Volatility Impulse Response Function Research Articles

Mutual volatility transmission between assets and trading places

Abstract This article proposes a framework to model the mutual volatility transmission between multiple assets and multiple trading places in different time zones. The model is estimated using a dataset containing daily returns of three stock indices – the MSCI Japan, the EuroStoxx 50, and the S&P 500 – each traded at three major trading places: the stock exchanges in Tokyo, London, and New York. Strong volatility transmission effects can be observed between New York and Tokyo, whereas current volatility in New York mostly depends on past volatility in New York. For the assets in consideration, spillovers are strong across trading zones, but weak across assets, suggesting a close connection between market places but only a loose volatility link between international stock indices. Volatility impulse response functions indicate a long-lasting and comparably large response of volatility in Tokyo, whereas they suggest a quicker volatility decay in London and New York.

Correlation impulse response functions

Volatility impulse response functions are a widely used tool for analyzing the temporal impact of shocks on (co-)volatilities of financial time series. This paper proposes an extension to correlation impulse response functions (CIRF), based on a multivariate GARCH modeling framework. As we show, CIRF and corresponding covariance impulse response functions can react quite differently to a given shock and even move in opposite directions. Due to the inherent nonlinearity, no analytical form is available for CIRF, but we propose a straightforward algorithm to estimate the CIRF numerically. In an empirical application we focus on the change of the consensus protocol of Ethereum in 2022 and its effect on the correlation with Bitcoin.

Structural sources of oil market volatility and correlation dynamics

This paper explores the structural driving sources of oil market volatility and correlation dynamics using our derived volatility impulse response and correlation decomposition functions. A focal point of this paper is to conduct hypothesis tests for the observed diverging dynamics of oil market volatilities (a steady decline in oil production volatility and a concurrent increase in oil price volatility). In order to circumvent the restriction of uncorrelated disturbances in traditional structural models with stochastic volatility, we specify a time-varying level-volatility transmission matrix that allows for time-varying correlations between level and volatility innovations. Our model generalization hence captures transmissions of unexpected changes in oil levels to oil volatilities, and proves more adequate than traditional models for estimating oil market dynamics. Our results reveal several new useful insights for policy-makers, firms and investors. First, we find that a decrease in oil price persistence is at the origin of the diverging pattern of oil volatilities. Second, decreased variability of flow supply shocks is responsible for about 32% of the reduction in oil production volatility, while increased variability of demand-side shocks explains almost 60% of the increase in oil price volatility. Lastly, we find a near-zero correlation between oil production and oil price since the mid-1980s – a result that supports the theoretical prediction that oil production is inelastic to oil price changes.

Asymmetric volatility impulse response functions

Asymmetric volatility impulse response functions

Volatility transmission and volatility impulse response functions in the main and the satellite Renminbi exchange rate markets

AbstractThe contributions of this paper are two‐fold, one methodological and the other substantive. First, we propose a novel way of estimating volatility impulse response functions (VIRF) employing the Markov chain Monte Carlo algorithm. As a useful byproduct, the associated confidence bands can be constructed. Second, we analyze volatility spillovers between the on‐ and offshore Renminbi exchange rates toward the US dollar. The VIRF show that the satellite CNH exchange rate promptly reflects the global market demand and supply, while the main CNY exchange rate reacts with a time lag.

Political uncertainty, COVID-19 pandemic and stock market volatility transmission

News about referendums and the ongoing evolution of a global contagious increase uncertainty about the development of economic fundamentals reflected by increased volatility in the financial markets. In this paper, employing volatility impulse response functions and assessing the volatility spillovers we examine intra-market volatility transmission in the Athens stock market. We employ a large sample period of daily data that spans from December 1999 to December 2020 and captures major events of the last 20 years especially related to the announcement of the two referendums during the Greek government-debt crisis in 2010 and the economic and political turmoil that increased country instability, the following years, the BREXIT referendum and the COVID-19 pandemic of 2020. Our results demonstrate that negative shocks during the announcement of the referendum produce larger impulse responses than during the announcement of the country lockdowns. Furthermore, we shed light on the existence of the dynamic relationship of volatility spillovers. Volatility spillovers peaked during the COVID-19 pandemic. Dynamic spillover plots demonstrate that during the COVID-19 pandemic, more volatility is transmitted by mid cap firms to large cap firms. Our findings have implications to market participants, policy makers and market regulators.

Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms

AbstractThis study introduces volatility impulse response functions (VIRF) for dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC‐GARCH) models. In addition, the implications with respect to network analysis—using the connectedness approach of Diebold and Y lmaz (Journal of Econometrics, 2014, 182(1), 119–134)—is discussed. The main advantages of this framework are (i) that the time‐varying dynamics do not underlie a rolling‐window approach and (ii) that it allows us to test whether the propagation mechanism is time varying or not. An empirical analysis on the volatility transmission mechanism across foreign exchange rate returns is illustrated. The results indicate that the Swiss franc and the euro are net transmitters of shocks, whereas the British pound and the Japanese yen are net volatility receivers of shocks. Finally, the findings suggest a high degree of comovement across European currencies, which has important portfolio and risk management implications.

Volatility Transmission and Volatility Impulse Response Functions in the Main and the Satellite Renminbi Exchange Rate Markets

We analyse volatility spillovers between the on- and offshore (CNY and CNH) Renminbi exchange rates towards the US dollar (USD). The volatility impulse response (VIRF) methodology introduced by Hafner and Herwatz (2006) is applied to several shocks between January 2012 and December 2019. Furthermore, we propose a novel way of estimating VIRFs based on Bayesian estimation of the MV-GARCH BEKK model. A simple Independence Chain Metropolis-Hastings algorithm allows drawing VIRFs in an efficient manner, allowing to analyse the significance and persistence of volatility shocks and associated volatility spillovers. The VIRF results show that the CNH exchange rate promptly reflects the global market demand and supply, while the CNY exchange rate reacts with a time lag. The VIRF results also show the existence of spillovers between the two markets as the co-volatility increases in response to shocks.

Volatility Impulse Responses for DCC-GARCH: The Role of Volatility Transmission Mechanisms

This study introduces volatility impulse response functions (VIRF) for DCC-GARCH models. In addition, the implications with respect to network analysis is illustrated by using the connectedness approach of Diebold and Yilmaz (2014) which rests on VIRFs. The advantage of this framework is that it does not underlie a rolling-window approach. An empirical analysis on the volatility trans- mission mechanism across foreign exchange rate returns is discussed. The findings point out that the CHF and the EUR are net volatility transmitters of shocks whereas the GBP and JPY are net volatility receivers of shocks.

Risk Transfer among Housing Markets in Major Cities in China

This study explored risk transfer among the housing markets of five major cities in China, comprising three first-tier cities (i.e., Beijing, Shanghai, and Shenzhen) and two second-tier cities (i.e., Tianjin and Chongqing). House price index data from January 2001 to June 2017 and a vector autoregressive–multivariate generalized autoregressive conditional heteroscedasticity model were employed to estimate correlations among these cities related to house price returns and volatility. In addition, volatility impulse-response functions were estimated to determine interactions among housing market risk in different cities. The results revealed that first-tier cities were more likely to transfer risk to second-tier cities, and that Beijing’s housing market exerted the greatest influence on risk in other cities’ housing markets. To consider the influence of the 2008 global financial crisis, data collected before and after the crisis were divided into two groups for subsequent investigation. The results revealed that these cities became more closely interrelated after the financial crisis, thereby escalating the risk of impulse influences. Finally, this study evaluated the influences of macroeconomic impulses on the housing markets of the three first-tier cities, indicating that real estate in these three cities can protect investors against inflation. The evidence presented in this paper can serve as a reference for the Chinese government regarding risk control.

Cross-commodity news transmission and volatility spillovers in the German energy markets

This study investigates volatility spillovers to electric power from large exogenous shocks in the prices of gas, coal, and carbon emission allowances in the German energy market. Our sample ranges from 2008 to 2016 and covers periods of different market conditions. We use a general VAR-BEKK model and the volatility impulse response function methodology to analyze and evaluate the spillover effects. Special attention is paid to selecting an appropriate econometric volatility model. Our results show that the spillover effects often are of a significant magnitude and display considerable variation over time and across commodities. Coal and gas generate non-negligible spillovers during almost the entire sample period. Carbon has very little impact during the early and late parts of the sample, but generates significant, and highly variable, spillovers during the period from 2011 to the end of 2014.

Modeling the Effect of Exchange Rate Liberalization on China’s Macro Economy

The empirical characteristics of domestic and foreign interest rate shocks are obtained by using VAR method: the domestic interest rate regulation is counter-cyclical, and the increase of foreign interest rate leads to the increase of domestic output and inflation. On this basis, we construct a small open dynamic stochastic general equilibrium theory framework which reflects the empirical characteristics, including exchange rate control, to analyze the macroeconomic effects of exchange rate liberalization reform. By volatility simulation, impulse response and social welfare loss function analysis, the empirical results show that: firstly, exchange rate reform would increase volatility of output and exchange rate, but reduce volatility of inflation and interest rate. Secondly, exchange rate reform enhances the impact of domestic interest rate shocks on output and inflation. Which means the reform would improve the control ability of interest rate as a monetary policy tool. Moreover, the reform increases loss of social welfare. The conclusion shows that the exchange rate liberalization should be implemented step by step. The government should accelerate the reform when the external macro economy is stable. Otherwise it will cause a larger economic volatility.

The Volatility Spillover Effect between Index Options and their Underlying Markets: Evidence from the US, the UK, and Taiwan

AbstractThis study examines the volatility spillover effect among five index options and their underlying markets. Results show that the bidirectional volatility spillover effect and the cross‐market leverage effect exist between index options and their underlying markets. Our findings confirm that the volatility spillover effect is generally outweighed by shocks in the underlying market, and that the options implied price volatility is provoked by the information shock occurring in both the cash and options markets to a higher degree. Through the volatility impulse response function, this study shows that the options implied prices are more sensitive to innovations in both markets.

Climate variability and the volatility of global maize and soybean prices

Volatility in the price of agricultural commodities is one of the main factors affecting food security. Many studies have analyzed agricultural market instability from different points of view, but the effect of climate oscillations on agricultural price volatility has been little studied. Climate anomalies, and in particular extreme events, can alter agricultural yields and stocks with related effects on prices. This paper presents a Volatility Impulse Response Function (VIRF) from a multivariate GARCH model to investigate the effects of variability in climatic shocks (El Nino/Southern Oscillation - ENSO) on international maize and soybean price volatility from 1960 to 2014. For both commodities, VIRF analysis was conducted splitting the effect of El Nino and La Nina events according to Spring-Summer and Autumn-Winter meteorological seasons. Both events increase expected price volatility of maize, showing the strongest impact during the El Nino phase in Spring-Summer. Soybean price volatility tends to slightly decrease during Autumn-Winter meteorological seasons and to increase during the Spring-Summer period. To minimize the impact of ENSO events on commodity price volatility, various measures can be taken, both political and technical. Financial aspects should also be considered. It is possible that financial agents can use the ENSO index as information for trading activity, creating a new link between this index and volatility in commodity prices.

Estimation of volatility causality in structural autoregressions with heteroskedasticity using independent component analysis

In this study, we developed a new statistical procedure to identify and estimate both the causal effects and the volatility structure in multivariate time series using structural autoregressions with heteroskedasticity (SVAR–GARCH) model. Specifically, using a recent method based on independent component analysis (ICA), we could simultaneously identify the causality and decompose the error terms into non-Gaussian independent components. We then used univariate GARCH to model the volatility of each independent component. The structure of volatility transmission along the SVAR model then was modeled using volatility impulse response function (VIRF) to independent component shocks. Simulation results were given to verify the effectiveness of the procedure. Empirically, we investigated the intraday volatility transmission effect along the US Treasury curve, based on high-frequency CME bond futures data. The result validated the general belief that intraday volatility transmission is a function of liquidity and maturity along the yield curve.

Price Discovery and Information Transmission in Stock Index Futures and Spot Markets: Evidence from China based on a VAR-GARCH Model with SSAEPD Margins

Using daily data, this paper empirically investigates the price discovery and information transmission in China's stock index futures and spot markets based on a VAR-GARCH model with SSAEPD margins. By comparing our model with classic VAR-GARCH model, we discover that our model can better capture the skewness, fat-tailness and asymmetric kurtosis in our data and has better in-sample fit than the VAR-GARCH model. Then, we use our model to conduct structural analysis. Causality Analysis, Impulse Response Function and Volatility Impulse Response Function indicate that there exists a significant bidirectional price causal relationship between the index futures and spot returns. And China's index futures market function very well in its price discovery performance, since the index futures market is found to lead the underlying spot market and plays a more dominant role in the price discovery process. Besides, Volatility Impulse Response Function also shows a higher investment risk in index futures market compared with index spot markets.

Stock markets’ bubbles burst and volatility spillovers in agricultural commodity markets

Our paper focuses on commodity financialization and the gradual integration between commodity and financial markets, investigating to what extent shocks in stock markets impact commodity price volatility, and the persistency of the phenomenon. To this end, we estimate Volatility Impulse Response Function from stock markets to agricultural commodity markets over a symmetric window before and after two of the most important bubble bursts since the new millennium, the 2000 dot.com bubble and the 2008 financial crises. Results highlight that volatility spillover increased significantly after the 2008 financial crises, signalling a rising interconnection between financial and agricultural commodity markets.

INVESTIGATING THE INTERDEPENDENCY OF AGRICULTURAL PRODUCTION VOLATILITY SPILLOVERS BETWEEN BANGLADESH, INDIA, AND PAKISTAN

This paper investigates the dynamics of agricultural production volatility spillovers between Bangladesh, India, and Pakistan. These countries were selected because of their agricultural economic importance to the South Asian region. This study uses per capita agricultural production data for the period 1961–2012, obtained from the Food and Agriculture Organization of the United Nations (FAO) statistical database, to construct and estimate a multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model. The MGARCH model is estimated utilizing the maximum likelihood method. Volatility impulse response functions are also applied to quantify the effects of independent shocks on expected conditional volatility. The model provides good statistical fit and the empirical results indicate significant cross‐country per capita agricultural production volatility spillovers among these countries.

Global financial crisis and emerging stock market contagion: A volatility impulse response function approach

By employing the volatility impulse response (VIRF) approach, this paper presents a general framework for addressing the extent of contagion effects between the BRICSs’ and U.S. stock markets and how the BRICSs’ stock markets have been influenced in the context of the 2007–2009 global financial crisis. Our empirical results show during the period of 2007–2009 global financial crisis, there are significant contagion effects from the U.S. to the BRICSs’ stock markets. Yet, the degree of stock market reactions to such shocks differs from one market to another, depending on the level of integration with the international economy. Besides, the strengthened degree of stock market integration among the U.S. and BRICS has adverse effect such that if the 2007–2009 global financial crisis occurs today it may result in heavier impact on stock market volatility nowadays compared to the crisis-era.

Volatility transmission and volatility impulse response functions among the Greater China stock markets

Using daily data from July 1993 to June 2013, we estimate a VAR-BEKK model and find evidence of return and volatility spillovers among the different stock markets in the Greater China region (China, Hong Kong, and Taiwan). We apply the volatility impulse response function proposed by Hafner and Herwartz (2006) to quantify the impact of financial crises on expected conditional volatility. We observe that financial crises have positive and large impacts on expected conditional variances, but the size and dynamics of the impact is largely market specific. We specifically find evidence in favour of increased market integration. Consequently, had a financial crisis occurred recently, the impact of it on expected conditional variance would have been significantly lower nowadays compared with the initial dates when the financial crisis occurred. Finally, we estimate the density of the VIRF at different forecast horizons. These fitted distributions are asymmetric and show that large increase in expected conditional volatilities are possible even if their probability is low. These features denote good efficiency and reaction to shocks from market participants.