Analysis and models of cross asset dependency structures in high-frequency data


The development of financial econometrics as a field of research has been
shaped by the availability of high-frequency data on any traded asset price
over the past one and a half decades. Due to this development in data
availability, a considerable amount of progress has been made in the
estimation of realized variance as a measure of financial volatility as well as
in the area of providing accurate forecasts of volatility based on these
measures. To a much lesser extent, the literature has been
asking which economic forces are at play in the dynamics of realized
variance, how realized (co)variances interact across different markets and
asset classes, how the parameters of the proposed models evolve over time,
and what implications realized variance has for asset and derivatives
pricing. The project "Analysis and models of cross asset dependency
structures in high-frequency data" contributes to the literature on
realized variance by filling these gaps.

For practical purposes, we structure the project into two subprojects. In
subproject one, we explore adaptive modeling techniques for realized
variance measures at the univariate and the multivariate level. Emphasis is
on parameter flexibility specified within a time-varying Bayesian
state-space model representation. This allows us to capture alternating lag
and weight structures and also macroeconomic risk factors that drive the
evolution of the economy. The second project focuses on the question whether realized variance measures obtained from derivatives and underlying markets can be reconciled under the assumptions of standard option pricing models. Moreover, what assumptions are needed in these models and how do intra-day jumps in the underlying process propagate to the derivatives market. Lastly, we seek to combine the insights gained from the two subprojects by developing sophisticated risk-management tools and flexible option pricing models based on information embodied in high-frequency data and the flexible
models fitted to it.

Additional Informationsunspecified
Commencement Date1 October 2012
Contributors Fengler, Matthias (Project Manager); Buncic, Daniel (Project Manager) & Audrino, Francesco (Project Manager)
Datestamp 24 Sep 2019 13:32
Institute/School ?? SEPS LS FM ??
University of St.Gallen
SEPS - School of Economics and Political Science
MS - Faculty of Mathematics and Statistics
?? SEPS LS FA ??
Completion Date 30 September 2015
Publications Fengler, Matthias & Okhrin, Ostap (2016) Managing Risk with a Realized Copula Parameter. Computational Statistics & Data Analysis, 100 131-152. ISSN 0167-9473
Audrino, Francesco & Knaus, Simon: Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics. Discussion paper series, 2012, 1224.
Corsi, Fulvio; Peluso, Stefano & Audrino, Francesco (2015) Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation. Journal of Applied Econometrics, 30 (3). 377-397. ISSN 0883-7252
Fengler, Matthias; Mammen, Enno & Vogt, Michael (2015) Specification and structural break tests for additive models with applications to realized variance data. Journal of Econometrics, 188 (1). 196-218. ISSN 0304-4076
Fengler, Matthias & Gisler, Katja (2015) A variance spillover analysis without covariances: what do we miss? Journal of International Money and Finance, 51 174-195. ISSN 0261-5606
Audrino, Francesco & Knaus, Simon (2014) Lassoing the HAR model: A Model Selection Perspective on Realized Volatility Dynamics. Econometric Reviews, (35). 1485-1521. ISSN 0747-4938
Audrino, Francesco; Camponovo, Lorenzo & Roth, Constantin: Testing the lag structure of assets' realized volatility dynamics. , 2015,
Fengler, Matthias & Herwartz, Helmut: Measuring spot variance spillovers when (co)variances are time-varying - the case of multivariate GARCH models. , 2015,
Buncic, Daniel & Gisler, Katja: Global Equity Market Volatility Spillovers: A Broader Role for the United States. SEPS Discussion Paper, 2015, Discussion Paper no. 2015-08.
Audrino, Francesco & Hu, Yujia (2016) Volatility Forecasting: Downside Risk, Jumps and Leverage Effect. Econometrics, 4 (1). 8. ISSN 2225-1146
HSG Profile Area SEPS - Quantitative Economic Methods
Keywords Realized covariance, realized volatility, time-varying parameter models, forecasting, Bayesian variable selection, option pricing
Methods Financial Econometrics
Funders SNF – National Research Project
Id 216969
Project Range Institute/School
Reference Number 100018_144033
Project Status ongoing
Subjects other research area
Topics Financial Econometrics; Realized volatility; Option pricing
Project Type applied research project
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