Time-varying Parameter Vector Autoregression Research Articles

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.

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.

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.

The Dynamic Impacts of Weather Changes on Vegetable Price Fluctuations in Shandong Province, China: An Analysis Based on VAR and TVP-VAR Models

In order to enrich the research on the influence of weather factors on agricultural economy and provide practical decision-making references for the relevant market entities, this study took pointed pepper, loofah, Chinese chives and tomato as examples, using weekly wholesale prices and corresponding weather factors data from one of the main production areas in China based on the vector autoregressive (VAR) and the time-varying parameter vector autoregressive (TVP-VAR) models to explore the dynamic impacts of weather changes on vegetable price fluctuations. It was found that price fluctuations of specific vegetable varieties were affected by changes in specific weather factors. The shock intensity of weather factor changes in the same lag period on the current vegetable price fluctuations was time-varying, which could describe the historical dynamics of the impacts of weather changes on vegetable price fluctuations. The dissipation characteristics of the dynamic impacts of weather factors changes occurring at selected time points on vegetable price fluctuations had strong consistency, and the obtained rules could be used to analyze the life cycle and dynamic characteristics of extreme weather impacts on vegetable price fluctuations. In addition, vegetable price fluctuations were affected mostly by their own lag periods, and the shock intensity of vegetable price fluctuations with equal lag periods on their own current price fluctuations were relatively stable. The dynamic impacts of vegetable price fluctuations occurring at chosen time points on its own later fluctuations were highly consistent in the variation from the beginning of the strongest positive shock to the final dissipation.

Will Demographic Changes Affect Monetary Policy with Interest Rate Shocks in Indonesia?

Changes in the demographic structure and its impact on the economy are becoming interesting issues in almost all countries in the world, including Indonesia. Until 2030, the working age group dominates almost 60 percent of the total population in Indonesia. This phenomenon is a valuable asset which should be used optimally. Moderate adjustment of interest rate policy is an effort applied by Bank Indonesia to maximize the access to credit and public savings. Therefore, this study aims to determine the effect of demographic aspects and the real sector on the effectiveness of monetary policy in the presence of interest rate shocks. This study employed time series data with a time period of 1987Q1 – 2020Q4 in Indonesia. The analysis was performed by using time varying parameter-vector autoregressive (TVP-VAR) and fully modified-ordinary least square (FM-OLS). The results revealed that the population and the elderly population aspects had significant positive effects on the monetary policy, while the variable of productive age and the number of dependence aspects has significant negative effects. The relationship between the real sector and the monetary policy has a different relationship, such as a positive relationship between the savings and technology variables and the domestic credit variable which has a significant negative relationship with the monetary policy.

CONNECTEDNESS BETWEEN CRUDE OIL AND US EQUITIES: THE IMPACT OF THE COVID-19 PANDEMIC

This paper contributes to the literature by employing a multifractal cross-correlation analysis (MFCCA) to study the effect of the global COVID-19 pandemic on cross-correlations between oil and US equity markets. First, we examine the detrended moving average cross-correlation coefficient between oil and S&P 500 returns before and during COVID-19 and find that US stock markets became more correlated with oil during the pandemic in the long term. Second, we find that the pandemic has caused an increase in the long-range cross-correlations over the small fluctuations. Third, the MF-DCCA method shows that the pandemic caused an increase in cross-correlations between the two markets. In sum, the pandemic caused a closer correlation between oil and US equities in the long range and a deeper dynamic connection between oil and US equity markets, as indicated by the multifractality tests. We also investigate the connectedness between oil and the S&P 500 using a dynamic procedure based on time-varying parameter vector autoregression. We find that oil is a net transmitter of shocks to the forecast error variance of the S&P 500 during March, April and May 2020, whereas the S&P 500 is a net transmitter of shocks to oil variance early in the pandemic (January and February 2020).

Macroeconomic forecasting and variable ordering in multivariate stochastic volatility models

We document five novel empirical findings on the well-known potential ordering drawback associated with the time-varying parameter vector autoregression with stochastic volatility developed by Cogley and Sargent (2005) and Primiceri (2005). First, the ordering does not affect point prediction. Second, the standard deviation of the predictive densities implied by different orderings can differ substantially. Third, the average length of the prediction intervals is also sensitive to the ordering. Fourth, the best ordering for one variable in terms of log-predictive scores does not necessarily imply the best ordering for another variable under the same metric. Fifth, the ordering problem becomes exacerbated in conditional forecasting exercises. Then, we consider three alternative ordering invariant models: a canonical discounted Wishart stochastic volatility model and two dynamic stochastic correlation models. When the forecasting performance of these ordering invariant models is compared to Cogley, Primiceri, and Sargent’s ordering variant model, the former underperforms relative to all orderings and the latter two have an out-of-sample forecasting performance comparable with the median outcomes across orderings.

An Application of a TVP-VAR Extended Joint Connected Approach to Investigate Dynamic Spillover Interrelations of Cryptocurrency and Stock Market in Vietnam

In this paper, we employ a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach to study interlinkages between the cryptocurrency and Vietnam’s stock market by characterizing their connectedness starting from January 1, 2018, to December 31, 2021. We report that the COVID-19 health shocks impact the system-wide dynamic connectedness, which reaches a peak during the COVID-19 pandemic. Net total directional connectedness suggests that the cryptocurrency market significantly impacts Vietnam’s stock market, especially those with the largest market capitalization like Bitcoin and Ethereum. This market can be held accountable for Vietnam’s stock market volatility. In encountering the COVID-19 pandemic, the effect of the three cryptocurrencies reduced before 2020, around the end of 2019 and the beginning of 2020. However, from the end of 2020–2021, while cryptocurrencies continued their roles as net transmitters for Vietnam’s stock market.

Time-frequency connectedness between food commodities: New implications for portfolio diversification

This study examines the time-frequency connectedness of food commodities (cereals, dairy, food, meat, vegetable oil, and sugar) in the Food and Agriculture Organization's world food indices. With a monthly-frequency dataset covering February 1990 to October 2021, we utilise the returns spillover index approach of Baruník and Křehlík (2018). The results are robust to the time-varying parameter vector autoregressive (TVP-VAR) connectedness approach. The findings explicate that food commodities are highly connected in the short- and long-term horizons. The dynamic total connectedness varies from 15% to 80% in the short term, 1% to 70%, in the medium-term, and 1% to 75% in the long-term, with high fluctuations in notable crisis periods. Contagious returns spillovers between food commodities are propagated by vegetable oil. Diversification and safe-haven properties of food commodities are consistently found in the meat market across tranquil and exuberant trading periods, respectively. The safe-haven role of sugar in recent episodes of financial crises is highlighted by our findings. The economic, policy, and portfolio implications of our results are discussed.

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 analysis of relationship between financial markets' illiquidity shocks and macroeconomic dynamics: Time-varying parameter vector autoregression (TVP-VAR) approach in Iran

تحقیقات اخیر نشان داده است که نقدشوندگی بازار‌های مالی عمده جهان در طول زمان متفاوت بوده و غیرقابل‌پیش‌بینی بودن نقدشوندگی این بازار‌ها منبع مهمی از ریسک برای سرمایه‌گذاران می‌باشد. این شوک نقدشوندگی بازار‌های مالی بر اقتصاد کلان تأثیرگذار است. در این تحقیق به تحلیل ارتباط شوک‌های عدم نقدشوندگی بازار مالی و پویایی‌های اقتصاد کلان با رویکرد خود رگرسیون برداری با ضرایب متغیر در زمان (TVP-VAR) در ایران پرداخته شده است. نتایج مطالعه با به‌کارگیری داده‌های سری زمانی فصلی طی سال‌های 1387:3 تا 1399:4 نشان می‌دهد که واکنش رشد تولید در دوره‌های مورد مطالعه به شوک عدم ‌نقدشوندگی منفی و کاهشی می‌باشد. همچنین شوک‌های عدم نقدشوندگی دارای اثرگذاری فزاینده بر تورم بوده اثرگذاری رشد حجم نقدینگی به این شوک‌ها نیز در دوره مورد مطالعه با افزایش نسبی همراه بوده است. سرانجام اثرگذاری شوک عدم نقدشوندگی بازار سهام بر نرخ بیکاری در ابتدا دوره‌ و سال‌های مورد بررسی افزایش بوده و در ادامه و انتهای سال‌ها و دوره‌ها این اثر کاهش یافته است.

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.

The volatility connectedness among fertilisers and agricultural crop prices: Evidence from selected main agricultural products

The price of fertiliser, which is one of the most important inputs of agricultural production, has increased significantly in recent years. In this study, we empirically analysed the effect of volatility in fertiliser prices on selected agricultural products by using the Diebold-Yilmaz connectedness approach, which is based on time-varying parameter (TVP) vector auto-regression (VAR). The findings showed that the spread of volatility and the interconnectedness between these variables increased in times of crisis and that the risk pass-through was due to fertiliser prices. However, empirical results showed that the price volatility of phosphate rock and urea was highly correlated to the volatility of other products. Furthermore, we found that sugar, soybean and cotton were the agricultural products most vulnerable to the effects of external shocks.

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.

Storm after the Gloomy days: Influences of COVID-19 pandemic on volatility of the energy market

Volatility is a common phenomenon in the energy market, but COVID-19 has cast a dark shadow over this characteristic. In light of this observation, individuals might have an incorrect impression of the impact of this shock on the energy markets. By applying a time-varying parameter vector autoregression (TVP-VAR) in combination with an extended joint connectedness approach to identify the sources of the energy market's volatility, we characterize the influences of COVID-19 health crisis and the volatility of the crude oil and precious metals (including gold and silver) market on the volatility of the energy market starting from January 1, 2020, to December 31, 2021. The total connectedness index, the net total, and pairwise directional connectedness measures obtained from the extended TVP-VAR allow us to monitor interlinkages from various variables in a designed network. The novel method has the benefit of distinguishing between a net recipient and a net transmitter. Our results demonstrate that the COVID-19 pandemic shocks first absorb the volatility from the energy and precious market to cause lagged but more severe consequences returning to these markets. Furthermore, there is a time-variant of system-wide interlinkages. Net total directional connectedness suggests that the oil and gold markets consistently appear to be a net transmitter of spillover shocks in the energy market. The COVID-19 pandemic shock first plays the role of shock receiver from other markets. However, this uncertainty shock acts as a shock transmitter, and its effects seem to be delayed but persistent for an extended period, thus making the energy and precious metal markets more volatile.

Do Rare Earths and Energy Commodities Drive Volatility Transmission in Sustainable Financial Markets? Evidence from China, Australia, and the US

The high volatility and energy usage of rare earths have raised sustainable and financial concerns for environmentalists and sustainable investors. Therefore, this paper aims to investigate time-varying volatility transmission among rare earths elements, energy commodities, and sustainable financial markets. The sample covers global and major financial markets, i.e., US, China, and Australia. Using daily log returns from 2018 to 2022, the paper considers the dynamic Time Varying Parameter-Vector Autoregression (TVP-VAR) connectedness approach to gauge the time-varying features of volatility spillovers. The findings of total spillovers index reveal weak connectedness among markets during the sampled period. US and China rare earth markets were net volatility transmitters, whereas the Dow Jones Australia Sustainability Index (ASI), China Sustainability Index (CSI), Dow Jones Sustainability World Index (SWI), and MVIS Global Rare Earth Index (MVISGREI) were net recipients. Moreover, energy commodities i.e., WTI Crude Oil, Gasoline, and Natural Gas were net volatility transmitters, while ASI, CSI, and SWI were major volatility recipients. The weak financial contagion effect and connectedness across financial markets uncovers possible diversification opportunities. However, the US sustainable financial market is persistently not affected by these volatility spillovers. Policymakers need to establish strict regulations to protect sustainable financial markets in China and Australia.