Heavy tailed time series with extremal independence
By Rafal Kulik, Philippe Soulier. 2013
47 citations recorded by the source corpus.
We consider heavy tailed time series whose finite-dimensional distributions are extremally independent in the sense that extremely large values cannot be observed consecutively. This calls for methods beyond the classical multivariate extreme value theory which is convenient only for extremally dependent multivariate distributions. We use the Conditional Extreme Value approach to study the effect of an extreme value at time zero on the future of the time series. In formal terms, we study the limiting conditional distribution of future observations given an extreme value at time zero. To this purpose, we introduce conditional scaling functions and conditional scaling exponents. We compute these quantities for a variety of models, including Markov chains, exponential autoregressive models, stochastic volatility models with heavy tailed innovations or volatilities.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/ec16d70c8aa6c818112ef1e4f159b98fe05d8201
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
In Finance
Citations in the corpus
- Graphical models for extremes (2018, background): “Conditional independence and graphical models have not been studied in this framework, except for the special case of Markov chains (Kulik and Soulier, 2015; Papastathopoulos et al., 2017).”