Time-varying Autoregressive Model Research Articles

Assessment of fit of the time-varying dynamic partial credit model using the posterior predictive model checking method.

Several new models based on item response theory have recently been suggested to analyse intensive longitudinal data. One of these new models is the time-varying dynamic partial credit model (TV-DPCM; Castro-Alvarez etal., Multivariate Behavioral Research, 2023, 1), which is a combination of the partial credit model and the time-varying autoregressive model. The model allows the study of the psychometric properties of the items and the modelling of nonlinear trends at the latent state level. However, there is a severe lack of tools to assess the fit of the TV-DPCM. In this paper, we propose and develop several test statistics and discrepancy measures based on the posterior predictive model checking (PPMC) method (PPMC; Rubin, The Annals of Statistics, 1984, 12, 1151) to assess the fit of the TV-DPCM. Simulated and empirical data are used to study the performance of and illustrate the effectiveness of the PPMC method.

Probabilistic State Estimation Under Varying Loading States via the Integration of Time-Varying Autoregressive and Gaussian Process Models

In this work, probabilistic damage quantification under varying loading conditions in a non-stationary, guided-wave environment is being tackled via the synergistic integration between Time-varying Autoregressive (TAR) models and Gaussian Process regression models (GPRMs). Applying these TAR-GPRMs onto an aluminum coupon with simulated damage under different loading conditions fitted with piezoelectric sensors/actuators, the TAR models show the capability of capturing the non-stationarity in the ultrasonic guided-wave signals generated by the actuators under varying plate-loading conditions. They also provide insights to the maintainer by showing when in time the guided-wave time series deviate the most. In order to take advantage of that, a time instant selection algorithm was developed to allow flexibility in choosing the time instant(s) at which probabilistic damage quantification should be done. Finally, this quantification task is tackled by GPRMs, in which multiple GPRMs are trained using the TAR model parameters under varying conditions, and then used to predict damage size and/or loading state. While this framework is much more powerful in terms of tapping into the dynamics of how guided-waves change with varying conditions compared to simpler forms of GPRMs (such as damage index-trained GPRMs), training of TAR-GPRMs is far more complex. The advantages and challenges associated with the proposed TAR-GPRM approach is presented herein along with potential open areas for research in this regard.

Spillover effects between fossil energy and green markets: Evidence from informational inefficiency

This paper builds an indicator of the market inefficiency degree by quantifying the extent of the market deviation from an efficient status. From a novel perspective of informational inefficiency, we further model the dynamic spillover effects between fossil energy and green markets by using a time-varying parameter vector autoregressive (TVP-VAR) model. Our findings indicate that the market inefficiency degree and its spillovers exhibit evident time-varying dynamics and are sensitive to extreme events. The international crude oil benchmarks serve as the most significant information transmitter and receiver in the spillover network of market inefficiency. Within green-related markets, carbon market demonstrates the strongest correlation with the fossil energy markets. Additional analyses involving consideration of economic policy uncertainty reassure the robustness of our findings.

Carbon volatility connectedness and the role of external uncertainties: Evidence from China

This paper investigates the volatility connectedness between China’s carbon pilot markets. Using Diebold and Yilmaz (2014)’s approach based on the time-varying parameter vector autoregression model with a variety of parameter sets, we obtain the average across 40 results to capture the volatility connectedness between the markets. We further use the linear and nonlinear autoregressive distributed lag models to assess the role of external uncertainties in shaping volatility connectedness. Several findings emerge: (1) Guangdong (Chongqing) is the largest net transmitter (receiver) in terms of volatility connectedness; (2) Volatility connectedness shows a declining trend, with its cycle fluctuations caused by compliance-driven trading; (3) Volatility connectedness correlates negatively with external uncertainties. Both economic policy and climate policy indices have impacts on volatility connectedness. We recommend introducing market makers to enhance market liquidity and reduce risk spreading. We also highlight the need for further research to pinpoint idiosyncratic factors that affect different markets.

The ability of energy commodities to hedge the dynamic risk of epidemic black swans

The outbreak of COVID-19 increased global financial risks and economic shocks. Although epidemic risk is on the decline, the likeliness of a similar black swan event occurring in the future cannot be ruled out. In this study, we constructed epidemic search indices using the Google Search Volume Index (SVI) to examine whether investor sentiment regarding the COVID-19 pandemic affected stocks, gold, silver, copper, crude oil, and natural gas. The results reveal that, over the entire epidemic period, energy assets (crude oil and natural gas) were the most “resistant” to epidemic panic, making them suitable for hedging the risk associated with epidemic panic risk. We employed a time-varying parameter structural vector autoregression model with stochastic volatility (TVP-SVAR-SV) to analyze the time-varying contagion effects of epidemic search indices on various asset prices at different points in time. The findings indicate that in the early stages of the COVID-19 pandemic when epidemic panic risk was highest, natural gas prices exhibited the most risk-averse characteristics. Only during the middle to later stages of the epidemic did natural gas prices begin to be positively affected by epidemic panic sentiment. However, during this period, the epidemic search indices show a stable declining trend, indicating a reduction in risk. We observe that natural gas prices only reflected panic sentiment during periods of stability. The results of this study suggest that energy commodities, especially natural gas, are suitable for hedging against unforeseen high epidemic risks. When epidemic risk decreases, profits can be realized and reinvested in assets with a higher contagion effect due to epidemic risks, further benefiting from the rebound in the prices of these assets. In addition to explaining why energy commodities are particularly suitable for mitigating epidemic-related black swan events, we provide a detailed investment strategy for hedging future epidemic risk and further elaborate on how investors should dynamically adjust their portfolios according to the severity of the epidemic and whether it is under control.

Dynamic asymmetric spillovers and connectedness between Chinese sectoral commodities and industry stock markets.

This study investigates the dynamic and asymmetric propagation of return spillovers between sectoral commodities and industry stock markets in China. Using a daily dataset from February 2007 to July 2022, we employ a time-varying vector autoregressive (TVP-VAR) model to examine the asymmetric return spillovers and dynamic connectedness across sectors. The results reveal significant time-varying spillovers among these sectors, with the industry stocks acting as the primary transmitter of information to the commodity market. Materials, energy, and industrials stock sectors contribute significantly to these spillovers due to their close ties to commodity production and processing. The study also identifies significant asymmetric spillovers with bad returns dominating, influenced by major economic and political events such as the 2008 global financial crisis, the 2015 Chinese stock market crisis, the COVID-19 pandemic, and the Russia-Ukraine war. Furthermore, our study highlights the unique dynamics within the Chinese market, where net information spillovers from the stock market to commodities drive the financialization process, which differs from the bidirectional commodity financialization observed in other markets. Finally, portfolio analysis reveals that the minimum connectedness portfolio outperforms other approaches and effectively reflects asymmetries. Understanding these dynamics and sectoral heterogeneities has important implications for risk management, policy development, and trading practices.

A Comparative Study of the Effects of Two Monetary Policy Tools on Money Market Interest Rates

This paper focuses on the regulation effect of money market interest rates from monetary policy tools. By employing Time-Varying Parameter Stochastic Volatility Vector Autoregressive model, we comparatively analyze the effects of the Standing Lending Facility (SLF) and the 7-Day Reverse Repurchase Agreements in the Open Market (RR7D) on the 7-Day Repo Rate of Deposit-taking Financial Institutions (DR007). It is found that SLF can marginally improve DR007 in the short term but can effectively lower it in the medium to long term by acting as the ceiling of the interest rate corridor. RR7D can improve DR007 with a stronger impact, compared to the SLF. The effectiveness of these monetary policy tools remains relatively stable under different socio-economic backgrounds, achieving the intended objectives of the central bank. Finally, the study proposes suggestions, close integration of the interest rate corridor and open market operations in monetary policy regulation, and encourages the use of SLFs to support the high-quality development of the national economy.

Dynamics between FinTech and financial market: Supply-driven or Demand-guided?

<p>This paper examined the dynamic relationship between FinTech development and financial development using the time-varying parameter structural vector autoregression (TVP-SV-VAR) model to analyze their impulse response relationship. The results showed that the impact of FinTech development on financial development varies across different periods. In China, before the first half of 2021, financial development mainly drove FinTech development through demand. Afterward, FinTech development promoted financial development by providing new technological tools and services. In the United States, FinTech innovation and application mainly influenced financial development through supply-driven mechanisms. After the second half of 2022, as FinTech infrastructure improved, its positive impact on the financial market strengthened. The study also found that the effects of policy changes and market fluctuations on impulse responses at specific time points differed even in countries with different systems. The findings of this paper provide valuable insights for policymakers to address the challenges and opportunities brought on by FinTech.</p>

The effect of oil price and macroeconomic factors on inflation rate in Azerbaijan: The application of time-varying coefficient VAR model and time-varying Granger causality analysis

This study analyzes inflation dynamics in Azerbaijan, focusing on the impact of oil price fluctuations and macroeconomic variables using advanced econometric methods, specifically a time-varying coefficient vector autoregressive model and time-varying Granger causality analysis. The research identifies significant inflation inertia, where past inflation rates shape future expectations. The relationship between oil prices and inflation has evolved, with contractionary policies mitigating the inflationary effects of rising oil prices after 2011, but oil price volatility and external inflationary pressures resurfacing post-2021. Azerbaijan's exchange rate policy reduced inflation by lowering import prices before 2017, but its effectiveness has since weakened. Fiscal policy, particularly changes in M2, has played a key role in inflation dynamics, with contractionary measures after 2015 reducing inflation, while expansionary policies in 2022 reignited inflation. The study also shows that growth in the non-oil sector helps alleviate inflation, though its influence has diminished over time. The study concludes that effective policy measures are essential to manage inflation inertia, reduce dependence on oil revenues, and strengthen economic resilience in a rapidly changing global landscape.

The impact of macroprudential cross-border capital flow management on the linkage of domestic and foreign bond markets

While cross-border financing activities have increased significantly in recent years, the inflow of capital also leads to accumulated financial risks. To mitigate financial risks and promote the opening of financial markets, macroprudential management policies for cross-border financing have been implemented since 2016. This paper examines the effectiveness of macroprudential management policies in opening financial markets and managing foreign financial risks. We employ a time-varying parameter vector autoregressive model (TVP-VAR) to quantitatively analyze changes in the spillover effects between China’s bond market and foreign bond markets under different implementation stages of cross-border financing macroprudential policy. Our analysis reveals that the implementation of macroprudential management of cross-border financing has increased the total spillover effect between different bond markets, as well as the spillover effect from other bond indices to the Chinese RMB Bond Index and the spillover effect from other indices to the Chinese USD index. Moreover, our findings indicate that macroprudential management has reduced the total volatility spillover effect and the volatility spillover effect from other indices to the Chinese RMB Bond Index. These results highlight the importance of preventing external risk transmission when China’s financial market is opening to the world.

Relationship between mateverse cryptocurruncies (meta coins) and sustainability indices

Purpose- The purpose of this study is to determine the relationship between metaverse and sustainability indices. MANA token was used as a metaverse indicator in the study. Dow Jones Sustainability Emerging Markets Index (DJS_EMI), Dow Jones Sustainability World Index (DJS_WI), Dow Jones Sustainability Europe Index (DJS_EI) were used as sustainability indices. Methodology- In this research, the dynamic relationships between Metaverse and 3 sustainable indices used in the study were analyzed with a time-varying parameter vector autoregressive (TVP-VAR) model. This method is based on the fixed parameter sliding window VAR approach, first developed by Antonakakis and Gabauer (2017) and later updated by Diebold and Yılmaz (2009, 2012, 2014). Findings- DSJ_EMI variable carries a volatility spread of 24.85% to “DSJ_WI”, 24.81% to “DSJ_EI” and 25.40% to “METAVERSE”. “DSJ_WI” has a 59.68% volatility spread to other variables. The variables “DSJ_EMI” 25.04%, “DSJ_EI” 25.10% and “METAVERSE” 24.69% carry out volatility spread. Finally, while 70.79% of the volatility change in the "METAVERSE" variable is due to itself, a total of 29.21% is due to other variables. The variables “DSJ_EMI” 9.74%, “DSJ_WI” 9.74% and “DSJ_EI” 9.73% perform volatility spread. Conclusion- Mana (Decentraland Token) is the cryptocurrency used in the virtual reality platform called Decentraland. Metaverse refers to a digital universe called virtual reality, augmented reality and virtual world. Decentraland is a decentralized virtual reality platform that allows users to create, trade and interact with their own digital content.As a result of the analysis, it was determined that MANA, a Metavarse crypto asset, spreads volatility to sustainability indices. In other words, the MANA token spreads volatility to sustainability indices. Keywords: Metaverse, metaverse cryptocurrincies, meta coins, sustainability ındex, TVP-VAR JEL Codes: C32, C55, C58

A Two-Step Estimation Method for a Time-Varying INAR Model

This paper proposes a new time-varying integer-valued autoregressive (TV-INAR) model with a state vector following a logistic regression structure. Since the autoregressive coefficient in the model is time-dependent, the Kalman-smoothed method is applicable. Some statistical properties of the model are established. To estimate the parameters of the model, a two-step estimation method is proposed. In the first step, the Kalman-smoothed estimation method, which is suitable for handling time-dependent systems and nonstationary stochastic processes, is utilized to estimate the time-varying parameters. In the second step, conditional least squares is used to estimate the parameter in the error term. This proposed method allows estimating the parameters in the nonlinear model and deriving the analytical solutions. The performance of the estimation method is evaluated through simulation studies. The model is then validated using actual time series data.

Gold Price, Oil Price, and Stock Market Return Spillovers: Empirical Evidence from Vietnam

This paper analyzes the spillovers of oil prices, gold prices and stock market returns in Vietnam. By adopting the time-varying parameter vector autoregression model (TVP-VAR), the results show a moderate interdependence among the variables from 2010-2022. Additionally, the relationship between oil prices, gold prices, and stock market returns changes over time and is influenced by economic and political events. Overall, stock market returns are net shock transmitters with the highest volatility among all the variables, while the oil and gold markets are net recipients. Finally, our results remain robust to Vietnam's alternative stock market index.

Changes in monetary policy implementation over time

In this study, we utilise a time-varying parameter vector autoregressive model to investigate the relationship between central bank liabilities and the overnight policy rate across three countries with distinct monetary policy frameworks. Our findings reveal that a consistent negative correlation between these variables is present only in the case of the conventional reserve regime. For the other regimes, an initial significant negative relationship between reserves and interest rates is observed, but this effect diminishes with the adoption of new frameworks. These results suggest that the emergence of novel operational structures for central banks has disrupted the traditional approach to monetary policy implementation. One practical consequence of this” decoupling” between interest rates and reserves is that central banks in the United States, Canada, and similar systems can effectively employ their balance sheets in conjunction with standard interest rate policies. However, this does not apply to reserve regimes that continue to depend on open market operations for steering interest rates

Dynamic spillovers in higher moments and jumps across ETFs and economic and financial uncertainty factors in the context of successive shocks

Hedging is a particularly important tool in the Exchange Traded Fund (ETF) markets where market makers seek the best ways to mitigate the uncertainty of their exposures. This study relies on high frequency data to assess the spillover effects among ten US sector ETFs and various economic and financial uncertainty indexes based on realized volatility, realized higher moments as well as jumps under a relatively new spillover framework. Next, a time-varying parameter vector autoregression (TVP-VAR) model is used to examine the dynamic connectedness among ETFs and uncertainty factors volatilities while avoiding the sensitivity of spillover results to the choice of the rolling window. Our results showcase higher total connectedness between the different uncertainty indexes and ETFs, though with varying sensitivities. Notably, skewness and kurtosis can spread from one market to another, especially during times of market turbulence, reflecting the significant spillovers in higher-order moments. Interestingly, the market’s 30-day forward looking expectations of US stock market volatility (VIX) has stronger effect on the US sector equity ETFs than the expected 30-day volatility of returns on oil and gold. This analysis emphasizes the implications and contributions of assessing the spillover in higher-order moments covering volatility, skewness, and kurtosis to portfolio hedging and financial risk management. Overall, the results are of considerable practical interest for economic and market agents who are keen to understand market integration and systemic risk propagation to infer asymmetric or fat tail risk related to extreme or downside/upside risks.

Does cycle superposition amplify bank liquidity risk under different levels of financial openness? Empirical evidence from China

PurposeThis paper aims to explore the impact of cycle superposition on bank liquidity risk under different levels of financial openness so that banks can better manage their liquidity risk. Meanwhile, it can also provide some ideas for banks in other emerging economies to better cope with the shocks of the global financial cycle.Design/methodology/approachEmploying the monthly data of 16 commercial banks in China from 2005 to 2021 and based on the time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR) model, the authors first examine whether the cycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. Subsequently, the authors investigate the impact of different levels of financial openness on cycle superposition amplification. Finally, the shock of the financial cycle of the world's major economies on the liquidity risk of Chinese banks is also empirically analyzed.FindingsCycle superposition can magnify the impact of China's financial cycle on bank liquidity risk. However, there are significant differences under different levels of financial openness. Compared with low financial openness, in the period of high financial openness, the magnifying effect of cycle superposition is strengthened in the short term but obviously weakened in the long run. In addition, the authors' findings also demonstrate that although the United States is the main shock country, the influence of other developed economies, such as Japan and Eurozone countries, cannot be ignored.Originality/valueFirstly, the cycle superposition index is constructed. Secondly, the authors supplement the literature by providing evidence that the association between cycle superposition and bank liquidity risk also depends on financial openness. Finally, the dominant countries of the global financial cycle have been rejudged.

Evolving efficiency of the BRICS markets

This paper investigates a time-varying version of weak-form market efficiency in the BRICS countries. A moving window test for sample autocorrelations is applied alongside a Kalman filter approach to recover the hidden dynamics of the market efficiency process through appropriate time-varying autoregressive models with both homoscedastic and heteroscedastic conditional variance. Monthly data cover the period from January 1995 to December 2020, which includes the 2008–2009 global financial crisis and the recent COVID-19 recession. The results reveal that all the BRICS stock markets were affected during both periods, but generally remained weak-form efficient, with the exception of China.

Integration of the international carbon market: A time-varying analysis

Emission Trading Schemes (ETSs) have become vital for meeting global emission reduction targets. They are gaining momentum, as witnessed by increasing market size and improving information mechanisms. Examining key emission markets — European Union, New Zealand, California, and Hubei (China) — from April 2014 to December 2021, a Time-Varying Parameter Vector Autoregressive (TVP-VAR) model is applied to discern the markets' connectedness. In a novel approach to global carbon market research, this study uniquely combines the TVP-VAR with the connectedness approach, overcoming fixed parameters estimation and ensuring precise parameter estimates. The approach sheds light on patterns of total, directional, and net return/volatility spillovers, striving to identify which markets act as transmitters and which are receivers. Linking market spillovers to market characteristics, events, and policies offers insights for investors and policymakers. The total connectedness index of 10–12 % suggests a relatively low level of spillover, when compared to other market integration studies. The dynamic nature of return and volatility spillovers is evident, especially during the energy crisis and Covid-19 outbreak. The EU's ETS consistently acts as a net transmitter, predominantly in return connectedness, while New Zealand's ETS emerges as a major shock receiver in both return and volatility systems. Global climate negotiations and carbon market events have only a minor impact on the level of connectedness, in contrast to energy or financial crises and the Covid-19 outbreak. By highlighting the intricacies of carbon price volatility and market transmissions, the findings equip stakeholders with invaluable, actionable insights.

Cryptocurrency Environmental Attention, Green Financial Assets, and Information Transmission: Evidence From the COVID-19 Pandemic

This paper explores the connectedness between the cryptocurrency environmental attention index and four major green financial assets using time-varying parameter vector autoregression model from January 2014 to December 2021. Findings reveal that connectedness follows a heterogeneous trajectory over time. Results show evidence of higher volatility transmission during the COVID-19 period relative to the entire sample period. The high volatility transmission is a source of concern to policymakers, green stakeholders, and investors, due to limited diversification options.

Analysis of the impact of central bank digital currency on stock markets: Dynamics and implications

The purpose of the study is to explore the influence of central bank digital currency on stock markets. To realize the purpose, the TVP-VAR model was built, which determines the impact of volatility of the CBDC attention index (CBDCAI) on the volatility of stock market indices. The study uses a time-varying vector autoregressive model that analyzes weekly data from the first week of January 2015 to the first week of July 2023. The endogenous vector to be assessed by VAR contains CBDCAI and stock market indices of different countries (France: CAC 40, The United States of America: S&P 500, Germany: DAX 40, United Kingdom: FTSE 100, China: SSEC, The Netherlands AEX 25, Switzerland: SMI 20, Japan: Nikkei 225, India: NIFTY 50, Brazil: BVSP, South Korea: KOSPI). The results of the TVP-VAR model show that compared to stock market indices, CBDCAI appeared to be relatively independent and isolated. Interdependence and mutual influence between the digital currency market of central banks and stock markets were also revealed. In addition, CBDC functions primarily as a volatility absorber rather than a source of volatility. Despite the overall ability of the CBDC market to absorb fluctuations in volatility, it may also change its function with the widespread adoption of central bank digital currencies in many countries.