Time-varying Autoregressive Research Articles

Geopolitical Risks and Stock Market Volatility in the SAARC Region

Abstract This study examined the stock market volatility of the member states of the South Asian Association for Regional Cooperation (SAARC) and geopolitical risk (GPR). The analysis period covered January 2014 to March 2024, and the time–frequency wavelet method was used to process the data. The time-varying parameter vector autoregression and spatial autoregressive models helped determine the dynamic connectedness of volatility in the analyzed states. The findings revealed similar stock market connections in Bangladesh and India. In addition, a comparative analysis of stocks in India and Pakistan led to the identification of common elements. The connection between geopolitical concerns and Sri Lankan stocks was the strongest and increased in intensity after 2019. GPR and Nepal’s stock market maintained a continuous but low-intensity relationship. The dynamic connectedness between member states’ stock markets was limited during the review period. The study results could encourage SAARC governments to bridge their political differences to ensure that South Asia becomes a strong partner in the global economy. Equally, our results can benefit investors, financial institutions, regulatory authorities, and governments.

The Spillover Effects of Public Attention and Real Estate Market: A Case Study from Shanghai, China

The real estate industry is closely linked to the national economy, and fluctuations in house prices have long been a major focus of public attention. This study explores the relationship between public attention and the real estate market, using public attention as a focal point. It systematically explains how public attention affects investment decisions in real estate. Taking Shanghai as a case, this study empirically examines the dynamic time-varying relationship between public attention and house prices, sales, investment and floor space completed by building a Time-Varying Parameter Vector Autoregression (TVP-VAR) model with data from 2011 to 2024.The research shows that the spillover index of house prices on public attention is positive and house prices acting as the sender of the shock, with rising house prices boost public attention. Conversely, the spillover index of public attention on house prices is negative, with public attention acting as the receiver of the shock. Based on the empirical results, it is recommended to incorporate public attention into the real estate market monitoring system, observe the real estate industry using multiple indicators, and enhance the regulation of online media to ensure policy transparency.

Research on the Impact of Economic Policy Uncertainty and Investor Sentiment on the Growth Enterprise Market Return in China—An Empirical Study Based on TVP-SV-VAR Model

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.

Motor-Related EEG Analysis Using a Pole Tracking Approach.

This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method is evaluated for its neurophysiological interpretation and effectiveness in motor-related brain-computer interface (BCI) applications. Specifically, we assess the ability of the tracked EEG poles to discriminate between rest, movement execution (ME) and movement imagination (MI) in healthy subjects, as well as movement attempts (MA) in individuals with spinal cord injury (SCI). Our results show that pole tracking effectively captures broad changes in EEG dynamics, such as transitions between rest and movement-related states. It outperformed traditional EEG-based features, increasing detection accuracy for ME by an average of 4.1% (with individual improvements reaching as high as 15%) and MI by an average of 4.5% (up to 13.8%) compared to time-domain low-frequency EEG features. Similarly, compared to alpha/beta band power, the method improved ME detection by an average of 5.9% (up to 10.4%) and MI by an average of 4.3% (up to 10.2%), with results averaged across 15 healthy participants. In one participant with SCI, pole tracking improved MA detection by 12.9% over low-frequency EEG features and 4.8% over alpha/beta band power. However, its ability to distinguish finer movement details within specific movement types was limited. Additionally, the temporal evolution of the extracted pole tracking features revealed event-related desynchronization phenomena, typically observed during ME, MA and MI, as well as increases in frequency, which are of neurophysiological interest.

Spatial Price Transmission and Dynamic Volatility Spillovers in the Global Grain Markets: A TVP-VAR-Connectedness Approach.

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.

Extreme downside risk connectedness between green energy and stock markets

This study addresses the need to understand the transmission of tail risk across financial markets, especially in the context of green energy and stock markets. Utilizing the Time-Varying Parameter Vector Autoregressive (TVP-VAR) methodology combined with the Conditional Autoregressive Value-at-Risk (CAViaR) framework, the model analyzes data from September 30, 2013, to October 11, 2023. This study focuses on five major stock indices (S&P500, CSI300, Nikkei225, STOXX50, and FTSE100), West Texas Intermediate (WTI) crude oil, and three sectors within green energy markets (Green bond, Global Clean Energy, and Renewable Energy and Clean Technology), highlighting the significant role of these sectors in risk propagation. The model can capture dynamic changes and asymmetries in financial returns, thus providing a precise estimation of extreme downside risks. Key findings show that three stock indices (S&P500, STOXX50, and FTSE100) and one green energy sector (Renewable Energy and Clean Technology sectors) are the predominant sources of risk, with significant connectedness around events, such as the COVID-19 pandemic. These insights are crucial for developing effective risk-management strategies and supporting the transition to a sustainable energy sector. This study concludes that understanding these risk dynamics is essential for strategic planning and market stability.

Impact of central bank digital currency uncertainty on international financial markets

Is the impact of central bank digital currency (CBDC) uncertainty on investment and financial stability significant? To address this pivotal question in the era of CBDC development, we employ a combination of models: the time-varying parameter vector autoregression with stochastic volatility (TVP-SV-VAR); the Baba, Engle, Kraft, and Kroner multivariate generalized autoregressive conditional heteroskedasticity (BEKK-MGARCH) model, and wavelet quantile correlation (WQC) analysis. Based on the findings, macrofinancial variables significantly respond to CBDC uncertainty shocks, exhibiting time-varying, heterogeneous, and stage-specific characteristics. These effects are most pronounced in the short term, but diminish over the long term. Notably, the impact of CBDC uncertainty shocks on other financial variables exceeds the reverse impact. In addition, asymmetric volatility spillovers are found, suggesting complex interdependencies. The implication of the findings is that interactions between CBDC uncertainty and financial variables evolve across short-, medium-, and long-term horizons, emphasizing the need for central banks to adopt a flexible approach in CBDC design to alleviate market concerns and enhance overall financial stability.

Harbor in the storm: How Bitcoin navigates challenges of climate change and global uncertainties

Understanding Bitcoin's (BTC) performance and hedging effects under various uncertainties is crucial. This study combines time-varying Granger causality (TVGC) tests and wavelet time-varying parameter vector autoregression (TVP-VAR) methods to examine the relationship between Bitcoin (BTC) and climate uncertainty (SOI), geopolitical risk (GPR), economic policy uncertainty (EPU), crude oil volatility (OVX), and market volatility (VIX) across both time and frequency domains, from December 19, 2017, to October 16, 2023. The results show that in recent years, the long-term impact of SOI on BTC, as well as political and economic uncertainties, is hard to ignore, but BTC cannot serve as a safe-haven asset against SOI. Wavelet decomposition indicates that during extreme events, BTC exhibits a leading positive co-movement with EPU, GPR, VIX, and OVX in the long term, showing potential as a hedging asset. However, TVGC and wavelet decomposition spillover results further suggest that BTC's hedging capacity against different economic and political uncertainties may be limited during certain extreme events.

WITHDRAWN: the Impact of Monetary Policy on the Liquidity of Bond Market——Based on Chinese Local Government Bond Market

The bond market serves dual roles in fiscal and financial spheres, playing a crucial role in coordinating monetary policy. This paper investigates the impact of quantitative and price-based monetary policies on the liquidity level of China’s bond market. A comprehensive index measuring the liquidity of the local bond market is constructed using a combination weighting method that integrates the entropy method and the coefficient of variation. Employing the time-varying stochastic volatility structure vector autoregression (TVP-SV-SVAR) model on data spanning from 2013 to 2021, the study empirically compares the impulse response of local bond market liquidity to monetary policy shocks. Findings reveal that both types of monetary policy operations exhibit asymmetric, nonlinear, and time-varying impacts on bond market liquidity. Quantitative monetary instruments induce deeper impulse responses, with longer-lasting effects. These conclusions offer insights for monetary policy reforms and bond market development in China.

Risk spillovers and optimal hedging in commodity ETFs: A TVP-VAR Approach

Despite the growing popularity of commodity exchange traded funds (ETFs), research on their risk transmission dynamics is lacking. The study employs a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model to analyze volatility transmission among commodity ETFs during significant events like the COVID-19 pandemic and geopolitical conflicts. It aims to minimize interconnectedness among ETFs to reduce associated risk using the Mean Co-skewness optimal portfolio (MCoP) technique. The findings emphasize the importance of adaptability in portfolio management and provide actionable information for investors seeking to optimize allocation and manage risk exposure effectively in dynamic market environments.

Unveiling asymmetric return spillovers with portfolio implications among Indian stock sectors during Covid-19 pandemic

This paper aims to provide a systematic inquiry into the return spillover dynamics between a network of Indian sectoral indices during the pre- and post-pandemic periods. To analyze the same, this paper uses the asymmetric time-varying parameter vector autoregressions (TVP-VAR) framework. Furthermore, in the spirit of Broadstock et al. (2020), we perform dynamic portfolio exercises based on common hedging techniques and the minimum connectedness portfolio approach to determine what better captures asymmetry. Our daily dataset includes 12 sectoral stocks spanning from January 01, 2017, to May 5, 2023. The findings reveal that negative connectedness dominates throughout the sample period, demonstrating that profit-maximizing agents and risk-averse investors are more likely to react negatively to news. We also show that in the network, the average net transmitters are the banking and other financial service sectors, whereas the net receivers are the information technology, pharmaceutical, and fast-moving consumer goods sectors throughout the period under consideration. Our results show that the minimum connectedness portfolio (MCoP) approach is a very useful method based on Sharpe ratios, as it is either the first or second most profitable among these three competing methods. These results, therefore, yield valuable insights for policymakers and investors

Good vs. bad volatility in major cryptocurrencies: The dichotomy and drivers of connectedness

Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. Our findings indicate that crypto connectedness reflects important events and oscillates substantially while reaching lower limit values when compared to traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that market downturns spill over substantially faster than comparable market surges. Overall, the connectedness dynamics are driven by a combination of both crypto (momentum, on-chain activity, off-chain activity) and legacy financial and economic (financial and economic uncertainty, and financial market performance) factors, while the asymmetry is more connected to the off-chain crypto activity and the combination of economic, financial, and monetary factors. In both the total connectedness and asymmetry modeling, these can serve as hands-on indicators to be further translated into specific portfolio re-balancing decisions, risk management, and regulatory frameworks.

Can stronger governance and institutional quality drive growth through inward foreign direct investments in BRICS nations?

Background: This research focusses on the significance of institutional quality (INSQ) and governance in shaping the relationship between foreign direct investments (FDI) and growth. It specifically examines the BRICS nations (Brazil, Russia, India, China and South Africa) because of their economic significance in the global economy.Aim: This study aims to analyse the impact of INSQ and governance on sustainable growth, with a particular focus on its effects through the channels of FDI in the BRICS countries.Setting: Annual panel data from the Organisation for Economic Cooperation and Development (OECD) statistics, the United Nations Conference on Trade and Development (UNCTAD) statistics and the World Bank indicators spanning two decades (2000–2022) are used to analyse BRICS nations.Methods: The research study employed the Bayesian time-varying coefficient vector autoregression (BTVC-VAR) model to achieve the objective of the study.Results: The findings indicate that there is no long-term relationship between FDI, INSQ and economic growth in the BRICS countries. However, there is a noticeable co-movement among these variables in the short run.Conclusion: Given the obtained results, the policymakers should prioritise efforts to strengthen institutional capacity in the short term while focussing on the Sustainable Development Goals (SDG-8 and SDG-16).Contribution: The existing studies have assumed static economic, social and political conditions, potentially failing to accurately depict the complexities of an actual economy. This study offers methodological innovation by employing Bayesian time-varying coefficient vector autoregression (BTVC-VAR), enabling coefficients to adapt to evolving economic conditions over time. This effectively captures the dynamic nature of variables and provides reliable estimates.

Spillover Effects between Oil, Gold, Stock, and Exchange Rate Returns in Thailand: An Extended Joint Connected TVP-VAR Approach

The objective of this study is to analyze the spillover effects among the returns of oil, gold, the stock market, and exchange rates in Thailand. Using the time-varying parameter vector autoregression model (TVP-VAR) with extended joint connectedness and data from April 2002 to March 2024, our analysis reveals a moderate level of the dynamic linkage among these returns. We observe that the dynamic connectedness among all returns varies over time, influenced by global economic events and country situations. Notably, in Thailand's landscape, stock market and gold returns act as net shock transmitters, with the stock market exhibiting the highest volatility among all variables, while oil and exchange rate markets function as net recipients. These insights significantly contribute to understanding asset and commodity markets and offer valuable policy implications for effectively managing these markets.

Time-frequency higher-order moment Co-movement and connectedness between Chinese stock and commodity markets

This study examines the higher-order moment co-movement and connectedness between China's stock and commodity markets across time and frequency domains. We propose wavelet decomposition to develop a multiscale time-varying parameter vector autoregression (TVP-VAR) approach for measuring higher-order moment connectedness. Our empirical findings are as follows: First, the co-movement of stock-commodity varies over time and across different frequencies, exhibiting heterogeneity at different moments. Stocks demonstrate robust co-movement with commodities over the medium- and long-term periods. Second, higher-order moment connectedness is stronger than return connectedness, whereas weaker than volatility connectedness. Finally, higher-order moment connectedness is highly event-dependent, peaking at COVID-19 onset. And long-run factors have the greatest effect on dynamic moment connectedness.

Cross-border ESG rating dynamics: An in-depth connectedness analysis of portfolio returns and volatilities in the USA and Canada

This study uses a time-varying parameter vector autoregression (TVP-VAR) model to examine the dynamic relationship between rating changes and portfolio returns in the US and Canada across the environmental (E), social (S), governance (G) and total ESG assessment pillars. The analysis includes both return and volatility spillovers and covers the period from March 2009 to October 2022. The study reveals a fluctuating pattern of connectedness, influenced by global financial events, such as the 2008 financial crisis. In particular, the US shows higher levels of connectedness. Rating changes, particularly in the ESG dimension, show stronger spillovers than returns, highlighting their importance in portfolio construction. The study further explores net connectedness profiles, identifying ESG rating changes as net transmitters. The results suggest that investors should prioritize rating changes over returns, highlighting the importance of considering ESG factors in portfolio management, especially the social criterion, to mitigate investment risks. The research contributes to the understanding of ESG dynamics in international equity markets and provides valuable insights for investors and market regulators.

On bank stock return spillovers in South Africa: Implications for portfolio hedging

The purpose of this study is to investigate the return spillovers among the five systemically important banks in South Africa. The study employs a time-varying parameter vector autoregression (TVP-VAR) framework utilising daily data between 2000 and 2024. A minimum return connectedness portfolio is constructed from the spillover indexes and compared with traditional portfolio optimisation methods in terms of risk reduction and reward-to-risk ratios. The research findings reveal that Capitec, the smallest bank in terms of assets, has weaker return connectedness with other banks while ABSA, FirstRand, Nedbank and Standard Bank are moderately connected. Moreover, the two largest banks in terms of assets, Standard and FirstRand, are more connected than any other pairwise connection in the network. During significant events such as national elections, the global financial crisis and the COVID-19 pandemic, system-wide connectedness increases.

EXCHANGE RATES AND STOCK MARKET DYNAMICS: ISLAMIC VERSUS CONVENTIONAL FINANCIAL SYSTEMS

This study investigates the changes and persistence in dynamic connectedness between stock market performance and exchange rate fluctuations, comparing conventional and Islamic financial systems. With an eye on a global financial landscape characterized by the integration of capital markets and the adoption of floating exchange rate regimes, it examines the effects of exchange rate variations on the dynamics of the stock market in nine different countries - the United Kingdom, Australia, Japan, Singapore, Canada, China, India, Korea, and South Africa. The study employs daily data spanning from November 2015 to July 2023 and uses a comprehensive analysis of three-step methodology, including nonparametric causality-in-quantiles tests, asymmetric slope Conditional Autoregressive Valueat-Risk (CAViaR), and Time-Varying Parameter Vector Autoregressive (TVP-VAR) Connectedness measure. Our results underline the asymmetric impact of exchange rate fluctuations on stock markets and highlights the distinctive characteristics of Islamic financial markets. Comparing Islamic and conventional stock markets in the context of exchange rate fluctuations, this study not only serves to fill a gap in the existing literature but also emphasizes the significance of currency exchange rate swings for global investors, policymakers, and practitioners trying to understand the intricacies of global financial markets.

Connectedness and frequency connection among green bond, cryptocurrency and green energy-related metals around the COVID-19 outbreak

We investigate the return interdependence among green bonds, cryptocurrency indices and green energy-related metals. We apply time-varying parametric vector autoregression (TVP-VAR) conenctedness, wavelet coherence, Wavelet Quantile Correlation （WQC) and Quantile on Quantile (QQR) Connectedness Methods. Our empirical findings show that return connectedness has become even stronger after the outbreak of COVID-19, with both green bonds and cryptocurrency indices acting as net receivers of return spillovers. Surprisingly, Copper functioned as a net sender of return spillovers over the entire observation period. Findings revealed that the cryptocurrency index exhibited a consistent positive correlation with the green energy-related metals market at medium to short-term frequencies, whereas green bonds showed a negative correlation with metals market at short-term frequencies and a positive correlation at long-term frequencies.

Harmony in diversity: Exploring connectedness and portfolio strategies among crude oil, gold, traditional and sustainable index

The research investigates dynamic connectedness and portfolio strategies among crude oil, gold, global traditional (DJGI) and sustainable (DJSI) indices. The time-varying parameter vector autoregression (TVP-VAR) findings reveal that the average total connectedness among the assets is 33.03%, with the total dynamic connectedness index exhibiting a notable surge during two crises and one stability period. The upsurge exhibited a predominant prevalence of short-run connectedness during crisis periods, notably amidst the COVID-19 pandemic and the Russia-Ukraine war. Conversely, the last and first quarters of 2017 and 2018 manifested as periods of stability, wherein long-term interconnectedness prevailed. Crude oil and gold emerge as the primary recipients of systemic shocks, while traditional and sustainable stock indices act as net transmitters of shocks. The BEKK and DCC GARCH models indicate short- and long-run persistence among the assets. Further, the study calculated hedge ratios and portfolio weights. Our empirical findings indicate that gold serves as a cost-effective hedging asset. Investors should invest more in the traditional stock index than in gold and crude oil to form an optimal portfolio. Notably, the global sustainable index offers results similar to those of the traditional index. Investors in the sustainable index would invest a similar amount for diversification and hedging with other traditional assets as they would for the traditional index.