Simultaneous Volatility Transmission and Spillover Effects

2010 ◽  
Vol 13 (01) ◽  
pp. 127-156 ◽  
Author(s):  
Gerard L. Gannon
Keyword(s):  
Futures Markets ◽  
Spillover Effects ◽  
The United States ◽  
Market Volatility ◽  
Multivariate Garch ◽  
Index Futures ◽  
Volatility Transmission ◽  
Volatility Models ◽  
Volatility Model ◽  
Intraday Data

Simultaneous volatility models are developed and shown to be separate from multivariate GARCH estimators. An example is provided that allows for simultaneous and unidirectional volatility and volume of trade effects. These effects are tested using intraday data from the Australian cash index and index futures markets. Overnight volatility spillover effects from the United States S&P500 index futures markets are tested using alternative estimates of this US market volatility. The simultaneous volatility model proves to be robust to alternative specifications of returns equations and to misspecification of the direction of volatility causality.

scholarly journals Market fear, investor mood, sentiment, economic uncertainty and tourism sector in the United States amid COVID-19 pandemic: A spillover analysis

2021 ◽  
pp. 135481662110528
Author(s):  
Faisal Nazir Zargar ◽  
Dilip Kumar
Keyword(s):  
Media Coverage ◽  
Spillover Effects ◽  
The United States ◽  
Equity Market ◽  
Market Volatility ◽  
Economic Uncertainty ◽  
Tourism Sector ◽  
The Us ◽  
Media Hype ◽  
Sector Returns

The study investigates and confirms the spillover effects from investor fear, mood, sentiment and uncertainty to the US tourism sector returns. The findings indicate that market fear, investor mood and sentiment are net transmitter of shocks and economic uncertainty and the tourism sector is net receiver of shocks. We also provide evidence that media-hype, infodemic, media-coverage related to COVID-19 and infectious disease equity market volatility impacts the total and directional spillover of information from fear, mood, sentiment and uncertainty to the tourism sector.

Structural Breaks in Volatility Transmission from Developed Markets to Major Asian Emerging Markets

2019 ◽  
Vol 18 (2) ◽  
pp. 172-209 ◽  
Author(s):  
Dilip Kumar
Keyword(s):  
Emerging Markets ◽  
Structural Breaks ◽  
Global Financial Crisis ◽  
Spillover Effect ◽  
Spillover Effects ◽  
The United States ◽  
Jel Classification ◽  
Volatility Transmission ◽  
Developed Markets ◽  
Asian Emerging Markets

The study investigates the volatility transmission from developed markets (the United States [US], the United Kingdom [UK] and Japan) to the major Asian emerging markets (India, China, Malaysia, Thailand and Indonesia) during a period from 1996 to 2015. We make use of the opening, high, low and closing prices to estimate unbiased extreme value volatility estimator and implement heterogeneous autoregressive distributed lag (HAR-DL) framework to study the spillover effects. Based on time-varying spillover analysis, we observe sudden changes in the spillover effect during the periods of major crises. Initially, we find evidence of contagion during the period of global financial crisis of 2007–2009. However, after accounting for conditional heteroscedasticity, we observe a decline in the strength of volatility transmission from developed markets to the Asian emerging markets. Moreover, the initial evidence of contagion is not detectable anymore. We also test the economic significance of the findings by implementing three trading strategies based on risk averse and risk-taking investors that make use of the forecasted variance based on HAR-DL specification. Our findings indicate that substantial average annualised gains in returns can be earned based on the lagged volatility components of the USA and the UK. JEL Classification: C32, C58, G01, G15

Dynamic co-movements and directional spillovers among energy futures

2020 ◽  
Vol 37 (4) ◽  
pp. 673-696 ◽  
Author(s):  
Sercan Demiralay ◽  
Nikolaos Hourvouliades ◽  
Athanasios Fassas
Keyword(s):  
Crude Oil ◽  
Futures Markets ◽  
Spillover Effects ◽  
Time Varying ◽  
Content Type ◽  
Energy Futures ◽  
Risk Transfer ◽  
Volatility Transmission ◽  
Heating Oil ◽  
Practical Implications

Purpose This paper aims to examine dynamic equicorrelations (DECO) and directional volatility spillover effects among four energy futures markets, namely, West Texas Intermediate crude oil, heating oil, natural gas and reformulated blendstock for oxygenate blending gasoline, by using a multivariate fractionally integrated asymmetric power ARCH–DECO–generalized autoregressive conditional heteroskedasticity (GARCH) model and the spillover index technique. Design/methodology/approach The empirical analysis uses the dynamic equicorrelation model of Engle and Kelly (2012) to examine time-varying correlations at equilibrium. The authors further analyze dynamic volatility transmission among energy futures by using Diebold and Yilmaz (2012) dynamic spillover index based on generalized value-at-risk framework. Findings The empirical results provide evidence of heightened equicorrelations at times of financial turmoil. More specifically, the dynamic spillover analysis shows that volatility is transmitted predominantly from crude oil to the other markets and risk transfer among four markets exhibits asymmetries. Spillovers are found to be highly responsive to dramatic events such as the 9/11 terror attack, 2008–2009 global financial crisis and 2014–2016 oil glut. Practical implications The results of this study have important practical implications for investors, portfolio managers and energy policymakers as the presence of time-varying co-movements and spillovers suggests the need for dynamic trading strategies. There are also implications regarding risk management practices, as there is evidence of increased volatility transmission at times of financial turmoil and uncertainty. Finally, the results provide insights to policymakers in a better understanding of the spillover dynamics. Originality/value This paper investigates the DECOs and spillover effects among crude oil, natural gas, heating oil and gasoline futures markets. To the best of the knowledge, this is one of a few studies that examine co-movements and risk transfer in energy futures in a comprehensive framework.

Does Index Futures Dominate Index Spot? Evidence from Taiwan Market

2002 ◽  
Vol 05 (02) ◽  
pp. 255-275 ◽  
Author(s):  
Ching-Chung Lin ◽  
Shen-Yuan Chen ◽  
Dar-Yeh Hwang ◽  
Chien-Fu Lin
Keyword(s):  
Futures Markets ◽  
Price Discovery ◽  
Stock Exchange ◽  
Impulse Response Function ◽  
Spot Market ◽  
Stock Index ◽  
Index Futures ◽  
Volatility Spillovers ◽  
Egarch Model ◽  
Intraday Data

By utilizing vector error correction model (VECM) and EGARCH model, this article uses 5-minute intraday data to examine the interaction of return and volatility between Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the newly introduced TAIEX futures. VECM model shows that there exists bi-directional Granger causality between index spot and index futures markets, but spot market plays a more important role in price discovery. The results of impulse response function and information share indicate that most of the price discovery happens in index spot market. The evidence of EGRACH shows that the impacts of spot and futures innovations are asymmetrical, and the volatility spillovers between spot and futures markets are bi-directional. However, the information flow from spot to futures is stronger. These results suggest that the TAIEX spot market dominates the TAIEX futures market in terms of return and volatility.

scholarly journals An Econometric Analysis of ETF and ETF Futures in Financial and Energy Markets Using Generated Regressors

2017 ◽  
Author(s):  
Chia-Lin Chang ◽  
Michael McAleer ◽  
Chien-Hsun Wang
Keyword(s):  
Futures Markets ◽  
Spillover Effects ◽  
Exchange Traded Funds ◽  
Data Set ◽  
Volatility Spillovers ◽  
Daily Data ◽  
Volatility Model ◽  
Management Perspective ◽  
The Us ◽  
Generated Regressors

It is well known that that there is an intrinsic link between the financial and energy sectors, which can be analyzed through their spillover effects, which are measures of how the shocks to returns in different assets affect each other’s subsequent volatility in both spot and futures markets. Financial derivatives, which are not only highly representative of the underlying indices, but can also be traded on both the spot and futures markets, include Exchange Traded Funds (ETFs), a tradable spot index whose aim is to replicate the return of an underlying benchmark index. When ETF futures are not available to examine spillover effects, “generated regressors” are useful for constructing both Financial ETF futures and Energy ETF futures. The purpose of the paper is to investigate the co-volatility spillovers within and across the US energy and financial sectors in both spot and futures markets, by using “generated regressors” and a multivariate conditional volatility model, namely Diagonal BEKK. The daily data used are from 1998/12/23 to 2016/4/22. The data set is analyzed in its entirety, and are also subdivided into three distinct subsets. The empirical results show there is a significant relationship between the Financial ETF and Energy ETF in the spot and futures markets. Therefore, financial and energy ETFs are suitable for constructing a financial portfolio from an optimal risk management perspective, and also for dynamic hedging purposes.

MEASURING VOLATILITY SPILLOVERS BETWEEN DEVELOPED AND SOUTHEAST ASIAN EMERGING STOCK MARKETS: A MULTIVARIATE GARCH APPROACH

2018 ◽  
Author(s):  
Anh Nguyễn Thị Hoàng ◽  
Huyền Trần Thị Thanh ◽  
Minh Huỳnh Ngọc Kim ◽  
Trân Nguyễn Thị Ngọc
Keyword(s):  
Emerging Markets ◽  
Stock Markets ◽  
Spillover Effects ◽  
The United States ◽  
Southeast Asian ◽  
Multivariate Garch ◽  
Volatility Spillover ◽  
Sample Analysis ◽  
Volatility Spillovers ◽  
Developed Markets

In this paper, we measure volatility spillovers among eleven stock markets, including five developed markets (the United States, Japan, Germany, the United Kingdom, Hong Kong) and six Southeast Asian developing markets (Indonesia, Malaysia, Philippines, Singapore, Thailand and Vietnam) over the 25-year period from January 1, 1993 to December 31, 2017. Employing the GARCH-DCC model and non-parametric sign tests on the correlations between developed markets and emerging markets, we find that correlations between developed markets and the Southeast Asian markets have risen sharply during periods of crisis, indicating the existence of volatility spillover effects from the developed markets to emerging ones. Full sample analysis suggests that volatility spillover from Japanese and the UK markets to the Southeast Asian emerging markets is stronger and more apparent than those transmitted from the US and Germany markets. Sub-sample analysis is able to identify the markets transmitting shocks to others. Results also suggest that Vietnam market is not fully integrated to the regional and global markets.

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