Estimation of limiting conditional distributions for the heavy tailed long memory stochastic volatility process

Details Estimation of limiting conditional distributions for the heavy tailed long memory stochastic volatility process Estimation of limiting conditional distributions for the heavy tailed long memory stochastic volatility process

2011-08-16

arxiv.org/abs/1108.3136v1

Mathematics

Statistics Theory

28 pages, 1 figure

Scientific paper

We consider Stochastic Volatility processes with heavy tails and possible long memory in volatility. We study the limiting conditional distribution of future events given that some present or past event was extreme (i.e. above a level which tends to infinity). Even though extremes of stochastic volatility processes are asymptotically independent (in the sense of extreme value theory), these limiting conditional distributions differ from the i.i.d. case. We introduce estimators of these limiting conditional distributions and study their asymptotic properties. If volatility has long memory, then the rate of convergence and the limiting distribution of the centered estimators can depend on the long memory parameter (Hurst index).

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