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Robust resampling methods for time series

Camponovo, Lorenzo; Scaillet, Olivier; Trojani, Fabio - 2010 - This version: May 9, 2010

We study the robustness of block resampling procedures for time series. We first derive a set of formulas to … applications. This renders inference based on standard resampling methods useless already in simple estimation and testing settings …. To solve this problem, we introduce a robust fast resampling scheme that is applicable to a wide class of time series …

Persistent link: https://www.econbiz.de/10003971115

A higher-order correct fast moving-average bootstrap for dependent data

La Vecchia, Davide; Moor, Alban; Scaillet, Olivier - 2020

We develop theory of a novel fast bootstrap for dependent data. Our scheme deploys i.i.d. resampling of smoothed moment …

Persistent link: https://www.econbiz.de/10012179669

Predictability hidden by anomalous observations

Camponovo, Lorenzo; Scaillet, Olivier; Trojani, Fabio - 2013

Testing procedures for predictive regressions with lagged autoregressive variables imply a suboptimal inference in presence of small violations of ideal assumptions. We propose a novel testing framework resistant to such violations, which is consistent with nearly integrated regressors and...

Persistent link: https://www.econbiz.de/10009721331

Dividend growth predictability and the price-dividend ratio

Piatti, Ilaria; Trojani, Fabio - 2012 - This version: October 2012

Conventional tests of present-value models over-reject the null of no predictability. In order to better account for the intrinsic probability of detecting predictive relations by chance alone, we develop a new nonparametric Monte Carlo testing method, which does not rely on distributional...

Persistent link: https://www.econbiz.de/10009684124

A specification test for nonparametric instrumental variable regression

Gagliardini, Patrick (contributor); … - 2007

We consider testing for correct specification of a nonparametric instrumental variable regression. In this ill-posed inverse problem setting, the test statistic is based on the empirical minimum distance criterion corresponding to the conditional moment restriction evaluated with a Tikhonov...

Persistent link: https://www.econbiz.de/10003550675

Spanning tests for Markowitz stochastic dominance

Arvanitis, Stelios; Scaillet, Olivier; Topaloglou, Nikolas - 2018 - Current draft: January 2018

Using properties of the cdf of a random variable defined as a saddle-type point of a real valued continuous stochastic process, we derive first-order asymptotic properties of tests for stochastic spanning w.r.t. a stochastic dominance relation. First, we define the concept of Markowitz...

Persistent link: https://www.econbiz.de/10011877232

Spanning analysis of stock market anomalies under prospect stochastic dominance

Arvanitis, Stelios; Scaillet, Olivier; Topaloglou, Nikolas - 2020 - This version: April 2020

We develop and implement methods for determining whether introducing new securities or relaxing investment constraints improves the investment opportunity set for prospect investors. We formulate a new testing procedure for prospect spanning for two nested portfolio sets based on subsampling and...

Persistent link: https://www.econbiz.de/10012219063

Anomalous returns in a neural network equity-ranking predictor

Satinover, Jeffrey; Sornette, Didier - 2008

Persistent link: https://www.econbiz.de/10003961709

Taking ambiguity to reality : robust agents cannot trust the data too much

Trojani, Fabio; Wiehenkamp, Christian; Wrampelmeyer, Jan - 2011

Ambiguity aversion in dynamic models is motivated by the presence of unknown time-varying features, which agents do not understand and cannot theorize about. We analyze the consequences of this assumption for economic agents and model builders, who typically need to estimate a model, e.g., to...

Persistent link: https://www.econbiz.de/10009273101

Machine learning with kernels for portfolio valuation and risk management

Boudabsa, Lotfi; Filipović, Damir - 2019

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a...

Persistent link: https://www.econbiz.de/10012052380