Search Results (37)

This paper investigates the impact of repatriated export proceeds on exchange rate volatility in Indonesia. By applying a time-varying parameter vector autoregression (TVP-VAR) model with stochastic volatility, we assess whether the impact of repatriated export proceeds can dampen the effect of short-term capital flows. Our findings indicate that the influence of export proceeds on exchange rate volatility varies over time, with no evidence supporting its ability to dampen the impact of short-term capital flows in the short and intermediate terms. Furthermore, we identify a reversal pattern in the impacts of both repatriated export proceeds and short-term foreign capital flows after 3–5 days, suggesting a potential need to evaluate policies aimed at dampening short-term capital flow impacts on exchange rate volatility. Our results are robust across a range of sensitivity and robustness checks, confirming the reliability of our findings. Full article

This paper employs a Time-Varying Parameter Vector Autoregression Directional–Spillover (TVP-VAR-DY) model to investigate the dynamic spillover effects among China’s energy, real estate, and stock markets from 2013 to 2023, with a focus on the impact of extreme events. The findings show that the total conditional spillover index (TCI) typically remains below 40% in the absence of extreme events, but significantly increases during such events, reaching 51.09% during the 2015 stock market crisis and nearing 60% during the COVID-19 pandemic in 2020. Specifically, the oil and gas market exhibited a net spillover index of 4.61%, emerging as a major source of risk transmission. In contrast, the real estate market, which had a net spillover index of −9.38%, became a net risk absorber. The net spillover index indicates that the risk transmission role of different markets towards other markets is dynamically changing over time and is closely related to significant global or domestic economic events. These results indicate that extreme events not only directly impact specific markets but also rapidly propagate risks through complex inter-market linkages, exacerbating systemic risks. Therefore, it is recommended to enhance market monitoring, improve transparency, and optimize risk management strategies to cope with uncertainties in the global economy and financial markets. Full article

This study examines the interconnectedness of African stock markets during three major global crises: the 2008 Global Financial Crisis (GFC), the COVID-19 pandemic, and the Russia–Ukraine conflict. We use daily stock index data from 2007 to 2023 for ten African countries and apply a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model. The results reveal that volatility connectedness among African markets intensified during all three crises, peaking during the COVID-19 pandemic followed by the 2008 GFC and the Russia–Ukraine conflict. Short-term connectedness consistently exceeded long-term connectedness across all crises. South Africa and Egypt acted as dominant transmitters of volatility, highlighting their systemic importance, while Morocco showed increased influence during the COVID-19 pandemic. These findings suggest that African markets are more globally integrated than previously assumed, making them vulnerable to external shocks. Policy implications include the need for stronger regional financial cooperation, the development of early warning systems, and enhanced intra-African investment to improve market resilience and reduce contagion risk. Full article

This study investigates the impact of cyber threats on the clean energy (CE) market, which is increasingly dependent on digital technologies and interconnected infrastructure. The sector’s growing digitalization makes it more susceptible to cyberattacks, leading to significant effects on market volatility and financial performance. Using time-varying parameter vector autoregression (TVP-VAR), wavelet coherence models, and rolling window analysis, this research examines the dynamic relationships between cyberattacks and the CE market over various timescales. The severity of cyberattacks is quantified using the OWASP risk rating methodology, providing a structured approach to assessing cyber risks. The findings reveal that high-severity cyberattacks targeting critical infrastructures generate pronounced short-term volatility, especially in concentrated indices such as TAN and ICLN. In contrast, diversified indices like PBW and RNRG demonstrate greater resilience, highlighting the protective role of portfolio diversification. Moreover, the impact of cyber threats is exacerbated during periods of macroeconomic instability, reinforcing the need for integrated risk management approaches. These results provide actionable insights for investors and policymakers, emphasizing the need for proactive risk management strategies to enhance market resilience and safeguard the CE sector from cybersecurity threats. Full article

Comprehending the dynamics of risk spillover across the value chain is indispensable for effective risk management, especially amid increasing economic and geopolitical uncertainty. This study investigates the mechanics of risk transmission within the value chain of the Chinese hospitality industry by employing a Time-Varying Parameter Vector Autoregression (TVP-VAR) model using daily data from January 2015 to December 2023. Our research identifies key sub-sectors, such as hotel resort and luxury cruises, film and entertainment, malls and supermarkets, environmental and facilities services, air freight and logistics, and road transportation, as significant risk transmitters that affect the overall stability of the industry. Conversely, sectors such as restaurants, liquor and wine services, leisure services, and railway transport are designated as risk receivers. These results offer critical insights for stakeholders, emphasizing the necessity of comprehensive risk management strategies to reduce negative spillover effects, particularly in the context of economic shocks like the COVID-19 pandemic and geopolitical events like the Russia–Ukraine conflict. Full article

Analyzing the influence of external shocks on the pricing dynamics of the live pig industry chain is essential for effective macroeconomic control. Utilizing monthly data spanning from January 2010 to August 2023, this study employs the TVP-SV-VAR (Time-Varying Parameter—Stochastic Volatility—Vector Autoregression) model to analyze the effects of EPU (Economic Policy Uncertainty) and INU (Live Pig Industry News Uncertainty) on industry pricing. The findings are as follows: Firstly, the impacts of EPU and INU on industry prices exhibit time variability and distinct characteristics. Specifically, the impact magnitude of EPU ranges between [−0.025, 0.025], and that of INU between [−0.01, 0.01]. These differences in impact magnitude elicit varied responses from manufacturers and consumers to the indices. Secondly, uncertainty shocks at particular time points show high consistency, suggesting a patterned influence of external shocks on industry pricing that aligns with historical trends. Thirdly, robustness tests with alternative explanatory variables confirm the reliability of the findings. An uncertainty index, crafted from more comprehensive information sources, more accurately captures the effects of external shocks on industry pricing. Additionally, the volume of live pig slaughters illustrates the potential interaction between external shocks and pricing dynamics. In an era marked by increasingly frequent external shocks, this research offers valuable insights for policymakers to implement macro-control and foster high-quality industrial development. Full article

Within the framework of high-frequency volatility modeling, this study investigates the realized volatility spillover dynamics across major cryptocurrencies over an extended period of time. Using a Time-Varying Parameter Vector Autoregression (TVP-VAR) model of the realized volatility (RV), this work constructs the Total Connectedness Index (TCI) and Pairwise Connectedness Index (PCI) to measure the intensity and direction of realized volatility transmission within this digital asset network. Our findings reveal a consistently high level of spillovers among these leading cryptocurrencies, with notable peaks during periods of global market turbulence. Notably, Ethereum emerges as the most influential volatility transmitter, challenging the traditional view of Bitcoin as a primary driver of volatility spillovers. This reflects Ethereum’s pivotal role in decentralized finance (DeFi), decentralized applications (dApps), and its growing trading activity, suggesting a shifting influence in the increasingly diversified cryptocurrency ecosystem. Full article

This paper selects daily stock market trading data of RCEP member countries from 3 December 2007 to 9 December 2024 and employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) model and transfer entropy to measure the time-varying volatility spillover effects among the stock markets of the sampled countries. The results indicate that the signing of the RCEP has strengthened the interconnectedness of member countries’ stock markets, with an overall upward trend in volatility spillover effects, which become even more pronounced during periods of financial turbulence. Within the structure of RCEP member stock markets, China is identified as a net risk receiver, while countries like Japan and South Korea act as net risk spillover contributors. This highlights the current “fragility” of China’s stock market, making it susceptible to risk shocks from the stock markets of economically developed RCEP member countries. This analysis suggests that significant changes in bidirectional risk spillover relationships between China’s stock market and those of other RCEP members coincided with the signing and implementation of the RCEP agreement. Full article

This study examines the dynamic interconnectedness of economic policy uncertainty (EPU) among Latin American economies—Brazil, Chile, Colombia, and Mexico—and significant international regions, including the United States, Europe, and Japan, as well as a global EPU index. Using a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with monthly data, this study reveals the evolving spillover effects and dependencies capturing how uncertainty in one market can transmit across others on both regional and global scales. The findings highlight the significant impact of external EPU, particularly from the U.S. and global EPU sources on Latin America, positioning it as a primary recipient of international uncertainty. These results underscore the need for Latin American economies to adopt resilience strategies—such as trade diversification and regional cooperation—to mitigate vulnerabilities to global shocks. This study offers valuable insights into the mechanisms of economic uncertainty transmission, guiding policymakers in developing coordinated responses to reduce the effects of external volatility and foster regional economic stability. Full article

The study examines the time-varying correlation and return spillover mechanism among developed (U.S.) and emerging (BRIC) stock markets during major crises from 2000 to 2023, namely the global financial crisis, COVID-19, and the Russia–Ukraine war. To do so, we used dynamic conditional correlation (DCC-GARCH) and time-varying parameter vector autoregression (TVP-VAR) models. This study finds that the nature of market crises plays a significant role in the interrelationship and return spillover mechanisms among the U.S. and BRIC stock markets. The interconnectedness of the stock markets was strengthened by crises such as the GFC and the COVID-19 pandemic. On the other hand, the Russia–Ukraine war temporarily disrupted the interrelationships between the markets. The study yields valuable insight to local and international investors in portfolio diversification and risk management strategies during market turbulence. Full article

With the advancement and development of the electricity market (EM), carbon emission market (CEM), and renewable energy certificate market (RECM), promoting the integration and growth of the EM alongside carbon emission trading, renewable energy certificate trading, and other related markets is becoming increasingly important for high-quality development of the power industry. Analyzing the intrinsic connections among these three types of markets can facilitate their coordinated development. In this study, we selected monthly data on European Union (EU) carbon emission futures, French electricity trading prices, and the price of Guarantees of Origin (GO) in France from March 2019 to March 2024 and utilized the Bayesian time-varying stochastic volatility vector autoregression model (TVP-SV-VAR) with time-varying parameters to effectively capture the dynamic changes among the three markets and to analyze the relationships and characteristics of the EM, CEM, and RECM across different historical contexts. Simulation results showed that the influences of the EM and CEM on the RECM were relatively low, with more pronounced short-term effects and relatively stable medium- and long-term effects. In contrast, the influences of the CEM and RECM on the EM were significant, with more pronounced short-term effects and stable medium- and long-term effects. The influences of the EM and RECM on the CEM were significant in the short term. Full article

In the context of a low-carbon economic transition and escalating uncertainties in financial markets, understanding the relationship between the long-term benefits of ESG (Environmental, Social, and Governance) investments and the stability of China’s financial markets emerges as a critical issue. This paper analyzes the risk contagion mechanisms within China’s financial system from the perspective of volatility spillovers associated with ESG investments. Initially, the study employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) model to calculate the variance decomposition spillover index, contrasting the dynamics and risk transmission mechanisms of market volatility between portfolios composed of ESG and conventional stocks. Building upon the analysis of risk spillover relations among financial sub-markets, the study utilizes the generalized forecast error variance decomposition method to construct a complex network of financial system risk spillovers, investigating the risk contagion characteristics within both financial systems through network topology. Empirical findings indicate a significant reduction in the risk and net spillover effects of China’s financial system when ESG stock indices replace conventional stock indices, with a notable mutation in the volatility spillover network structure during extreme risk events and even more substantial changes during the COVID-19 pandemic. Furthermore, based on volatility spillover analysis, the study computes optimal weights and hedging strategies for portfolios incorporating the ESG volatility index and other market volatility indices. The conclusions of this research are instrumental for regulatory authorities in establishing early warning mechanisms and for investors in avoiding financial investment risks. Full article

This study employs the economic policy uncertainty index to gauge the level of economic policy uncertainty in China. Utilizing textual data from the growth enterprise market internet community, we construct the growth enterprise market investor sentiment index by applying the deep learning ERNIE (Enhanced Representation through Knowledge Integration) model, thereby capturing investors’ sentiment within the growth enterprise market. The dynamic interplay between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is scrutinized via the TVP-SV-VAR (time-varying parameter stochastic volatility vector auto-regression) model, and the asymmetric response of different industries’ stock returns within the growth enterprise market to economic policy uncertainty and investor sentiment shock. The findings of this research are that economic policy uncertainty exerts a negative influence on both investor sentiment and returns of the growth enterprise market. While it may trigger a temporary decline in stock prices, the empirical evidence suggests that the impact is of short duration. The influence of investor sentiment on the growth enterprise market returns is characterized by a reversal effect, suggesting that improved sentiment may initially boost stock prices but could lead to a subsequent decline over the long term. The relationship between economic policy uncertainty, investor sentiment, and returns of the growth enterprise market is time-variant, with heightened sensitivity observed during bull markets. Lastly, the effects of economic policy uncertainty and investor sentiment on the returns of different industries within the growth enterprise market are found to be asymmetric. These conclusions contribute to the existing body of literature on the Chinese capital market, offering a deeper understanding of the complex dynamics and the factors influencing market behavior. Full article

The global food market’s escalating volatility has led to a complex network of uncertainty and risk transmission across different grain markets. This study utilizes the Time-Varying Parameter Vector Autoregression (TVP-VAR)-Connectedness approach to analyze the price transmission and volatility dynamics of key grains, including wheat, maize, rice, barley, peanut, soybean, and soybean meal, and their dynamic spillover directions, intensity, and network. By integrating the TVP-VAR-Connectedness model, this research captures the time-varying variability and interconnected nature of global grain price movements. The main findings reveal significant spillover effects, particularly in corn prices, with prices of soybean dominating other grains while prices of peanut and corn experience higher external spillover effects from other grains. The conclusions drawn underscore the imperative for policymakers to consider a holistic perspective of all types of grains when addressing global food security, with this study providing valuable insights for risk management in the grain sector at both global level and country level. Full article

We examine the impact of physical climate risks on energy markets in China, distinguishing between traditional energy and new energy stock markets, and the energy commodity market, utilizing a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR). Specifically, we investigate the dynamic effects of five specific subtypes of physical climate risks, namely waterlogging by rain, drought, typhoon, cryogenic freezing, and high temperature, on WTI oil prices and coal prices. The findings reveal that these physical climate risks exhibit time-varying similar effects on the returns of traditional energy and new energy stocks, but heterogeneous effects on the returns of WTI oil prices and coal prices. Finally, we categorize and examine the impact of both acute and chronic physical risks on the energy commodity market. Full article