Time-varying Parameter Vector Autoregressive Model Research Articles

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.

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.

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.

The determinants of cross-border bond risk premia

Although cross-border bond issuance by emerging market economies (EMEs) has surged and dominated financing since the financial crisis of 2008, the sources of variation in cross-border bond risk premia have been ignored. This study fills this gap in the literature by examining the dynamic impact of interest rate differentials, exchange rates, and the broad dollar index on cross-border bond risk premia by applying a time-varying parameter vector autoregression (TVP-VAR) model with stochastic volatility. Generally, the risk premia responses are time-varying, lagged, and heterogeneous, with a reduction as the lag time horizon expands and the most significant in major global events. The empirical findings highlight that the broad dollar index dominates in explaining the variation of cross-border bond risk premia rather than the other two potential factors; we call this the “cross-border bond risk premia puzzle.” Furthermore, we find that the broad dollar index positively impacts cross-border bond risk premia via two transmission channels: 1) exchange rate expectations, which are significantly negatively driven by the broad dollar index, and 2) risk sentiment, which is negatively reflected by the index itself; both effects are negative. Moreover, the effect of the broad dollar index on risk premia is more pronounced for high-yield bonds than for investment-grade bonds. High-yield bond risk premia are negatively driven by exchange rate expectations, while investment-grade bond risk premia are positively driven by the local currency spread. Finally, these findings provide fresh insights into the aforementioned relationships; thus, we outline the implications.

Dynamic risk spillover among crude oil, economic policy uncertainty and Chinese financial sectors

Using daily return, realized volatility, realized skewness and realized kurtosis as risk proxies, we analyze the risk spillovers among crude oil, gold, economic policy uncertainty (EPU) and four Chinese financial sectors including bank, trust, insurance and security under different market conditions. Our analysis spans from January 10, 2008 to June 30, 2020 and is based on the combination of quantile VAR model and time-varying parameter vector autoregressive (TVP-VAR) model based on generalized forecast error variance decomposition. We find that: (1) The risk spillovers of the four risk proxies under 0.01-quantile and 0.99-quantile are much larger than those under mean and median (0.5-quantile); (2) The four risk proxies capture different information of the systemic shocks; (3) Term spread and credit spread have significant forecasting power on the total return spillovers and the total volatility spillovers.

The dynamic impact of monetary policy on financial stability in China after crises

This research investigates the role that China's monetary policy has played in maintaining financial stability after crises in the 21st century. We construct a financial stability index (FSI) with eight variables from four characteristics that reflect variations in the financial stability of China, and analyze the dynamic impacts of China's monetary policies on financial stability with a time-varying parameter vector-autoregressive (TVP-VAR) model. The three major crises that China has been involved in the past 15 years (i.e., the 2008 global financial crisis, 2015 stock market crash in China, and the ongoing COVID-19 crisis) indicate that the scale of social financing, stock market performance, and the degree of financial deepening in China have great influences on financial stability. We observe supportive evidence, moreover, that both quantitative and price-based monetary policies can promote financial stability after crises in China, especially in the short term. The quantitative policy is more effective than price-based monetary policy and has become the main policy tool in China. At a time when the world economy is still suffering from the COVID-19 crisis, the quick economic recovery is an imperative task for all countries. It is of great theoretical and practical significance to understand the relationship between monetary policy and financial stability after crises.

Dynamic linkages among oil price, green bond, carbon market and low-carbon footprint company stock price: Evidence from the TVP-VAR model

Achieving peak carbon dioxide emissions and carbon neutrality is essential for China to achieve high-quality economic development. This paper uses the Time-Varying Parameter Vector AutoRegression (TVP-VAR) model to explore the dynamic relationship between oil price, green bond index, carbon emission trading price and carbon efficiency index. The time-varying impulse response analysis results show that the oil price has a negative effect on the green bond index and carbon price and a positive impact on the carbon efficiency index in the short and medium term. The green bond index has a positive impact on the carbon price in the short and medium term, and a negative impact on the carbon efficiency index. In addition, carbon price shocks have a positive impact on the carbon efficiency index in the short and medium term. The robustness test indicates that our results are robust. These findings can help formulate relevant policies and prevent financial risks under the influence of energy prices.

Economic policy uncertainty and price pass-through effect of exchange rate in China

Using the Chinese economic policy uncertainty (EPU) index, this paper investigates the spillover effect on the price pass-through effect of the RMB exchange rate and uncertainty, using the time-varying parameter vector autoregressive (TVP-VAR) model. We find that EPU has a significant effect on the price pass-through effect of the RMB exchange rate, which indicates that an increase in uncertainty will significantly enhance the price pass-through effect of the exchange rate. In addition, EPU itself can have a significant spillover effect on the exchange rate. Finally, the price pass-through effect of the RMB exchange rate is incomplete, and its impact on the price level gradually decreases along the circulation chain. The Chinese government should pay close attention to the impact of economic policy uncertainty on the price pass-through effect of the RMB exchange rate and the exchange rate and further strengthen the risk prevention and early warning mechanism to minimize the impact of uncertainty on China's macroeconomy.

Price discovery in bitcoin spot or futures during the Covid-19 pandemic? Evidence from the time-varying parameter vector autoregressive model with stochastic volatility

ABSTRACT This paper examines price discovery between bitcoin spot and futures using static measures, namely information share (IS), component share (CS), modified information share (MIS), information leadership share (ILS), impulse response, and a time-varying parameter vector autoregressive (TVP-VAR) model with stochastic volatility and Markov Chain Monte Carlo (MCMC) sampling algorithm. Our one-minute and daily datasets cover 16 months before and 16 months during the Covid-19 pandemic (November 2018 to June 2021). Our IS, CS, MIS and impulse response results indicate a stronger bitcoin spot leadership, whereas our ILS results point to a weaker bitcoin futures dominance, during the Covid-19 pandemic. We construe this, as far as microstructure noise is concerned, as meaning that the bitcoin price is discovered in the spot market, and its dominance appears to have strengthened during the pandemic. However, as far as ‘pure speed’ is concerned, price discovery takes place in the bitcoin futures market, and its leadership seems to have weakened during the Covid-19 pandemic. The results of the time-varying measure (TVP-VAR) imply that, before the pandemic, price discovery took place within bitcoin futures but, during the pandemic, price discovery leadership has changed course, to occur within bitcoin spot.

Dynamic Influence of Network Public Opinions on Price Fluctuation of Small Agricultural Products Based on NLP-TVP-VAR Model—Taking Garlic as an Example

In recent years, the price of small agricultural products has both plummeted and skyrocketed, which has a great impact on people’s lives. Studying the factors affecting the price fluctuation of small agricultural products is of great significance for stabilizing their price. With the development and application of social media, farmers and consumers are more greatly influenced by online public opinion, resulting in irrational planting behavior or purchasing behavior, which has a complex impact on the price of small agricultural products. Taking garlic as an example, we crawled through network public opinions about garlic price from January 2015 to December 2020 using web crawler technology. Then, the network public opinions were quantified using a natural language processing and time-varying parameter vector autoregression (NLP-TVP-VAR) model to empirically analyze their dynamic influence on garlic price fluctuation. It was found that both public attitude and public attention have a short-term influence on garlic price fluctuation, and the influences of each differ according to direction, intensity and timing. The influence of public attitude on garlic price fluctuation is positive, while the influence of public attention on garlic price fluctuation is largely negative. The influence intensity of public attitude is stronger than of public attention on garlic price fluctuation. The influence of public attitude on garlic price fluctuation shows a trend of intensifying, while that of public attention has been weaker than in previous years. In addition, based on the results of our study, we present some recommendations for improving the comprehensive information platform and price fluctuation early warning system for the whole industry chain of small agricultural products.

Economic policy uncertainty and stock market sector time-varying spillover effect: Evidence from China

This paper investigates the volatility spillover effect among the Chinese economic policy uncertainty index, stock markets, gold and oil by employing the time-varying parameter vector autoregressive (TVP-VAR) model. Three main results are obtained. Firstly, the optional consumption, industry, public utility and financial sectors are systemically important during the sample period. Secondly, among the four policy uncertainties, the uncertainty of fiscal policy and trade policy contributes more to the spillover effect, while the uncertainty of monetary policy and exchange rate policy contributes less to the spillover effect. Thirdly, during COVID-19, oil spillovers from other sources dropped rapidly to a very low point, it also had a significant impact on the net volatility spillover of the stock market. This paper can provide policy implication for decision-makers and reasonable risk aversion methods for investors.

Impact of Oil Financialization on Oil Price Fluctuation: A Perspective of Heterogeneity

A large number of studies have confirmed that oil speculation has played a vital role in oil price fluctuation in recent years. However, the heterogeneous impact of oil financialization on oil price fluctuation has not received enough attention. Based on time series data from January 1990 to October 2021, this paper adopts the Time-Varying Parameter Vector Auto-Regression (TVP-VAR) model and the Ensemble Empirical Mode Decomposition (EEMD) method to study the heterogeneous impact of oil financialization on oil price fluctuation from three perspectives: different periods, different frequencies, and different time points of major events. The research results are as follows. First, the impact of oil financialization on oil price fluctuation in different periods is heterogeneous in terms of fluctuation amplitude and intensity. During major events such as the financial crisis or the COVID pandemic, the impact of oil financialization on oil price fluctuation is volatile and intense. Second, the impact of oil financialization on the oil price fluctuation of different frequencies is mainly reflected in the direction and duration. Oil financialization mainly promotes high-frequency oil price fluctuation in the short term, and it mainly suppresses low-frequency oil price fluctuation in the long term. Third, the impact of oil financialization on oil price fluctuation is heterogeneous in terms of duration, intensity, and transmission speed at different time points of major events.

Response of Runoff to Meteorological Factors Based on Time-Varying Parameter Vector Autoregressive Model with Stochastic Volatility in Arid and Semi-Arid Area of Weihe River Basin

This study explores the response characteristics of runoff to the variability of meteorological factors. A modified vector autoregressive (VAR) model is proposed by combining time-varying parameters (TVP) and stochastic volatility (SV). Markov chain Monte Carlo (MCMC) is used to estimate parameters. The TVP-SV-VAR model of daily runoff response to the variability of meteorological factors is established and applied to the daily runoff series from the Linjiacun hydrological station, Shaanxi Province, China. It is found that the posterior estimates of the stochastic volatility of the four variables fluctuate significantly with time, and the variance fluctuations of runoff and precipitation have strong synchronicity. The simultaneous impact of precipitation and evaporation on the pulse of runoff is close to 0. Runoff has a positive impulse response to precipitation, which decreases as the lag time increases, and a negative impulse response to temperature and evaporation with fluctuation. The response speed is precipitation > evaporation > temperature. The TVP-SV-VAR model avoids the hypothesis of homoscedasticity of variance and allows the variance to be randomly variable, which significantly improves the analysis performance. It provides theoretical support for the study of runoff response and water resource management under the conditions of climate change.

Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network

This paper is the first attempt to forecast the time-varying total return and volatility connectedness between the oil prices and the Islamic stock indices of seven oil-exporting countries, namely Iran, Oman, Saudi Arabia, Qatar, Kuwait, Bahrain, and United Arab Emirates. The entire analysis follows two main stages. The first carries out the connectedness analysis using the Bayesian time-varying parameter vector autoregressive (BTVP-VAR) model, while the second stage applies the training process in a cascade-forward backpropagation network (CFBPN) to five groups of input (spillover TO others, spillover FROM others, both spillovers TO & FROM others, raw returns, and raw volatilities, and ALL inputs) to forecast the dynamic total connectedness of returns/volatilities. The results of the connectedness analysis show that the Islamic stock index of Iran is not connected to the oil market and the Islamic stock indices of other Islamic oil-exporting countries. The Islamic stock indices of the United Arab Emirates and Saudi Arabia have a leadership role in the network. Besides, oil demonstrates a net receiving spillover status. In addition, connectedness increases during periods of crisis and significant oil price changes. Finally, the forecast analysis shows that the transmitted return and volatility spillovers from markets (TO spillovers) are the most important factor information in predicting the total connectedness of the network. The findings showcase important implications for investors, policy makers, and future studies.

International monetary policy and cryptocurrency markets: dynamic and spillover effects

Using daily data over the period August 5, 2013 – September 27, 2019, this study investigates the dynamic spillovers between international monetary policies across four major economies (i.e. Eurozone, Japan, UK and US) and three key cryptocurrencies (i.e. Bitcoin, Litecoin and Ripple). In doing so, we apply a Time-Varying Parameter Vector Auto-Regression (TVP-VAR) model, a dynamic connectedness approach and network analysis. The empirical results indicate that cryptocurrency returns and monetary policy spillovers were particularly large when shadow policy rates became negative, moderated during the Fed's ‘tapering process’, and sharpened again more recently as cryptocurrency buoyancy returned. Gross directional spillovers suggest that shadow policy rates have more ‘to give than to receive’, while those from and to cryptocurrency returns are naturally volatile. There is also strong interconnectedness between monetary policy in either the US or the Eurozone and the UK, and between Bitcoin and Litecoin. However, the spillovers across monetary policy and cryptocurrencies tend to be muted. Finally, spillovers were only slightly larger during the Fed's ‘unconventional’ policy compared to the ‘standard’ era, but their composition qualitatively changed over time.

Do the time-varying effects of oil prices affect the trade balances of ASEAN-5 countries?

ABSTRACT This study investigates the effects of oil price shocks on five Association of Southeast Asian Nations (ASEAN-5) trade balances by employing a time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR). The time-varying responses derived from this model indicate that the impacts of oil price shocks vary significantly across economies due to their distinct characteristics in the oil market. The results also demonstrate the significant time-varying impact of economic activity and the real exchange rate on the trade balances of the five countries. The time-varying responses show that the effects of oil prices on trade balances are more pronounced during global and local economic events. Our findings have important policy implications for each country to avoid the risks associated with fluctuations in oil prices.

Credit Policy and Housing Market Liquidity: An Empirical Study in Beijing Based on the TVP-VAR Model

Although there is a consensus that the housing market is deeply affected by credit policies, little research is available on the impact of credit policies on housing market liquidity. Moreover, housing market liquidity is not scientifically quantified and monitored in China. To improve the government's intelligence in monitoring the fluctuation of the housing market and make more efficient policies in time, the dynamic relationship between credit policy and housing liquidity needs to be understood fully. On the basis of second-hand housing transaction data in Beijing from 2013 to 2018, this paper uses a time-varying parameter vector autoregressive model and reveals several important results. First, loosening credit policies improves the housing market liquidity, whereas credit tightening reduces the housing market liquidity. Second, both the direction and the duration of the impacts are time-varying and sensitive to the market conditions; when the housing market is downward, the effect of a loose credit policy to improve market liquidity is weak, and when the housing market is upward, market liquidity is more sensitive to monetary policy. Finally, the housing market confidence serves as an intermediary between credit policy and housing market liquidity. These results are of great significance to improve the intelligence and efficiency of the government in monitoring and regulating the housing market. Several policy recommendations are discussed to regulate the housing market and to stabilize market expectations.

The dynamic connectedness and hedging opportunities of implied and realized volatility: Evidence from clean energy ETFs

This paper aims to examine dynamic connectedness and hedging opportunities between the realized volatilities of clean energy ETFs and energy implied volatilities through Time-Varying Parameter Vector Autoregression Model (TVP-VAR) and Asymmetric Dynamic Conditional Correlation (ADCC) GARCH models. TVP-VAR analysis results show that dynamic connectedness increases during turbulence periods. We also determine that clean energy ETFs such as PBW, QCLN, SMOG, and TAN are net volatility transmitters. Surprisingly, OVX is a net volatility receiver, especially with the developments after the Paris Agreement in 2016.As a result of the ADCC GARCH analysis, we determine that the conditional correlation between clean energy ETFs and implied volatility ETFs is asymmetric, and negative information shocks increase the conditional correlation. Although OVX is a cheap alternative for hedging long position risks in clean energy ETFs, VXXLE is more effective than OVX in terms of hedging effectiveness. These findings provide insight for individual and institutional investors, and portfolio managers on how negative and positive shocks change the conditional correlation between assets at different levels.