Motivated by prediction problems for time series with heavy-tailed marginal distributions, we consider methods based on `local least absolute deviations' for estimating a regression median from dependent data. Unlike more conventional `local median' methods, which are in effect based on locally...

We discuss moving-maximum models, based on weighted maxima of independent random variables, for extreme values from a time series. The models encompass a range of stochastic processes that are of interest in the context of extreme-value data. We show that a stationary stochastic process whose...

We consider local least absolute deviation (LLAD) estimation for trend functions of time series with heavy tails which are characterised via a symmetric stable law distribution. The setting includes both causal stable ARMA model and fractional stable ARIMA model as special cases. The asymptotic...

In certain cases partial sums of i.i.d. random variables with finite variance are better approximated by a sequence of stable distributions with indices \alpha_n \to 2 than by a normal distribution. We discuss when this happens and how much the convergence rate can be improved by using...

Estimators of the extreme-value index are based on a set of upper order statistics. We present an adaptive method to choose the number of order statistics involved in an optimal way, balancing variance and bias components. Recently this has been achieved for the similar but somewhat less...

The paper characterizes first and second order tail behavior of convolutions of i.i.d. heavy tailed random variables with support on the real line. The result is applied to the problem of risk diversification in portfolio analysis and to the estimation of the parameter in a MA(1) model.

We analyze a bias in transaction-based price indexes due to the presence of seller reservation prices. We develop a model in which the ratio of sellers' reservation prices to the market value affects trading volume and biases of observed transaction prices: when trading volume decreases...

The weighted least absolute deviations estimator is studied for an AR(1) process with ARCH(1) errors ϵ-sub-t. Unlike for the quasi maximum likelihood estimator, the estimator's, limiting distribution is shown to be normal even when E(ϵ-sub-t-super-4) = ∞. Furthermore, the estimator can be...

Modeling and estimating a tail copula play an important role in forecasting rare events. Due to their easy simulation, elliptical copulas have been employed in risk management. Recently, Klppelberg, [Klppelber, C., Kuhn, G., Peng, L., 2007. Estimating the tail dependence function of an...