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...

We study the asymptotic properties of a Tikhonov Regularized (TiR) estimator of a functional parameter based on a minimum distance principle for nonparametric conditional moment restrictions. The estimator is computationally tractable and takes a closed form in the linear case. We derive its...

Driven by the rise in computational power, it has become popular to measure integrated variance with high-frequency squared returns. Though the squared return is a natural choice as a variance estimate, it is not the most efficient one for a given interval length. Extreme-value based estima-...

We propose a general robust semiparametric bootstrap method to estimate conditional predictive distributions of GARCH-type models. Our approach is based on a robust estimator for the parameters in GARCH-type models and a robustified resampling method for standardized GARCH residuals, which...

In this paper, we consider an investor who plays in a market that involves a risky asset whose instantaneous rate of return changes at unknown random times. This return rate is assumed to follow the law of a Compound Poisson Process. We construct optimal mathematical strategies in this context...

This paper shows that in financial markets with endogenous asset supply and demand, both rational and noise traders do coexist in the long run. The finding implies that financial markets are neither informationally nor allocationally efficient. While rational traders have a consistently higher...

We aim to compare financial technical analysis techniques to strategies which depend on a mathematical model. In this paper, we consider the moving average indicator and an investor using a risky asset whose instantaneous rate of return changes at an unknown random time. We construct...

We consider consistent tests for stochastic dominance efficiency at any order of a given portfolio with respect to all possible portfolios constructed from a set of assets. We justify block bootstrap approaches to achieve valid inference in a time series setting. The test statistics are computed...

We introduce a new approach on shape preserving estimation of cumulative distribution functions and probability density functions using the wavelet methodology for multivariate de- pendent data. Our estimators preserve shape constraints such as monotonicity, positivity and integration to one,...

We propose a class of new robust GMM tests for endogenous structural breaks. The tests are based on supremum and average statistics derived from robust GMM estimators with a bounded influence function. They imply a bounded linearized asymptotic bias of size and power under local model...