Search Results (9)

The energy sector, as a major source of carbon emissions, has a significant impact on the operation of the carbon market and the management of carbon emissions. With the introduction of the “dual carbon” goals, the Chinese government has actively implemented measures to reduce carbon emissions, making the carbon market an important tool for emission reduction. Therefore, characterizing the inter-market relationships helps enhance decision-making for market participants and promotes sustainable economic development. This study selects the price of the Chinese carbon emission trading market, which began trading on 16 July 2021, as a representative of the carbon market price. In terms of energy market selection, the prices of electricity, new energy, and coal are chosen as representatives of the energy market. From the perspective of the nonlinear dependency structure between market prices, a “carbon ↔ electricity ↔ new energy ↔ coal market” multi-to-multi interaction model is constructed, and the MSVAR model is employed to study the nonlinear dependency characteristics between market prices under interactive influences. The results show that there is a significant nonlinear dependency structure between the four market prices, especially between the carbon market and the new energy market. These market prices exhibit different behavioral characteristics under different states, with non-stationary states being the most common. There is a strong positive correlation between the electricity market and new energy market prices, while the relationship between the carbon market and other market prices is relatively weaker. The relevant conclusions provide valuable insights for policymakers and investors, helping them better understand and predict future market dynamics. Full article

This study examines the synchronization of economic and growth cycles between China and the United States of America amid ongoing economic and geopolitical tensions. Using a Markov-Switching–Vector Autoregression (MS-VAR) model, the analysis applies the Hodrick–Prescott and Baxter–King filters to monthly data from January 2000 to December 2024, capturing trends and cyclical fluctuations. The findings reveal asymmetries in economic synchronization, with differences in recession and expansion durations influenced by trade disputes, financial integration, and external shocks. As the rivalry between the two nations intensifies, marked by trade wars, technological competition, and geopolitical conflicts, understanding their economic co-movement becomes crucial. This study contributes to the literature by providing empirical insights into their evolving interdependence and offers policy recommendations for mitigating asymmetric shocks and promoting global economic stability. Full article

The co-movement of multi-asset markets in emerging markets has become an important determinant for investors seeking diversified portfolios and enhanced portfolio returns. Despite this, studies have failed to examine the determinants of the co-movement of multi-asset markets such as investor sentiment and changing market conditions. Accordingly, this study investigates the effect of investor sentiment on the co-movement of South African multi-asset markets by introducing alternating market conditions. The Markov regime-switching autoregressive (MS-AR) model and Markov regime-switching vector autoregressive (MS-VAR) model impulse response function are used from 2007 March to January 2024. The findings indicate that investor sentiment has a time-varying and regime-specific effect on the co-movement of South African multi-asset markets. In a bull market condition, investor sentiment positively affects the equity–bond and equity–gold co-movement. In the bear market condition, investor sentiment has a negative and significant effect on the equity–bond, equity–property, bond–gold, and bond–property co-movement. Similarly, in a bull regime, the co-movement of South African multi-asset markets positively responds to sentiment shocks, although this is only observed in the short term. However, in the bear market regime, the co-movement of South African multi-asset markets responds positively and negatively to sentiment shocks, despite this being observed in the long run. These observations provide interesting insights to policymakers, investors, and fund managers for portfolio diversification and risk management strategies. That being, the current policies are not robust enough to reduce asset market integration and reduce sentiment-induced markets. Consequently, policymakers must re-examine and amend current policies according to the findings of the study. In addition, portfolio rebalancing in line with the findings of this study is essential for portfolio diversification. Full article

The banking crisis experienced at the beginning of 2023 in the aftermath of the global 2008 crisis served as a stark reminder of the importance of systemic risk once again across the world. This study examines the dynamics of systemic risk in the Turkish banking system and its impact on sustainable economic growth between the period of 2007 and 2022. Through the Component Expected Shortfall (CES) method and quantile spillover analysis, private banks, such as Garanti Bank (GARAN), Akbank (AKBNK), İş Bank (ISCTR), and Yapı ve Kredi Bank (YKBNK), are identified as major sources of systemic risk. The analysis reveals a high level of interconnectedness among the banks during market downturns, with TSKB, Vakıfbank (VAKBNK), İş Bank (ISCTR), Halk Bank (HALKB), Akbank (AKBNK), Yapı ve Kredi Bank (YKBNK), and Garanti Bank (GARAN) serving as net risk transmitters, while QNB Finansbank (QNBFB), ICBC Turkey Bank (ICBCT), Şekerbank (SKBNK), GSD Holding (GSD), and Albaraka Türk (ALBRK) act as net risk receivers. Employing the Markov switching VAR (MS-VAR) model, the study finds that increased systemic risk significantly reduces economic growth during heightened financial periods. These findings underscore the importance of monitoring systemic risks and implementing proactive measures in the banking sector. The policy implications highlight the requirement for regulators and policymakers to prioritize systemic risk management. Close monitoring helps detect weaknesses and imbalances that could put financial stability at risk. Timely implementation of policies and rules is crucial in the prevention of the accumulation of systemic risks and in dealing with the existing hazards. Such measures protect the stability of the banking sector and mitigate potential negative effects on the broader economy. Full article

The effects of environmental pollution and Industry 4.0 on a sustainable environment are the main topic of this study, which may be regarded as a complement to the literature on energy and the environment. The paper aims to investigate the relation between Industry 4.0 (I4.0) and environmental sustainability, which is very important for policymakers, practitioners, and company executives in the period of Industry 4.0 in Turkey. To this end, natural gas consumption and technology patents as control variables of Industry 4.0, in addition to the variables of environmental pollution and economic growth, were selected during the period of 1988 to 2022 using Markov switching VAR (MS-VAR), Markov switching Granger causality (MS-GC), Fourier VAR (FVAR), and Granger causality (FGC) techniques. The reason for covering the period starting in 1988 is its recognition as the beginning of the Industry 4.0 era with AutoIDLab in 1988. According to the causality results, there was unidirectional causality running from technology patents to environmental pollution in the results of both MS-GC and FGC. However, the directions of causality between natural gas consumption and environmental pollution, and between economic growth and environmental pollution differed between regimes in the MS-GC model. Bidirectional causality was determined between economic growth and environmental pollution in the first MS-GC regime. However, in the second regime, unidirectional causality from economic growth to environmental pollution was determined. The causality direction determined by Fourier causality gave the same result with the second regime. A similar finding was observed in the direction of causality between natural gas consumption and CO₂ emissions. While MS-GC determined unidirectional causality from natural gas consumption to environmental pollution in the first regime, a bidirectional causality result between GC and environmental pollution was determined in the second regime. The FGC result was similar to the second regime result. And lastly, the MS-GC and FGC methods determined unidirectional causality from Industry 4.0 to environmental pollution. Full article

The question of how to proactively respond to population aging has become a major global issue. As a country with the largest elderly population in the world, China suffers a stronger shock from population aging, which makes it more urgent to transform its industrial and economic development model. Concretely, in the context of the new macroeconomic environment that has undergone profound changes, the shock of population aging makes the traditional industrial structure upgrading model (driven by large-scale factor inputs, imitation innovation and low-cost technological progress, and strong external demand) more unsustainable, and China has an urgent need to transform it to a more sustainable one. Only with an in-depth analysis of the influence mechanism of population aging on the upgrading of industrial structure can we better promote industrial structure upgrading under the impact of population aging. Therefore, six MSVAR models were constructed from each environmental perspective based on data from 1987 to 2021. The probabilities of regime transition figures show that the influencing mechanisms have a clear two-regime feature from any view; specifically, the omnidirectional environmental transition occurs in 2019. A further impulse–response analysis shows that, comparatively speaking, under the new environment regime the acceleration of population aging (1) aggravates the labor shortage, thus narrowing the industrial structure upgrading ranges; (2) has a negative, rather than positive, impact on the capital stock, but leads to a cumulative increase in industrial structure upgrading; (3) forces weaker technological progress, but further leads to a stronger impact on the industrial structure upgrading; (4) forces greater consumption upgrading, which further weakens industrial structure upgrading; (5) narrows rather than expands the upgrading of investment and industrial structures; and (6) narrows the upgrading of export and industrial structures. Therefore, we should collaboratively promote industrial structure upgrading from the supply side relying heavily on independent innovation and talent, and the demand side relying heavily on the upgrading of domestic consumption and exports. Full article

In recent years, more and more funds circulate internally in the financial field, which is called “financial hoarding”. After calculations, the scale of China’s financial hoarding was 242,178 billion yuan in the first quarter of 2003 and jumped to 1,801,706 billion yuan in the fourth quarter of 2016, which increased by nearly 7.4 times in the past 14 years and accelerated after 2014. The phenomenon that large amounts of money deviate from the real economy to virtual economy is called “shift from real economy to virtual economy”. The large scale of financial hoarding will inevitably influence the economic growth in China. Does financial hoarding promote or inhibit the economy? Does the relationship change with the economic growth rate? To address this issue, this paper first provided theoretical analysis of the relationship between financial hoarding and economic growth. Then, it used the data of the first quarter of 2003 through the fourth quarter of 2016 in China for empirical analysis. The results revealed two facts. Firstly, the simultaneous equations model showed that financial hoarding and economic growth promote each other in the long run and financial hoarding can be conducive to economic growth. Secondly, the MS-VAR model showed that the relationship between financial hoarding and economic growth changed with the economic growth rate. In addition, financial hoarding had a positive effect on the economic growth under both medium and high economic growth regimes, but to a greater extent in high economic growth regimes. Full article

In this study, we aim to investigate the impacts of credit default swaps (CDS) premium as a risk financial indicator on the fluctuations of value of the Turkish lira against the Euro. We try to answer the following questions: Is the CDS premium change among the drivers of EUR/TL exchange rate and what are the possible effects of CDS premium volatility on EUR/TL exchange rate stability in different conditions? In this regard, we developed a MS-VAR regime change model and asymmetric, frequency domain and rolling windows causality analysis methods. Results obtained from all tests imply that risk premium is partially a driver of the EUR/TL exchange rate between the years 2009 and 2015. Full article

Despite recent efforts to record wind at finer spatial and temporal scales, stochastic realizations of wind are still important for many purposes and particularly for wind energy grid integration and reliability studies. Most instances of wind generation in the literature focus on simulating only wind speed, or power, or only the wind vector at a particular location and sampling frequency. In this work, we introduce a Markov-switching vector autoregressive (MSVAR) model, and we demonstrate its flexibility in simulating wind vectors for 10-min, hourly and daily time series and for individual, locally-averaged and regionally-averaged time series. In addition, we demonstrate how the model can be used to simulate wind vectors at multiple locations simultaneously for an hourly time step. The parameter estimation and simulation algorithm are presented along with a validation of the important statistical properties of each simulation scenario. We find the MSVAR to be very flexible in characterizing a wide range of properties in the wind vector, and we conclude with a discussion of extensions of this model and modeling choices that may be investigated for further improvements. Full article