Jeannine Polivka
Last Name
Polivka
First name
Jeannine
Email
jeannine.polivka@unisg.ch
Phone
+41 71 224 2413
7 results
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Item type:Publication, Structural Volatility Impulse Response Analysis(2025-01-31); We make three contributions to the volatility impulse response function (VIRF) developed by Hafner and Herwartz (2006). First, we derive its law for multivariate GARCH models of the BEKK type. Second, we present a structural embedding of the VIRF, leveraging recent advancements in the identification of multivariate generalized autoregressive conditional heteroskedasticity models. Third, we show how to endow the VIRF with a causal interpretation. We illustrate the merits of a structural VIRF analysis by investigating the impacts of historical and out-of-sample shock scenarios on the U.S. equity, government bond, and foreign exchange markets.Type:journal articleJournal:Journal of Financial EconometricsScopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Identifying structural shocks to volatility through a
proxy-MGARCH modelWe extend the classical multivariate GARCH (MGARCH) specification for volatility modeling by developing a structural MGARCH model targeting identification of shocks and volatility spillovers in a speculative return system of daily frequency. Similarly to the proxy-SVAR framework, we work with auxiliary proxy variables constructed from news-related measures to identify the underlying shock system. Our identification strategy targets full identification. We estimate the underlying structural rotation matrix by means of Givens rotations, which ensures orthogonality of the resulting shocks. In an empirical application, we identify an equity, bond and currency shock. We study the volatility spillovers implied by these labeled structural shocks. Our analysis shows that symmetric spillover regimes are rejected.Type:conference speech - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Structural Volatility Impulse Response Analysis(2022-06-16); We make three contributions to the volatility impulse response function (VIRF) of Hafner and Herwartz (2006), the most widely applied impulse response function in the context of multivariate volatility models. Firstly, we derive its law in the BEKK model. Secondly, we present a structural embedding of the VIRF by relying on recent developments for identification in MGARCH models. This broadens the use case of the VIRF, to date limited to historical analyses, by allowing for counterfactual and out-of-sample scenario analyses of volatility responses. Thirdly, we show how to endow the VIRF with a causal interpretation. We illustrate the merits of a structural VIRF analysis by investigating the impacts of historical shock events as well as the consequences of well-defined future shock scenarios on the U.S. equity, government bond and foreign exchange market. Our findings suggest that it is vital to be able to assess the statistical significance of volatility impulse responses.Type:conference speech - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Structural Volatility Modelling(2019-09-23); Research on GARCH-type multivariate volatility and correlation models typically concentrates on accurate estimation and prediction of return dynamics. To a much lesser extent, the literature has focused on the economic forces at play in the background and potential volatility transmission mechanisms. We propose to extend the classical MGARCH specification for volatility modelling by developing a news tone based structural MGARCH model targeting structural identification of volatility spillovers. Instead of relying on widely used ad-hoc decomposition techniques of the conditional covariance matrix based on ordering of variables, we suggest, similar to the proxy-SVAR framework, to obtain the structural decomposition of the conditional covariance matrix of a system of asset returns using instrumental variables. In the spirit of sentiment econometrics, which has sparked considerable progress in estimation and forecasting of financial volatility in recent years, we investigate news tone based measures as proxy variables for identification of our structural MGARCH model. In an empirical application, we assess the the impact of structural shocks by investigating volatility impulse response functions based on economic turmoil events. Keywords: Proxy-MGARCH, structural innovations, identification, news tone, volatility impulse responsesType:conference speech - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Proxy-identification of a structural MGARCH model for asset returns(2024-10-17); We extend the multivariate GARCH (MGARCH) specification for volatility modeling by developing a structural MGARCH model that targets the identification of shocks and volatility spillovers in a speculative return system. Similarly to the proxy-SVAR framework, we leverage auxiliary proxy variables to identify the underlying shock system. The estimation of structural parameters, including an orthogonal matrix, is achieved through techniques derived from Riemannian optimization. Our analysis of daily S&P 500 returns, 10-year Treasury yields, and the U.S. Dollar Index, employing news-driven instrument variables, identifies an equity and a bond market shock.Type:working paperJournal:Swiss Finance Institute Research Paper Series - Some of the metrics are blocked by yourconsent settings
Item type:Publication,