Volatility contagion: A range-based volatility approach

Chiang, Min Hsien; Wang, Li Min

doi:10.1016/j.jeconom.2011.07.004

Cited by 52 publications

(23 citation statements)

References 26 publications

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“…The focus should be put on the relationship between short-term conditional volatility and range volatility. A starting point could be the range volatility-based GARCH models such as the E-GARCH model used in Brandt and Jones (2006) and the conditional autoregressive range model used in Chiang and Wang (2011). In any case, the volatility spillover indices are a useful addition to the hitherto GARCH-centered analysis of volatility relationships.…”

Section: Discussionmentioning

confidence: 99%

“…The classical volatility proxy is calculated as the variance of daily returns, which may be associated with large, non-Gaussian measurement errors (cf. Parkinson 1980;Alizadeh et al 2002;Chiang and Wang 2011). The range is calculated as:…”

Section: Description Of the Methodology And Datamentioning

confidence: 99%

“…It may also affect linkages between agricultural and energy markets as both commodity groups are included in indices such as the Standard and Poor's (S&P) GSCI or the Dow Jones UBS (DJ UBS) Commodity index, which are replicated by index-linked products and funds. In any case, volatility, rather than returns, is the more interesting linkage due to its closer relation to information flows (Chiang and Wang 2011;Cheung and Ng 1996). Also, the development of ETP assets suggests a steadily emerging financial interest and motivates the search for a gradual change rather than a sudden structural break in market linkages.…”

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confidence: 99%

See 2 more Smart Citations

Directional Volatility Spillovers Between Agricultural, Crude Oil, Real Estate, and Other Financial Markets

Grosche

Heckelei

2016

Food Price Volatility and Its Implications for Food Security and Policy

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Section: Discussionmentioning

confidence: 99%

Section: Description Of the Methodology And Datamentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Directional Volatility Spillovers Between Agricultural, Crude Oil, Real Estate, and Other Financial Markets

Grosche

Heckelei

2016

Food Price Volatility and Its Implications for Food Security and Policy

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“…Similarly, Kenourgios et al (2011) andHamori (2013) provide evidence of increased comovements during the crisis periods between emerging and developed markets. In contrast to the return-based volatility studies, Chiang and Wang (2011) use a timevarying logarithmic conditional autoregressive range model with the lognormal distribution (TVLCARR) and examine the volatility contagion of the G7 stock markets. They use smooth transition copula functions to detect the volatility contagion.…”

Section: Sources Of Time Varying Return Comovements During Different…mentioning

confidence: 99%

Sources of time varying return comovements during different economic regimes: evidence from the emerging Indian equity market

Poshakwale

Mandal

2016

Rev Quant Finan Acc

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We study the economic and non-economic sources of stock return comovements of the emerging Indian equity market and the developed equity markets of the US, UK, Germany, France, Canada and Japan. Our findings show that the probability of extreme comovements in the economic contraction regime is relatively higher than in the economic expansion regime. We show that international interest rates, inflation uncertainty and dividend yields are the main drivers of the asymmetric return comovements. Findings reported in the paper imply that the impact of interest rates and inflation on return comovements could be used for anticipating financial contagion and/or spillover effects. This is particularly critical since during extreme market conditions, the tail return comovements can potentially reveal critical information for active portfolio management.Keywords Emerging Indian equity market Á Asset return comovements Á Economic and non-economic sources Á Copula models Á Markov switching stochastic volatility model JEL Classification G15 Á F36 Á E44

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“…By accounting for the interdependence between the variances as well as the covariance our approach differs from existing empirical studies on volatility transmissions across stock markets with overlapping trading hours like those of Engle, et al (2012), Dimpfl and Jung (2012) and Chiang and Wang (2011), which solely investigate the dynamic interdependence of variance measures. As such, our approach allows to account for two potential channels of volatility spillovers, namely, via a direct volatility transmission from one market to the other through its variance and via an indirect transmission through its covariance.…”

Section: Introductionmentioning

confidence: 99%

Intra-Daily Volatility Spillovers between the US and German Stock Markets

2012

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractUsing a novel three-phase model based upon a conditional autoregressive Wishart (CAW) framework for the realized (co)variances of the US Dow Jones and the German stock index DAX, we analyze intra-daily volatility spillovers between the US and German stock markets. The proposed model explicitly accounts for three distinct intraday periods resulting from the non-synchronous and partially overlapping opening hours of the two markets. We find evidence of significant short-term volatility spillovers from one intraday period to the next within both markets ('heat-wave effects') as well as across the two markets ('meteor-shower effects'). Furthermore, we find that during the subprime crisis the general persistence of short-term volatility shocks is considerably higher and the spillovers effects between the US and the German stock markets are significantly larger than before the crisis, indicating substantial volatility contagion effects.JEL classification: C32, C58, G17

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Volatility contagion: A range-based volatility approach

Cited by 52 publications

References 26 publications

Directional Volatility Spillovers Between Agricultural, Crude Oil, Real Estate, and Other Financial Markets

Directional Volatility Spillovers Between Agricultural, Crude Oil, Real Estate, and Other Financial Markets

Sources of time varying return comovements during different economic regimes: evidence from the emerging Indian equity market

Intra-Daily Volatility Spillovers between the US and German Stock Markets

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