Time-varying Parameter Vector Autoregressive Model Research Articles

Effects of monetary policy on the exchange rates: A Time-varying analysis

Employing a time-varying parameter vector autoregression (VAR) model, which is identified by high frequency monetary policy surprises, we examine the effects of monetary policy on the exchange rates. Our results indicate that a contractionary monetary policy shock leads to an appreciation of the exchange rate during both conventional and unconventional monetary policy periods in the US. During the period 2008–2012, when unconventional monetary policy was practiced, monetary policy shocks produced greater negative effects on the exchange rate compared with other periods. We find that the maximal appreciation of the exchange rate occurs within 3–4 months during most of the period, providing favorable evidence to support Dornbusch’s (1976) overshooting hypothesis.

Time-Varying Impacts of Carbon Price Drivers in the EU ETS: A TVP-VAR Analysis

This study analyzes the impacts of different drivers on the pricing of EU carbon futures in various periods by using the time-varying parameter vector autoregressive (TVP-VAR) model. The results indicate that: (1) The relationships between oil, gas, electricity, stock prices and carbon price have significant time-varying characteristics and those relationships have experienced an inversion in 2016. This might be due to the pressure of achieving the “EU 20-20-20” targets and the signing of the Paris Agreement as well as the fine-tuning of the European Union Emissions Trading Scheme (EU ETS). (2) The impacts of different drivers on carbon price are various. The carbon price is more sensitive to oil, gas, electricity prices as well as the stock price before the inversion in the short-term, while its response to changes in the stock price after the inversion is more obvious in the mid-long term. (3) After the signing of the Paris Agreement in the second quarter of 2016, the carbon price has a greater response to changes in its drivers. The oil price’s impact on carbon price became the most significant one among them.

Spillovers in higher moments and jumps across US stock and strategic commodity markets

This paper examines the dynamics of spillover effects on realized estimators of return distributions across US stock, crude oil and gold markets. Using 5-min data from April 11, 2006 to April 29, 2019, daily realized volatility, realized skewness, realized kurtosis, and jumps are computed. The dynamics of spillovers are revealed using the time-varying parameter vector autoregression (TVP-VAR) model, which reflects the way spillovers are shaped by various crisis periods. The results show that all spillovers seem to intensify during crisis periods. Besides the importance of realized volatility spillovers, spillovers in skewness, kurtosis and jumps are notable, although realized volatility spillovers are relatively stronger. Specifically for realized volatility and kurtosis, the stock index is the main net transmitter, whereas for realized skewness and jumps, oil is the main net transmitter. Gold is a net receiver of all realized higher moments and jumps. The findings suggest the utility of considering spillovers of higher moments and jumps when studying US stock, crude oil, and gold markets, which has implications for portfolio and risk management.

Oil prices, policy uncertainty and travel and leisure stocks in China

By utilizing the time-varying parameter vector autoregressive (TVP-VAR) model, we investigate the time-varying effects of oil price shocks and policy uncertainty on travel and leisure stocks from January 2000 to December 2018 in China. The results suggest that the impacts of oil price shocks on travel and leisure stock returns are mainly positive, which support the market inertia theory. However, the effects of policy uncertainty on travel and leisure stock returns fluctuate between positive and negative. Furthermore, the impacts of oil price shocks and policy uncertainty vary significantly across different sub-industries. For classified travel and leisure stocks, oil price shocks and policy uncertainty both have the greatest influences on scenic spot stock returns. Among classified policy uncertainty, the influences of trade policy uncertainty on travel and leisure stocks are the most significant. • • The impacts of oil price shocks on travel and leisure stock returns are mainly positive. • • The effects of policy uncertainty on travel and leisure stock returns vary between positive and negative. • • The impacts of oil price shocks and policy uncertainty vary significantly across different sub-industries. • • Oil price shocks and policy uncertainty both have the greatest influences on scenic spot stock returns. • • The influences of trade policy uncertainty on travel and leisure stocks are the most significant.

The realized volatility of commodity futures: Interconnectedness and determinants

Using high frequency data and connectedness measures based on a time-varying parameter vector autoregression (TVP-VAR) model, we study dynamic connectedness among the realized volatility of 15 commodity futures (Gold, Heating oil, Light crude oil, Natural gas, Copper, Platinum, Cocoa, Coffee, Corn, Cotton, Orange Juice, Soybean, Soybean meal, Sugar, and Wheat) from September 22, 2008 to May 28, 2020. The results show strong and moderate levels of volatility connectedness among energy and metals and moderate connectedness levels within the group of agricultural commodities. Cross-commodity connectedness can explain a large portion of volatility connectedness in some cases, highlighting the importance of conducting realized volatility connectedness within a model allowing realized volatilities to be endogenously and simultaneously determined. Connectedness is robust to alternative specifications and varies with time. It is mostly driven by macroeconomic variables and uncertainty, including the term spread of interest rates and real economic activity. However, the analysis shows that some of the drivers of connectedness differ between upper and lower quantiles.

A closer look into the global determinants of oil price volatility

Abstract In this paper we investigate global determinants of oil price volatility by employing a time-varying parameter vector autoregressive (TVP-VAR) model. We focus on realised volatility and consider the impact from a set of potential determinants including oil supply, oil demand, oil inventory, financial market uncertainty, financial interbank stress, as well as, financial trends in different currencies. We investigate the impact of these factors on realised volatility utilising monthly data over the period 1990:1–2019:5. Findings show that all factors can be conducive to higher levels of realised oil price volatility particularly in the short run. What can further be noticed, is that the magnitude of the corresponding impulse response functions may differ across time and this could largely be attributed to specific intervals of financial crises and economic recessions. Nevertheless, we show that shocks originating to the financial markets tend to be more important for oil price volatility. Our findings are closely linked to the implications regarding the financialisation of the oil market.

Time-varying impact of U.S. financial conditions on China's inflation: a perspective of different types of events

<abstract> <p>In recent years, the frequency adjustment of U.S. monetary policy has a dynamic and global impact on other countries' economy. Based on the financial conditions index (FCI), the paper employs the time-varying parameter vector autoregressive model with stochastic volatility (TVP-VAR-SV) and spillover index respectively to investigate the time-varying impact of U.S. financial conditions (UFCI) on China's inflation (CINF) and its impact mechanisms. Some results are achieved as follows: first, the impacts of UFCI on CINF vary greatly over time both in the dimension of action duration and time point. Second, the effects of UFCI on CINF directly relate to different types of major events, and they are heterogeneous in action duration, degree, direction as well as the trend and range of fluctuations. In addition, UFCI can work on CINF through trade flow and China's financial market, and the China's financial market plays a main conductive role, and its conductive effect changes over time.</p> </abstract>

Time-varying impact of oil shocks on trade balances: Evidence using the TVP-VAR model

This paper investigates the effects of oil supply and demand shocks on the current account balances of China and Russia for the period between the first quarter of 1993 and the third quarter of 2018 using a time-varying parameter vector autoregression (TVP-VAR) model with stochastic volatility. To facilitate comprehension of the results and to anchor them in well-known events, this analysis focuses on two countries with different oil trade characteristics—Russia as an oil exporter and China as an oil importer. We find that identifying their sources plays an important role in understanding the impact of oil price shocks on trade balances. The results indicate that oil demand shocks have a much larger effect on trade balances and are more attributable to oil price shocks than oil supply shocks. Due to their different positions in the global oil market—one an oil exporter and the other, an oil importer—China’s and Russia’s individual responses differ substantially. In line with findings regarding impulse responses, the time-varying forecast error decompositions demonstrate that both oil supply and demand shocks have their greatest influence during and immediately after periods of crisis.

A Study on the Pass-Through Rate of the Exchange Rate on the Liquid Natural Gas (LNG) Import Price in China

The Chinese liquid natural gas (LNG) import price has been unstable because the stability of LNG import prices is related to changes in the exchange rates. This paper analyzes the pass-through rate of the Chinese Yuan (CNY) and Japanese Yen (JPY) on the Chinese LNG import price. The Time-Varying Parameter vector autoregressive (TVP-VAR) model is adopted to verify the pass-through rate of the exchange rates on the LNG import price using the Markov chain Monte Carlo (MCMC) method. Since September 2005, the JPY pass-through rate on the Chinese LNG import price has been decreasing while that of the CNY has been increasing. Notably, the pass-through rate of CNY began to exceed that of JPY after 2008. Moreover, since 2005, the lag effect of the CNY on the Chinese LNG import price became longer compared to JPY. If any new currency reform of the CNY is implemented in the future, then the impact of JPY on the Chinese LNG import price could be reduced and the lag effect of the CNY on the Chinese LNG import price could become longer. Therefore, the fluctuation of the CNY is becoming an important factor in understanding the movements of the Chinese LNG import price. This implies the significance of considering the effect of the exchange rate on an energy market when the market is influenced by a monetary reform of the importing country.

Why the Effects of Oil Price Shocks on China’s Economy are Changing

Some studies on developed economies have revealed that the impacts of oil price shocks have decreased while conclusions about China remain occluded. We investigate the changing effects of oil price shocks on China’s macroeconomy and discuss the causes. A time-varying parameter vector autoregressive (VAR) model reveals that impacts of oil price shocks on China’s economy have shown a downward trend since 1997. The responses of the real output are much greater and last longer than those of inflation. Then a new Keynesian dynamic stochastic general equilibrium model is developed to synthetically explore the causes. The results indicate that decreasing oil intensity and monopoly power reduce the effects of oil price shocks, while increasing capital intensity in production amplifies them. Other factors-such as changing price stickiness, deregulation of refined oil prices, and shifts in monetary policy targets-have limited effects on the relationship between oil price shocks and China’s macroeconomy.

The effects of uncertainty measures on commodity prices from a time-varying perspective

Based on a time-varying parameter vector autoregression (TVP-VAR) model, this paper analyses the time-varying relationships between commodity prices and uncertainty measures (namely, economic policy uncertainty (EPU), macroeconomic uncertainty (MU), equity market uncertainty (EMU), and stock market volatility (VXO)). We confirm that the effects of uncertainty shocks on commodity prices are time-varying, except for MU. In particular, compared with other major uncertain events, the 2008 financial crisis had a greater impact on commodity prices. Finally, we also find that the time-varying impact is greater in agricultural commodity prices compared to metals and energy commodities prices. These findings have important reference meanings for policymakers and investors.

Quantile spillovers and dependence between Bitcoin, equities and strategic commodities

This paper has two aims. We first examine the dynamic spillovers between Bitcoin and 12 developed equities, gold, and crude oil for different market conditions using a Bayesian Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with daily spot prices. Our econometric approach enables us to capture the left and right tails as well as the shoulders of the return distribution corresponding to volatility spillovers under the bear, normal, and bull market states among these financial assets. We quantify and trace the dependence and directional predictability from Bitcoin to other assets using the sample cross-quantilogram. Our key findings offer convincing evidence of time variation in the level of volatility. Spillovers between Bitcoin and other financial assets intensify during extreme global market conditions. Secondly, results from the cross-quantilogram indicate strong dependence and positive directional predictability between Bitcoin and most equities and crude oil when market returns are bullish. However, during the bearish market period, there is negative dependence and predictability from Bitcoin to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market only. This implies that Bitcoin can act as a hedge to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market. However, insignificant dependence and directional predictability from Bitcoin to the remaining assets indicate that Bitcoin may act as a safe-haven to these assets during bearish markets. Our findings hold important implications for both international investors and portfolio managers who consider Bitcoin as part of their portfolio diversification and other investment strategies.

The effects of geopolitical risks on the stock dynamics of China's rare metals: A TVP-VAR analysis

Adopting a time-varying parameter vector autoregression (TVP-VAR) model, we analyze the dynamic effects of geopolitical risks (GPRs) on the stock returns and volatility of China's rare metals at three time horizons and four time points. In addition, we consider the differences among five GPRs indexes and five rare metal stock markets. The results show that the impulse responses are time-varying and heterogeneous across rare metal stock markets and GPRs indexes. In general, in the short term, the effects of a benchmark GPRs index on the stock returns of China's aggregate rare metals (RM) are positive before 2012 and negative afterwards, while its influences on the stock volatility are negative in most cases. GPRs generated from the four events exert significant and different influences on both the stock returns and stock volatility of RM. Among the four indexes, act-based GPRs index affects the stock returns more negatively, while broad GPRs index has stronger positive effects on the stock volatility of RM. The research results indicate that dynamic and targeted risk-hedging measures should be taken for China's rare metal stock markets.

Dynamic connectedness and portfolio strategies: Energy and metal markets

In this paper, we investigate the volatility spillover effect among the global commodity futures (including both energy and metal futures; global stock markets (covering both Developed and Emerging Markets); the US bond market and the US Dollar index by employing the dynamic connectedness approach of (Diebold and Yilmaz, 2012, 2014) based on the time-varying parameter vector autoregressive (TVP-VAR) model and using daily data for the period from January 3, 1992 to December 27, 2019. Our results indicate a moderate connectedness among the volatilities changing over time and approaching its peak level during 2007/08 global financial crises. In addition, we determine the optimal hedge ratios and portfolio weights for the commodity investors and portfolio managers. Our results indicate that for the equity market volatility investors, the highest hedging effectiveness can be reached by taking short positions in energy futures (such as natural gas), on the other hand for both the US bond and US Dollar volatility investors it can be reached by taking short positions in metal futures (such as gold).

Applying deep learning method in TVP-VAR model under systematic financial risk monitoring and early warning

In order to improve the effectiveness and accuracy of financial status indicators to measure the degree of fiscal tightening, financial market situation and systemic financial risk, the logistic regression method is used to screen the target variables of the indicators. The model improves the objectivity of the selection of target variables. The model chooses 18 alternative indicators such as three-month weighted average interest rate, national real estate prosperity index, money supply M2, declared effective exchange rate and Shenzhen Component Index, and establishes the financial status index. This model validates China’ s financial situation from 2013 to 2017. The results indicate that the dynamic weighted financial condition index based on time-varying parameter vector autoregressive model includes five variables: interest rate, real estate price, money supply, exchange rate and stock price, which effectively reflect the actual financial situation of China. It also proves that the degree of fiscal tightening and financial market conditions can be measured and warned in advance by changes in financial indicators. To sum up, it can be concluded that it is necessary to pay attention to the changes of interest rates, real estate prices and stock prices when monitoring the systemic financial risks in China. In order to promote early warning and effectively control financial risks, China should establish an information system and a flexible macro-prudential supervision system. This study is of great significance to the prediction and supervision of systemic financial risks in China.

Age Structural Transitions and Copayment Policy Effectiveness: Evidence from Taiwan's National Health Insurance System.

Background: Population ageing is a worldwide phenomenon that could influence health policy effectiveness. This research explores the impact of age structural transitions on copayment policy responses under Taiwan’s National Health Insurance (NHI) system. Methods: The time-varying parameter vector autoregressive model was applied to create two measures of the copayment policy effectiveness, and multiple linear regression models were used to verify the nonlinear effect of age structural transitions on copayment policy responses. Results: Our results show that copayment policy effectiveness (in terms of the negative response of medical center outpatient visits to upward adjustments in copayment) is positively correlated with the proportions of the population in two older age groups (aged 55–64 and ≥ 65) and children (age < 15), but negatively correlated with the proportion of the population that makes up most of the workforce (aged 15‒54). These tendencies of age distribution, which influence the responses of medical center outpatient visits to copayment policy changes, predict that copayment policy may have a greater influence on medical center outpatient utilization in an ageing society. Conclusions: Policymakers should be concerned about the adverse effects of copayment adjustments on the elderly, such as an increasing financial burden and the effect of pricing some elderly patients out of Taiwan’s NHI system.

Impact of the COVID-19 Pandemic on the Crude Oil and Stock Markets in the US: A Time-Varying Analysis

This research explores the interaction among the COVID-19 pandemic, crude oil market and stock market in the U.S. by utilizing a time-varying parameter vector autoregression (TVP-VAR) model. Our results indicate that there is a negative connection between crude oil returns and stock returns. Interestingly, contrary to our intuition, we find that the COVID-19 pandemic cannot exert a negative effect but has a statistically significantly positive effect on crude oil returns and stock returns.

Time-Varying Relationship between Crude Oil Price and Exchange Rate in the Context of Structural Breaks

This paper examines the dynamic relationship between crude oil prices and the U.S. exchange rate within the structural break detection context. Based on monthly data from January 1996 to April 2019, this paper identifies structural breaks in movements of oil price and examines the dynamic relationship between crude oil prices and the U.S. exchange rate movement by introducing the economic policy uncertainty and using the TVP-VAR (Time-Varying Parameter-Vector Auto Regression ) model. Empirical results indicate that shocks to crude oil prices have immediate and short-term impacts on movements in the exchange rate which are emphasized during the confidence intervals of structural breaks. Oil price shocks and economic policy uncertainty are interrelated and influence movements in the U.S. exchange rate. Since the U.S. dollar is the main currency of the international oil market and the U.S. has become a major exporter of crude oil, the transmission of price shocks to the U.S. exchange rate becomes complicated. In most cases, the relationship between oil prices and the U.S. exchange rate movements is negative.

Refined Measures of Dynamic Connectedness based on Time-Varying Parameter Vector Autoregressions

In this study, we enhance the dynamic connectedness measures originally introduced by Diebold and Yılmaz (2012, 2014) with a time-varying parameter vector autoregressive model (TVP-VAR) which predicates upon a time-varying variance-covariance structure. This framework allows to capture possible changes in the underlying structure of the data in a more flexible and robust manner. Specifically, there is neither a need to arbitrarily set the rolling-window size nor a loss of observations in the calculation of the dynamic measures of connectedness, as no rolling-window analysis is involved. Given that the proposed framework rests on multivariate Kalman filters, it is less sensitive to outliers. Furthermore, we emphasise the merits of this approach by conducting Monte Carlo simulations. We put our framework into practice by investigating dynamic connectedness measures of the four most traded foreign exchange rates, comparing the TVP-VAR results to those obtained from three different rolling-window settings. Finally, we propose uncertainty measures for both TVP-VAR-based and rolling-window VAR-based dynamic connectedness measures.

The Time-series Linkages between US Fiscal Policy and Asset Prices

This article studies the interplay of fiscal policy and asset price returns of the United States in a time-varying parameter vector autoregressive (VAR) model. Using annual data from 1890 to 2013, we study the effects of dynamic shocks to both fiscal policy on asset returns and asset returns on fiscal policy. Distinguishing between low-volatility (bull market) and high-volatility (bear market) regimes together with a time-varying parameter VAR model enables us to isolate the different sizes and signs of responses to shocks during different time periods. The results indicate that increases in the primary surplus-to-gross-domestic-product ratio decrease house returns over the entire sample and at each impulse horizon. Unlike the house return responses, stock returns only decrease in the first year after the fiscal shock but then increase for the following eight years. Furthermore, the findings show that asset return movements affect fiscal policy, whereby fiscal policy responds more to equity returns than to house returns. The response of fiscal policy to asset returns proves relatively stable and constant over time while controlling for various asset return regimes. Asset returns respond uniformly to fiscal policy shocks since the 1900s.