Abstract
A new state space approach is proposed to model the time-dependence in an extreme value process. The generalized extreme value distribution is extended to incorporate the time-dependence using a state space representation where the state variables either follow an autoregressive (AR) process or a moving average (MA) process with innovations arising from a Gumbel distribution. Using a Bayesian approach, an efficient algorithm is proposed to implement Markov chain Monte Carlo method where we exploit an accurate approximation of the Gumbel distribution by a ten-component mixture of normal distributions. The methodology is illustrated using extreme returns of daily stock data. The model is fitted to a monthly series of minimum returns and the empirical results support strong evidence of time-dependence among the observed minimum returns.
Originalsprache | Englisch |
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Seiten (von - bis) | 3241 - 3259 |
Fachzeitschrift | Computational Statistics and Data Analysis |
Jahrgang | 56 |
Ausgabenummer | 11 |
Publikationsstatus | Veröffentlicht - 1 Aug. 2012 |