Time-varying Parameter Vector Research Articles

The Time-Lag Effect of Climate Factors on the Forest Enhanced Vegetation Index for Subtropical Humid Areas in China.

Forests represent the greatest carbon reservoir in terrestrial ecosystems. Climate change drives the changes in forest vegetation growth, which in turn influences carbon sequestration capability. Exploring the dynamic response of forest vegetation to climate change is thus one of the most important scientific questions to be addressed in the precise monitoring of forest resources. This paper explores the relationship between climate factors and vegetation growth in typical forest ecosystems in China from 2007 to 2019 based on long-term meteorological monitoring data from six forest field stations in different subtropical ecological zones in China. The time-varying parameter vector autoregressive model (TVP-VAR) was used to analyze the temporal and spatial differences of the time-lag effects of climate factors, and the impact of climate change on vegetation was predicted. The enhanced vegetation index (EVI) was used to measure vegetation growth. Monthly meteorological observations and solar radiation data, including precipitation, air temperature, relative humidity, and photosynthetic effective radiation, were provided by the resource sharing service platform of the national ecological research data center. It was revealed that the time-lag effect of climate factors on the EVI vanished after a half year, and the lag accumulation tended to be steady over time. The TVP-VAR model was found to be more suitable than the vector autoregressive model (VAR). The predicted EVI values using the TVP-VAR model were close to the true values with the root mean squares error (RMSE) < 0.05. On average, each site improved its prediction accuracy by 14.81%. Therefore, the TVP-VAR model can be used to analyze the relationship of climate factors and forest EVI as well as the time-lag effect of climate factors on vegetation growth in subtropical China. The results can be used to improve the predictability of the EVI for forests and to encourage the development of intensive forest management.

The spillover effect of international monetary policy on China's financial market

&lt;abstract&gt; &lt;p&gt;This study analyzes the impact of global financial integration and monetary policies from the United States, European Union and Japan on China's financial markets post-pandemic. Using TVP-FAVAR (Time-Varying Parameter Factor Augmented Vector Autoregression) and TVP-VAR-DY (Time-Varying Parameter Vector Autoregression DY) models, a Chinese financial market stress index was developed, showing that developed nations' monetary policies influence China's financial stress. The impact varies based on the economy's size and policy effectiveness. The spillovers occur mainly through accelerated short-term capital flows and foreign exchange reserve fluctuations. These effects have evolved over two decades, particularly noticeable during economic crises and the COVID-19 pandemic, highlighting the need for emerging economies, like China, to protect against international financial spillovers.&lt;/p&gt; &lt;/abstract&gt;

Asymmetric volatility spillover between oil prices and regional renewable energy stock markets: A time-varying parameter vector autoregressive-based connectedness approach

&lt;abstract&gt; &lt;p&gt;The rapid expansion of renewable energy sources and their integration into the energy mix has generated scholarly interest in comprehending the interplay between renewable and conventional energy markets. This research aims to examine the (a)symmetric volatility spillover between the oil market and various regional renewable energy stock markets, namely the US, Europe and Asia. To achieve this objective, we employ the time-varying parameter vector autoregressive-based connectedness (TVP-VAR) approach, which allows analysing the interconnection and transmission of shocks between the different markets. Based on an analysis of daily data relative to the different regional renewable energy stock markets and international oil prices, the findings suggest the presence of a dynamic volatility connectedness between the green and brown energy stock markets. The extent of connectedness is contingent upon the specific regional renewable energy market under consideration. Moreover, the decomposition of the volatility series into &lt;italic&gt;good&lt;/italic&gt; and &lt;italic&gt;bad&lt;/italic&gt; volatility emphasizes an asymmetric pattern, which becomes more pronounced during periods of major events. On average, the oil market and the Asian renewable energy stock market are net receivers of volatility shocks. In contrast, the US and European renewable energy stock markets are net transmitters of shocks. Our findings provide investors with valuable insights for portfolio design and risk management decisions.&lt;/p&gt; &lt;/abstract&gt;

Output and price volatility spillovers among ECOWAS members: The role of global and regional uncertainties

This paper examines the role of global and regional uncertainties on output and price dynamics connectedness between members of the Economic Community of West African States (ECOWAS) over the period of 1990 to 2020. The paper employs the Time-Varying Parameter Vector Autoregression connectedness framework to compute the spillover indexes and the non-parametric causality-in-quantile approach to analyze the causal effect of Global and Africa uncertainties on the output and price connectedness between the countries. Our findings indicate a high degree of interdependence between ECOWAS members in terms of output and prices with the exception of Togo and Mali which show the weakest vulnerability to idiosyncratic shocks from other West African countries. Also, the causal analysis provides evidence supporting a non-linear causal relationship between economic and trade uncertainties and the connectedness between ECOWAS economies, mostly at lower and median quantiles. Policymakers are advised to ensure stability in economic policies and rapid policy intervention to tackle economic crises to reduce economies' exposure to risks and speculative shocks.

VOLATILITY SPILLOVER BETWEEN GERMANY, FRANCE, AND CEE STOCK MARKETS

The CEE stock markets are more and more integrated in the European financial markets. The growth of the integration of financial markets favours the volatility and return spillover between them. The current study analyses the volatility spillover among the stock markets in the countries from Central and East Europe (CEE) and Germany and France with the aim to identify the possibilities of reduction of a portfolio risk. A special attention is granted to the analysis during the pandemic caused by COVID-19. The time-varying parameter vector autoregressive (TVP-VAR) model on which is based the methodology proposed by Antonakakis and Gabauer (2017) is used to estimate the evolution in time of volatility spillover. The empirical results obtained for the period January 2001 – September 2021 highlight the increase in volatility spillover between the countries analysed when the pandemic caused by COVID-19 was confirmed. The lack of volatility integration of the markets analysed enables the making of arbitrages in order to reduce the risk of a portfolio. The results obtained are important in the management of financial asset portfolios.

The Time-Varying Effect of Interest Rates on Housing Prices

This study analyzes the time-varying effect of interest rates on housing prices. As housing prices are too high for most consumers to afford with income alone, they use bank loans. Consequently, when interest rates fall, the demand for housing increases, causing prices to rise. This effect of interest rates was common in countries that implemented low-interest rates in response to the COVID-19 pandemic. Using Korean data from March 1991 to March 2022, this study examined the impact of interest rate shocks on housing prices by employing a time-varying parameter vector autoregressive model. According to the analysis, in Korea, while the impact of the interest rate shocks on housing prices was not significant before the global financial crisis, it increased dramatically afterward. Particularly, the impact of interest rate shocks was strongest relative to the past during the period of the increase in house prices from 2020 to 2021. The rise in the effects of interest rate shocks on housing prices is attributed to the increased dependence on loans for housing purchases. The results suggest that given the recent substantial increments in interest rates due to inflation, an interest rate shock would likely cause a global housing market recession.

On the volatility of WTI crude oil prices: A time-varying approach with stochastic volatility

This study investigates the impacts of crude oil-market-specific fundamental factors and financial indicators on the realized volatility of West Texas Intermediate (WTI) crude oil price. A time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV) is applied to weekly data series spanning January 2008 to October 2021. It is found that the WTI oil price volatility responds positively to a shock in oil production, oil inventories, the US dollar index, and VIX but negatively to a shock in the US economic activity. The response to the EPU index was initially positive and then turned slightly negative before fading away. The VIX index has the most significant effect. Furthermore, the time-varying nature of the response of the WTI realized oil price volatility is evident. Extreme effects materialize during economic recessions and crises, especially during the COVID-19 pandemic. The findings can improve our understanding of the time-varying nature and determinants of WTI oil price volatility.

Connectedness between geopolitical risk, financial instability indices and precious metals markets: Novel findings from Russia Ukraine conflict perspective

Precious metals and traditional energy prices are shaken by geopolitical tension, financial instability, rising inflation, declining economic growth, and financial markets. In addition, conventional energy, such as oil and gas, are intensive inputs to metal production, making these variables increasingly interconnected for economies and financial investment. This study analyzes the dynamics of the connectedness generated by economic instability and geopolitical risk on the traditional non-renewable energy and precious metals markets using Time-Varying Parameter Vector Autoregressive (TVP-VAR) and Wavelets Coherences from 11 June 2012 to 23 May 2022. The results show that the total connectedness index was higher during the Russia-Ukrainian conflict in February–May 2022. Moreover, the geopolitical risk, financial instability, and oil return are net transmitters of shocks, while gold, gas, and silver are the net received. The wavelet coherence results show strong co-movement during the Russia-Ukrainian conflict between geopolitical risk, oil, gas, and silver returns at various scales, given its properties for diversification at the time-frequency domain. Gold appears a stable asset and serves as a haven ability against geopolitical risk and financial instability. Findings indicate that geopolitical risk and financial instability are crucial in determining metals, precious, and energy markets that the investors, managers, funds, and producers need to consider in their investment and production decisions. Policymakers and macroprudential authorities should consider a shifting macroeconomic environment.

Quantifying interconnectedness and centrality ranking among financial institutions with TVP-VAR framework.

Financial risk is spread and amplified through the interconnectedness among financial institutions. We apply a time-varying parameter vector autoregression model to analyze the dynamic spillover effects in the Chinese financial system. We find that the 2017 house price control policies have significantly increased the risk of China's financial system. Before 2017, with the prosperity of the real estate market, the interconnectedness of the Chinese financial system continued to decline, while after 2017, with the slowdown of house price growth and the downturn of the real estate market, the interconnectedness turned to increase. For different sectors, the trends and the magnitudes of the spillover effects are diverse, and any sector can contribute to systemic risk in a dynamic way. Finally, we rank 20 systemically important financial institutions according to two centrality measures. The stable institution ranking provides less noisy information for regulators to formulate a policy and intervene in the market effectively.

Dynamic path through of oil price shocks into inflation in Iran: application of Markov switching and TVP-VAR models

PurposeThe main purpose of this paper is to investigate the dynamic effects of the oil price shocks on Iran’s inflation in the period 1993:2–2018:2Design/methodology/approachThe main purpose of this paper is to investigate the dynamic effects of the oil price shocks on Iran’s inflation in the period 1993:2–2018:2 using the time-varying parameter vector autoregressive (TVP-VAR) model. The dynamics of the results enable us to study the amount and severity of the impact of the oil price shocks on inflation with the distinction of the sanctioned and non-sanctioned periods. The volume of oil export is used to identify the effective oil sanctions. The period is divided into sanctioned and non-sanctioned periods by Markov switching model.FindingsThe results show that the pass-through of oil price shocks into Iran’s inflation are time-varying, and there are significant differences at sanction period from other time horizons. An increase in oil price has a positive effect on inflation and its effects are stronger during the sanctions period. It is also observed that the producer price index is more sensitive to changes in the oil price than the consumer price index. The necessity of the government’s earnest efforts to improve international relations to lift the sanctions, along with diversification of exports, and making the economy of Iran independent of oil revenues is obvious.Originality/valueIn addition to the exogenous oil price shocks, Iran’s economy faces numerous restrictions for its oil exports due to the sanctions. The main purpose of this paper is to investigate the dynamics effects of the oil price shocks on Iran’s inflation in the period 1993:2–2018:2 using the time-varying parameter vector autoregressive (TVP-VAR) model. The dynamics of the results enable us to study the amount and severity of the impact of the oil price shocks on inflation with the distinction of the sanctioned and non-sanctioned periods. The volume of oil export is used to identify the effective oil sanctions.

The volatility connectedness between agricultural commodity and agri businesses: Evidence from time-varying extended joint approach

This paper investigates the volatility connectedness between ten major agribusiness common stock prices and various agricultural commodity prices between August 11, 2005, and November 4, 2022. We employ the time-varying parameter vector autoregressions (TVP-VAR) extended joint connectedness framework. The results show that agribusiness stocks are net volatility transmitters, whereas agricultural commodities are net volatility receivers. The results provide significant implications for investors and policymakers concerned with commodity prices.

COVID-19 and risk spillovers of China's major financial markets: Evidence from time-varying variance decomposition and wavelet coherence analysis

COVID-19 has influenced financial markets drastically; however, this influence has received little attention, particularly in China. This study investigates risk spillovers across China's financial and shipping markets through dynamic spillover measures based on time-varying parameter vector autoregression and generalized forecast error variance decompositions. Stock, fund, and futures markets are identified as major risk senders, whereas other markets are identified as major risk receivers. Surprisingly, bonds, gold, and shipping are safe havens that facilitate portfolio optimization. Furthermore, using wavelet coherence analysis, we find that the coherence between dynamic total spillover and COVID-19 varies across time and frequency domains.

Do biofuel production and financial speculation in agricultural commodities influence African food prices? New evidence from a TVP-VAR extended joint connectedness approach

Many African countries experienced social disorder and subsequent political instability as a result of global commodity price inflation in 2007–2008, which reaffirmed the importance of overseas factors such as biofuel production, international food and energy prices, and financial speculation. Biofuel, in particular, is often placed at the center of the debate around identifying potential determinants of food price hikes. We apply a time-varying parameter vector autoregressive (TVP-VAR) extended joint connectedness approach to uncover the dynamic connectivity of African food prices, US biofuel production, global energy and food prices, and financial speculation. The key findings are; 1) the results of averaged connectedness suggest that US biofuel production and financial speculation in agricultural commodities significantly influence African food prices; 2) the hefty surges in the dynamic connectedness between African food prices and four cross-border factors are triggered by global events like the 2000 dot-com bubble, the 2008 global commodity boom, and the 2020 COVID-19 pandemic; 3) arbitrage transactions transmitted intense shocks to African food prices between 2001 and 2012, while biofuel production constantly affected African food prices between 2001 and 2021. We draw pragmatic policy implications to prevent or mitigate market shock transmissions to African food markets.

Institutional enforcement of environmental fiscal stance and energy stock markets performance: Evaluating for returns and risk among connected markets

Energy markets are critical avenues for achieving the goal of climate action. Nevertheless, effective green policies by the government may significantly influence the demand and supply side of the market differently. Using the time-varying parameter vector autoregression (TVP-VAR) and panel autoregressive distributed lag (PARDL), this study examines the role of institutional enforcement of environmental taxes and government spending on environmental protection on the returns and risk connectedness of energy stocks of 12 countries. The results show that networks of energy stocks primarily influence the returns and risk of each country's energy stock. The analysis further reveals that institutional enforcement of environmental fiscal laws reduces the long-run total returns connectedness of the energy market only through the net transmitting markets. The opposite is observed in net receiving markets. On the other hand, the risk spillover of energy markets increases with enforcing environmental protection spending only through the net receiving markets in the long run. A detailed analysis of the net receiving markets demonstrates that, depending on the period and the instruments employed, the impact of enforcing environmental fiscal laws on risk spillover of these markets is heterogeneous. The results of this study emphasize the policy implications for investors and climate change.

Dynamic volatility connectedness among cryptocurrencies and China's financial assets in standard times and during the COVID-19 pandemic

In this paper, we use the time-varying parameter vector autoregressions (TVP-VAR) model to examine volatility connectedness among 5 cryptocurrencies and 5 China's financial assets in static and dynamic scenarios. We find that the dynamic total connectedness of the system exhibits large dynamic variability. When the total connectedness breaks through 50%, it will move down rapidly. Ethereum and Litecoin are increasing their influence, whereas Bitcoin is losing its leadership. The impact of the cryptocurrency market on China's financial market has become very small since 2022Q1. Furthermore, the COVID-19 outbreak has a long-term (short-term) impact on the gold market (the other markets).

Dynamic connectedness among regional FinTech indices in times of turbulences

ABSTRACT This study examines the connectedness among several regional FinTech indices and analyses the impact of Russia’s invasion of Ukraine on the dynamic spillovers. We employ the time-varying parameter vector autoregression and time-frequency connectedness network approach using data spanning from 2 September 2021 until 4 April 2022. We find that the returns and volatility spillovers among FinTech indices burst with the outbreak of the invasion. During this invasion, Asia-Pacific and Japan FinTech appeared to be the largest recipients of the spillovers. In contrast, Europe and North America FinTech appeared to be the largest transmitters of spillovers. The short-term effect among regional FinTech indices is stronger than the long-term relationship.

Time-frequency transmission mechanism of EPU, investor sentiment and financial assets: A multiscale TVP-VAR connectedness analysis

This article examines the transmission mechanism of economic policy uncertainty (EPU), investor sentiment and Chinese financial assets from time-frequency and static-dynamic perspectives. The multiscale connectedness method based on time-varying parameter vector autoregression (TVP-VAR) is introduced to explore the time–frequency and static-dynamic spillovers. The empirical results are as follows: First, there is an interdependence between EPU and high-risk assets. Additionally, EPU and high-risk assets spillover risk to investor sentiment individually or in chains, ultimately affecting low-risk assets. Second, high-risk assets spill to low-risk assets in the short term but reverse in the long term. Third, EPU spills over to the system the most around 2008, especially in the long term. In addition, high-risk assets are the largest risk spillover and recipient at each frequency over the last decade. Overall, investors and regulators should consider real-time financial monitoring solutions in China based on economic policy uncertainty and investor sentiment factors.

Oil shocks and global economy

This paper analyzes how global economic activity reacts to shocks in the crude oil market, allowing that such reactions may change over time by using a Time-Varying Parameter Vector Autoregression model. Our findings show that an unanticipated disruption in oil supply leads to a relevant decline in global economic activity at any period of time considered (1973:08–2019:06) and at any response horizon, with impacts being relatively similar over time. An unexpected expansion in aggregate demand meaningfully boots global economic activity at any period of time and at any response horizon. In contrast, an unexpected oil-specific demand increase only has a short-lived substantial effect on global economic activity for the shocks produced in the early 2000s, the global financial crisis and the shocks occurred in the last years of the 2010s. These findings are in line with those of Baumeister and Hamilton (2019) who, using a time-invariant model, found that oil supply shocks reduce global economic activity, but oil consumption-demand shocks do not.

Can treasury inflation-protected securities safeguard investors from outward risk spillovers? A portfolio hedging strategy through the prism of COVID-19

Documenting the interlinkages among assets that are widely used to hedge against inflation is crucial for investors, as the necessity to protect the investment portfolio is stronger under inflationary conditions. For this purpose, we investigate the volatility spillovers between treasury inflation-protected securities (TIPS) and a battery of other assets perceived as inflation hedges, including bonds, gold, real estate, oil and equities. The applied methodology comprehends the time-varying parameter vector autoregressive (TVP-VAR) extension of the Diebold and Yilmaz (Int J Forecast 28:57–66, 2012, 10.1016/j.ijforecast.2011.02.006) approach for the period 1/1/2010–3/31/2022. Our results indicate that the assets under consideration are moderately interconnected and subjected to several exogenous shocks, such as the US–China trade war, the COVID-19 pandemic and the Russia–Ukraine war. Furthermore, we assess the hedging effectiveness of TIPS against each asset by estimating hedge ratios and optimal portfolios weights, before and after the spread of COVID-19 pandemic, by using conditional variance estimations (DCC-GARCH). The empirical findings show that the short position in the volatility of TIPS is proved to be an excellent hedge for all the sampled assets, with the exception of short-term Treasury bonds, and their hedging ability was improved during COVID-19.

Dynamic interlinkages between the crude oil and gold and stock during Russia-Ukraine War: evidence from an extended TVP-VAR analysis.

The Russia-Ukraine significantly influences the oil market. We employ a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach to identify the sources of the oil market's volatility by studying interlinkages between the crude oil and gold and stock market by characterizing the connectedness of four markets starting from January 1, 2018 to April 8, 2022. Our attention is mostly paid to the period marked by the event that Russia invaded Ukraine on 24 February, 2022. Our results demonstrate that the war shocks appear to influence the system-wide dynamic connectedness, which signifies the interlinkages among the considered markets. Net total directional connectedness suggests that the oil and gold markets appear to be the net transmitter of spillover shocks in the system. However, there are shifts in the roles of these two markets during the time of the Russia-Ukraine war shock. Pairwise connectedness highlights the significance of the oil market in transmitting the adverse influences of shocks to other markets, especially during the Russia-Ukraine war.