Multivariate autoregressive extreme value process and its application for modeling the time series properties of the extreme daily asset prices
DOI10.1080/03610926.2013.791370zbMath1342.62143OpenAlexW2092255802MaRDI QIDQ5739165
Taras Bodnar, Rostyslav Bodnar, Wolfgang Schmid
Publication date: 15 July 2016
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610926.2013.791370
asymptotic normalityprediction intervalautoregressive processmultivariate extreme value distribution
Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Extreme value theory; extremal stochastic processes (60G70) Economic time series analysis (91B84)
Cites Work
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- The Wishart autoregressive process of multivariate stochastic volatility
- Nonparametric estimation of multivariate extreme-value copulas
- A spectral representation for max-stable processes
- Time series: theory and methods.
- The extremes of a triangular array of normal random variables
- Entropy densities with an application to autoregressive conditional skewness and kurtosis.
- Stochastic volatility duration models
- Asymptotic efficiency of the two-stage estimation method for copula-based models
- Spatial extremes: models for the stationary case
- Copula-Based Multivariate Input Models for Stochastic Simulation
- Modelling multivariate extreme value distributions
- Structural Laplace Transform and Compound Autoregressive Models
- Bayesian Spatial Modeling of Extreme Precipitation Return Levels
- Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
- Multivariate extreme‐value distributions with applications to environmental data
- Autoregressive Conditional Density Estimation
- Econometric Analysis of Realized Volatility and its Use in Estimating Stochastic Volatility Models
- An Empirical Likelihood Goodness-of-Fit Test for Time Series
- A Conditional Approach for Multivariate Extreme Values (with Discussion)
- TAIL INDEX OF AN AR(1) MODEL WITH ARCH(1) ERRORS
- Fitting Time-Series Input Processes for Simulation
- Heavy-Tail Phenomena
- Stochastic volatility models as hidden Markov models and statistical applications
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