Time-varying Parameter Vector Autoregression Research Articles

The determinants of volatility connectedness of South African equity super sectors

The paper aims to explore the determinants of total volatility connectedness of nine super sectors on the Johannesburg Stock Exchange (JSE) market from 3rd January 2006 to 31st December 2021. These sectors are Automobile and Parts, Chemical, Telecommunication, Technology, Energy, Health, Finance, Insurance, and General Industrials. The paper applied Diebold and Yilmaz connectedness matrix and the time-varying parameter – vector autoregressive (TVP-VAR) model to determine the sectorial total volatility connectedness index (STVCI). After that, the nonlinear autoregressive distributed lag model (NARDL) was used to determine the asymmetric effects and the drivers of STVCI. It was found that the partial sum decomposition of the South African volatility index (SAVI) and Economic Policy Uncertainty Index (EPU) are the key determinants of the STVCI both in the long and short run. However, domestic market return (DMR) shows no significant asymmetric effect on STVCI. The study concluded that SAVI and EPU are the key determinants of volatility connectedness among the JSE super-sectors. The results unveil important implications for sectorial investors and policymakers on potential regulations and stability of the significant determinants of spillover risk.

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.

Coal price, economic growth and electricity consumption in China under the background of energy transition

With the increasing share of renewable energy generation in China's electricity consumption and the dramatic fluctuation of coal prices in recent years, the discussion of the relationship between coal prices, economic growth and electricity consumption in China becomes more relevant. This paper collects monthly data from 2016 to 2023, uses industrial value added to represent economic growth, and explore the relationship between the three through a time-varying parameter vector autoregressive (TVP-VAR) model, which shows that: (1) The mutual impact effects among coal prices, economic growth, and electricity consumption vary across short-, medium- and long-term. This finding underscores the importance of considering different time horizons when evaluating the interactive effects of these variables. (2) Compared to 2017 and 2022, the lagged impact effects among the three have undergone certain changes, indicating that the understanding of their relationships should be contextualized within specific developmental phases. (3) The relationship between coal prices, economic growth, and different categories of electricity consumption exhibits heterogeneous characteristics, suggesting that recognition of these relationships should be based on specific types of power generation. This research enhances the understanding of the dynamic relationship between coal prices and the macroeconomy in China at the current stage.

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.

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

Connectedness and Shock Propagation in South African Equity Sectors during Extreme Market Conditions

This study examined the connectedness and propagation of risk in the South African equity sectors during the Global Financial Crisis (GFC), the European Debt Crisis (EDC), the US–China trade war, and the COVID-19 pandemic. Daily returns of nine Johannesburg Stock Exchange (JSE) super sectors were examined from 3 January 2006 to 31 December 2021. Applying the connectedness matrix and time-varying parameter vector autoregressive (TVP-VAR) model, in full sample and sub-periods, the study showed that dynamic total connectedness of the super sectors is high in absolute form (62%). Furthermore, it was found that the highest volatility connectedness was during the EDC (68.83%) and during the COVID-19 pandemic (68.57%), followed by the GFC (63.16%) and lastly the US–China trade war (42.09%), respectively. This suggests that the tendency for a systemic risk is highest during the EDC, COVID-19, and GFC periods, and lowest during the US–China trade war. The financial sector was the primary net-transmitter of shocks during the COVID-19 period, while the automobile and parts sector was the strongest net-transmitter of shocks during the GFC, EDC, and US–China trade war. Similarly, the strongest net recipient of shocks during GFC, EDC, and COVID-19 is the chemical super sector. The study concludes that there is a significant volatility connectedness among JSE super sectors. In addition, the JSE super sectors exhibit time-varying connectedness during extreme events. Moreover, the net-transmitter and net-receiver of shock do not change significantly during different crisis periods. The policy implications of the findings are highlighted in the concluding section.

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.

Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations

This paper employs wavelet coherence, Cross-Quantilogram (CQ), and Time-Varying Parameter Vector-Autoregression (TVP-VAR) estimation strategies to investigate the dependence structure and connectedness between investments in artificial intelligence (AI) and eight different energy-focused sectors. We find significant evidence of dependence and connectedness between the stock returns of AI and those of the energy-focused sectors, especially during intermediate and long-term investment horizons. The relationship has become stronger since the COVID-19 pandemic. More specifically, results from the wavelet coherence approach show a stronger association between the stock returns of energy-focused sectors and AI, while results from the CQ analysis show that directional predictability from AI to energy-focused sectors varies across sectors, investment horizons, and market conditions. TVP-VAR results show that since the COVID-19 outbreak, AI has become more of a net shock receiver from the energy market. Our study offers crucial implications for investors and policymakers.

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.

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.

DYNAMIC VOLATILITY SPILLOVERS BETWEEN CURRENCIES OF ASEAN MEMBER COUNTRIES AND CHINA: EVIDENCE FROM TVP-VAR APPROACH

Driven by the importance of analyzing interregional volatility spillover effects, this study investigates dynamic volatility spillovers between the currencies of China– ASEAN Free Trade Area (FTA) member countries by applying the time-varying parameter vector autoregressive (TVP-VAR) connectedness approach to a sample spanning January 1, 2006, to October 31, 2022. The results show that (1) dynamic volatility connectedness varies over time and reaches peaks due to economic events, such as the 2008 global financial crisis (GFC) and the COVID-19 pandemic; (2) the Chinese yuan (CNY) market assumes more of a net receiving role for volatility spillovers after the 811 exchange rate reform, while the Singapore dollar (SGD) market assumes more of a net transmitting role for volatility spillovers in the whole sample period and (3) negative spillovers are dominant during the study period, except during the European debt crisis. Our findings can help China and ASEAN establish an exchange rate coordination mechanism to prevent the cross-country transmission of exchange rate risk.

Sparse-structured time-varying parameter vector autoregression for high-dimensional network connectedness measurement

This paper proposes a new sparse-structured time-varying parameter vector autoregression (SS-TVP-VAR) model to effectively measure high-dimensional network connectedness of financial enterprises. Previous VAR based network connectedness methods require high computational cost and are not feasible for high-dimensional networks of a large number of enterprises. The proposed SS-TVP-VAR not only reduces computational cost but also selects key coefficients when building the VAR model. As a result, it produces a more adaptive and effective connectedness to measure systemic risk caused by extreme events. Studies on 57 listed major financial enterprises in China mainland during a 15-year period show that the proposed SS-TVP-VAR achieves reliable and useful results, and it successfully identifies all the major market events during this time.