This paper focuses on the robust Effcient Method of Moments (EMM) estimation of a general parametric stationary process and proposes a broad framework for constructing robust EMM statistics in this context. This extends the application field of robust statistics to very general time series...

Inspired by findings of lowdimensional nonlinearities and the Theorem of Takens (1983) forecasting models of financial time series are often built upon nonparametric, i.e. universal nonlinear, univariate relationships. Empirical investigations, however, are seriously contaminated by the problem...

Conventional time series analysis, focusing exclusively on a time series at a given scale, lacks the ability to explain the nature of the data generating process. A process equation that successfully explains daily price changes, for example, is unable to characterize the nature of hourly price...

In this paper, we investigate the relative performance of Value-at-Risk (VaR) models with the daily stock market returns of nine di.erent emerging markets. In addition to well-known modeling approaches such as variance-covariance method and historical simulation, we study the extreme value...

This paper presents an empirical investigation of scaling and multifractal properties of U.S. Dollar-Deutschemark (USD-DEM) returns. The data set is ten years of 5-minute returns. The cumulative return distributions of positive and negative tails at di.erent time intervals are linear in the...