVP-VAR Model
- Frequency-based
TVP-VAR Network
Connectedness
Cepni, Demirer, Pham and
Rognone (2023)
- MSCI Europe ESG
Various Euro-based
Leaders Index
- ESG Bond Indexes
03.01.2014 -
30.09.2021 (daily)
- TVP-VAR Model
- ADCC Model
- Portfolio analysis
Liu, Guo, Ping and Luo (2023)
- ESG Stock
- General Stock
- Interbank Market
- Bond Market
- FX Market
01.09.2010 -
31.08.2022 (monthly)
- TVP-VAR Model
Akhtaruzzaman, Boubaker and
Umar (2022)
- Media Coverage Index
- ESG leader Index
(EMU, China, Brazil,
Russia, India, and South
Africa, UK and US)
01.01.2020-
21.04.2021 (daily)
- TVP-VAR Model
Shipping Market and TVP-VAR- (SV) Model
Xie, Cheng, Liu, Zheng and Li
(2023)
- Stock, - Fund
- Bond, - Interbank
- Forex, - Futures
- Gold, - Shipping
20.01.2020 -
26.05.2022 (daily)
- TVP-VAR Model
- GFEVD
Samitas, Papathanasiou,
Koutsokostas and Kampouris
(2022-a)
- Fine wine, Bonds -
Equities, Gold
- Bonds, - Crude oil,
- Commodities, -,
Shipping
- Copper, -,
- Real estate
01.01.2010-
31.05.2021 (monthly)
- TVP-VAR Model
Samitas, Papathanasiou,
Koutsokostas and Kampouris
(2022-b)
- Timber, - Water
- Equities, - Bonds
- Crude oil, - Gold
- Real estate, - Shipping
- Currency
01.01.2010 -
30.09.2021 (monthly)
- TVP-VAR Model
Chen, Zhang and Chai (2021)
- World oil market
- Global bulk shipping
market
- Stock market
- Economic growth
01.01.1998 -
31.12.2020 (quarterly)
- TVP-VAR-SV Model
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
48
3. Research Methodology
This section provides an elaborate description of the time-varying parameter vector autoregressive (TVP-VAR)
model, elucidating its conceptual framework. Figure 2 below illustrates the methodological structure employed in
the study.
Figure 2. Methodological Framework
In this study, time-varying parameter vector autoregressive (TVP-VAR) model was used to investigate the time-
dependent dynamic relationship between the returns of the series, proposed by Antonakakis et al. (2020). The
TVP-VAR approach proposed by Antonakakis et al. (2020) allows variation of the variance-covariance matrix
over time through a Kalman Filter estimation based on Koop and Korobilis (2014) forgetting factors. In fact, it
extends the connectedness approach proposed by Diebold and Yılmaz (2009, 2012, 2014). In this way, the model
avoids the possibility that the rolling-windows technique, which has no consensus in the literature about the
selection criteria and which is usually chosen arbitrarily, leads to irregular or flattened parameters and the loss of
valuable observations (Antonakakis & Gabauer, 2017; Gabauer & Gupta, 2018; Korobilis & Yilmaz, 2018).
Accordingly, the model can be used to examine dynamic connectivity measures for both low-frequency data and
limited time-series data. The lag length of the series was determined as 1 according to the Bayes information
criterion (BIC) and the TVP-VAR (1) estimation was performed.
The TVP-VAR model is expressed as follows:
(1)
(2)
with
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
49
Here, respectively, it represents; all available information till , t-1; and m × 1 and mp × 1 vectors;
and , m × mp and m × m dimensional matrix; one m × 1 vector and one p × 1 dimensional matrix. The
time-varying variance-covariance matrices ve are m × m and m × m and p × p dimensional matrixes,
respectively. Also, , p × 1 is a vectorization of , which is a p × 1 vector.
Prior prediction is used to initialize the Kalman filter. Based on equations, with ,
and equal to the
VAR estimate for the first 20 months:
To ensure numerical stability in the Kalman filter algorithm, the decay factors were applied as =0,99 and
=0,99 recommended by Koop and Korobilis (2014)
Time-varying coefficients and time-varying variance-covariance matrices, Koop et al. (1996) and Pesaran and
Shin (1998), based on generalized impulse response functions (GIRF) and generalized prediction error variance
decompositions (GFEVD) to estimate the generalized connectivity procedure. For the calculation of GIRF and
GFEVD, TVP-VAR needs to be converted to a vector moving average (VMA) representation within the
framework of the Wold Decomposition theorem. The VMA representation is converted as follows (Dogan et al.,
2023; Akkus and Dogan, 2023):
(3)
(4)
(5)
(6)
indicate a dimension matrix of the mp × mp, is dimention vector of a mp × 1, indicate a dimension matrix
of the mp × m
GIRFs express the response in all variables to a shock in variable . it is not a structural model, an
estimate of H (H step ahead) is calculated in which variable i is both shock and non-shock, and the difference
between is attributed to variable . This is as follows
(7)
(8)
(9)
Calculating GFEVD ( which represents bidirectional dependency from to . The effect of variable '
on variable is explained in terms of estimation error variance shares. The said variance shares are normalized and
all variables together explain 100% of the estimation error variance of the variable . Its mathematical expression
is as follows:
(10)
Ve
. The denominator in this equation is the cumulative effect of all shocks;
The numerator represents the cumulative effect of a shock in the variable . Using the GFEVD, the Total
Connectedness Index (TCI) is calculated as follows:
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
50
(11)
This connectedness approach shows the spread of the shock in one variable to other variables. Based on this
approach, Total Directional Connectedness to Others (TO), which shows the spread of the shock in variable to
all other variables , is calculated as follows:
(12)
Total Directional Connectedness from Others (FROM), that shows the spread of shock in all variables to variable
, is calculated as follows:
(13)
To unveil the Net Total Directional Connectedness, which signifies the impact a variable exerts on the examined
network, the disparity between Total Directional Connectedness to Other Variables (TO) and Total Directional
Connectedness from Other Variables (FROM) is computed:
(14)
In this equation, if takes a positive value, it indicates that variable directs the network by affecting other
variables more than it has; If takes a negative value, it means that variable is driven by the network under the
influence of other variables.
Finally, Net Pairwise Directional Connectedness is calculated by decomposing Net Total Directional
Connectedness to examine bidirectional relationships:
(15)
NPDC indicate the situation where variable dominates variable or variable dominates variable (Antonakakis
et al., 2020, pp. 4–7).
4. Analysis
In this section, firstly, information about the variables and data set used in the study was given. Then, explanations
were made regarding the findings obtained in the analyses.
4.1. Data Set
This study examined the asymmetric dynamic spillover relationship between China Ocean Shipping Company
(COSCO) stock returns, Climate Policy Uncertainty (CPU) and MSCI China ESG Leaders Index (China ESG
Leaders). This index is a publicly adjusted, market capitalization weighted index. It was created by taking into
account the performance of companies selected from a core index based on Environmental, Social and Governance
(ESG). Factor Groups (e.g. Value, Size, Momentum, Quality, Return and Volatility) that have been extensively
documented in academic literature. Calculated by taking into account data in the literature and confirmed by MSCI
Research as the main drivers of risks and returns in stock portfolios.
A dataset containing monthly data was used for the sampling period from October 2007 to July 2022. First of all,
it was determined that the series were not stationary according to the ERS unit root test (Elliott et al., 1996), which
takes into account an unknown mean or trend in the series. and the logarithmic return of the series was calculated
with the formula ln (/ .The graph expressing the logarithmic return of the series is given in Figure 3.
Adekoya et al. (2022)'s approach was followed to observe the asymmetric spread in the series and monthly returns
were decomposed into positive and negative as follows;
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
51
Figure 3. Logarithmic Return of Series
Descriptive statistics of the return series are given in Table 2. According to the ERS unit root test, it was determined
that each series has a unit root, that is, the null hypothesis was rejected for all series at 1% or 5% significance level.
Fisher and Gallagher's weighted Ljung-Box results show that the returns of all series have significant
autocorrelation in their squares. This means that each series has time-varying variances and it is appropriate to use
the TVP-VAR model in the study.
Table 2. Descriptive Statistics
COSCO
CPU
CHINA.ESG.LEADERS
Mean
-0.008
0.008
0.001
Varyans
0.024
0.132
0.004
Skewness
0.185
0.089
-0.524***
-0.298
-0.616
-0.005
Ex.Kurtosis
3.586***
0.326
0.365
0
-0.285
-0.248
JB
96.372***
1.022
9.142***
0
-0.6
-0.01
ERS
-4.231***
-5.208***
-1.479*
0
0
-0.141
Q(10)
9.523*
27.712***
4.025
-0.087
0
-0.661
Q2(10)
5.24
13.443**
24.735***
-0.47
-0.013
0
Note: *, ** and *** represent significance level at 1, 5 and 10% respectively
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
52
Within Table 2, you will find an overview of the descriptive statistics pertaining to the variables utilized in this
research. The results show that the variables are not normally distributed and do not include unit root. In addition,
Q and Q2 test statistics show that it contains autocorrelation.
4.2. Findings
4.2.1. Average connectedness
Average connectivity results are given in Table 3. The upper section of Table 3 displays the comprehensive
outcomes, disregarding any asymmetry. The lower sections, however, focus on assessing measures of asymmetric
interconnectedness concerning both positive and negative returns. The values along the diagonal represent the
impact of past shocks from the variables on their respective error variance. Conversely, off-diagonal values
indicate the binary correlation between variables within the network. It is evident that the diagonal values surpass
other values within the network, highlighting the significant contribution of variables' own shocks to the estimation
error variance. For example, the largest value in the COSCO column is 87.49, which represents an 87%
contribution to its own forecast error variance. A value of 22.77 in the column indicates a 23% return spillover
from COSCO to China ESG Leaders.
The Corrected Total Connected Index (cTCI) value shows the total connectivity within the network regardless of
time. The cTCI value at the top of the table is 29%, implying that the interdependence of the variables within the
network is partially high. The “From” column represents the connectedness to a variable that is passed from all
other variables in the network; The “To” line indicates the transfer of connectivity from one variable to all other
variables in the network. When the relationship between “From” and “To” is examined for each variable, the
correlation of COSCO stock returns to other variables in the network; It is observed that the China ESG Leaders
index is the variable that is transferred from the other variables in the network. However, looking at the average
net donors and net buyers, the main net giver within the network is COSCO stock returns. China ESG Leaders and
CPU indices are net buyers in the network.
The middle and bottom of Table 3 represent positive and negative returns, respectively. It shows that both the main
findings and the findings from positive and negative returns are similar in terms of ranking of network transmitters
and network receivers. It should be noted, however, that cTCI is greater in connectivity based on negative returns
(31%) compared to that based on positive returns (ie 16%). Negative returns have higher overall network
connectivity, implying evidence for asymmetric effects.
Table 3. Average Connectivity
COSCO
CPU
CHINA.ESG.LEADERS
FROM
COSCO
87.49
1.74
10.77
12.51
CPU
4.4
85.42
10.18
14.58
CHINA.ESG.LEADERS
22.77
8.4
68.82
31.18
TO
27.17
10.14
20.95
58.27
Inc.Own
114.66
95.56
89.78
cTCI/TCI
NET
14.66
-4.44
-10.22
29.13/19.42
NPT
2
0
1
+
COSCO
CPU
CHINA.ESG.LEADERS
FROM
COSCO
91.94
4.15
3.91
8.06
CPU
5.12
88.32
6.56
11.68
CHINA.ESG.LEADERS
7.04
6.14
86.82
13.18
TO
12.16
10.29
10.47
32.92
Inc.Own
104.1
98.61
97.29
cTCI/TCI
NET
4.1
-1.39
-2.71
16.46/10.97
NPT
2
0
1
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
53
Table 3. Average Connectivity (Continuing)
-
COSCO
CPU
CHINA.ESG.LEADERS
FROM
COSCO
77.17
2.1
20.73
22.83
CPU
4.83
92.29
2.89
7.71
CHINA.ESG.LEADERS
29.5
2.21
68.29
31.71
TO
34.33
4.31
23.61
62.25
Inc.Own
111.5
96.6
91.9
cTCI/TCI
NET
11.5
-3.4
-8.1
31.12/20.75
NPT
2
0
1
Note: The outcomes are derived from an analysis utilizing a TVP-VAR model with a lag length determined by the Bayesian
Information Criterion (BIC) and a generalized prediction error variance decomposition conducted up to 20 steps ahead.
4.2.2. Dynamic total connectivity
The above-mentioned measures of mean connectivity are time-independent and, therefore, it is not possible to
observe the dynamic evolution of spillovers between variables. Considering that various economic and political
events occurred during the sample period that could affect the returns of the series positively or negatively, it
would be more accurate to focus on dynamic measurements. From this perspective, the Total Connectedness Index
(TCI) results, which show the change in the dynamic total interconnectedness between the returns of the variables
during the sample period, are given in Figure 4. The black shaded area shows the evolution of the TCI, which
includes both positive and negative values. With this, the green line on the Chart represents the TCI consisting of
only positive returns, while the red line represents only negative returns.
Focusing on the black shaded area to observe the evolution of connectivity within the network throughout the
sample period, it is seen that the connectivity between the variables exhibits a decreasing trend over time. Despite
the downtrend from 80% to 20%, the correlation between the variables is relatively strong. Also, although the
three different series exhibit a qualitatively downward trend, the correlation between positive returns is very low,
while the correlation between negative returns is much higher, supporting previous findings. The fact that the
correlation between negative returns is relatively higher and volatile compared to positive returns reveals that
negative news is more effective on stability in the network.
0
20
40
60
80
100
2008 2010 2012 2014 2016 2018 2020 2022
Figure 4. Total Connectedness Index (TCI)
Notes: Results presented herein are derived from a TVP-VAR model utilizing a lag length of one (selected based on the
Bayesian Information Criterion) and a generalized prediction error variance decomposition conducted up to 20 steps ahead. In
the visual representation, the black region corresponds to the symmetrical total interconnectedness, while the green and red
lines signify positive and negative total interconnectedness, respectively.
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
54
4.2.3. Net total and net bidirectional connectivity
Net Total Directional Connectivity provides a dynamic view of the net-receiving or net-transmitting role of a
variable. The results are given in Figure 5. A positive value in Figure 5 indicates that the variable is a net transmitter
in the network, and a negative value indicates that it is a net receiver. It should be noted that the role of a variable
in the network may change over time.
In Figure 5, according to the total returns without considering the asymmetric relations; During the entire sample
period, China ESG Leaders are net receiver and COSCO is net transmitters. However, the role of the CPU index
changes over time, but is usually in the receiver position.
According to the results of asymmetrical interconnectedness relations, a role change occurs between the positive
returns of all variables. In terms of negative returns; There is no role change in the negative returns of COSCO
and China ESG Leaders. Unlike general findings, especially in some periods, COSCO is the receiver of positive
returns from China ESG Leaders.
-20
0
20
40
60
80
100
2008 2010 2012 2014 2016 2018 2020 2022
COSCO
-60
-50
-40
-30
-20
-10
0
10
20
30
2008 2010 2012 2014 2016 2018 2020 2022
CHINA ESG LEADERS
-100
-80
-60
-40
-20
0
20
2008 2010 2012 2014 2016 2018 2020 2022
CPU
Figure 5. Net Total Directional Connectivity
Notes: The outcomes stem from an analysis employing a TVP-VAR model with a lag length determined by the Bayesian
Information Criterion (BIC) and a generalized prediction error variance decomposition extending up to 20 steps ahead. The
shaded region depicted in black signifies the overall balanced interconnectedness, while the presence of green and red lines
denotes positive and negative interconnectedness, respectively.
For more detailed inferences, we focus on Net Bidirectional Connectivity results. The Net Bidirectional
Connectivity results given in Figure 6 show the dynamic interconnectedness between two variables in the network.
Each graph represents the net transmitter/receiver role of the variable named in the first row with respect to the
variable named in the second row. For example, when we examine the black shaded area in the "COSCO-CHINA
ESG LEADERS" graph, we can say that China ESG Leaders were net recipients of emissions from COSCO during
the entire sample period. Findings from both general and positive and negative returns support previous findings.
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
55
-10
0
10
20
30
40
50
60
2008 2010 2012 2014 2016 2018 2020 2022
NET COSCO-CPU
-30
-20
-10
0
10
20
2008 2010 2012 2014 2016 2018 2020 2022
NET CPU-CHINA ESG LEADERS
-20
-10
0
10
20
30
40
2008 2010 2012 2014 2016 2018 2020 2022
NET COSCO-CHINA ESG LEADERS
Figure 6. Net Bidirectional Connectivity
Notes: The findings presented in this study are derived from an analysis conducted using a TVP-VAR model, employing a lag
length determined by the Bayesian Information Criterion (BIC), and a generalized prediction error variance decomposition
carried out up to 20 steps ahead. The shaded region in black illustrates the comprehensive interconnectedness, while the
presence of green and red lines indicates positive and negative connectivity, respectively.
Figure 7. Network graph representation of volatility spillover of variables.
In Figure 7, the connectivity transfer network between the variables is presented. The blue dots represent the
transmitter variables that conduct connectivity to other variables, and the yellow dots represent the receiver
variables that are linked from other variables. The size of the variable circles indicates the effect size of the
transmitter or receiver variable. The arrows drawn from the circles show the direction of the relationship between
the variables, while the thickness of these arrows shows the strength of the relationship. When the graph is
examined, COSCO is the variable that transmits the shock, while the China ESG Leaders and the CPU are the
variables that receive the shock. Shock transfer takes place from the COSCO variable to the China ESG Leaders
variable.
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
56
5. Conclusion
This study explored the link between CPU, ESG and COSCO stock return spread. An autoregressive model with
time-varying parameter vector was used in monthly data covering the period October 2007-July 2022. As a result
of the analysis, it was seen that the volatility spread to the COSCO variable, China ESG Leaders and CPU
variables. In other words, shock transfer occurred from the COSCO variable to the China ESG Leader and CPU
variables.
As a result of industrialization, environmental degradation increases significantly with the developing economic
development. Countries need to realize their management and strategies within the framework of sustainability.
Since the logistics sector acts as a bridge in trade, it is important to take measures to prevent environmental
degradation. In addition, determining the leading criteria in the realization of green logistics activities and ensuring
the selection of areas according to these criteria is one of the steps that should be taken in order to prevent
environmental degradation caused by the logistics sector. Based on the findings obtained as a result of the study,
the evaluation of the logistics sector in terms of green transformation and the centralization of the sector with this
awareness will make a great contribution to solving the greenhouse gas problem in the world.
There are a number of limitations to this research, which examines the correlation spread between CPU, ESG and
COSCO stock returns. First of all, the results should be evaluated in terms of COSCO share returns, which
represent the logistics industry. Future studies on green logistics activities will help environmental sustainability
policy makers. In addition, examining the relationship between the logistics sector index and sustainability indices,
clean energy indices and carbon emission indices will contribute to the literature.
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
57
References
Adekoya, O. B., Akinseye, A. B., Antonakakis, N., Chatziantoniou, I., Gabauer, D., & Oliyide, J. (2022). Crude
oil and Islamic sectoral stocks: Asymmetric TVP-VAR connectedness and investment strategies. Resources
Policy, 78, 102877. https://doi.org/10.1016/J.RESOURPOL.2022.102877
Agyabeng-Mensah, Y., Afum, E., & Ahenkorah, E. (2020). Exploring financial performance and green logistics
management practices: examining the mediating influences of market, environmental and social performances.
Journal of cleaner production, 258, 120613. https://doi.org/10.1016/j.jclepro.2020.120613.
Akhtaruzzaman, M., Boubaker, S., & Umar, Z. (2022). COVID–19 media coverage and ESG leader indices.
Finance Research Letters, 45, 102170. https://doi.org/10.1016/j.frl.2021.102170
Akkus, H. T., & Dogan, M. (2023). Analysis of dynamic connectedness relationships between cryptocurrency,
NFT and DeFi assets: TVP-VAR approach. Applied Economics Letters, 1-6.
Alphaliner (2023). https://alphaliner.axsmarine.com/PublicTop100/ Accessed: 19 May 2023.
Antonakakis, N., & Gabauer, D. (2017). Refined Measures of Dynamic Connectedness based on TVP-VAR. In
MPRA Paper (No. 78282; MPRA Paper). University Library of Munich, Germany.
Antonakakis, N., Chatziantoniou, I., & Gabauer, D. (2020). Refined Measures of Dynamic Connectedness based
on Time-Varying Parameter Vector Autoregressions. Journal of Risk and Financial Management, 13(4), 84.
https://doi.org/10.3390/JRFM13040084.
Cepni, O., Demirer, R., Pham, L., & Rognone, L. (2023). Climate uncertainty and information transmissions across
the conventional and ESG assets. Journal of International Financial Markets, Institutions and Money, 83,
101730. https://doi.org/10.1016/j.intfin.2022.101730.
Chen, Z., Zhang, L., & Weng, C. (2023). Does climate policy uncertainty affect Chinese stock market volatility?.
International Review of Economics & Finance, 84, 369-381. https://doi.org/10.1016/j.iref.2022.11.030.
Chen, Z., Zhang, X., & Chai, J. (2021). The dynamic impacts of the global shipping market under the background
of oil price fluctuations and emergencies. Complexity, 2021, 1-13. https://doi.org/10.1155/2021/8826253.
Christopher, M. (2011). Logistics & Supply Chain Management (4th edition), Pearson Education, London, UK.
Diebold, F. X., & Yilmaz, K. (2009). Measuring Financial Asset Return and Volatility Spillovers, with Application
to Global Equity Markets. The Economic Journal, 119(534), 158–171. https://doi.org/10.1111/J.1468-
0297.2008.02208.X
Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility
spillovers. International Journal of Forecasting, 28(1), 57–66.
https://doi.org/10.1016/J.IJFORECAST.2011.02.006.
Diebold, F. X., & Yilmaz, K. (2014). On the network topology of variance decompositions: Measuring the
connectedness of financial firms. Journal of Econometrics, 182(1), 119–134.
https://doi.org/10.1016/J.JECONOM.2014.04.012.
Doğan, M., Raikhan, S., Zhanar, N., & Gulbagda, B. (2023). Analysis of Dynamic Connectedness Relationships
among Clean Energy, Carbon Emission Allowance, and BIST Indexes. Sustainability, 15(7), 6025.
Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient Tests for An Autoregresive Unit Root. Econometrica,
64(4), 813–836.
Felicio, J. A., Rodrigues, R., & Caldeirinha, V. (2021). Green shipping effect on sustainable economy and
environmental performance. Sustainability, 13(8), 4256. https://doi.org/10.3390/su13084256.
Fisher, T. J., & Gallagher, C. M. (2012). New Weighted Portmanteau Statistics for Time Series Goodness of Fit
Testing. Journal of the American Statistical Association, 107, 777–787.
https://doi.org/10.1080/01621459.2012.688465.
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
58
Gabauer, D., & Gupta, R. (2018). On the transmission mechanism of country-specific and international economic
uncertainty spillovers: Evidence from a TVP-VAR connectedness decomposition approach. Economics Letters,
171, 63–71. https://doi.org/10.1016/J.ECONLET.2018.07.007.
Gavriilidis, K. (2021). Measuring climate policy uncertainty. Available at SSRN 3847388.
http://dx.doi.org/10.2139/ssrn.3847388.
Guo, J., Long, S., & Luo, W. (2022). Nonlinear effects of climate policy uncertainty and financial speculation on
the global prices of oil and gas. International Review of Financial Analysis, 83, 102286.
https://doi.org/10.1016/j.irfa.2022.102286.
Gürsoy, S. (2021). Küresel Ekonomik Politik Belirsizliğin (Gepu) Döviz Kuru, Enflasyon Ve Borsa Etkisi:
Türkiye`Den Kanitlar. Türkiye Mesleki Ve Sosyal Bilimler Dergisi (5), 120-131.
https://doi.org/10.46236/jovosst.877608.
IEA, (2021). Improving the sustainability of passenger and freight transport, https://www.iea.org/topics/transport
Accessed 8 April 2023.
IEA, (2022). Global CO2 emissions in transport by mode in the sustainable development scenario,
https://www.iea.org/data-and-statistics/charts/global-co2-emissions-in-transport-by-mode-in-the-sustainable-
development-scenario-2000-2070 Accessed 8 April 2023.
IEA Transport Report (2022). Sectoral overview, https://www.iea.org/reports/transport
Koop, G., & Korobilis, D. (2014). A new index of financial conditions. European Economic Review, 71, 101–116.
https://doi.org/10.1016/J.EUROECOREV.2014.07.002.
Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models.
Journal of Econometrics, 74(1), 119–147. https://doi.org/10.1016/0304-4076(95)01753-4.
Korobilis, D., & Yilmaz, K. (2018). Measuring Dynamic Connectedness with Large Bayesian VAR Models. SSRN
Electronic Journal. https://doi.org/10.2139/SSRN.3099725.
Li, X. (2022). Dynamic spillovers between US climate policy uncertainty and global foreign exchange markets:
the pass-through effect of crude oil prices. Letters in Spatial and Resource Sciences, 15, 665–673.
https://doi.org/10.1007/s12076-022-00318-4.
Liu, M., Guo, T., Ping, W., & Luo, L. (2023). Sustainability and stability: Will ESG investment reduce the return
and volatility spillover effects across the Chinese financial market?. Energy Economics, In Press, Journal Pre-
proof, 106674. https://doi.org/10.1016/j.eneco.2023.106674.
Marine Insight, (2023). 20 Largest container shipping companies in the world in 2023,
https://www.marineinsight.com/know-more/10-largest-container-shipping-companies-in-the-
world/#1_MSC_%E2%80%93_Mediterranean_Shipping_Company Accessed 19 May 2023.
McKinnon, A. (2015). Green Logistics: Improving the Environmental Sustainability of Logistics (third edition).
McKinnon, A., Browne, M., Piecyk, M.& Whiteing, A. (Eds.), Environmental sustainability: A new priority
for logistics managers (pp. 3-31). Kogan Page.
Pang, K., Lu, C.-S., Shang, K.-C. & Weng, H.-K. (2021). An empirical investigation of green shipping practices,
corporate reputation and organisational performance in container shipping. International Journal of Shipping
and Transport Logistics, 13(3/4), 422–444. https://doi.org/10.1504/IJSTL.2021.113996.
Pesaran, H. H., & Shin, Y. (1998). Generalized impulse response analysis in linear multivariate models. Economics
Letters, 58(1), 17–29. https://doi.org/10.1016/S0165-1765(97)00214-0.
Polat, O., El Khoury, R., Alshater, M. M., & Yoon, S. M. (2023). Media Coverage of COVID-19 and Its
Relationship with Climate Change Indices: A Dynamic Connectedness Analysis of Four Pandemic Waves.
Journal of Climate Finance, In Press, Journal Pre-proof, 100010. https://doi.org/10.1016/j.jclimf.2023.100010.
Ren, X., Zhang, X., Yan, C., & Gozgor, G. (2022). Climate policy uncertainty and firm-level total factor
productivity: Evidence from China. Energy Economics, 113, 106209.
https://doi.org/10.1016/j.eneco.2022.106209.
Citation / Atıf: ALTIN F. G., GÜRSOY S., DOĞAN M., ERGÜNEY E. B. (2023). The Analysis of the Relationship Among Climate Policy
Uncertainty, Logistic Firm Stock Returns and ESG Scores: Evidence from the TVP-VAR Model. İstatistik Araştırma Dergisi, 13 (2), 42-59.
59
Samitas, A., Papathanasiou, S., Koutsokostas, D., & Kampouris, E. (2022-a). Volatility spillovers between fine
wine and major global markets during COVID-19: A portfolio hedging strategy for investors. International
Review of Economics & Finance, 78, 629-642. https://doi.org/10.1016/j.iref.2022.01.009.
Samitas, A., Papathanasiou, S., Koutsokostas, D., & Kampouris, E. (2022-b). Are timber and water investments
safe-havens? A volatility spillover approach and portfolio hedging strategies for investors. Finance Research
Letters, 47, 102657. https://doi.org/10.1016/j.frl.2021.102657.
Seroka-Stolka, O., & Ociepa-Kubicka, A. (2019). Green logistics and circular economy. Transportation Research
Procedia, 39, 471-479. https://doi.org/10.1016/j.trpro.2019.06.049.
Shahbaz, M., Dogan, M., Akkus, H.T. et al. The effect of financial development and economic growth on
ecological footprint: evidence from top 10 emitter countries. Environmental Science and Pollution Research.
30, 73518–73533 (2023). https://doi.org/10.1007/s11356-023-27573-2.
Xiao, J., & Liu, H. (2023). The time-varying impact of uncertainty on oil market fear: Does climate policy
uncertainty matter?. Resources Policy, 82, 103533. https://doi.org/10.1016/j.resourpol.2023.103533.
Xie, Q., Cheng, L., Liu, R., Zheng, X., & Li, J. (2023). COVID-19 and risk spillovers of China's major financial
markets: Evidence from time-varying variance decomposition and wavelet coherence analysis. Finance
Research Letters, 52, 103545. https://doi.org/10.1016/j.frl.2022.103545.
Yan, W. L. & Cheung, A. (W.K.) (2023). The dynamic spillover effects of climate policy uncertainty and coal
price on carbon price: Evidence from China. Finance Research Letters, 53, 103400.
https://doi.org/10.1016/j.frl.2022.103400.
Yingfei, Y., Mengze, Z., Zeyu, L., Ki-Hyung, B., Avotra, A. A. R. N., & Nawaz, A. (2022). Green logistics
performance and infrastructure on service trade and environment-measuring firm’s performance and service
quality. Journal of King Saud University-Science, 34(1), 1-10. https://doi.org/10.1016/j.jksus.2021.101683.
Yontar, E. (2022). Assessment of the logistics activities with a structural model on the basis of improvement of
sustainability performance. Environmental Science and Pollution Research, 29(45), 68904-68922.
https://doi.org/10.1007/s11356-022-20562-x.
Yu, J., Zhang, M., Liu, R., & Wang, G. (2023). Dynamic Effects of Climate Policy Uncertainty on Green Bond
Volatility: An Empirical Investigation Based on TVP-VAR Models. Sustainability, 15(2), 1692.
https://doi.org/10.3390/su15021692.
Zhou, D., Siddik, A. B., Guo, L., & Li, H. (2023). Dynamic relationship among climate policy uncertainty, oil
price and renewable energy consumption - findings from TVP-SV-VAR approach. Renewable Energy, 204,
722-732. https://doi.org/10.1016/j.renene.2023.01.018.
